# The neuropsychological evaluation of face identity recognition

Angélique Volfart, Bruno Rossion

► To cite this version:

Angélique Volfart, Bruno Rossion. The neuropsychological evaluation of face identity recognition. Neuropsychologia, 2024, 198, pp.108865. 10.1016/j.neuropsychologia.2024.108865. hal-05029756

HAL Id: hal-05029756

https://hal.science/hal-05029756v1

Submitted on 10 Apr 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
## Review article

# The neuropsychological evaluation of face identity recognition

Angélique Volfart<sup>a, **</sup>, Bruno Rossion<sup>b, c, *</sup>

<figure><img src="image_2.png" /></figure>

## ARTICLE INFO

### Keywords:

Face identity recognition

Neuropsychological evaluation

Prosopagnosia

Cortical face memories

EEG

## ABSTRACT

Facial identity recognition (FIR) is arguably the ultimate form of recognition for the adult human brain. Even if the term prosopagnosia is reserved for exceptionally rare brain-damaged cases with a category-specific abrupt loss of FIR at adulthood, subjective and objective impairments or difficulties of FIR are common in the neuropsychological population. Here we provide a critical overview of the evaluation of FIR both for clinicians and researchers in neuropsychology. FIR impairments occur following many causes that should be identified objectively by both general and specific, behavioral and neural examinations. We refute the commonly used dissociation between perceptual and memory deficits/tests for FIR, since even a task involving the discrimination of unfamiliar face images presented side-by-side relies on cortical memories of faces in the right-lateralized ventral occipito-temporal cortex. Another frequently encountered confusion is between specific deficits of the FIR function and a more general impairment of semantic memory (of people), the latter being most often encountered following anterior temporal lobe damage. Many computerized tests aimed at evaluating FIR have appeared over the last two decades, as reviewed here. However, despite undeniable strengths, they often suffer from ecological limitations, difficulties of instruction, as well as a lack of consideration for processing speed and qualitative information. Taking into account these issues, a recently developed behavioral test with natural images manipulating face familiarity, stimulus inversion, and correct response times as a key variable appears promising. The measurement of electroencephalographic (EEG) activity in the frequency domain from fast periodic visual stimulation also appears as a particularly promising tool to complete and enhance the neuropsychological assessment of FIR.

## 1. Introduction

The present paper presents a (critical) review of the neuropsychological assessment of an essential function of the human brain: facial identity recognition (FIR). It begins by reminding the reader of the importance and fundamental characteristics of FIR for our species, emphasizing the reasons that make FIR both an impressive and very fragile brain function for neurotypical human adults (section 2). Although in a symptom-based definition FIR disorders are commonly associated with the term 'prosopagnosia' (section 3), we reserve this term to an extremely rare, necessarily acquired, category-specific neurological condition occurring suddenly at adulthood (Rossion, 2018a). According to this view, FIR disorders are likely to be observed

following many causes beyond prosopagnosia (section 4). The body of the present review article is devoted to an extensive review of current behavioral tests aimed at assessing FIR, most of these tests having been developed in the last two decades in computerized versions (section 5). In terms of clinical neuropsychological assessment, these tests, such as the Cambridge Face Memory Test (CFMT), suffer from significant limitations due to their development outside of a clinical context, an incoherent distinction between (deficits of) perception and memory, and their failure to take into account the high speed, automaticity and qualitative aspects of FIR. The most widely used tests, such as the CFMT and the Benton Face Recognition Test (BFRT) (and its computerized version, BFRT-c) are evaluated in detail in terms of their strengths and weaknesses. A recently developed test that takes into account these

<footnote><sup>a</sup> School of Psychology and Counselling, Faculty of Health, Queensland University of Technology, Australia</footnote>

<footnote><sup>b</sup> Centre for Biomedical Technologies, Queensland University of Technology, Australia</footnote>

<footnote><sup>c</sup> Université de Lorraine, CNRS, IMoPA, F-54000, Nancy, France</footnote>

<footnote>* Corresponding author. Pavillon Krug (1er étage - entrée CC-1), Hôpital Central, CHRU Nancy - University Hospital of Nancy, 29 Avenue du Maréchal de Lattre de Tassigny, 54000 NANCY, France.</footnote>

<footnote>** Corresponding author. School of Psychology and Counselling, Faculty of Health, O Block, A Wing, OA221, Kelvin Grove Campus, Queensland University of Technology, Brisbane, QLD, 4059, Australia.</footnote>

<footnote>E-mail addresses: angelique.volfart@qut.edu.au (A. Volfart), bruno.rossion@univ-lorraine.fr (B. Rossion).</footnote>
limitations, with natural images, a measurement of response time (RT) and a manipulation of familiarity and orientation is also proposed. Finally (section 6), we explain why the measurement of electroencephalographic (EEG) activity in the frequency domain following fast periodic visual stimulation appears particularly promising to complement the neuropsychological assessment of FIR.

## 2. Importance and challenge of facial identity recognition (FIR) in neuropsychology

In the vast majority of human societies, dozens of people are identified on a daily basis: family members, colleagues, friends, but also celebrities in the media (TV, internet, etc.). Most of the time, this recognition of identity is based on the *face*, technically the anterior part of the head which includes most of the sensory organs and is limited by the hair and the ears (often considered external facial features), and the top of the chin. For an adult human being with no neurological or psychiatric history, FIR constitutes a common, extremely frequent operation of the brain. Young adults are able to recognize the identity of several thousands of faces (Jenkins et al., 2018). Even if there are times when one explicitly search for a known face identity in the environment, FIR is largely *automatic* – think of some people you do not like but cannot – unfortunately – avoid recognizing when you encounter their face. FIR is also extremely fast: a single glance is more than enough to recognize a facial identity (Hsiao and Cottrell, 2008; Orban de Xivry et al., 2008; Retter et al., 2021) and behavioral and electrophysiological data show that these identities are recognized as being familiar (or not) in about 1/3 of a second (Ramon et al., 2011; Caharel et al., 2014; Besson et al., 2017; Wiese et al., 2019; Yan and Rossion, 2020). Although some animal species also spontaneously use the face to recognize conspecific identities (e.g., wasps of the species Polistes Fuscatus; Sheehan and Tibbetts, 2011), no species appear to be able to compete with humans regarding this function. In fact, even compared to other primates, our human species appear to have developed a genuine expertise and specificities in terms of performance, processes and neural structures for FIR (Rossion and Taubert, 2019; Griffin, 2020; Rossion, 2022a).

Nevertheless, FIR presents a considerable challenge for our brain because of several reasons that are important to mention in order to understand the origin of FIR difficulties encountered in the neuropsychological population. First, FIR requires being able to differentiate, or *discriminate*, between human faces, which are complex (i.e., multidimensional) visual patterns. Every human face is morphologically unique – even the faces of identical twins are different – and human faces show

the greatest amount of morphological and genetic variability compared to other parts of the body (Sheehan and Nachman, 2014). However, individual faces also have great structural similarities, especially within a genetically homogeneous population: the same facial features (eyes, eyebrows, nose, mouth, ...) organized according to the same spatial configuration (i.e., a roughly symmetrical layout, with two eyebrows above the eyes, a central vertically-oriented nose above a mouth and chin) (Fig. 1). This morphological similarity can be fully appreciated when we are confronted with the difficulty of differentiating faces of another 'race', for example faces of the population of sub-Saharan Africa for Europeans of the Caucasian type or vice versa (Meissner and Brigham, 2001; Rossion and Michel, 2011; see Stelter and Schweinberger, 2021 for a recent special issue on this other-race face effect). Second, the same facial identity can vary greatly under different viewing conditions and over time, to the point that two views of the same facial identity often differ physically to a greater extent than two different facial identities (i.e., "within-person variability" may be greater than "between-person variability"; Burton et al., 2016; White et al., 2022). Therefore, to recognize a given facial identity, our brain has to *generalize* its response across very different exemplars of the same individual (Fig. 1).

These first two key reasons for the difficulty of FIR are linked to the notions of *discrimination* and *generalization*, which form the core of the definition of this function: *the ability to produce a specific and reproducible response (behavioral or neural) to a given individual based on their face*. This definition of FIR differs from the more restrictive one of the term 'recognition' that is generally used in psychology ('judgment of previous occurrence') and which necessarily relies on the notion of past experience (Mandler, 1980). Critically, the above definition englobes the recognition of *unfamiliar faces* (i.e., faces that have never been encoded in memory before), which actually form the main material of the neuropsychological assessment tests for FIR (section 5).

A third reason for the extreme difficulty of FIR is that in most modern societies the *number* of face identities to be recognized is very high, typically from several hundreds to thousands of individual faces (Jenkins et al., 2018). Each identity thus constitutes a category whose different exemplars are the different views of the same individual (Fig. 1). In comparison, other face-related recognition functions are based on a very small number of categories (e.g., two gender categories, six basic emotional facial expressions, etc.) (Rossion, 2022a). Fourth, in modern societies, the number of recognizable identities, i.e., categories, is often *indeterminate*, that is, it changes in different contexts and over time, with familiar faces mixed in with an indefinite number of unknown

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** An illustration of the challenge of facial identity recognition (FIR). This function requires both to discriminate between the face of a known identity and other identities (known or unknown) and to generalize this discriminative (i.e., specific or selective) response through sometimes very different views of the same facial identity. In this sense, FIR is a **categorization** function for the human brain, with a very high and indeterminate number of categories (Rossion 2022a, 2022b). Face photographs are licensed under Creative Commons, attribution left to right, top to bottom: Ed Van-West Garcia, David Shankbone, Sean Reynolds, John Mathew Smith, John Bauld, Dick Thomas Johnson, John Bauld, Nicolas Genin, Gage Skidmore, CelebrityABC, David Shankbone, Paul Sherwood.</figcaption></figure>
faces in various contexts. As the studies of prosopagnosic patients discussed below will show, this factor plays a critical role in FIR difficulties (Rossion, 2022a). Fifth, compared to the face, other cues of people's identity are not visible (fingerprints, for example), not very informative, or convey diagnostic information too slowly. For example, compared to the face, the body shape or posture, which can quickly signal a person's gender and emotional state, convey relatively few diagnostic cues regarding a specific identity (Sheehan and Nachman, 2014). Despite the parallels (but also differences) often drawn between face and voice recognition (Yovel and Belin, 2013; Young et al., 2020; Jenkins et al., 2021), the human voice is also a largely insufficient, slow and inefficient indicator of identity compared to the face. Indeed, in many social circumstances, we have to recognize people's identity long before they articulate or we can hear their voices. And while a few vocal snippets may be enough to distinguish an adult male individual from a female individual, or different accents or emotional tones, identifying specific people by their voice is time-consuming and much more difficult than identifying them by their face (Barsics, 2014).

For all these reasons, FIR is arguably the ultimate form of recognition for the human brain (Rossion, 2022a, 2022b). Thus, even though, contrary to written language, this function is learned naturally, i.e., without explicit instructions, it depends on a long and progressive development and may not reach its maturity until adulthood (e.g., Carey et al., 1980; Carey, 1992; Ellis et al., 1992; O'Hearn et al., 2010; Germine et al., 2011; Hills and Lewis, 2018). More important for the present paper, this difficulty explains why complaints and deficits of FIR are common in the neuropsychological population, especially as there is social pressure to perform this function appropriately.

### 3. Prosopagnosia

(Human) Neuropsychology is a scientific discipline concerned with how mental/cognitive functions relate to the brain, and has traditionally been based on the study of clinical (i.e., neurological) patients (Hécaen, 1972; Berlucchi, 2017; Vallar and Caputi, 2022). The neuropsychology of FIR is often associated, sometimes completely redundantly, with that of prosopagnosia, a term introduced by Bodamer in 1947 following a century of clinical observations of sudden loss of the ability to recognize (identify) faces in patients who had suffered a stroke or head trauma (since Wigan, 1844; Quaglino and Borelli, 1867; see the first review in English by Hécaen and Angelergues, 1962). As originally defined, prosopagnosia (from the Greek 'prosopon': face; and 'a-gnosia': without knowledge) is a very rare neurological condition that cannot be explained by sensory disturbances (i.e., poor vision) or intellectual disorders, and which is specific to the recognition (of the identity) of the face (Bodamer, 1947). By specific, Bodamer (1947) conceived that other visual forms, living or non-living, are still recognizable from vision by the patient, who therefore does not suffer from visual (object) agnosia. However, whether from the analysis of other reported cases or from his own observations, when he introduced this term, Bodamer (1947) was unable to put forward a convincing example of a prosopagnosic patient, and the very existence of true cases of prosopagnosia have long been disputed (as has the existence of visual agnosia; Bay, 1953; Bender and Feldman, 1972). Despite this absence of clear cases, for a very long time the term prosopagnosia was adopted in the scientific literature, and applied to define the FIR deficit in neurological patients also suffering from other visual pattern recognition difficulties (i.e., visual object agnosia; e.g., Damasio et al., 1982; Levine and Calvanio, 1989; Clarke et al., 1997; Gauthier et al., 1999; Barton, 2008a) and/or more general person recognition difficulties (e.g., Sergent and Poncet, 1990; Schroeger et al., 2022; see Gainotti et al., 2010; Gainotti, 2013a).

For the last fifteen years, this symptom-based definition of prosopagnosia has become even more widespread in the scientific literature and the general public, and the term is now commonly used to designate any difficulty of FIR, objectively demonstrated by behavioral tests or even simply arising from subjective experience. The source of this shift

in definition is the observation of people who, with no neurological history, complain of having always experienced profound FIR difficulties, these people being therefore qualified as suffering from "Developmental Prosopagnosia" (DP) or "Congenital Prosopagnosia" (CP) (McConachie, 1976; for reviews, see Behrmann and Avidan, 2005; Duchaine and Nakayama, 2006; Murray et al., 2018). There is currently a substantial amount of research on DP/CP, which, as for dyslexia, would constitute a prevalent neurodevelopmental disorder in the population: some authors have even put forward the impressive figure of around 2-3% of people in the population suffering from DP/CP (e.g., 2.47% in Kennerknecht et al., 2006; 2-2.9% in Bowles et al., 2009; but see DeGutis et al., 2023a for a recent discussion on this topic). However, a caveat to this figure is that it could simply reflect the 2.5% of people in the normal population who complain the most of FIR difficulties, or who are the worst performers on a behavioral test with a normal distribution of performance in the population (Barton and Corrow, 2016a). At the other end of the spectrum, some people who excel in FIR have been defined as "super recognizers" and are sometimes studied specifically for this ability (Russell et al., 2009; Ramon, 2021).

While some studies report that people who are very poor or excel at FIR exhibit face category-specific difficulties/abilities (respectively) (e. g., Bobak et al., 2016a; Barton et al., 2019; Fry et al., 2020; Hendel et al., 2019), others have found an association between face and visual object recognition difficulties (e.g., Bobak et al., 2016a; Geskin and Behrmann, 2018; Barton et al., 2019; Hendel et al., 2019). A similar debate is found as to whether these poor (or excellent) performers differ qualitatively or only quantitatively from the general population, with inconsistent findings so far (e.g., Biotti et al., 2017; Bobak et al., 2017; Towler et al., 2018; Hendel et al., 2019; Tardif et al., 2019; Bennetts et al., 2022). The considerable heterogeneity of results reported across and within studies coupled with the absence of consensual diagnostic criteria for both DP/CP and 'super recognizers' (e.g., see Bate and Tree, 2017; Bate et al., 2018; Burns et al., 2022) further complicates the picture.

In summary, in the absence of biological criteria and clear evidence of qualitatively different processes from the rest of the population, whether people who have always had difficulties at (rather than an absence of ability to perform) FIR should be considered as patients, let alone neuropsychological patients, is debatable. Thus, without denying the considerable FIR difficulties of developmental nature that can be found in the neurotypical population (with severe psychosocial consequences for some individuals, e.g., Yardley et al., 2008; Murray et al., 2018) and their interests for research purposes, we consider that neuropsychologists must above all be aware that there is substantial variability of this capacity in the neurotypical adult population (with a slight advantage of performance for females than males according to most studies; e.g., McBain et al., 2009; Megreya et al., 2011; Herlitz and Lovén, 2013; but see also Mishra et al., 2019). For sake of clarity, if a significant FIR difficulty of developmental origin, without neurological condition, is objectively demonstrated (see below), the senior author of the present paper and other authors have suggested to refer to these cases as having prosopidygsnosia (or simply visual dysgnosia if the deficit is not specific to the category of faces) (Rossion, 2018b; Sørensen and Overgaard, 2018). In addition, the term prosopagnosia should be reserved for a very rare neurological condition, only defined following in-depth observations and on the basis of inclusion and exclusion criteria that are added to those initially defined by Bodamer (1947): a massive deficit of FIR objectified by several behavioral tests (see below), occurring abruptly in an adult individual, stable (i.e., not evolving as in a neurodegenerative disorder), which concerns people known before and encountered after the accident (i.e., retrograde and anterograde), and their faces only (i.e., recognition of other visual forms or from other sensory modalities preserved) (Rossion, 2018a). Given the considerable progress made at this level (see section 5.2 below), we consider that the cause and location of the objectively identified brain damage or dysfunction should also play a role in the diagnosis of prosopagnosia.

The behavioral performance of different patients labeled with
prosopagnosia can obviously vary considerably at different FIR tests (e. g., Sergent and Signoret, 1992; Barton, 2008a; Busigny and Rossion, 2010a; Barton and Corrow, 2016b). This variability simply reflects what is already observed in the normal population, amplified by various factors (associated deficits, causes and extent of brain damage, etc.). Despite this variability, neuropsychologists must nevertheless be aware that there is no consistent behavioral and neural evidence in favor of different *types* of prosopagnosia, in particular the classical distinction between 'apperceptive' and 'associative' forms of prosopagnosia as advocated by several authors (Hécaen, 1981; De Renzi, 1986; Barton, 2008a; Davies-Thompson et al., 2014). Instead, beyond the inability to recognize familiar face identities, the condition of prosopagnosia necessarily causes difficulties even to perform simple tasks requiring to match side-by-side pictures of unfamiliar faces for their identity (Davidoff and Landis, 1990; Farah, 1990; Delvenne et al., 2004). In a coherent theoretical framework, prosopagnosia is attributed to impairment of (cortical) memories of faces in right-lateralized ventral occipito-temporal brain regions (Rossion, 2022a, 2022c; see also Rossion et al., 2024), while damage to anterior temporal lobe regions, even when predominant or restricted to the right hemisphere, do not cause 'associative' prosopagnosia but multimodal semantic impairments (e.g., Sergent and Poncet, 1990; Schroeger et al., 2022; see Gainotti et al., 2010; Gainotti, 2010, 2013a). While in practice most neuropsychologists will never encounter a true case of prosopagnosia during their career, the in-depth study of such cases, such as the patient PS (Rossion, 2022a, 2022b for review), provides a unique way to understand the foundations of FIR and design the most appropriate tests for assessing this function in other neuropsychological populations.

## 4. Other causes of FIR difficulties

FIR involves many structures/circuits of the adult human brain, only a subset of which are category-specific. FIR deficits/difficulties can therefore be found in numerous conditions (Table 1).

While the extent of the association between general intelligence and FIR measures varies across studies (e.g., Wilhelm et al., 2010; Hildebrandt et al., 2011; Gignac et al., 2016; see Walker et al., 2023 for a meta-analytic review), a general cognitive impairment is likely to impact performance on FIR tasks that involve understanding complex instructions and various cognitive abilities (e.g., visuospatial attention, decision making, short-term visual memory; see Manzanero et al., 2012 for a discussion on task format and importance of instructions in such context). Most studies that investigated face recognition abilities in people with intellectual disability have focused on emotion recognition, and the variability in studies that have included FIR tests (e.g., variable causes and severity of intellectual disability, choice of control groups, tasks, etc.; Hippolyte et al., 2008; Carvajal et al., 2012; Gawrylowicz et al., 2013) makes it complicated to generalize results to the entire population with general cognitive impairment. Most importantly, even if limitations of intellectual capacities are likely to affect performance at behavioral measures of FIR, this does not imply that FIR is impaired in real life circumstances.

We have already mentioned above FIR difficulties of (neuro)developmental origin, which could be defined as *visual dysgnosia* or, if the deficit appears to be category-specific, *prospopdysgnosia* (Rossion, 2018b; Sørensen and Overgaard, 2018). The neural basis of these developmental difficulties in FIR is controversial, with some neuroimaging studies reporting various structural alterations in grey matter (e.g., Behrmann et al., 2007; Garrido et al., 2009; Dinkelacker et al., 2011; Parker et al., 2023) or white matter (e.g., Thomas et al., 2009; Gomez et al., 2015; Song et al., 2015). Other studies have rather reported functional alterations such as reduced face-selectivity in core face-selective brain regions (e.g., Bentin et al., 2007; Furl et al., 2011; Jiahui et al., 2018; Haeger et al., 2021), attenuated repetition suppression for faces (Zhao et al., 2022), reduced (e.g., Lohse et al., 2016) or increased (e.g., Rosenthal et al., 2017) functional connectivity in posterior face-selective areas. Yet other studies have reported normal face-selectivity in core face-selective regions but abnormal patterns of activation and connectivity outside of these regions (e.g., Avidan et al., 2005, 2014; Avidan and Behrmann, 2009; Rosenthal et al., 2017). As discussed above, these substantial differences between studies may be largely due to the lack of objective and harmonized criteria in the literature for the definition of cases of prosopdysgnosia/developmental prosopagnosia.

Difficulties with FIR have long been reported in autism spectrum disorder (ASD; e.g., Feldman and Dimitropoulos, 2014; Vettori et al., 2019; Zagury-Orly et al., 2022; see Weigelt et al., 2012; Griffin et al., 2021 for reviews), although most studies have focused on other types of face recognition functions (i.e., emotional expressions, gaze direction; e. g. Sachse et al., 2014; Forgeot d'Arc et al., 2017). However, both the nature of these deficits and their generalization to the wider, highly variable ASD population are controversial (Jemel et al., 2006; Tang et al., 2015).

FIR difficulties have also been reported in other psychiatric disorders such as delusional misidentification syndromes (e.g., Capgras or Fregoli syndromes). Capgras syndrome has been the most investigated in relation with FIR performance and is characterized by the delusion that relatives have been replaced by other people who are physically the same. In Capgras cases without an underlying neurological condition, some early case studies have reported deficits or borderline scores at the BFRT (e.g., Waziri, 1978; Silva et al., 1992; Young et al., 1993b) while others have found normal performance (e.g., Ellis et al., 1997; Oyebode et al., 1998). In cases related to an underlying neurological condition, FIR performance (as assessed by the BFRT) has generally been found to be preserved (e.g., Fiacconi et al., 2014; Thiel et al., 2014; Metin et al., 2019; but see Silva et al., 1993). Group studies assessing FIR in

**Table 1**

Causes of FIR difficulties. These are potential causes: FIR difficulties are not observed systematically in the conditions listed above. According to one view, all of the objective (or even subjective) difficulties of FIR in these populations constitute 'prosopagnosic' deficits (i.e., a symptom-based definition of prosopagnosia). The alternative position, adopted in the present review, is to reserve the term prosopagnosia for a very rare deficit, occurring abruptly in adulthood due to brain damage or dysfunction, with strict inclusion and exclusion criteria (Rossion, 2018a; see section 3).

- General cognitive impairment, limitations of intellectual capacities

- Neurodevelopmental disorders: prosopdysgnosia

- Autism spectrum disorders

- Psychiatric disorders (e.g., delusional misidentification syndromes)

- Low-level visual disorders (e.g., glaucoma, cataracts, amblyopia, etc.)

- Cerebral Visual Impairment (CVI)

- Stroke/head injury with posterior lesions (right hemispheric dominance)

- (Occipito)-Temporal lobe epilepsy (right hemispheric dominance)

- (Occipito)-Temporal lobe brain tumors (right hemispheric dominance)

- Surgery of the occipito-temporal cortex (right hemispheric dominance)

- Herpes encephalitis (right hemispheric dominance)

- Alzheimer's disease

- Semantic variant of fronto-temporal dementia (right hemispheric dominance)
delusional misidentification syndromes are rare and have reported mixed results (Bidault et al., 1986; Lykouras et al., 2008; Walther et al., 2010), with highly variable methodology (e.g., grouping patients with syndromes from psychiatric and neurological origin, grouping all delusional misidentification syndromes together, use of standardized FIR tasks or not), making their results hard to interpret.

Vision deficits due to cataract, maculopathy, age-related macular degeneration or glaucoma can cause severe FIR deficits, as reported by patients and confirmed by behavioral tests (e.g., Bullimore et al., 1991; Elliott et al., 1997; Taylor et al., 2018; Logan et al., 2020; Hirji et al., 2021). Amblyopia (strabismic) or lack of early visual experience due to cataract associated with vision deficits in adulthood can also cause significant FIR deficits (Cattaneo et al., 2013; de Heering and Maurer, 2014). Individuals with cerebral visual impairment (CVI), which refers to a variety of visual dysfunctions resulting from brain injury, malformation, and/or genetic disorders, may also demonstrate significant difficulties with FIR and subsequent difficulties with social interactions (Bauer et al., 2023).

In the adult population with well-identified brain lesions, several cohort studies of patients with posterior head trauma or (most often) a cerebrovascular accident at the level of the posterior cerebral artery (supplying the occipital lobe and the ventral occipito-temporal cortex, including the fusiform gyrus) have found FIR deficits, although not as severe as prosopagnosia or visual agnosia (Benton and Van Allen, 1972; Valentine et al., 2006). The study by Valentine et al. (2006) on a large sample of patients (n = 91) with acquired brain injury tested with various FIR tests identified a very high proportion (between 21% and 80%, depending on the tests) of patients presenting with FIR deficits. Right hemispheric lesions are usually more likely to cause (more severe) FIR deficits (Benton and Van Allen, 1972; Tranel et al., 2009; Robotham et al., 2023).

Extremely severe FIR disorders are obviously present in patients who, following a stroke or head trauma, suffer from visual object agnosia. At this level, we distinguish two types of patients, which are rather rare. On the one hand, patients whose visual recognition function is very severely impaired, often following carbon monoxide (CO) poisoning particularly affecting the primary areas of the visual cortex, a particularly high consumer of oxygen. This type of patient has often been defined as suffering from apperceptive visual agnosia (e.g., Benson and Greenberg, 1969; the famous patient DF: Goodale and Milner, 2013; tested for face recognition in Steeves et al., 2006). However, even though these patients are not blind and their deficit is not due to damage to the peripheral visual pathways (retina, optic nerve, optic radiations), they actually have very significant low-level vision deficits subtending their impairments at visual object recognition (see Farah, 1990). On the other hand, there are patients with stroke or head trauma whose low-level vision is largely preserved but who are nevertheless unable to recognize objects through vision only (cases of 'visual integrative agnosia'; e.g., the patient HJA, Riddoch and Humphreys, 1987; LH, Levine and Calvanio, 1989; SM and CR, Behrmann and Williams, 2007). Although their deficit is not specific to the face category, these patients have often been studied for their FIR difficulties and defined (again, erroneously in our opinion) as cases of prosopagnosia (LH: Farah et al., 1995; SM & CR: Gauthier et al., 1999; HJA: Boutsen and Humphreys, 2002; see also Delvenne et al., 2004; Barton, 2008a; see the discussion in Rossion, 2018a).

Herpes encephalitis, which primarily involves the medial and anterior temporal regions (Noppeney et al., 2007), may also be associated with severe FIR deficits (e.g., Hanley et al., 1989; Broks et al., 1998). Most significantly, several studies have shown that patients with temporal lobe epilepsy (TLE) are impaired at recognizing the identity of familiar faces. While left-sided TLE has been more consistently related to difficulties at *naming* famous faces, right-sided TLE has been frequently linked to difficulties at *judging familiarity*, or providing semantic information about a face (Gainotti, 2007; Drane et al., 2008, 2013). A similar pattern of famous face identification difficulties has also been found in

TLE patients following ATL resection (Glosser et al., 2003; Drane et al., 2008, 2013; Rice et al., 2018a). Additionally, these patients exhibit difficulties at explicitly encoding and subsequently recognizing pictures of unfamiliar faces, regardless of the epileptogenic focus side (Milner, 1968; Chiaravalloti and Glosser, 2004). However, these deficits remain relatively modest compared to cases of prosopagnosia, with no group-level impairment at unfamiliar face matching tasks in TLE (Volfart et al., 2020). Even though right TLE patients are generally slowed down at such tasks, these effects are not category-specific, and face inversion effects are preserved (Volfart et al., 2020; Volfart et al., 2023a).

Regarding neurodegenerative conditions, FIR deficits have been found in Alzheimer's disease, even at early stages, and may even help for its early diagnosis (Becker et al., 1995; Greene and Hodges, 1996; Lavallée et al., 2016). FIR difficulties have also been reported in association with the semantic variant of fronto-temporal dementia ('semantic dementia'), especially when the atrophy begins at the level of the right temporal pole (Barbarotto et al., 1995; Evans et al., 1995; Gentileschi et al., 2001; Gorno-Tempini et al., 2004; Joubert et al., 2006; Gainotti et al., 2008; Busigny et al., 2009; Snowden et al., 2018).

## 5. Evaluation of FIR: in practice

Here we mainly consider the evaluation of human *adults*, knowing that FIR abilities develop gradually during childhood and adolescence as mentioned above, and that their evaluation before adulthood in a neuropsychological context therefore presents additional difficulties linked to this slow development and that of other cognitive functions, in particular those involved in understanding and responding to behavioral tasks assessing FIR.

### 5.1. *Clinical history and subjective report of the patient*

Taking into account the subjective experience of the patient before formal neuropsychological assessment through the use of interviews and questionnaires will allow the neuropsychologist to estimate the level of subjective awareness that a patient has about potential FIR difficulties and to contrast it with objective test results (Valentine et al., 2006). A first element to consider is pre-morbid FIR skills. Was the patient fairly good or bad at recognizing faces before their neurological impairment? Did they consider themselves to be good at face identity recognition or not? Do they feel like their performance worsened? If so, was it abrupt or gradual? It is important in this regard to dissociate FIR deficits from difficulties at retrieving someone's proper name, which is a common complaint in the healthy population (e.g., Condret-Santi et al., 2013) and a frequent symptom in neurological patients (e.g., Kapur and Pearson, 1983; Condret-Santi et al., 2014 in patients with temporal lobe epilepsy; Ahmed et al., 2008 in patients with mild cognitive impairment). It is also crucial to corroborate the patient's subjective report with those of their relatives or caregivers, who may provide valuable information about FIR difficulties occurring in everyday life, their frequency and evolution.

In the neurological population, the study of Valentine et al. (2006) found significant correlations between subjective ratings of face difficulties (4 questions rated) and objective FIR measures (including the BFRT and the Warrington Recognition Memory Test for Faces; correlation range: 0.23–0.35) but not with other cognitive measures in patients with acquired brain injury. More recently, a questionnaire including 20 questions aiming to identify FIR difficulties has been released (PI20, Shah et al., 2015; see also the Stirling Face Recognition Scale, SFRS, adapted from the PI20 to also identify super-recognizers; Bobak et al., 2019), with higher scores reflecting greater FIR difficulties. In the normal population, the association between subjective report with the PI20 and objective behavioral performance (e.g., as assessed with the CFMT) is significant but of considerable variability across studies (with higher PI20 scores associated with lower CFMT performance; e.g.,
ranging from −0.18 to −0.43 in Gray et al., 2017; Ventura et al., 2018; Marschollek et al., 2019; Estudillo and Wong, 2021; Matsuyoshi and Watanabe, 2021; Nørkær et al., 2023; Tagliente et al., 2023). Similar correlation values have been found using another questionnaire, the Cambridge Face Memory Questionnaire (CFMQ; r = 0.44 with CFMT scores in Arizpe et al., 2019), although the correlation strength varies with the participant's age (lower correlations of r = 0.258 and 0.322 in 10–18 and 51–70 year olds, respectively, as compared to, e.g., r = 0.455 in 23–26 years old; DeGutis et al., 2023b), warranting caution when interpreting subjective reports. In the context of developmental FIR disorders, some studies on the PI20 have shown that suspected participants with prosopdygnosia (usually self-reported) score higher on the PI20 and lower on the CFMT than control participants (Shah et al., 2015; Tsantani et al., 2021) while others found no significant correlation between the PI20 and the CFMT (r = 0.246 for males and –0.111 for females in Murray and Bate, 2019). In addition, a relatively high number of individuals who self-identify as suffering from prosopdygnosia do not meet commonly used behavioral criteria for a diagnosis, leading some authors to argue for more emphasis on subjective reports (see Burns et al., 2022; but see also Arizpe et al., 2019). While the relationship between subjective and objective FIR measures is still debated (Kramer and Tree, 2023), taking into consideration the patient's subjective experience, ideally using standardized questionnaires, may provide valuable information even if behavioral FIR tests should be systematically performed. When subjective FIR difficulties are not corroborated by the performance at a given FIR test, they should still be

acknowledged, and further assessment may be conducted with additional (FIR and more general cognitive) measures to better characterize the patient's complaints.

## 5.2. Neurological information

Thanks to lesion studies, functional neuroimaging in healthy subjects (essentially functional magnetic resonance imaging, fMRI) and intracranial electrical stimulations, the brain structures specifically (and causally) involved in FIR are now well defined (Fig. 2). Neuropsychologists cannot therefore remain in a strict 'cognitivist' approach which would make the economy of this knowledge providing additional information on the nature of the deficit experienced by the patient and objectified by the neuropsychological tests (Tramo, 1990; Benton, 1992).

The key brain structures for FIR are in the ventral occipito-temporal cortex (VOTC), specifically the lateral portions of the inferior occipital gyrus (IOG) and the fusiform gyrus (FG) (Fig. 2). The FG, a cortical region unique to hominoids or great apes (Bryant and Preuss, 2018), is the largest macro-anatomical structure within the VOTC, forming part of the temporal and occipital lobes in Brodmann's area 37. It is bounded by the collateral sulcus (CoS) and the occipito-temporal sulcus (OTS). The OTS separates the FG from the inferior temporal gyrus (located laterally to the FG) and the CoS separates the FG from the parahippocampal gyrus (located medially to the FG) (Palejwala et al., 2020). It is, above all, the lateral part of the FG, in particular in its middle section on the

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Brain regions critically and specifically or dominantly involved in FIR. These regions are identified by lesion studies of reported cases of prosopagnosia (e.g., panels A and B), functional neuroimaging (fMRI) studies, and intracranial electroencephalographic recordings (e.g., panel C). A. Lesion overlap in patients with reported prosopagnosia (n = 52). Figure adapted from Bouvier and Engel (2006) with permission. B. Lesion overlap (at z = -19) in reported cases of prosopagnosia, either intersecting (left, n = 29) or not (right, n = 15) with an a priori right FFA (blue outline). Adapted from Cohen et al. (2019) with permission. C. On the left, significant response amplitude difference between conditions with upright and inverted faces over ventral occipito-temporal intracerebral electrode contacts responding to (unfamiliar) face identity discrimination (n = 69). On the right, ventral and lateral views showing the comparison of local proportions of contacts responding to upright and inverted conditions in the intracerebral recording study of Jacques et al. (2020). Note the right hemispheric dominance, with a focus on both the fusiform (amplitude) and inferior occipital gyri (proportion of significant contacts).</figcaption></figure>
postero-anterior axis, that appears to be crucial for FIR: it is in this region that face-selective responses are the most robust in fMRI ('Fusiform face area', FFA, according to Kanwisher et al., 1997; in reality, often several subregions of variable size depending on each individual; Weiner and Grill-Spector, 2013; Gao et al., 2022) and intracranial electro-physiological recordings (Jonas et al., 2016; Jacques et al., 2022). Like the posterior region of the IOG ('Occipital Face Area', OFA), these FG regions are sensitive to differences between facial identities, as demonstrated in numerous fMRI-adaptation studies (e.g., Gauthier et al., 2000; Schiltz et al., 2006; Davies-Thompson et al., 2009; Rostalski et al., 2019; see Kovács, 2020 for review). A key region that is typically overlooked by neuroimaging studies because of fMRI magnetic susceptibility artifacts is the anterior fusiform gyrus, anterior to the 'FFA'. Intracerebral electrical stimulation in the right anterior fusiform gyrus can give rise to temporary, specific and dramatic interruptions of FIR (Jonas et al., 2015; Jonas and Rossion, 2021; Volfart et al., 2022). Although they result from focal stimulation, these effects can be attributed to the more global disruption of a cortical network specialized in face recognition. Therefore, beyond cortical grey matter, several white matter fiber bundles most certainly play a key role in this network (Fox et al., 2008; Palejwala et al., 2020; Wang et al., 2020b). These bundles of white matter fibers locally connect the FG to the lingual gyrus and the IOG, while the inferior longitudinal fasciculus (ILF) and the inferior fronto-occipital fasciculus (IFOF), parts of which come from the lingual gyrus and IOG, meet in the FG before reaching their terminations in the temporal pole and frontal lobe respectively (Herbet et al., 2018; Palejwala et al., 2020).

The dominance of the right hemisphere in FIR was highlighted for the first time from the observations and inferences made by Henri Hécaen and his colleagues on clinical signs of brain lesion localization (i. e., dominance of left upper visual field defects; Hécaen et al., 1957; Hécaen and Angelergues, 1962; see also Bouvier and Engel, 2006) and is now very well established by a multitude of evidence from lesion localization in imaging (e.g., Sergent and Signoret, 1992; Cohen et al., 2019), divided visual field presentation (e.g., Kolb et al., 1983; Yovel et al., 2003), EEG (e.g., Rossion et al., 2003b), positron emission tomography (PET) and fMRI (e.g., Sergent et al., 1992; Kanwisher et al., 1997), intracranial recordings (Jonas et al., 2016; Jacques et al., 2020) and direct intracranial stimulation studies (Jonas and Rossion, 2021) (see also the recent review of Rossion and Lochy, 2022) (Fig. 2). Although there is no conclusive evidence of a link between this right-hemispheric dominance and the localization of reading in the left hemisphere (Rossion and Lochy, 2022), the right-hemispheric dominance for FIR (and all functions of face recognition in general) is specific to the human species and less frequent in left-handed subjects (see Bukowski et al., 2013; Johnstone et al., 2021). For this reason, the assessment of handedness and, if possible, of language lateralization, should be considered during the neuropsychological assessment.

Damage to other brain regions can affect performance in FIR in a less specific way. In particular, regions of the medial temporal lobe (MTL), including the hippocampus, are thought to be involved in the encoding, consolidation and explicit recollection of cortical memories in a general (i.e., non-categorical) way. Although subregions of the MTL (perirhinal, entorhinal, parahippocampal cortices, hippocampus) may play different roles in memory for various visual items (faces, objects, scenes, words) (Robin et al., 2019), damage to these structures will generally lead to failures to recognize facial identities encoded relatively recently (Crane and Milner, 2002), the episodes during which they have been encoded, and in learning arbitrary associations between faces and other domains (e.g., semantic information, voice, names) (e.g., Vargha-Khadem et al., 1997; Mayes et al., 2002, 2007) rather than in specifically recognizing that a long-term known face identity is familiar (Montaldi and Kafkas, 2022) or even identifying this face identity.

As noted above, a progressive inability to recognize faces of familiar people in cases of fronto-temporal dementia has also been well-documented (Barbarotto et al., 1995; Evans et al., 1995; Gentileschi

et al., 2001; Gorno-Tempini et al., 2004; Joubert et al., 2006; Gainotti et al., 2008; Busigny et al., 2009; for a review, see Gainotti, 2007). This is in line with regions from the ventral anterior temporal lobe (VATL), including the temporal pole, being critically involved in semantic memory, including person-related knowledge (e.g., Ross and Olson, 2012; Wang et al., 2017; Rice et al., 2018a; Pisoni et al., 2020; see Olson et al., 2013; Lambon Ralph et al., 2017 for reviews). At this level, the recognition deficit extends to multiple cues and generally concerns both face and voice as well as proper name, with evidence of right hemispheric dominance for the first two modalities and left hemispheric dominance for names (Snowden et al., 2012; Gainotti, 2013b; Piccininni et al., 2023). However, the nature of the task also appears to play a key role in lateralization effects (i.e., familiarity judgments vs. semantic judgments or naming; see Borghesani et al., 2019).

At the VOTC level, there is no consensual anatomical boundary between the occipito-temporal and anterior temporal regions. Similarly, at the functional level, we do not adopt here the classic conception according to which there would be a distinction between posterior and anterior regions corresponding to a Perception vs. Memory dichotomy, respectively (e.g., Haxby et al., 2000; Barton, 2008a; Davies-Thompson et al., 2014; Duchaine and Yovel, 2015). Instead, VOTC regions, including the VATL, become selective to faces and facial identities through experience-related synaptic modulations, and therefore all constitute our cortical memories (Fuster, 1997) of faces. Along the postero-anterior axis, rather than a boundary between perception and memory/recognition (for faces), we propose a gradient of increasing/decreasing sensitivity of these cortical memories to semanti-c/physical properties of faces, respectively (Rossion, 2022a; Rossion et al., 2024).

On a practical level, an examination by magnetic resonance imaging (MRI) is essential to define the location of the cerebral lesion(s) or cortical degeneration (without requiring a precise volumetric analysis). It should be noted that different MRI sequences (T1-weighted, T2-weighted, FLAIR) may sometimes be necessary to fully reveal the extent of brain lesions (the patient in Barton, 2008b for example apparently presented only a left hemispheric lesion of the VOTC in T1 MRI examination and T2-weighted, but a FLAIR sequence nevertheless revealed another lesion in the right hemisphere). Even if a structurally healthy tissue may be dysfunctional, an fMRI examination (e.g., of face-selectivity or even sensitivity to face identity differences using adaptation paradigms) is not necessary for the diagnosis of FIR impairment.

When brain lesions are present, their cause must also be taken into consideration: in case of focal lesions (stroke, traumatic injury, tumor, cortical surgery, etc.), behavioral measures can be administered at several stages during the recovery, but at least 6 months after the occurrence of the injury to be considered stable (e.g., see discussion in Brainin et al., 2015 for post-stroke assessment; Baxendale et al., 2019 for recommendations in epilepsy surgery). In the case of neurodegenerative diseases, regular cognitive assessments should be carried out taking into account the test-retest issues with most FIR evaluations (sections 5.4 and 5.5).

### 5.3. General intellectual and sensory abilities

Neuropsychological examination to diagnose a FIR disorder should first include a general cognitive assessment to ensure that the patient exhibits normal intellectual efficiency. The administration of an intelligence scale such as Wechsler's (WAIS-IV in its most recent version; Wechsler, 2008) is therefore recommended in the first place. To our knowledge, there is no evidence of FIR difficulties caused by a general intellectual deficit. However, as mentioned above, it is important to consider in the context of a specific behavioral assessment of FIR, often including complex tasks to perform and instructions that may be difficult to understand.

The evaluation of low-level visual capacities will be ensured by an
ophthalmological examination of visual acuity and visual field, and ideally a measurement of contrast sensitivity (e.g., Freiburg Visual Acuity and Contrast Test, FrACT; Bach, 1996). While possible weaknesses at this level (e.g., scotomas, below normal range contrast sensitivity and acuity) should be taken into consideration during the FIR assessment, their presence does not necessarily imply difficulties at FIR, which does not require a full visual field, high visual acuity and contrast sensitivity (De Haan et al., 1995). Facial identities can indeed be recognized accurately and rapidly even under degraded viewing conditions (middle or even low range of spatial frequencies; see Yan et al., 2022; low contrast; Peterson et al., 2023). In contrast, some manipulations that preserve low-level visual cues, such as picture-plane inversion or contrast-reversal (even with equated luminance) have long been known to dramatically affect FIR performance (Yin, 1969; Galper, 1970, respectively; see also Johnston et al., 1992).

Some battery subtests such as those found in the VOSP (Visual Object and Space Perception battery; Warrington and James, 1991) or the BORB (Birmingham Object Recognition Battery; Riddoch and Humphreys, 1993) can be used to assess other low-level visual abilities (judgments of orientation, size, length on simple stimuli). However, any assessment based on the use of geometric (circle, square, triangle, etc.) or known visual shapes (familiar objects) (e.g., size judgment test of the BORB; incomplete letters of the VOSP) is likely to involve visual knowledge (Gregory, 1966) based on our experience of the environment (Purves et al., 2015) and therefore high-level visual processes (Herzog et al., 2016). Thus, neuropsychologists should always be aware that impaired performance at these tests may not be solely attributable to low-level visual deficits.

## 5.4. Quantitative assessment: FIR tests

Quantitative FIR tests using photographs of faces first appeared in the late 1960s to assess groups of brain-damaged patients (De Renzi and Spinnler, 1966; Warrington and James, 1967; Benton and Van Allen, 1968; De Renzi et al., 1968; Milner, 1968) and it is within this framework that these first tests were made available to clinical neuropsychologists. However, until 20 years ago, these neuropsychologists essentially had access to only two standardized and commercially available tests: the Benton Facial Recognition Test (BFRT; Benton and Van Allen, 1968) and the Warrington Recognition Memory Test for Faces

(WRMT-faces; Warrington, 1984). A larger battery of facial cue recognition tests was also subsequently proposed (Bruyer and Schweich, 1991). However, its FIR tests, in particular when they involved matching unknown facial identities presented alongside, were very poorly controlled, so that even well-established cases of prosopagnosia or visual agnosia could easily pass them (PS: Mayer et al., 1999; NS: Pesenti et al., 2000) while these same patients were highly impaired on similar, better-controlled tests (PS: Rossion et al., 2003a; NS: Delvenne et al., 2004). To our knowledge, Bruyer and Schweich (1991)'s battery is not used in current clinical studies and examinations.

The original BFRT involves matching grayscale photographs of individual (Caucasian) faces, first with a target face appearing on an upper panel to be found among six faces presented simultaneously on a lower panel (6 elements), then with a face target to be found three times among six faces (16 items) (Fig. 3A), for a total score of 54 (i.e., $(6 \times 1) + (16 \times 3)$). The two images to be matched are practically identical in the first six trials, which are relatively easy and can almost serve as a practice. However, the next 16 trials require matching across changes in head orientation (8 trials) or lighting conditions (8 trials) and are much more difficult. A score greater than 40 out of 54 (≥76%) on the BFRT is considered to reflect normal performance at matching face identities, 39–40 is a borderline score, 37–38 reflects moderate impairment, and subjects scoring lower than 37 out of 54 (i.e., less than 68.5%) are considered to be impaired (Benton et al., 1983). A short version of the test, with half of the items (i.e., 27) can also be administered in case of limited time, with the total correct doubled in order to convert scores to those obtained from the long version of the test (Benton et al., 1983).

The WRMT-faces involves presenting grayscale photos of 50 unknown men at a frequency of one item every 3 s, and subjects are asked to respond "yes" or "no" depending on whether they find the face to be pleasant or not. Immediately after the presentation of the target faces, a forced choice is presented between a target photograph identical to the one presented previously and a distractor photograph (of an unknown face, not presented previously). The number of correct choices determines the subject's score, with a maximum of 50. Average scores vary between 42.4 and 44.3 depending on the age group, with standard deviations around 3.5 (Warrington, 1984). While this is a difficult test, it is limited because the stimuli contain non-facial diagnostic cues (e.g., the person's clothing) and the images presented at encoding and recognition are exactly the same (Fig. 4). As with the battery of Bruyer and Schweich

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** A. The Benton Facial Recognition Test (BFRT) is the oldest behavioral FIR test. It was developed for the evaluation of prosopagnosia and for contrasting groups of right and left brain-damaged patients (Benton and Van Allen, 1968; Benton et al., 1983). Most of the test involves identifying the three face photographs among six that represent the same identity as the target face shown above. Figure reproduced from Duchaine and Weidenfeld (2003) with permission. B. The brain lesions that are most (in red) or least (in blue) associated with BFRT deficits in the large-scale study from Tranel et al. (2009). In addition to the dominance of the right ventral occipito-temporal regions, note the involvement of the right occipito-parietal cortex, whose critical involvement may reflect the important visuospatial and attentional demands of the task. Adapted from Tranel et al. (2009), with permission.</figcaption></figure>
Forced-choice phase of the WRMT-faces

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 4. Example of a forced-choice trial from the WRMT-faces (Warrington, 1984). Faces are presented with their external features and non-facial cues (clothing). The presence of these cues is not a problem in itself for the validity of the test, but the fact that the images to be encoded (here the face on the left) are strictly identical during the encoding and recognition phases is problematic. Reproduced from Duchaine and Weidenfeld (2003), with permission.</figcaption></figure>

(1991), the key problem is not so much the presence of non-facial elements but the fact that these elements are stable between the images to match and therefore highly diagnostic for recognition.

In addition to the hundreds of experimental tasks developed by FIR researchers and described in scientific publications for about 5 decades, a whole series of tests have appeared over the last 20 years with the specific goal of providing diagnostic measures of FIR to researchers and clinicians (Table 2). Their great advantage over the traditional WRMT-faces and BFRT is that they exist in computerized version, and several of them are available online (Table 2).

The vast majority of these tests use photographs of faces that are unfamiliar to the participants/patients, which has considerable advantages in terms of test design (i.e., it facilitates control and manipulation of images, it is easier to deal with copyrights/photographic rights) and allows better comparison between individuals' performance (i.e., all participants have the same level of prior knowledge on test faces, that is none). These faces are mostly presented with a so-called "neutral" expression, with their mouths closed, most often in frontal views. In accordance with the definition of FIR above (see also Rossion, 2022a), these tests are all based on the functions of discrimination (between different identities) and generalization (between several – most often different – images of the same identity).

Broadly, two types of tasks can be distinguished, reflecting the difference between the BFRT and WRMT-faces mentioned above, respectively: 1. Matching/discrimination of faces presented side-by-side (often by pairs), used for example in the Glasgow Face Matching Test (GFMT; Burton et al., 2010), the Expertise in Facial Comparison Test (EFCT; White et al., 2015), the Person Identification Challenge Test (PICT; White et al., 2015), the Kent Face Matching Test (KFMT; Fysh and Bindemann, 2018), and the Oxford Face Matching Test (OFMT; Stantić et al., 2022a) with natural images (Fig. 5A), or in the University of Southern California Face Perception Test (USCFPT; Yue et al., 2012), and the Caledonian Face Test (CFT; Logan et al., 2016) with artificial faces (Fig. 5B); 2. Encoding of several individual faces to subsequently match them with (identical or different) photographs of the same identities by discriminating them from distracting faces. The most well-known test using this approach is the Cambridge Face Memory Test (CFMT; Duchaine and Nakayama, 2006) and its many variants (e.g., CFMT+ with an additional, more difficult recognition phase; Russell et al., 2009; CFMT-Aus, adapted for the Australian population; McKone et al., 2011, CFMT-Chinese; McKone et al., 2012; CFMT-C, for children

<caption>Table 2</caption>

List of currently available tests evaluating FIR. The reference of the original study, the principle of the task and its availability are specified for each test. Besides these tests, hundreds of experimental FIR tasks have been developed since the first neuropsychological experiments from as early as the late 1960s in the context of fundamental research.

<table><thead><tr><th>Tests targeting FIR</th><th>Original study</th><th>Task</th><th>Free online access?</th></tr></thead><tbody><tr><td>Benton Face Recognition Test (BFRT)</td><td>Benton et al., (1983); computerized version in Rossion and Michel (2018)</td><td>Matching</td><td>On request to the author</td></tr><tr><td>Caledonian Face Test (CFT)</td><td>Logan et al. (2016)</td><td>Detection of odd one out among 4 face images</td><td>No</td></tr><tr><td>Cambridge Face Memory Test (CFMT)</td><td>Duchaine and Nakayama (2006)</td><td>Encoding new faces and forced-choice recognition</td><td>On request: https://www.testable.org/library#911527</td></tr><tr><td>Cambridge Face Perception Test (CFPT)</td><td>Duchaine et al. (2007)</td><td>Ranking of faces according to similarity with target face</td><td>On request: https://www.testable.org/library#911527</td></tr><tr><td>Expertise in Facial Comparison Test (EFCT)</td><td>White et al. (2015)</td><td>Same/different identity judgment on 2 faces side by side</td><td>No</td></tr><tr><td>Facial Identity Card Sorting Test (FICST)</td><td>Jenkins et al. (2011)</td><td>Grouping of facial images according to perceived identity</td><td>No</td></tr><tr><td>Glasgow Face Matching Test (GFMT)</td><td>Burton et al. (2010)</td><td>Same/different identity judgment on 2 faces side by side</td><td>Yes, https://sites.google.com/site/mikeb.urtonfacelab/glasgow-face-matching-test</td></tr><tr><td>Kent Face Matching Test (KFMT)</td><td>Fysh and Bindemann (2018)</td><td>Same/different identity judgment on 2 faces side by side</td><td>Yes, https://www.kent.ac.uk/school-of-psychology/kentfacematch/index.html?ta=b=downloads</td></tr><tr><td>Local Heroes Test (LOA)</td><td>White et al. (2017)</td><td>Same/different identity judgment on 2 faces side by side</td><td>No</td></tr><tr><td>MEM-III Face Memory Subtest</td><td>Wechsler (1997)</td><td>Encoding new faces and forced-choice recognition</td><td>No</td></tr><tr><td>Models Memory Test (MMT)</td><td>Bate et al. (2018)</td><td>Encoding new faces and forced-choice recognition</td><td>No</td></tr><tr><td>Oxford Face Matching Test (OFMT)</td><td>Stantić et al., 2022a</td><td>Same/different identity judgment on 2 faces side by side</td><td>On request: https://app.gorilla.sc/openmateria ls/134286</td></tr><tr><td>Person Identification Challenge Test (PICT)</td><td>White et al. (2015)</td><td>Same/different identity judgment on 2 faces side by side</td><td>No</td></tr><tr><td>Philadelphia Face Perception battery, Similarity subtest</td><td>Thomas et al. (2008)</td><td>Matching</td><td>Yes, https://cfn.upenn.edu/aguirre/wiki/doku.php?id=public:stimuli_archclinneuropsychology_2008_thomas</td></tr></tbody><tfoot><tr><td colspan="4" style="text-align: right;">(continued on next page)</td></tr></tfoot></table>
<table><caption>**Table 2 (continued)**</caption><thead><tr><th>Tests targeting FIR</th><th>Original study</th><th>Task</th><th>Free online access?</th></tr></thead><tbody><tr><td>Same/different face matching test (SDFMT)</td><td>Mishra et al. (2021)</td><td>Same/different identity judgment on 2 faces side by side</td><td>On request to the author</td></tr><tr><td>UNSW Face Test</td><td>Dunn et al. (2020)</td><td>Two tasks: (1) Encoding new faces followed by old/new judgment, and (2) Encoding new faces followed by pile sorting</td><td>Yes, https://facetest.ps y.unsw.edu.au/</td></tr><tr><td>University of Southern California Face Perception Test (USCFPT)</td><td>Yue et al. (2012)</td><td>Matching</td><td>Yes, https://www.test able.org/experiment /373/598216/start</td></tr><tr><td>Warrington Face Recognition Memory Test (WRMT-faces)</td><td>Warrington (1984)</td><td>(Implicit) encoding of new faces and forced-choice recognition</td><td>No</td></tr><tr><td>Yearbook Test (YT)</td><td>Bruck et al. (1991)</td><td>Pairing</td><td>No</td></tr></tbody></table>

between 5 and 12 years old; Croydon et al., 2014; CFMT-films, with short films shown during the encoding phase, rather than static images; Palermo et al., 2017; CFMT-MY, with Chinese Malaysian faces; Kho

et al., 2023)(Fig. 6).

Generally, in the scientific literature, the first type of task is considered as a 'perception' test (sometimes even stated explicitly, e.g., USCFPT, CFPT), and the second as a 'memory' test (in line with tasks performed by Milner 1968 with patient HM and other patients who had surgery in the medial temporal lobe(s); e.g., Crane and Milner 2002). These two types of tasks are usually presented as two very different constructs (e.g., Petersen and Leue, 2022; Stantić et al., 2022b; Stantić et al., 2023). Since the second type of test is much more difficult, this dissociation often leads to a hasty conclusion that some subjects/patients do not present with a deficit in the perception of facial identities, but rather a deficit in the memory of these identities (e.g., Weigelt et al., 2012). At a theoretical level, this dissociation has also led many researchers to consider that prosopagnosia was a deficit of memory rather than perception (Benton, 1980; Damasio et al., 1982) or that there would be a dissociation between two forms of prosopagnosia (apperceptive vs. associative) (Hécaen, 1981; De Renzi, 1986; Barton, 2008a; Davies-Thompson et al., 2014), in connection with the classical distinction made in the field of visual agnosia (Lissauer, 1890; see Lissauer and Jackson, 1988).

As mentioned above (end of section 3), and in line with Lissauer (1890)'s own qualification of the dissociation between apperceptive and associative forms of visual agnosia (see Farah, 1990; Rossion, 2022a), we consider here that this interpretation and its theoretical implications are highly problematic. Indeed, apart from the level of difficulty which differs between these two major types of tasks, the cognitive operations involved in each task are very different. While it is indeed important to evaluate FIR with a test including unfamiliar faces presented simultaneously to avoid the involvement of generic learning/encoding and short-term memory retention processes, satisfactory performance at this

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Examples of stimuli and tasks from a series of recent FIR tests. A. The GFMT (Burton et al., 2010), PICT (White et al., 2015) and EFCT (White et al., 2015) require a "same" vs. "different" identity judgment. Examples on the top row should elicit a "same" judgment, while those on the bottom row should be answered as "different". Figure adapted from White et al. (2015), with permission. B. Examples of stimuli from the USCFPT (Yue et al., 2012) and CFT (Logan et al., 2016). In the USCFPT, the subject has to match one of the two bottom images with the target face on the top row and all images are derived from a single base identity, modified across eight levels of two dimensions and used throughout all trials of the test. In the CFT, the subject has to spot the different face among three identical faces. Adapted from Yue et al. (2012); Logan et al. (2016), with permission.</figcaption></figure>
CFMT with male faces

<figure><img src="image_9.png" /></figure>

CFMT with female faces

<figure><img src="image_10.png" /></figure>

Training

Training

Block 1
(no noise)

Block 2
(noise added)

Long Form Blocks
(noise, hair, expression,
new view added and
repeating distractors)

<caption>**Fig. 6.** Examples of stimuli from the Cambridge Face Memory Test (CFMT), with the 4 stages of the original version on the top left (Duchaine and Nakayama, 2006) and examples of alternative versions (long form on the bottom, variant with female faces on the right). Adapted from Scherf et al. (2017).</caption>

type of test can still be achieved using unusual recognition strategies, for example by scrutinizing local facial features to differentiate between faces. This issue has long been noted in studies of brain-damaged reported cases of prosopagnosia (e.g., Newcombe, 1979; Christen et al., 1985; Davidoff, 1988; Delvenne et al., 2004). If the stimuli are presented for a relatively long or unlimited time and RT are not measured, absolute performance in this type of test should be subject to great caution (at least when this performance falls within the normal range). Moreover, in order to compensate for the fact that these tests are usually easy to perform with randomly selected pictures of face identities in the population, many researchers have artificially increased their difficulty by maximizing the differences between images of the same face and the similarities between different identities, which limits the ecological validity of the test (Fig. 5; see section 5.5 below and also the critical view in Rossion, 2022a).

Other factors that vary between these two categories of tests are the number of distractors, the number of trials, the use of natural, modified or artificial images, in color or black-and-white, the use of faces presented only in frontal view or from different viewing angles, etc. (Figs. 3–6).

While FIR has also been assessed using known faces (mostly from famous people), their recognition involves additional cognitive processes (e.g., proper name retrieval or access to semantic information) that likely contribute to the subject/patient's performance. Famous face recognition tasks are usually very similar, with several stages (the order of which may vary depending on the task) including a stage that requires the participant to make a familiarity judgment about the face, one in which the participant is asked to name the famous face, and a stage at which the participant gives as much semantic information as possible about the famous person (e.g., occupation, nationality, etc.). Since they involve a strong culture/country-specific component, examples of such tasks have been developed with pictures of many different famous faces (e.g., Boston or Iowa Famous Face Tests in English; Albert et al., 1979; Tranel et al., 1995; CELEB in French; Busigny et al., 2014a; Famous People Recognition Battery in Italian; Quaranta et al., 2016; Famous Face Test for older adults in Dutch; van den Elzen et al., 2023). However, the constant evolution of celebrity status across cultures and generations

makes it hard to design famous face recognition tests that would last in the long run and could be used in all populations, hence the difficulty of assessing this function and standardizing such tests. This does not imply that using pictures of familiar faces should be excluded from the neuropsychological examination of FIR, as will be discussed later (section 5.6).

## 5.5. Test limitations

Unlike the original FIR tests (BFRT and WRMT-faces), all the unfamiliar face tests that have appeared in the last 15–20 years in the scientific literature and are available in computerized version (Table 2) have not been developed in a clinical neuropsychological framework (i.e., developed and evaluated from the onset with neurological populations) but rather to better assess the variability of performance in the normal population in order to identify and characterize possible cases of developmental prosopagnosia/prosopdysgnosia or, on the contrary, "super recognizers". For this reason, most of these tests are not suitable for assessing clinical populations, with instructions that are sometimes difficult to understand, ambiguities on the number of responses (e.g., see the FICST; Jenkins et al., 2011) and complex tasks (CFPT, FICST, etc.). In addition, an important limitation about the GFMT and all the tests that have been derived from it in recent years (EFCT, KFMT, LHT, OFMT, PICT, SDFMT; also the FICST) is that the 'different/same' task on two items is likely to lead to significant response biases, particularly in clinical populations (or children), as some patients may for example consider all faces presented to be different and thus repeatedly press the same ("different") response key. According to our experience in assessing neuropsychological patients, it is always preferable to have them perform forced-choice tasks, with at least two alternatives ('2 alternative forced choice, 2AFC) and balancing response keys. This approach allows patients (and their control subjects) to calibrate their judgments.

Interestingly, while a large number of experimental measures of FIR, including studies investigating patients with prosopagnosia or visual agnosia, use matching tasks with a short delay (e.g., presentation of a target face that disappears before the appearance of two probe faces, one of which corresponds to this target; e.g., Delvenne et al., 2004; Busigny
et al., 2010b), none of the FIR tests currently available and listed in Table 2 are based on this type of manipulation. The main reason seems to be the - vain - desire of researchers to eliminate any memory component, even minimal, in unfamiliar face identity matching tasks. However, this simple manipulation could minimize spatial exploration time of a target face (presented first), preventing unusual strategies based on a slow featured-based detailed analysis of the face. In this case, probe identities would remain on the screen until the response, so that the patient would not be stressed out by a temporal constraint on the time allocated to answer. This issue is discussed below, together with other limitations of the available computerized FIR tests.

### 5.5.1. Manipulation and selection of distractors

In order to minimize the involvement of memory processes and supposedly measure “pure perceptual processes”, a large number of tests consist in discriminating between different facial identities presented simultaneously, side-by-side (e.g., BFRT, GFMT, KFMT, PICT, etc.). The problem inherent to this type of task is that even though subjects/patients may present with significant FIR difficulties in everyday life, they still could perform at a very satisfactory level on these tests: the concurrent presentation of targets and distractors makes it possible to use a detailed/analytic strategy, with several (tens of) seconds available to identify reliable local differences (e.g., eyebrow thickness; Sadr et al., 2003) and respond correctly. This problem had already been pointed out by Arthur Benton, a pioneer in the neuropsychology of face recognition (Sivan et al., 2007), who admitted that even some reported cases of prosopagnosia who could no longer recognize familiar faces in everyday life could nevertheless pass his test, the BFRT, at a satisfactory level (Benton and Van Allen, 1972; see also Davidoff and Landis, 1990).

On a theoretical perspective, this type of observation raised two major problems: first, it reinforced Benton and other researchers' belief that familiar and unfamiliar faces were processed fundamentally differently by the brain (Benton, 1980; Hancock et al., 2000; Young and Burton, 2018), and supported the weak and highly debatable distinction between apertive and associative forms of prosopagnosia (De Renzi, 1986; see the critical views by Davidoff and Landis, 1990; Farah, 1990; Delvenne et al., 2004; Rossion, 2022a). To counter this methodological difficulty, the majority of BFRT items exhibit facial photographs that also differ significantly from the target to be matched (Fig. 3A). However, without a time limit, this is not enough to avoid an honorable performance even in the event of an otherwise recognized FIR deficit (Duchaine and Weidenfeld, 2003). Rather than minimizing spatial exploration time of a target face identity as suggested above, in order to avoid ceiling effects as well as to maximize performance variability in the general population, recent computerized tests have introduced artificial manipulations to make distractors more similar to targets in terms of their internal traits, mainly using morphing techniques (CFPT; Duchaine et al., 2007; USCFPPT; Yue et al., 2012; CFT; Logan et al., 2016, Figs. 5 and 6). This technique is also based on the theoretical framework of 'face-space', according to which our face processing system would represent face identities according to a physical multidimensional space (Valentine, 1991; Valentine et al., 2016). With respect to the GFMT, which is not morphing-based, the distractors are also extremely similar to the targets in the "different" trials (Fig. 5) because they were selected based on the worst-performing trials in the normal population (Bruce et al., 2001). The short version of the GFMT goes even further by using a sub-selection of the 20 pairs of different faces associated with the highest error rate (Burton et al., 2010).

However, studies of genuine cases of prosopagnosia show that artificially increasing physical similarity between unfamiliar faces, especially through morphing, does not lead to more sensitive measurements and may in fact decrease the contrast between patients and control subjects' performance (Busigny et al., 2010b; see also Busigny et al., 2010a; Busigny et al., 2014b; Rossion, 2022a). This is completely understandable: when distractors are artificially similar to target faces, a typical observer, i.e., without FIR deficit, especially if given long or

unlimited time, will tend to analyze local facial features more and increase their number of eye fixations, similarly to what a prosopagnosic patient would do (Orban de Vivry et al., 2008) – just think about how you would try to tell the faces of identical twins apart. On this subject, it is important to insist on the fact that FIR deficits in everyday life are generally not due to physical confusion (i.e., not recognizing someone because they look like another person in terms of internal facial features), and that we do not live in environments where the faces we encounter are objectively very similar in terms of their internal features. For these and other reasons (e.g., external features removed or absence of color cues, which play a diagnostic role in FIR; Yip and Sinha, 2002), (ecological) validity of these tests is limited (Figs. 3–5).

### 5.5.2. Strengths and limits of the CFMT

Due to its popularity among face recognition researchers, the CFMT is available in many different versions adapted to different ethnicities (e. g., McKone et al., 2011, 2012; Cheng et al., 2016; Kho et al., 2023; see also section 5.5.6), which is a considerable advantage. Its psychometric properties have also been extensively investigated, demonstrating an excellent internal consistency (Cronbach's alpha = 0.89 in Bowles et al., 2009; 0.88 for the CFMT-Aus in McKone et al., 2011, 0.86 to 0.94 for the original CFMT or CFMT-Chinese with Asian and European observers in McKone et al., 2012; 0.90 to 0.94 for the CFMT + administered in-lab or online in Petersen and Leue, 2021, 2022; 0.86 for the CFMT-MY in Kho et al., 2023) and satisfactory test-retest reliability (although it depends on the interval and version administered; r = 0.70 for a 6-months test-retest interval in Wilmer et al., 2010; 0.84 for a 24-hr delay with the CFMT-Aus in McKone et al., 2011; 0.68 for longer but variable intervals across participants with the CFMT administered in-lab or online in Murray and Bate, 2020; 0.89 for a 12-weeks interval with the online CFMT+ in Petersen and Leue, 2022; 0.67 for a 14-days interval with the online CFMT in Stantić et al., 2022a). It has also been found to correlate well with other FIR measures (e.g., Bowles et al., 2009; Palermo et al., 2017) but not with similarly constructed non-face measures (e.g., an equivalent task with abstract art stimuli; Wilmer et al., 2010; or the Cambridge Car Memory Test; McKone et al., 2011; Dennett et al., 2012). Its inconsistent correlation strength with self-report as assessed by the PI-20 (e.g., ranging from r = -0.50 to -0.14 depending on studies; Gray et al., 2017; Palermo et al., 2017; Tsantani et al., 2021) is in line with other studies mentioned in section 5.1 suggesting that typical participants have moderate insight into their face identity recognition abilities (but note the current debate about the relevance of subjective measures in DP/CP participants; Burns et al., 2022; DeGutis et al., 2023b).

Despite these undeniable strengths and its popularity, the CFMT also has significant limitations for neuropsychological practice. First, it is very difficult to perform, involving several FIR processes (explicit learning and retention of new face identities in addition to discrimination of target faces from others) and requires the integrity of many cognitive functions (visual analysis, short-term visual memory, selective and spatial attention, decision making, etc.). The high level of difficulty has been in place from the start for two reasons. First, to prevent reported cases of developmental prosopagnosia/prosop dysgnosia to obtain satisfactory performance on the test (Duchaine and Nakayama, 2006), an objective certainly achieved. Second, to maximize interindividual variability across neurotypical individuals precisely for the purpose of identifying people with prosop dysgnosia. However, in neuropsychology, the value of a test is rather measured in terms of the contrast it highlights between the performance of people who are truly deficient at the targeted function - here, FIR - and the normal population, requiring to minimize the variability of performance in the latter population. Since the CFMT leads to high variability in the normal population and can even be failed by individuals who do not have objective FIR difficulties (e.g., Elbich and Scherf, 2017), this contrast is not the highest compared to other tests (e.g., Fysh and Ramon, 2022). Moreover, besides the fact that many cognitive operations beyond FIR are involved to perform the CFMT, the core of the test does not rely on solid theoretical
foundations: the targets to be matched simply vary in terms of depth rotation of the head (e.g., frontal view and ¾ profile view), a manipulation that largely preserves low-level features and with relatively moderate changes to lighting conditions (a latter manipulation that is more challenging for FIR; see Rossion and Michel, 2018; Mishra et al., 2021). Similarly, adding white noise to face photographs (see Fig. 6) has no theoretical basis, and this subpart of the test does not seem to add any diagnostic element (Corrow et al., 2018; Murray and Bate, 2020). Finally, like all tests using unfamiliar faces (or known faces with a single version using the same images), a major problem concerns the test-retest effect: even though the CFMT is often lauded for having a high reliability coefficient (intra- and inter-session) across a population of participants (e.g., Wilmer et al., 2010; Petersen and Leue, 2022), there may nevertheless be an overall improvement in performance just because participants become familiar with the facial identities to be explicitly encoded in memory when first taking the test (see practice effects in Wilmer et al., 2010; Murray and Bate, 2020; but see also Stantić et al., 2022a for an absence of improvement). Moreover, re-administering this same test means that the distracting faces will have already been seen a first time (i.e., familiarized), blurring the distinction between old and new faces.

In summary, although the BFRT has undeniable advantages due to its use in many studies (reporting performance standards for different populations) and its availability in many versions, it also has a number of weaknesses and we have found it generally difficult to administer in clinical populations (i.e., with neurological patients suffering from Alzheimer's disease, TLE, fronto-temporal 'semantic' dementia, ...).

### 5.5.3. Strengths and limits of the BFRT (classic version)

Despite the proliferation of FIR tests over the past two decades, the BFRT still appears to be one of the most (if not the most) relevant (Mishra et al., 2021), especially in its computerized version taking into account the overall RT of participants (BFRT-c; Rossion and Michel, 2018). A new computerized version of this test (i.e., with new, more recent photographs) is also available (BFRT-r; Murray et al., 2022). Although the BFRT is difficult to perform (as illustrated by the absence of a ceiling effect; Benton and Van Allen, 1972), its instructions are relatively straightforward. Overall, the stimuli used are also well controlled, since they have no external features such as hair or obvious clues (facial hair, glasses, etc.) but the general shape of the faces is more visible than when external features are trimmed. The original BFRT also has the advantage of correcting for participant age although separate norms for female and male participants would also be welcome (a limitation of most available tests, see section 5.5.6). While slightly lower than the CFMT, its internal consistency is also satisfactory (Cronbach's alpha = 0.72 in Christensen et al., 2002; 0.608 in Albonico et al., 2017; 0.69 in Palermo et al., 2017; 0.74 in Mishra et al., 2021), as is its test-retest reliability (r = 0.71 for a 13 months interval in Christensen et al., 2002).

Yet, the BFRT is not without limitations. A caveat of the test is that it is not possible for the patient to know which faces they have already selected among the six that are presented (i.e., there is no visual indication allowing the patient to know that they have already pointed/clicked on specific face items). In other words, it is necessary for the patient to have good short-term visuospatial memory retention as well as good visuospatial attention capacities ("Where are the faces on which I have already clicked?"), at the risk of seeing them pointing/clicking several times on the same face item. This issue could explain the important involvement of the right parietal regions when performing the BFRT (Tranel et al., 2009, Fig. 3B). If the test is adapted to take this factor into consideration in the future (e.g., indication of items already selected, limit to one target per trial instead of three), we think that it will be improved although this would require the collection of new normative data. In any case, concurrent presentation of 7 pictures of faces to a participant (Fig. 3) is overwhelming from a cognitive standpoint. Possibly for this reason, performance at the BFRT has been reported to be poor in some aphasic patients with oral comprehension

difficulties (Hamsher et al., 1979). In our recent experience, a number of patients diagnosed with Alzheimer's disease (Mini-Mental State Examination [MMSE] between 16 and 27) also show difficulties to perform the task, in part because they tend to forget which of the three faces has been already selected (although see Lavallée et al., 2016 for normal performance of AD patients - with relatively high MMSE scores - at the BFRT). Finally, the BFRT may also be affected by test-retest effects: again, although measures of test-retest reliability and internal consistency may be satisfactory (e.g., Christensen et al., 2002; Albonico et al., 2017; Mishra et al., 2021), performance may also improve overall because participants become familiar with the specific face stimuli of the test.

### 5.5.4. Recognition speed

As mentioned above, for the adult human being, FIR is very fast: a single glance is usually enough to recognize a facial identity (Hsiao and Cottrell, 2008; Orban de Xivry et al., 2008; Chakravarthula et al., 2021; Retter et al., 2021), and electrophysiological data suggests that the function can be largely accomplished in 1/3 of a second (Caharel et al., 2014; Barragan-Jason et al., 2015; Wiese et al., 2019; Yan and Rossion, 2020). If you systematically take a few seconds to recognize that the faces of your friends (or foes) are familiar in everyday life, your FIR ability is certainly deficient.

FIR processing speed is therefore an important element, which should be taken into account in neuropsychological evaluation. Unfortunately, a behavioral test does not provide a direct measure of processing speed. At best, it measures a response time (RT), which can be informative but is sometimes difficult to interpret. An abnormally slow behavioral RT on a FIR test may be due to multiple causes. For example, the FIR function may be perfectly functional and fast, but the individual wants to be certain of their answer, or prefers to take their time, or lacks confidence, or is uncomfortable with keyboards and needs to look at the keys on which they want to press to answer during the task, etc. Or, this person may have attention difficulties, concentration issues, fatigue, or is generally slowed down due to heavy medication, etc. In other words, if FIR is deficient, RT will generally be longer in a diagnostic task (unless the participant responds immediately at random to each item without even trying to do the task). However, the reverse is not true: a prolonged RT does not necessarily imply a deficit at FIR. This is one of the reasons why neuropsychological FIR tests – like many tests – do not include RT measures. Another reason is the absence of norms and the difficulty of collecting them. While it is relatively simple to define accuracy thresholds for a task, which can be easily understood by anyone looking at the scores, it is more complicated for RT (e.g., while 80% accuracy may give an initial idea of the subject's performance, using a normality threshold of, e.g., 1100 ms per item, may seem meaningless if norms are not easily accessible). In addition, RT vary more than accuracy rates, depending on age for example (e.g., Ebaid et al., 2017) and require proper norms for each age group. Another reason for the lack of attention paid to RT is that most of the classic neuropsychological tests, such as the BFRT and the WRMT-faces, were developed before the proliferation of computer resources and existed only in "paper/pencil" versions (but see below for a discussion on the computerized version of the BFRT). This is the case for many tests, and at this level, it is regrettable that clinical neuropsychology has evolved (too) little in recent decades in its current practice.

Almost all the FIR tests listed in Table 2 exist in computerized version and should technically allow RT to be taken into account. However, they were not designed to do so, and are therefore seriously limited. Currently, the BFRT is one of the only tests to have standards in terms of percentage of correct answers and average RT (BFRT-c; Rossion and Michel, 2018; BFRT-r; Murray et al., 2022), which makes it advantageous for FIR assessment. However, as mentioned above, the test requires three responses per panel for most trials (Fig. 3A) and, even in its computerized version (BFRT-c or BFRT-r), one cannot isolate RT for each response unambiguously so that the RT measured in the BFRT-c is
global (i.e., total time to perform the test, generally around 3 min in young adults, with a normal distribution of RT; see Rossion and Michel, 2018). This prevents the removal of abnormally slow RT ('outliers'). This measure should nevertheless be considered during FIR assessment, especially considering that norms exist (at least for the young adult

population; Rossion and Michel, 2018; see also the BFRT-r, Murray et al., 2022). The CFMT does not include RT measurements (and norms) (with the exception of the Chinese version; McKone et al., 2017). According to a recent study, such measurements would bring little to no benefit (DeGutis et al., 2022) but this statement is only valid at the level

<figure><img src="image_11.png" /><figcaption>**Fig. 7.** Examples of stimuli from a new FIR test (see Volfart et al. 2022; Volfart et al., 2023b). **A.** Familiar and unfamiliar face matching test in upright and inverted orientation (above). Performance of the prosopagnosic patient PS on this test, compared with age-matched controls (below) (Volfart et al., in preparation). The graph on the left represents the accuracy rate, while the graph on the right represents the average RT for each condition. Colored dots represent individual performance of control subjects. Face photographs are licensed under Creative Commons, attribution left to right for each triplet: Sylvi Listhaug: M. Brakestad/Stortinget, P. Sønstrød; Mélanie Joly: Province of British Columbia; Lars Ohly: Frankie Fouganthin; Lars Leijonborg: Arild Vågen; François Hollande: A. Bouirabdane, C. Truong-Ngoc; Jean Reno: El Hormiguero; Angela Merkel: Aleph, politik.in2pic.com; Theresa May: Annika Haas. **B.** Face and name familiarity tests (left). Performance of PS on these two tests compared to control participants (right). Face photographs are licensed under Creative Commons, attribution left to right, top to bottom: Terje Riis-Johansen: https://flic.kr/p/wh35rD (CC BY-ND 2.0), BildeforBilde, Kåre S. Pedersen; Nicolas Sarkozy: https://flic.kr/p/A61tiY (CC BY 2.0), European People's Party; M. Lupi: https://flic.kr/p/bDbP6n (CC BY 2.0), Maurizio Lupi; Claudia Roth: https://flic.kr/p/ddEFy7 (CC BY-SA 2.0), Gruene Berlin; Marissa Mayer: https://flic.kr/p/c4mq3h (CC BY 2.0), P. Zimmerman; Marine Le Pen: Wikimedia Commons (CC BY-SA 4.0), C. Truong-Ngoc.</figcaption></figure>
of the normal population when trying to identify cases of (prosop) dysgnosia, and may not be true in a clinical setting.

Going back to unfamiliar face tests with photographs presented side-by-side like the BFRT, we consider that completely ignoring (correct) RT is a mistake: studies clearly show that even brain-damaged patients with prosopagnosia/visual object agnosia, who are profoundly deficient in FIR, can obtain satisfactory performance in this type of test if they are given enough time (e.g., Lhermitte et al., 1972; Newcombe, 1979; Christen et al., 1985; Delvenne et al., 2004; Bukach et al., 2006; Jansari et al., 2015; Liu-Shuang et al., 2016). When adding a RT constraint to respond, the same patients can become highly deficient at the same test (e.g., BFRT with 17 s per item in Bukach et al., 2006). However, putting a subject (Bindemann et al., 2016; Fysh and Bindemann, 2017), or a *fortiori* a neuropsychological patient, under time pressure to perform a task may lead to unusual strategies or increased stress leading to completely random responses. For this reason, it is better to measure RT during a given task without crudely interpreting it (i.e., slow = deficit) but rather comparing it to RT obtained at other tasks (i.e., is the patient generally slowed down? Or specifically on this task, with these items?). For example, due to sensory deficits, general visual recognition, or attentional/visual-spatial impairments, patients with right-hemisphere posterior brain damage are typically slowed down in tasks involving visual recognition, regardless of the material tested (Arrigoni and De Renzi, 1964; De Renzi and Faglioni, 1965; Young et al., 1993a; see also Benton, 1986). Similarly, patients with TLE are typically slowed down in visual recognition tasks across the board, especially when their epilepsy is lateralized to the right hemisphere (Volfart et al., 2020). Before diagnosing FIR deficits, it is therefore necessary to take into account the degree of specificity of these slowdowns as well as qualitative aspects, as discussed below.

### 5.5.5. Qualitative information: inversion

One of the most glaring, and surprising, shortcomings of all of the tests mentioned in Table 2 is the lack of comparison between the performance for upright faces and that for the same faces presented upside-down (i.e., with a rotation of 180° or a reversal along the horizontal axis of the image; see Fig. 7A for an example). Human adults' FIR ability is profoundly disrupted by stimulus inversion, regardless of the task and the type of stimuli used (e.g., Yin, 1969; Farah et al., 1995; Freire et al., 2000; Rossion, 2008 for review). This Face Inversion Effect (FIE; Yin, 1969)<fnref n="1" /> is arguably the most robust effect in the field of facial recognition, and constitutes one of the most robust effects in experimental psychology in general. In standard tasks, face inversion typically causes a decrease in performance of the order of 20%, also with a significant RT increase (Robbins and McKone, 2007; Rossion, 2008). While the functional account of the FIE has been debated for decades (e.g., does it affect FIR qualitatively or quantitatively? If the effect is qualitative, which features/processes are most affected by inversion?; see Rossion, 2008; Tanaka and Gordon, 2011; Civile et al., 2016; Murphy et al., 2020), the brain regions underlying the FIE appear to be located in the VOTC, with a right hemispheric predominance (Fig. 2) (Yovel and Kanwisher, 2005; Mazard et al., 2006; Jacques et al., 2020). Most importantly perhaps for the present purpose, inversion completely preserves the physical differences between the facial identities to be compared, and is therefore an excellent control, which can also help differentiating the causes of FIR deficits. If cases of prosopagnosia or visual object agnosia generally present a substantially reduced or even completely abolished face inversion effect (Busigny and Rossion, 2010b), this is not the case for patients suffering from a general person recognition deficit (e.g., in the right variant of fronto-temporal dementia, also referred to as semantic dementia; Busigny et al., 2009). In

principle, the magnitude of the inversion effect should not be affected in case of low-level sensory deficits or general intellectual impairment (unless performance is at floor regardless of stimulus orientation). The magnitude of the effect is also preserved in TLE patients (left or right-sided; Volfart et al., 2020; Volfart et al., 2023a). However, the FIE can be reduced in patients with Alzheimer's disease (Lavallée et al., 2016). This could be due to the fact that, while Alzheimer's disease primarily concerns regions of the medial temporal lobe, structural and functional alterations extend to lateral and posterior cortical structures of the temporal lobe such as the fusiform gyrus, even in the early stages of the disease (e.g., Bokde et al., 2006; Whitwell et al., 2007; Ma et al., 2020).

### 5.5.6. Norms and diagnostic cutoffs

Many of the FIR tests reviewed in Table 2 lack up-to-date (if any) norms taking into consideration demographic characteristics such as age, gender and/or ethnicity that have been found to influence FIR performance (e.g., McKone et al., 2012; Wang et al., 2020a). Although the BFRT and CFMT have recently benefited from such initiatives (e.g., Bobak et al., 2016b in young British adults with the CFMT+; Albonico et al., 2017 in young Italian adults with the BFRT and CFMT; Rossion and Michel, 2018 in young Belgian adults with the BFRT-c; Wang et al., 2020a in children and adults with the BFRT), the proposed norms are rarely comprehensive, i.e., including norms by age and gender in a given ethnicity.

The multiplicity and diversity of human ethnic groups also call for versions of these tests using different, adapted face stimuli and providing associated norms. The CFMT format has been popular in this regard, with many research teams creating their own version such as the CFMT-Aus with Caucasian Australian faces (McKone et al., 2011), the CFMT-Chinese with Han Chinese face (McKone et al., 2012, 2017), the Taiwanese Face Memory Test (TFMT) with Taiwanese faces (Cheng et al., 2016) or the CFMT-MY with Chinese Malaysian faces (Kho et al., 2023). However, efforts remain to be made to provide clinical neuro-psychologists with alternative versions and norms using faces from other underrepresented ethnic groups in the FIR literature (e.g., populations from Africa using, e.g., Muhammad et al., 2021 recent database of 38, 546 African face images with multiple views and lighting conditions).

In addition to this lack of norms, few FIR tests provide neuropsychologists with clear cutoff recommendations for identifying impaired FIR performance. Notably, a recent study highlighted that, while percentile cutoffs are more robust to the sample distribution deviating from normality, and thus less likely to overestimate the prevalence of FIR disorders, using z-score cutoffs (based on mean performance minus 2 standards deviations (SD)) remains the preferred approach in most studies interested in diagnosing FIR disorders (DeGutis et al., 2023a).

In the BFRT manual (Benton et al., 1983), scores above 40/54 are considered as reflecting normal performance. Recent studies have highlighted similar findings, with normative data showing a cutoff between 39 and 40 (based on the 5th percentile) or at 38/54 (-2SD from the mean) in young Belgian adults (Rossion and Michel, 2018), at 41/54 (-2SD from the mean) in young Italian adults (Albonico et al., 2017), or at 38/54 (-2SD from the mean) in American adults between 16 and 55 years old (Wang et al., 2020a).

Regarding the CFMT, cutoffs used to diagnose FIR disorders are highly variable between studies, which either used between 1.7 and 2 SD away from the mean, percentiles or a set numerical score (see Barton and Corrow, 2016a; DeGutis et al., 2023a). In addition to these variations in cutoff criteria chosen by the researcher or neuropsychologist, the population to whom the test is administered is likely to influence the cutoff on this task: among participants from different countries (USA, Israel, Italy, Australia/New Zealand, Germany) tested with the standard CFMT, cutoffs (at -2SD from the mean) have been found to range between 35/72 (Germany; Herzmann et al., 2008) and 42/72 (USA; Duchaine and Nakayama, 2006) (see Bowles et al., 2009; Albonico et al., 2017), further urging for the use of appropriate, ethnicity-matched norms.

<footnote n="1">¹ Technically, the inversion effect was originally defined by Yin (1969) as the greater deficit for the category of faces than for other categories of mono-oriented objects.</footnote>
While issues about cutoffs have been raised by other researchers (see e.g., Burns et al., 2022; DeGutis et al., 2023a), most of these discussions gravitate towards better identifying and diagnosing cases of developmental prosopagnosia/prospdysgnosia. In comparison, studies investigating diagnostic cutoffs for acquired FIR disorders in patients with brain lesions are, to our knowledge, surprisingly dated (e.g., Benton and Van Allen, 1968).

## 5.6. Specificity of FIR and multimodal person recognition disorder

When a patient presents with FIR disorders, it is legitimate to ask how specific the disorder is, i.e., is the recognition of other visual forms impaired?

Since the original definition of prosopagnosia by Bodamer (1947; section 3), this issue has been raised repeatedly: is prosopagnosia really 'pure', i.e., only affecting the category of faces? Asking this question is basically asking whether prosopagnosia exists or not (Rossion, 2018a). We know now that the answer to this question is positive, i.e., there are cases where the deficit does not concern the visual recognition of other objects (Rossion et al., 2003a; Riddoch et al., 2008; Busigny et al., 2010a), even when RTs are taken into consideration (see Busigny et al., 2010a). In addition, the hypothesis that FIR deficits could be explained by a higher visual similarity between exemplars of the face category than between members of other visual categories (Faust, 1955; Damasio et al., 1982; Gauthier et al., 1999) has been refuted (Busigny et al., 2010b; see also Busigny et al., 2010a; Busigny et al., 2014b; see Rossion, 2018a for review). Nevertheless, it remains that most patients defined as cases of prosopagnosia also present with visual object recognition disorders (although their recognition of written letters and words is generally preserved) (e.g., Levine and Calvanio, 1989; Gauthier et al., 1999; Barton, 2008a). The fact that their impairment is less severe (in appearance) for the recognition of objects than that of facial identities does not matter: these patients should rather be diagnosed as cases of visual (object) agnosia – even though they are not impaired to the point of 'taking their husband for a hat' (Rossion, 2018a). An erroneous diagnosis (i.e., when these patients are defined as cases of prosopagnosia) can lead to significant errors of interpretation about the nature of prosopagnosia (e.g., "prosopagnosia is a general deficit in recognition of visually similar exemplars", Gauthier et al., 1999; or "prosopagnosia is a general deficit in holistic/configural processing", Levine and Calvanio, 1989; Behrmann et al., 2005; Barton, 2009) and FIR (e.g., "FIR would essentially be an issue of fine-grained visual discrimination" or "would involve the same processes as those involved in Navon tasks", respectively).

To ascertain the specificity of the disorder, basic-level naming tests with living and non-living objects (e.g., colored Snodgrass & Vanderwart images; Rossion and Pourtois, 2004) can suffice. These tests can possibly be supplemented by tests assessing the recognition of fruits and vegetables (visually similar objects whose recognition is often highly deficient in cases of visual agnosia; Barton, 2008a) and forced-choice discrimination test with non-face exemplars (e.g., cars), taking (correct) RTs into consideration (Busigny et al., 2010b; Volfart et al., 2020).

Still relating to specificity, another issue concerns general person recognition disorders in the context of semantic deficits linked to anterior temporal lobe damage. Many studies have shown the involvement of these anterior temporal regions in semantic memory processes, whether by analyzing the neuropsychological profile of patients with impairment in this domain (e.g., in the semantic variant of frontotemporal dementia: Hodges and Patterson, 2007; in herpetic encephalitis: Noppeney et al., 2007; in epilepsy: Jaimes-Bautista et al., 2015; following a stroke: Patterson et al., 2015; or following temporal lobectomy: Rice et al., 2018a), using functional neuroimaging (e.g., Rice et al., 2018b), or performing electrical or magnetic stimulation on these regions during semantic tasks (e.g., Woollams et al., 2017).

Since recognizing the identity of a person by their face, name, voice or any other information requires access to semantic memory to some

extent, whenever anterior temporal regions are affected, it is crucial to consider possible alteration of person recognition processes involving faces but also other inputs. Indeed, brain damage to anterior temporal lobe regions often leads to a *multimodal person recognition disorder* (Gainotti, 2007, 2013a; Blank et al., 2014; Rapcsak, 2019), and many patients who have been defined as cases of (associative) prosopagnosia are actually better characterized by this more general profile (e.g., the patient PV in Sergent and Poncet, 1990; see Boudouresques, 1979; see also Schroeger et al., 2022). Unlike cases of prosopagnosia, these patients have difficulty recognizing people by other non-visual clues such as their name or their voice, or when presented with biographical facts about these people (e.g., "this person is the current president of the French Republic") (e.g., Neuner and Schweinberger, 2000; Gainotti et al., 2003; Giovanello et al., 2003; Joubert et al., 2006; see also Gainotti, 2013a). Interestingly, many studies report a laterality effect of the brain lesion(s) on the pattern of performance at person recognition tasks: difficulties at recognizing someone from their name are more prevalent if the lesion/atrophy is *left*-lateralized, whereas contralateral *right* involvement is more frequently associated with difficulty identifying someone from their face or voice (Gainotti, 2007; Luzzi et al., 2017; Snowden et al., 2018; Schroeger et al., 2022).

In this context of multimodal person recognition disorder, cognitive disorders are no longer restricted to visual recognition but are rather attributed to a degradation or loss of knowledge stored in semantic memory. Although these multimodal person recognition disorders have sometimes been observed apparently in isolation (e.g., Neuner and Schweinberger, 2000; Thompson et al., 2004), such cognitive pattern is more frequently found in the context of general semantic disorders, with impaired performance in other tasks assessing semantic memory (Busigny et al., 2009; Blank et al., 2014; Mendez et al., 2015; Rice et al., 2018a; Snowden et al., 2018).

To better assess people recognition disorders, especially in the presence of an anterior temporal lesion/atrophy, it is recommended to evaluate this function with several tasks (e.g., retrieving biographical information about a person from their name, voice or face), in conjunction with other tasks assessing the integrity of semantic functions more generally. Ideally, the neuropsychologist should assess the recognition of people by their name, for example by proposing a multiple-choice task where the patient selects the name of the famous person among fictitious names (Volfart et al., 2022; Volfart et al., 2023b; see Fig. 7B), or by asking the patient to provide as much semantic information as possible about famous people with their name as input. The assessment of voice recognition may also be an important aspect in distinguishing between FIR disorder and multimodal person recognition disorder (Boudouresques, 1979; Gainotti, 2013a; Schroeger et al., 2022). Unfortunately, voice recognition tests are very language-dependent and, to our knowledge, are available only in English (Tsantani and Cook, 2020), German (Schweinberger et al., 1997) or Italian (Quaranta et al., 2016). A number of voice discrimination tests based on learning and recognition of unfamiliar voices, such as the Bangor Voice Matching Test (Mühl et al., 2018), the Glasgow Voice Memory Test (Aglieri et al., 2017), or the Jena Voice Learning and Memory Test (Humble et al., 2022), can also be used to compare the subject's performance at discriminating unfamiliar faces or voices when semantic memory is not required.

However, recognizing people by their voice is a much more difficult task than recognizing people by their face (Barsics, 2014 for review; see also Papagno et al., 2021), which must be taken into account when comparing several recognition modalities. In addition, naming tasks such as the Boston Naming Test (Kaplan et al., 1983) or semantic matching tasks such as the Pyramids and Palm Trees Test (Howard and Patterson, 1992) or the Camel and Cactus test (Bozeat et al., 2000) can be used to assess general semantic memory functioning. A famous monument recognition task (see Mina et al., 2010 for an example) may also be interesting to administer because this type of stimuli is one of the few to represent a single entity (e.g., like a face identity, the Eiffel tower
is a unique entity, well identifiable among the general category of towers, and associated with specific semantic information).

In conclusion, in line with Gainotti (2007, 2010), we think that it is important to differentiate between recognition disorders that are limited to the visual modality of faces, the typical example being prosopagnosia (section 2), and multimodal person recognition disorders, which can affect any modality (face, voice, name, etc.) and are often found associated with other, more general semantic impairments.

## 5.7. A new FIR test

Based on the above considerations, we have recently developed a new FIR test in which the participant is presented with triplets of images, with a photograph of the target face above two other photographs of faces (Fig. 7A; Volfart et al., 2022; Volfart et al., 2023b). One of these two photographs represents the same facial identity as the target face, while the other photograph is a distractor face. The participant's task is to indicate, by pressing a key (left or right) on a computer keyboard or on a response box, which of the two photographs below represents the same person as the photograph above (the correct answer is on the left in 50% of the trials). RT is measured for each item and the participant is invited to respond as soon as they think they know the correct answer.

Twenty-two triplets show pictures of familiar faces, i.e., celebrities known to the French population (from politics, TV shows, cinema, sports, etc.), and twenty-two other triplets show pictures of unfamiliar faces, i.e., celebrities (also Caucasians) from other countries and unknown to the French community. Regardless of familiarity, distractor faces are selected based on their general physical resemblance with the target (gender, age, similar haircut and color, presence of glasses or beard) to avoid an easy, non-identity-based decision. However, unlike the GFMT, the selection is not based on a resemblance of internal facial features and, most importantly, there is no pre-selection of the most difficult items among a larger sample. Photographs are taken under natural conditions (collected from the internet): no image manipulation (other than orientation), presence of external facial elements, natural changes in lighting and facial expression, slight variations in orientation of the face (but mainly frontal view). The pictures include elements of clothing and a natural background, but these elements are not diagnostic since they vary between images of the same face identity (Fig. 7A). Each triplet is presented once at upright orientation and once upside-down, in pseudorandom order to avoid repetition of the same identity within the same block.

The test therefore provides several measures aimed at evaluating FIR both quantitatively (i.e., accuracy rates and mean correct RTs) and qualitatively (i.e., advantage for familiar faces, a well-known behavioral effect, e.g., Bruce et al., 2001; Jenkins et al., 2011; and the FIE, e.g., Yin, 1969; Rossion, 2008; see section 5.5.5).

Furthermore, the administration of this test to the case of prosopagnosia PS revealed a clear contrast between her performance and that of age-matched controls (Volfart, Michel & Rossion, in preparation; Fig. 7A; see also Rossion, 2022a). First, PS does not detect the presence of trials with familiar faces during the task, all faces appearing unknown to her. Second, although PS's correct response rate is overall above chance level (80.7% correct responses across the 88 trials, for a chance level at 50%), it is significantly lower than controls for upright trials (79.5% for PS vs. 97.7% for control subjects), while her correct response rate for upside-down trials is similar to that of control subjects (81.5% vs. 82.1%). In other words, PS do not show a typical FIE, in agreement with all the data observed in this patient in other tests (see Busigny and Rossion, 2010b). Unlike controls, PS performance also does not show any face familiarity effect, with similar performance on familiar and unfamiliar trials. Finally, PS is 4.6 times slower than control subjects to complete the task.

To this familiar/unfamiliar face identity matching test can be added two tests assessing face and name familiarity. The face familiarity test consists of three faces presented side-by-side, one representing a famous

face and two others representing unfamiliar distractor faces (Fig. 7B). The participant indicates (without verbal output) which of the three faces is the famous person. As with the matching test, photographs are taken under natural conditions, and face identity triplets are created based on general physical resemblance between distractors and target. The name familiarity test follows the same principle, replacing the famous face by its written name, and the two distractor faces by two fictitious names, phonologically similar to the famous name (Fig. 7B). The famous identities presented in the face familiarity and name familiarity tests are identical, thus allowing the comparison of performance obtained from two different types of inputs (faces and names).

Evaluated with these two tasks, PS performed at chance level on the face familiarity test (36% accuracy, versus 33% at chance level) while her control subjects performed at ceiling. In contrast, PS obtained a perfect score (100% correct responses) on the name familiarity test, clearly illustrating the dissociation between face and name identity recognition in this patient (Rossion, 2022a). The FIR tasks described here have also shown their validity in two recently reported cases of 'transient prosopagnosia' due to electrical intracerebral stimulation in face-selective regions of the right (anterior and middle) fusiform gyrus (Volfart et al., 2022; Volfart et al., 2023b).

Beyond its validation with such rare cases, the novel FIR test shown here, with different subtasks, aims at evaluating FIR in neuropsychological populations in general. It appears to have many advantages in this endeavor: both accuracy and RT are measured, the instructions are easy to understand and perform, there is only one face to select/exclude, no naming is involved, and it is not necessary to explicitly retrieve the identity of famous people to be able to succeed in the face matching task, the familiarity effect being reflected by the relative difference in performance between familiar and unfamiliar triplets. Admittedly, and although they were specifically developed to overcome the current limitations of FIR tests (taking into consideration the FIE and the familiar/unfamiliar comparison, presenting natural images, etc.), these three tests are not devoid of limitations, the main one being their current lack of published norms. Another limitation that generally applies to the presentation of famous faces is that celebrities are different within different populations/cultures and change over time (e.g., celebrities from the 1980s are different from celebrities from the 2020s), which implies that this type of tasks must be regularly updated. Also, the face matching test compares different identities and images in the familiar and unfamiliar face conditions, and thus the level of difficulty is likely to be different in these two conditions. For this reason, a systematic evaluation of the test with participants who are unfamiliar with all faces (or cases of prosopagnosia) would be required to assess this difference and balance trials based on performance that is not biased by familiarity. Finally, the face matching task could benefit from a limited exposure time procedure, showing for instance the target face first for one gaze fixation at most (200 ms), followed by the pair of probe faces either until the participant's response or for a few seconds (without time constraint on the motor response).

# 6. Electrophysiology as a future development path for cognitive assessment of FIR?

If behavioral tests are key tools for understanding FIR disorders, their use of explicit tasks requires the patient to use a whole set of other cognitive functions such as understanding (verbal or written) instructions, attention, visuospatial abilities, decision-making, execution of a motor response, etc. Moreover, if some of these tests can be performed relatively quickly by neurotypical adult subjects, this is not necessarily the case for some patients (e.g., almost 1 min per item for subject NS to perform the BFRT; Delvenne et al., 2004; 14 min for the case of prosopagnosia subject PS to perform the BFRT-c; Liu-Shuang et al., 2016). Moreover, these tests do not capture two essential aspects of FIR, i.e., its automaticity and high speed, especially since, as explained above (section 5.5.4), they do not constrain/measure processing time for
this function.

In this context, the non-invasive recording of electrophysiological activity (with electroencephalography, EEG) combined with fast periodic visual stimulation (FPVS) could become a complementary tool for neuropsychological evaluation in the near future. The principle of this FPVS-EEG approach is based on the old observation (Adrian and Matthews, 1934) that the presentation of visual stimuli at a fixed frequency (for example 10 times/second) produces EEG responses at exactly this frequency (10 Hz) and its harmonics (e.g., 20 Hz, 30 Hz, etc.), which allows (after Fourier transform) to objectively identify and quantify (in amplitude, microvolts, μV) these responses in the frequency domain (Regan, 1966, 1989; Norcia et al., 2015). The high signal-to-noise ratio (SNR) of this technique compared to conventional EEG measurements (spontaneous oscillations; evoked potentials expressed in the time domain) is due to the large number of stimuli presented in a relatively short period of time and (especially) to the concentration of all the activity of interest within a reduced frequency band, therefore preventing it from being affected by the background EEG (Regan, 1989). Moreover, contrary to time-domain analyses, estimation of the reference noise is conducted only on the frequency bands that are close to the signal's band (Regan, 1989; Rossion et al., 2020).

Initially restricted to the evaluation of low-level vision (and hearing) and their modulation by attention (Norcia et al., 2015 for review), this technique has been extended over the past ten years to high-level recognition processes, in particular relating to faces (since Rossion and Boremanse, 2011). A standard 'oddball-like' paradigm developed in this context can be used to measure, in a few minutes, the different EEG response to periodic 'oddball' unfamiliar facial identities appearing

among a stream of a repeated 'base' identity (Fig. 8A; Liu-Shuang et al., 2014). Measuring such a discrimination response requires the brain to generalize its response to the same face identity despite size (and/or luminance) changes (see Rossion et al., 2020). The face images are presented at a relatively fast frequency (6 images/second; 6 Hz), allowing only one eye fixation per image. The subject/patient performs a simple, implicit task, which generally consists of detecting a change in color (or size/shape) of a fixation cross.

With this FPVS-EEG paradigm, 'oddball' responses (e.g., at 1.2 Hz and harmonics for identity changes occurring every 5 stimuli presented at 6 Hz) can be observed within a few (<5) minutes, with statistical significance at the individual level in the vast majority of participants (Xu et al., 2017; see Rossion et al., 2020 for review: a significant response at p < 0.05 was observed in 93% of 130 individuals tested with 4 1-min stimulation sequences). These response amplitude measures are highly reliable within a test session or between sessions spaced several months apart: they show no increase or decrease in overall response, and the focal topography is stable across individuals. While amplitude variations are large between individuals, they are highly stable for a given individual (Dzhelyova et al., 2019; Stacchi et al., 2019).

Across a population of young neurotypical participants, the FPVS-EEG response to unfamiliar face identity discrimination correlates positively but not significantly with accuracy at the BFRT-c, while a modest but significant negative correlation is observed with RTs of the test (i.e., slower individuals exhibiting a weaker EEG response; Dzhelyova et al., 2020). These weak correlations are not surprising since both the behavioral measures and the magnitude of the oddball response depend on general factors. The validity of this FPVS-EEG paradigm is

<figure><img src="image_12.png" /><figcaption>**Fig. 8.** A. The case of prosopagnosia PS tested with FPVS-EEG in September 2013 (Liu-Shuang et al., 2016). PS was presented with the unfamiliar face discrimination paradigm shown on the right, in which different face identities interrupt a fast (5.88 Hz) train of the same face identity (through substantial changes in stimulus size) every 5 stimuli. B. Like age-matched normal controls, PS showed a significant response at 5.88 Hz corresponding to the general visual stimulation frequency. C. However, unlike her controls, PS showed no face discrimination response at 1.18 Hz. Importantly, the lack of a significant EEG response at 1.18 Hz and harmonics for PS is not due to her brain's inability to respond to this frequency: if faces are presented at 1.18 Hz among non-face objects, she shows a clear face-selective response similar to what is observed in normal controls, and in accordance with her normal ability at recognizing faces as faces (see Liu-Shuang et al., 2016).</figcaption></figure>
rather supported by a number of key observations: the FIR response is significantly decreased in amplitude (53%) when faces are inverted (Liu-Shuang et al., 2014; Rossion et al., 2020), it is localized over the right occipito-temporal cortex at the group level and in most participants, consistent with the neural basis of face recognition (section 5.2), and largely reduced in amplitude – in a non-specific way – for subjects defined as cases of prosop dysgnosia (Fisher et al., 2020). It also shows the largest amplitude in the right ventral occipital and lateral fusiform gyri at the group level (Jacques et al., 2020; see Fig. 2C) and on intracerebral electrode contacts associated with transient FIR impairments during electrical stimulation (three cases: Jonas et al., 2018; Volfart et al., 2022; Volfart et al., 2023b). Finally, and perhaps most importantly, despite the case of prosopagnosia PS showing typical responses at the ‘base’ frequency and at ‘oddball’ frequencies in another paradigm assessing discrimination of faces among visual non-face objects, she showed no automatic face identity discrimination responses at all in this paradigm (Liu-Shuang et al., 2016, Fig. 8). While a complete absence of response may appear surprising – given that PS’s performance at behavioral unfamiliar face identity matching tasks is well above chance-level (e.g., Rossion et al., 2003a; Busigny and Rossion, 2010b; Rossion, 2022a, also Fig. 7), it could certainly be attributed to the rapid presentation of each face identity in this paradigm (i.e., a single glance at each oddball face identity), allowing to put severe processing time constraints without any response pressure for the patient (see Christen et al., 1985; Davidoff, 1988).

In summary, this type of FPVS-EEG paradigm, or others following a similar methodology (e.g., Zimmermann et al., 2019; Yan and Rossion, 2020 for familiar faces presented among unfamiliar faces) seem(s) to offer objective, valid, implicit and sensitive measures to reliably assess FIR. These measures are relatively simple to understand and to analyze and are usually restricted to a few channels on the scalp. As in other domains (e.g., reading; Lochy et al., 2015), these measures could very well complement neuropsychological assessment and even become a key method in populations that are difficult to test (e.g., young children).

## 7. Cognitive rehabilitation

Over the past decade, several studies have addressed the cognitive rehabilitation of people with FIR disorder, either from acquired or developmental origin. Two cognitive rehabilitation approaches are commonly distinguished. On the one hand, the *compensatory approach* aims to help patients overcoming their FIR difficulties by relying on other intact cognitive processes (e.g., associating a face identity with semantic information, directing their attention to distinctive facial features and verbalizing them; Francis et al., 2002; Powell et al., 2008). On the other hand, the *remedial approach* aims at restoring or improving the defective function (e.g., through training centered on holistic processing of faces; DeGutis et al., 2014b).

For acquired FIR disorders, the various studies conducted (about a dozen, mostly involving single cases) have produced mixed results (see Bate and Bennetts, 2014; DeGutis et al., 2014a for reviews; see also Davies-Thompson et al., 2017; Heutink et al., 2019). Although the compensatory approach has led to improvements in some cases (Francis et al., 2002; Mayer and Rossion, 2007; Powell et al., 2008), other studies have reported only limited or even non-existent effects (e.g., Experiment 4 in Wilson, 1987; Polster and Rapcsak, 1996). Fewer studies used a remedial approach, focusing on perceptual training, and also reported variable results (e.g., complete absence of improvement in Ellis and Young, 1988; or limited improvement and no generalization to daily life in DeGutis et al., 2013; Bate et al., 2015). More recently, a group study was conducted in 10 reported cases of prosopagnosia exposed to a training program for 11 weeks (involving discrimination of morphed faces, across different rotation angles and changes in expression; Davies-Thompson et al., 2017). This study reported improved FIR performance generalizing to novel faces presented within the same context

(but not to other FIR tasks such as the CFMT), which remained stable at 3-month post-training follow-up and was associated with subjective improvement in some patients. Training was also more beneficial for patients with more pronounced pre-training deficits.

Although some promising results have been observed, the benefits of cognitive rehabilitation in people with acquired FIR disorders appear extremely variable and in no case lead to a 'return to normality' of FIR processes. There is also little evidence that the effects of rehabilitation generalize to new faces or are sustained over time for more than a few months.

A larger number of studies has shown positive effects of cognitive rehabilitation on subjects with developmental FIR disorders. In their review, DeGutis and his colleagues (2014a) report positive outcomes of attempts at compensatory rehabilitation (i.e., verbalizing facial characteristics to learn faces) in children with a developmental FIR disorder (e.g., Brunsdon et al., 2006; Schmalzl et al., 2008). In adults, studies have more often used a remedial approach (i.e., training subjects to process spacing information in the mouth and eyes). Contrary to what is observed in the case of an acquired disorder, these studies have highlighted improvements for single cases (DeGutis et al., 2007) and at the group level (DeGutis et al., 2014b). An innovative approach consisting of having subjects inhale oxytocin via an intranasal spray before testing them with FIR tasks also showed interesting results, with improved performance after inhalation of the oxytocin spray but not after inhalation of a placebo spray (Bate et al., 2014). More recently, another group study also showed improved FIR performance after perceptual training, maintaining at 3-month follow-up and generalizing to a lesser extent to novel faces (Corrow et al., 2019).

Although these studies involved a limited number of subjects and do not offer a definitive cure for developmental FIR disorders, their results are promising and suggest that people with a developmental FIR disorder may benefit more from cognitive rehabilitation approaches than patients with an acquired disorder. More studies are needed to assess to what extent improvements on experimental tasks generalize to daily life and are maintained over the long term.

Among the studies that have considered cognitive rehabilitation in developmental FIR disorders, several involved young subjects (<15 years old) and have generally reported an improvement in FIR after rehabilitation (e.g., McConachie, 1976 in a 12-year-old child; Ariel and Sadeh, 1996 in a 8-year-old; Jones and Tranel, 2001 in a 5-year-old; Brunsdon et al., 2006 in a 8-year-old; Schmalzl et al., 2008 in a 4-year-old). It should be noted, however, that the evaluation of FIR capacities is complicated in children and adolescents due to the slow evolution and maturation of this function during development (Hills and Lewis, 2018), and that it will therefore be necessary to remain cautious when interpreting improvements in a subject whose system is not yet mature.

Furthermore, some studies have focused on the spontaneous recovery of FIR capacities after an injury, that is to say without any attempt at treatment. Seven studies are reported in DeGutis et al., 2014a's review, and four of them tend to show that it may be possible for some patients to improve spontaneously after an injury that has led to an FIR disorder, especially in cases where the injury only affected one hemisphere. It should be noted, however, that the limited number of studies and the large variability in their methodology (i.e., from a few weeks (e.g., Malone et al., 1982) to several decades (Sparr et al., 1991) between two evaluations; difference in the criteria used to diagnose prosopagnosia; studies solely based on the subjective feelings of a patient (e.g., Glowic and Violon, 1981)) encourage some caution when interpreting these results.

## 8. Conclusions

Face identity recognition (FIR) is a key function of the human brain, arguably the ultimate form of recognition. The neurotypical human adult achieves an impressive level of performance in this function, which nevertheless varies considerably in the population and is very
fragile. In addition, there is significant social pressure for being suc-
cessful at FIR, a function for which there is not (yet?) - unlike topo-
graphic orientation - technological aids to compensate for deficits.

To correctly approach the evaluation of this function in neuropsychology, it is important to move away from the notion of prosopagnosia, a term that in our view should not be used to define a symptom (i.e., FIR difficulties), but reserved for a very rare neurological condition. Prosopagnosia is useful as a theoretical reference for understanding FIR processes and validating tests (e.g., section 5), but the most frequent FIR disorders are observed in other neurological, psychiatric, or neuro-developmental pathologies.

As in many other domains of cognition, the neuropsychology of face recognition has remained in the stone age, such that researchers, and clinicians especially, are deprived of valid, sensitive and reliable tools to assess FIR in neurological populations. In addition, the evaluation of this function suffers from problems with image licensing for creating new tests, and with familiar faces that vary according to cultures, generations and individuals.

While a very large number of FIR tests have recently appeared in computerized format (Table 2), they are unfortunately based on an obsolete notion of dissociation between perception and memory, and have significant limitations that reflect their development outside the context of clinical neuropsychology (e.g., maximization of variability of performance between normal participants by making the tests artificially difficult). Despite some limitations, the classic test of Benton and Van Allen (1968) in its computerized version (BFRT-c) has many advantages and could therefore be recommended in clinical practice. However, the use or adaptation of simple tests as described in section 5.7 has more advantages and should lead to the development of norms in the years to come. Ideally, we recommend that several FIR tests are administrated for evaluation. These tests should have demonstrated a sufficiently high sensitivity to identify a deficit in patients with a proven FIR disorder, to be simple, free from potential response bias, taking RTs into account and limiting processing time without response time pressure, using images with natural variations in viewing angles, lighting, facial expressions, etc., and assessing the inversion effect. The use of the FPVS-EEG approach in the context of neuropsychology also seems to be a particularly promising method to complement the assessment of FIR in an implicit, valid and reliable way.

CRediT authorship contribution statement

Angélique Volfart: Writing – review & editing, Writing – original draft, Conceptualization. Bruno Rossion: Writing – review & editing, Writing – original draft, Funding acquisition, Conceptualization.

Data availability

No data was used for the research described in the article.

Acknowledgments

This research was supported by a grant from the European Research Council (ERC, HUMANFACE 101055175) awarded to Bruno Rossion. The authors thank two anonymous reviewers for their helpful comments on previous versions of this manuscript.

References

Adrian, E.D., Matthews, B.H., 1934. The Berger rhythm: potential changes from the occipital lobes in man. Brain 57, 355-385.

Aglieri, V., Watson, R., Pernet, C., Latinus, M., Garrido, L., Belin, P., 2017. The Glasgow Voice Memory Test: assessing the ability to memorize and recognize unfamiliar voices. Behav Res 49, 97-110.

Ahmed, S., Arnold, R., Thompson, S.A., Graham, K.S., Hodges, J.R., 2008. Naming of objects, faces and buildings in mild cognitive impairment. Cortex 44, 746-752.

Albert, M.S., Butters, N., Levin, J., 1979. Temporal gradients in the retrograde amnesia of patients with alcoholic Korsakoff's disease. Arch. Neurol. 36, 211-216.

Albonico, A., Malaspina, M., Daini, R., 2017. Italian normative data and validation of two neuropsychological tests of face recognition: Benton Facial Recognition Test and Cambridge Face Memory Test. Neurol. Sci. 38, 1637-1643.

Ariel, R., Sadeh, M., 1996. Congenital visual agnosia and prosopagnosia in a child: a case report. Cortex 32, 221-240.

Arizpe, J.M., Saad, E., Douglas, A.O., Germine, L., Wilmer, J.B., DeGutis, J.M., 2019. Self-reported face recognition is highly valid, but alone is not highly discriminative of prosopagnosia-level performance on objective assessments. Behav Res 51, 1102-1116.

Arrigoni, G., De Renzi, E., 1964. Constructional apraxia and hemispheric locus of lesion. Cortex 1, 170-197.

Avidan, G., Behrmann, M., 2009. Functional MRI reveals compromised neural integrity of the face processing network in congenital prosopagnosia. Curr. Biol. 19, 1146-1150.

Avidan, G., Hasson, U., Malach, R., Behrmann, M., 2005. Detailed exploration of face-related processing in congenital prosopagnosia: 2. Functional neuroimaging findings. J. Cognit. Neurosci. 17, 1150-1167.

Avidan, G., Tanzer, M., Hadj-Bouziane, F., Liu, N., Ungerleider, L.G., Behrmann, M., 2014. Selective dissociation between core and extended regions of the face processing network in congenital prosopagnosia. Cerebr. Cortex 24, 1565-1578.

Bach, M., 1996. The Freiburg visual acuity test-automatic measurement of visual acuity. Optom. Vis. Sci. 73, 49-53.

Barbarotto, R., Capitani, E., Spinnler, H., Trivelli, C., 1995. Slowly progressive semantic impairment with category specificity. Neurocase 1, 107-119.

Barragan-Jason, G., Cauchoy, M., Barbeau, E.J., 2015. The neural speed of familiar face recognition. Neuropsychologia 75, 390-401.

Barsics, C., 2014. Person recognition is easier from faces than from voices. Psychol. Belg. 54, 244-254.

Barton, J.J.S., 2008a. Structure and function in acquired prosopagnosia: lessons from a series of 10 patients with brain damage. J. Neuropsychol. 2, 197-225.

Barton, J.J.S., 2008b. Prosopagnosia associated with a left occipitotemporal lesion. Neuropsychologia 46, 2214-2224.

Barton, J.J.S., 2009. What is meant by impaired configural processing in acquired prosopagnosia? Perception 38, 242-260.

Barton, J.J.S., Albonico, A., Susilo, T., Duchaine, B., Corrow, S.L., 2019. Object recognition in acquired and developmental prosopagnosia. Cogn. Neuropsychol. 36, 54-84.

Barton, J.J.S., Corrow, S.L., 2016a. The problem of being bad at faces. Neuropsychologia 89, 119-124.

Barton, J.J.S., Corrow, S.L., 2016b. Selectivity in acquired prosopagnosia: the segregation of divergent and convergent operations. Neuropsychologia 83, 76-87.

Bate, S., Bennetts, R., Mole, J.A., Ainge, J.A., Gregory, N.J., Bobak, A.K., Bussunt, A., 2015. Rehabilitation of face-processing skills in an adolescent with prosopagnosia: evaluation of an online perceptual training programme. Neuropsychol. Rehabil. 25, 733-762.

Bate, S., Bennetts, R.J., 2014. The rehabilitation of face recognition impairments: a critical review and future directions. Front. Hum. Neurosci. 8, 491.

Bate, S., Cook, S.J., Duchaine, B., Tree, J.J., Burns, E.J., Hodgson, T.L., 2014. Intranasal inhalation of oxytocin improves face processing in developmental prosopagnosia. Cortex 50, 55-63.

Bate, S., Frowd, C., Bennetts, R., Hashnim, N., Murray, E., Bobak, A.K., Wills, H., Richards, S., 2018. Applied screening tests for the detection of superior face recognition. Cognitive Research: Principles and Implications 3, 22.

Bate, S., Tree, J.J., 2017. The definition and diagnosis of developmental prosopagnosia. Q. J. Exp. Psychol. 70, 193-200.

Bauer, C.M., Manley, C.E., Ravenscroft, J., Cabral, H., Dilks, D.D., Bex, P.J., 2023. Deficits in face recognition and consequent quality-of-life factors in individuals with cerebral visual impairment. Vision 7, 9.

Baxendale, S., Wilson, S.J., Baker, G.A., Barr, W., Helmstaedter, C., Hermann, B.P., Langfitt, J., Reuner, G., Rzezak, P., Samson, S., Smith, M.-L., 2019. Indications and expectations for neuropsychological assessment in epilepsy surgery in children and adults. Epileptic Disord. 21, 221-234.

Bay, E., 1953. Disturbances of visual perception and their examination. Brain: J. Neurol. 76, 515-550.

Becker, J.T., Lopez, O.L., Boller, F., 1995. Understanding impaired analysis of faces by patients with probable Alzheimer's Disease. Cortex 31, 129-137.

Behrmann, M., Avidan, G., 2005. Congenital prosopagnosia: face-blind from birth. Trends Cogn. Sci. 9, 180-187.

Behrmann, M., Avidan, G., Gao, F., Black, S., 2007. Structural imaging reveals anatomical alterations in inferotemporal cortex in congenital prosopagnosia. Cerebr. Cortex 17, 2354-2363.

Behrmann, M., Avidan, G., Marotta, J.J., Kimchi, R., 2005. Detailed exploration of face-related processing in congenital prosopagnosia: 1. Behavioral findings. J. Cogn. Neurosci. 17, 1130-1149.

Behrmann, M., Williams, P., 2007. Impairments in part-whole representations of objects in two cases of integrative visual agnosia. Cognitive Neuropsychology 24 (7), 701-730.

Bender, M.B., Feldman, M., 1972. The so-called "visual agnosias". Brain 95, 173-186.

Bennetts, R.J., Gregory, N.J., Tree, J., Di Bernardi Luft, C., Banissy, M.J., Murray, E., Penton, T., Bate, S., 2022. Face specific inversion effects provide evidence for two subtypes of developmental prosopagnosia. Neuropsychologia 174, 108332.

Benson, D.F., Greenberg, J.P., 1969. Visual form agnosia. A specific defect in visual discrimination. Arch. Neurol. 20, 82-89.

Bentin, S., DeGutis, J.M., D'Esposito, M., Robertson, L.C., 2007. Too many trees to see the forest: performance, event-related potential, and functional magnetic resonance
imaging manifestations of integrative congenital prosopagnosia. J. Cognit. Neurosci. 19, 132–146.

Benton, A.L., 1980. The neuropsychology of facial recognition. Am. Psychol. 35, 176–186.

Benton, A.L., 1986. Reaction time in brain disease: some reflections. Cortex 22, 129–140.

Benton, A.L., 1992. Clinical neuropsychology: 1960-1990. J. Clin. Exp. Neuropsychol. 14, 407–417.

Benton, A.L., Sivan, A.B., Hamsher, K., Varney, N.R., Sreen, O., 1983. Benton Facial Recognition: Stimulus and Multiple Choice Pictures. Psychological Assessment Resources Inc, Lut, FL.

Benton, A.L., Van Allen, M.W., 1968. Impairment in facial recognition in patients with cerebral disease. Cortex 4, 344–358.

Benton, A.L., Van Allen, M.W., 1972. Prosopagnosia and facial discrimination. J. Neurol. Sci. 15, 167–172.

Berlucchi, G., 2017. Neuropsychology. In: Reference Module in Neuroscience and Biobehavioral Psychology. Elsevier. B9780128093245024652.

Besson, G., Barragan-Jason, G., Thorpe, S.J., Fabre-Thorpe, M., Puma, S., Ceccaldi, M., Barbeau, E.J., 2017. From face processing to face recognition: comparing three different processing levels. Cognition 158, 33–43.

Bidault, E., Luauté, J.P., Tzavaras, A., 1986. Prosopagnosia and the Delusional Misidentification Syndromes. Bibl Psychiatr, pp. 80–91.

Bindemann, M., Fysh, M., Cross, K., Watts, R., 2016. Matching Faces against the Clock. I-Perception, vol. 7, 2041669516672219.

Biotti, F., Wu, E., Yang, H., Jiahui, G., Duchaine, B., Cook, R., 2017. Normal composite face effects in developmental prosopagnosia. Cortex 95, 63–76.

Blank, H., Wieland, N., von Kriegstein, K., 2014. Person recognition and the brain: merging evidence from patients and healthy individuals. Neurosci. Biobehav. Rev. 47, 717–734.

Bobak, A.K., Bennetts, R.J., Parris, B.A., Jansari, A., Bate, S., 2016a. An in-depth cognitive examination of individuals with superior face recognition skills. Cortex 82, 48–62.

Bobak, A.K., Mileva, V.R., Hancock, P.J., 2019. Facing the facts: naive participants have only moderate insight into their face recognition and face perception abilities. Q. J. Exp. Psychol. 72, 872–881.

Bobak, A.K., Pampoulov, P., Bate, S., 2016b. Detecting superior face recognition skills in a large sample of young British adults. Front. Psychol. 7.

Bobak, A.K., Parris, B.A., Gregory, N.J., Bennetts, R.J., Bate, S., 2017. Eye-movement strategies in developmental prosopagnosia and “super” face recognition. Q. J. Exp. Psychol. 70, 201–217.

Bodamer, J., 1947. Die Prosop-Agnosie; die Agnosie des Physiognomieerkennens. Arch Psychiatr Nervenk Z Gesamte Neurol Psychiatr 118, 6–53.

Bokde, A.L.W., Lopez-Bayo, P., Meindl, T., Pechler, S., Born, C., Faltraco, F., Teipel, S.J., Möller, H.-J., Hampel, H., 2006. Functional connectivity of the fusiform gyrus during a face-matching task in subjects with mild cognitive impairment. Brain 129, 1113–1124.

Borghesani, V., Narvid, J., Battistella, G., Shwe, W., Watson, C., Binney, R.J., Sturm, V., Miller, Z., Mandelli, M.L., Miller, B., Gorno-Tempini, M.L., 2019. “Looks familiar, but I do not know who she is”: the role of the anterior right temporal lobe in famous face recognition. Cortex 115, 72–85.

Boudouresques, J., 1979. L'agnosie des visages: un témoin de la désorganisation fonctionnelle d'un certain type de connaissance des éléments du monde extérieur.

Boutsen, L., Humphreys, G.W., 2002. Face context interferes with local part processing in a prosopagnosic patient. Neuropsychologia 40, 2305–2313.

Bouvier, S.E., Engel, S.A., 2006. Behavioral deficits and cortical damage loci in cerebral achromatopsia. Cerebr. Cortex 16, 183–191.

Bowles, D.C., McKone, E., Dawel, A., Duchaine, B., Palermo, R., Schmalzl, L., Rivolta, D., Wilson, C.E., Yovel, G., 2009. Diagnosing prosopnosis: effects of ageing, sex, and participant-stimulus ethnic match on the Cambridge face memory test and Cambridge face perception test. Cogn. Neuropsychol. 26, 423–455.

Bozeat, S., Lambon Ralph, M.A., Patterson, K., Garrard, P., Hodges, J.R., 2000. Non-verbal semantic impairment in semantic dementia. Neuropsychologia 38, 1207–1215.

Brainin, M., Tuomilehto, J., Heiss, W.-D., Bornstein, N.M., Bath, P.M.W., Teuschl, Y., Richard, E., Guekht, A., Quinn, T., Group the PSCS. 2015. Post-stroke cognitive decline: an update and perspectives for clinical research. Eur. J. Neurol. 22, 229–e16.

Broks, P., Young, A.W., Maratos, E.J., Coffey, P.J., Calder, A.J., Isaac, C.L., Mayes, A.R., Hodges, J.R., Montaldi, D., Cezayirli, E., Roberts, N., Hadley, D., 1998. Face processing impairments after encephalitis: amygdala damage and recognition of fear. Neuropsychologia 36, 59–70.

Bruce, V., Henderson, Z., Newman, C., Burton, A.M., 2001. Matching identities of familiar and unfamiliar faces caught on CCTV images. J. Exp. Psychol. Appl. 7, 207–218.

Bruck, M., Cavanagh, P., Ceci, S.J., 1991. Fortysomething: recognizing faces at one's 25th reunion. Mem. Cognit. 19, 221–228.

Brunsdon, R., Coltheart, M., Nickels, L., Joy, P., 2006. Developmental prosopagnosia: a case analysis and treatment study. Cogn. Neuropsychol. 23, 822–840.

Bruyer, R., Schweich, M., 1991. A clinical test battery of face processing. Int. J. Neurosci. 61, 19–30.

Bryant, K.L., Preuss, T.M., 2018. A comparative perspective on the human temporal lobe. In: Bruner, E., Ogihara, N., Tanabe, H.C. (Eds.), Digital Endocasts: from Skulls to Brains. Replacement of Neanderthals by Modern Humans Series. Springer Japan, Tokyo, pp. 239–258.

Bukach, C.M., Bub, D.N., Gauthier, I., Tarr, M.J., 2006. Perceptual expertise effects are not all or none: spatially limited perceptual expertise for faces in a case of prosopagnosia. J. Cognit. Neurosci. 18, 48–63.

Bukowski, H., Dricot, L., Hanseeuw, B., Rossion, B., 2013. Cerebral lateralization of face-sensitive areas in left-handers: only the FFA does not get it right. Cortex 49, 2583–2589.

Bullimore, M.A., Bailey, I.L., Wacker, R.T., 1991. Face recognition in age-related maculopathy. Invest. Ophthalmol. Vis. Sci. 32, 2020–2029.

Burns, E.J., Gaunt, E., Kidane, B., Hunter, L., Pulford, J., 2022. A new approach to diagnosing and researching developmental prosopagnosia: excluded cases are impaired too. Behav. Res. 55, 4291–4314.

Burton, A.M., Kramer, R.S.S., Ritchie, K.L., Jenkins, R., 2016. Identity from variation: representations of faces derived from multiple instances. Cognit. Sci. 40, 202–223.

Burton, A.M., White, D., McNeill, A., 2010. The Glasgow face matching test. Behav. Res. Methods 42, 286–291.

Busigny, T., Graf, M., Mayer, E., Rossion, B., 2010a. Acquired prosopagnosia as a face-specific disorder: ruling out the general visual similarity account. Neuropsychologia 48, 2051–2067.

Busigny, T., Joubert, S., Felician, O., Ceccaldi, M., Rossion, B., 2010b. Holistic perception of the individual face is specific and necessary: evidence from an extensive case study of acquired prosopagnosia. Neuropsychologia 48, 4057–4092.

Busigny, T., Prairial, C., Nootens, J., Kindt, V., Engels, S., Verplancke, S., Mejias, S., Mary, G., Mahau, P., Coyette, F., 2014a. Celeb : une batterie d'évaluation de la reconnaissance des visages célèbres et de l'accès aux noms propres. Rev. Neuropsychologia 6, 69–81.

Busigny, T., Robaye, L., Dricot, L., Rossion, B., 2009. Right anterior temporal lobe atrophy and person-based semantic defect: a detailed case study. Neurocase 15, 485–508.

Busigny, T., Rossion, B., 2010a. Acquired prosopagnosia is not due to a general impairment in fine-grained recognition of exemplars of a visually homogeneous category. Behav. Neurol. 23, 229–231.

Busigny, T., Rossion, B., 2010b. Acquired prosopagnosia abolishes the face inversion effect. Cortex 46, 965–981.

Busigny, T., Van Belle, G., Jemel, B., Hosein, A., Joubert, S., Rossion, B., 2014b. Face-specific impairment in holistic perception following focal lesion of the right anterior temporal lobe. Neuropsychologia 56, 312–333.

Caharel, S., Ramon, M., Rossion, B., 2014. Face familiarity decisions take 200 msec in the human brain: electrophysiological evidence from a Go/No-go speeded task. J. Cognit. Neurosci. 26, 81–95.

Carey, S., 1992. Becoming a face expert. Philos. Trans. R. Soc. Lond. B Biol. Sci. 335, 95–103.

Carey, S., Diamond, R., Woods, B., 1980. Development of face recognition: a maturation component? Dev. Psychol. 16, 257–269.

Carvajal, F., Fernández-Alcaraz, C., Rueda, M., Sarrión, L., 2012. Processing of facial expressions of emotions by adults with Down syndrome and moderate intellectual disability. Res. Dev. Disabil. 33, 783–790.

Cattaneo, Z., Vecchi, T., Monegato, M., Pece, A., Merabet, L.B., Carbon, C.-C., 2013. Strabismic amblyopia affects relational but not featural and Gestalt processing of faces. Vis. Res. 80, 19–30.

Chakravarthula, P.N., Tsank, Y., Eckstein, M.P., 2021. Eye movement strategies in face ethnicity categorization vs. face identification tasks. Vis. Res. 186, 59–70.

Cheng, Y.-H., Shyi, G.C.-W., Cheng, K.-H., 2016. Age differences in face memory and face processing between younger and older adults in Taiwan. Chin. J. Psychol. 58, 233–262.

Chiaravalloti, N.D., Grosser, G., 2004. Memory for faces dissociates from memory for location following anterior temporal lobectomy. Brain Cognit. 54, 35–42.

Christen, L., Landis, T., Regard, M., 1985. Left hemispheric functional compensation in prosopagnosia? A tachistoscopic study with unilaterally lesioned patients. Hum. Neurobiol. 4, 9–14.

Christensen, K.J., Riley, B.E., Heffernan, K.A., Love, S.B., McLaughlin, Sta, Maria, M.E., 2002. Facial Recognition Test in the elderly: norms, reliability and premorbid estimation. Clin. Neuropsychol. 16, 51–56.

Civile, C., McLaren, R., McLaren, I.P.L., 2016. The face inversion effect: roles of first and second-order configurable information. Am. J. Psychol. 129, 23–35.

Clarke, S., Lindemann, A., Maeder, P., Borruat, F.-X., Assal, G., 1997. Face recognition and postero-inferior hemispheric lesions. Neuropsychologia 35, 1555–1563.

Cohen, A.L., Soussand, L., Corrow, S.L., Martinaud, O., Barton, J.J.S., Fox, M.D., 2019. Looking beyond the face area: lesion network mapping of prosopagnosia. Brain 142, 3975–3990.

Condret-Santi, V., Barbeau, E.J., Matharan, F., Goff, M.L., Dartigues, J.-F., Amieva, H., 2013. Prevalence of word retrieval complaint and Prediction of dementia in a population-based study of elderly subjects. Dement. Geriatr. Cogn. Disord 35, 313–324.

Condret-Santi, V., Barragan-Jason, G., Valton, L., Denuelle, M., Crout, J., Nespoulous, J.-L., Barbeau, E.J., 2014. Object and proper name retrieval in temporal lobe epilepsy: a study of difficulties and latencies. Epilepsy Res. 108, 1825–1838.

Corrow, S.L., Albonico, A., Barton, J.J.S., 2018. Diagnosing prosopagnosia: the utility of visual noise in the Cambridge face recognition test. Perception 47, 330–343.

Corrow, S.L., Davies-Thompson, J., Fletcher, K., Hills, C., Corrow, J.C., Barton, J.J.S., 2019. Training face perception in developmental prosopagnosia through perceptual learning. Neuropsychologia 134, 107196.

Crane, J., Milner, B., 2002. Do I know you? Face perception and memory in patients with selective amygdalo-hippocampectomy. Neuropsychologia 40, 530–538.

Croydon, A., Pimperton, H., Ewing, L., Duchaine, B.C., Pellicano, E., 2014. The Cambridge Face Memory Test for Children (CFMT-C): a new tool for measuring face recognition skills in childhood. Neuropsychologia 62, 60–67.

Damasio, A.R., Damasio, H., Van Hoesen, G.W., 1982. Prosopagnosia: anatomic basis and behavioral mechanisms. Neurology 32, 331–341.
Davidoff, J., 1988. Prosopagnosia: a disorder of rapid spatial integration. In: Perspectives on Cognitive Neuropsychology. Routledge.

Davidoff, J., Landis, T., 1990. Recognition of unfamiliar faces in prosopagnosia. Neuropsychologia 28, 1143-1161.

Davies-Thompson, J., Fletcher, K., Hills, C., Pancaroglu, R., Corrow, S.L., Barton, J.J.S., 2017. Perceptual learning of faces: a rehabilitative study of acquired prosopagnosia. J. Cognit. Neurosci. 29, 573-591.

Davies-Thompson, J., Gouws, A., Andrews, T.J., 2009. An image-dependent representation of familiar and unfamiliar faces in the human ventral stream. Neuropsychologia, Perception and Action 47, 1627-1635.

Davies-Thompson, J., Pancaroglu, R., Barton, J., 2014. Acquired prosopagnosia: structural basis and processing impairments. Front. Biosci. 6, 159-174.

De Haan, E.H., Heywood, C.A., Young, A.W., Edelstyn, N., Newcombe, F., 1995. Ettlinger revisited: the relation between agnosia and sensory impairment. J. Neurol. Neurosurg. Psychiatr. 58, 350-356.

de Heering, A., Maurer, D., 2014. Face memory deficits in patients deprived of early visual input by bilateral congenital cataracts. Dev. Psychobiol. 56, 96-108.

De Renzi, E., 1986. Current issues on prosopagnosia. In: Ellis, H.D., Jeeves, M.A., Newcombe, F., Young, A. (Eds.), Aspects of Face Processing. NATO ASI Series. Springer Netherlands, Dordrecht, pp. 243-252.

De Renzi, E., Faglioni, P., 1965. The comparative efficiency of intelligence and vigilance tests in detecting hemispheric cerebral damage. Cortex 1, 410-433.

De Renzi, E., Faglioni, P., Spinnler, H., 1968. The performance of patients with unilateral brain damage on face recognition tasks. Cortex 4, 17-34.

De Renzi, E., Spinnler, H., 1966. Visual recognition in patients with unilateral cerebral disease. J. Nerv. Ment. Dis. 142 (6), 515-525.

DeGutis, J., Bahierathan, K., Barahona, K., Lee, E., Evans, T.C., Shin, H.M., Mishra, M., Likitlersuang, J., Wilmer, J.B., 2023a. What is the prevalence of developmental prosopagnosia? An empirical assessment of different diagnostic cutoffs. Cortex 161, 51-64.

DeGutis, J., Bentin, S., Robertson, L.C., D'Esposito, M., 2007. Functional plasticity in ventral temporal cortex following cognitive rehabilitation of a congenital prosopagnosia. J. Cognit. Neurosci. 19, 1790-1802.

DeGutis, J., Chiu, C., Grosso, M.E., Cohan, S., 2014a. Face processing improvements in prosopagnosia: successes and failures over the last 50 years. Front. Hum. Neurosci. 8, 561.

DeGutis, J., Cohan, S., Kahn, D.A., Aguirre, G.K., Nakayama, K., 2013. Facial expression training improves emotion recognition and changes neural tuning in a patient with acquired emotion recognition deficits and prosopagnosia. J. Vis. 13, 993.

DeGutis, J., Cohan, S., Nakayama, K., 2014b. Holistic face training enhances face processing in developmental prosopagnosia. Brain 137, 1781-1798.

DeGutis, J., Li, X., Yosef, B., Mishra, M.V., 2022. Not so fast! Response times in the computerized Benton Face Recognition Test may not reflect face recognition ability. Cogn. Neuropsychol. 39, 155-169.

DeGutis, J., Yosef, B., Lee, E.A., Saad, E., Arizpe, J., Song, J.S., Wilmer, J., Germine, L., Esterman, M., 2023b. The rise and fall of face recognition awareness across the life span. J. Exp. Psychol. Hum. Percept. Perform. 29, 22-33.

Delvenne, J.-F., Seron, X., Coyette, F., Rossion, B., 2004. Evidence for perceptual deficits in associative visual (prosop)agnosia: a single-case study. Neuropsychologia 42, 597-612.

Dennett, H.W., McKone, E., Tavashmi, R., Hall, A., Pidcock, M., Edwards, M., Duchaine, B., 2012. The Cambridge Car Memory Test: a task matched in format to the Cambridge Face Memory Test, with norms, reliability, sex differences, dissociations from face memory, and expertise effects. Behav Res 44, 587-605.

Dinkelacker, V., Grüter, M., Klaver, P., Grüter, T., Specht, K., Weis, S., Kennerknecht, L., Elger, C.E., Fernandez, G., 2011. Congenital prosopagnosia: multistage anatomical and functional deficits in face processing circuitry. J. Neurol. 258, 770-782.

Drane, D.L., Ojemann, G.A., Aylward, E., Ojemann, J.G., Johnson, L.C., Silbergeld, D.L., Miller, J.W., Tranel, D., 2008. Category-specific naming and recognition deficits in temporal lobe epilepsy surgical patients. Neuropsychologia 46, 1242-1255.

Drane, D.L., Ojemann, J.G., Phatak, V., Loring, D.W., Gross, R.E., Hebb, A.O., Silbergeld, D.L., Miller, J.W., Voets, N.L., Saindane, A.M., Barsalou, L., Meador, K.J., Ojemann, G.A., Tranel, D., 2013. Famous face identification in temporal lobe epilepsy: support for a multimodal integration model of semantic memory. Cortex 49, 1648-1667.

Duchaine, B., Germine, L., Nakayama, K., 2007. Family resemblance: ten family members with prosopagnosia and within-class object agnosia. Cogn. Neuropsychol. 24, 419-430.

Duchaine, B., Nakayama, K., 2006. The Cambridge Face Memory Test: results for neurologically intact individuals and an investigation of its validity using inverted face stimuli and prosopagnosic participants. Neuropsychologia 44, 576-585.

Duchaine, B., Weidenfeld, A., 2003. An evaluation of two commonly used tests of unfamiliar face recognition. Neuropsychologia 41, 713-720.

Duchaine, B., Yovel, G., 2015. A revised neural framework for face processing. Annu Rev Vis Sci. 1, 393-416.

Dunn, J.D., Summersby, S., Towler, A., Davis, J.P., White, D., 2020. UNSW Face Test: a screening tool for super-recognizers. PLoS One 15, e0241747.

Dzhelyova, M., Jacques, C., Dormal, G., Michel, C., Schiltz, C., Rossion, B., 2019. High test-retest reliability of a neural index of rapid automatic discrimination of unfamiliar individual faces. Vis. Cognit. 27, 127-141.

Dzhelyova, M., Schiltz, C., Rossion, B., 2020. The relationship between the Benton Face Recognition Test and electrophysiological unfamiliar face individuation response as revealed by Fast Periodic Stimulation. Perception 49, 210-221.

Ebaid, D., Crewther, S.G., MacCalman, K., Brown, A., Crewther, D.P., 2017. Cognitive processing speed across the lifespan: beyond the influence of motor speed. Front. Aging Neurosci. 9.

Elbich, D.B., Scherf, S., 2017. Beyond the FFA: brain-behavior correspondences in face recognition abilities. Neuroimage 147, 409-422.

Elliott, D.B., Patla, A., Bullimore, M.A., 1997. Improvements in clinical and functional vision and perceived visual disability after first and second eye cataract surgery. Br. J. Ophthalmol. 81, 889-895.

Ellis, H.D., Bruce, V., De Schonen, S., Bruce, V., Cowey, A., Ellis, A.W., Perrett, D.I., 1992. The development of face processing skills. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 335, 105-111.

Ellis, H.D., Young, A.W., 1988. Training in face-processing skills for a child with acquired prosopagnosia. Dev. Neuropsychol. 4, 283-294.

Ellis, H.D., Young, A.W., Quayle, A.H., De Pauw, K.W., 1997. Reduced autonomic responses to faces in Capgras delusion. Proc. Biol. Sci. 264, 1085-1092.

Estudillo, A.J., Wong, H.K., 2021. Associations between self-reported and objective face recognition abilities are only evident in above- and below-average recognisers. PeerJ 9, e10629.

Evans, J.J., Heggs, A.J., Antoun, N., Hodges, J.R., 1995. Progressive prosopagnosia associated with selective right temporal lobe atrophy. A new syndrome? Brain 118, 1-13.

Farah, M.J., 1990. Visual Agnosia: Disorders of Object Recognition and what They Tell Us about Normal Vision. MIT Press, Cambridge, Massachusetts.

Farah, M.J., Wilson, K.D., Maxwell Drain, H., Tanaka, J.R., 1995. The inverted face inversion effect in prosopagnosia: evidence for mandatory, face-specific perceptual mechanisms. Vis. Res. 35, 2089-2093.

Faust, C., 1955. Die zerebralen Herdstörungen bei Hinterhauptsverletzungen und ihre Beurteilung. Thiemer.

Feldman, B.H., Dimitropoulos, A., 2014. Face discrimination skills in prader-willi syndrome and autism spectrum disorder. Journal of Mental Health Research in Intellectual Disabilities. 7, 264-285.

Fiacconi, C.M., Barkley, V., Finger, E.C., Carson, N., Duke, D., Rosenbaum, R.S., Gilboa, A., Köhler, S., 2014. Nature and extent of person recognition impairments associated with Capgras syndrome in Lewy body dementia. Front. Hum. Neurosci. 8.

Fisher, K., Towler, J., Rossion, B., Eimer, M., 2020. Neural responses in a fast periodic visual stimulation paradigm reveal domain-general visual discrimination deficits in developmental prosopagnosia. Cortex 133, 76-102.

Forgeot d'Arc, B., Delorme, R., Zalla, T., Lefebvre, A., Amsellem, F., Moukawane, S., Letellier, L., Leboyer, M., Mouren, M.-C., Ramus, F., 2017. Gaze direction detection in autism spectrum disorder. Autism 21, 100-107.

Fox, C.J., Iaria, G., Barton, J.J.S., 2008. Disconnection in prosopagnosia and face processing. Cortex 44, 996-1009.

Francis, R., Riddoch, M.J., Humphreys, G.W., 2002. "Who's that girl?" Prosopagnosia, person-based semantic disorder, and the reacquisition of face identification ability. Neuropsychol. Rehabil. 12, 1-26.

Freire, A., Lee, K., Symons, L.A., 2000. The face-inversion effect as a deficit in the encoding of configural information: direct evidence. Perception 29, 159-170.

Fry, R., Wilmer, J., Xie, I., Verfaellie, M., DeGutis, J., 2020. Evidence for normal novel object recognition abilities in developmental prosopagnosia. R. Soc. Open Sci. 7, 200988.

Furl, N., Garrido, L., Dolan, R.J., Driver, J., Duchaine, B., 2011. Fusiform gyrus face selectivity relates to individual differences in facial recognition ability. J. Cognit. Neurosci. 23, 1723-1740.

Fuster, J.M., 1997. Network memory. Trends Neurosci. 20, 451-459.

Fysh, M.C., Bindemann, M., 2017. Effects of time pressure and time passage on face-matching accuracy. R. Soc. Open Sci. 4, 170249.

Fysh, M.C., Bindemann, M., 2018. The kent face matching test. Br. J. Psychol. 109, 219-231.

Fysh, M.C., Ramon, M., 2022. Accurate but inefficient: standard face identity matching tests fail to identify prosopagnosia. Neuropsychologia 165, 108119.

Gainotti, G., 2007. Different patterns of famous people recognition disorders in patients with right and left anterior temporal lesions: a systematic review. Neuropsychologia 45, 1591-1607.

Gainotti, G., 2010. Not all patients labeled as "prosopagnosia" have a real prosopagnosia. J. Clin. Exp. Neuropsychol. 32, 763-766.

Gainotti, G., 2013a. Is the right anterior temporal variant of prosopagnosia a form of "associative prosopagnosia" or a form of "multimodal person recognition disorder". Neuropsychol. Rev. 23, 99-110.

Gainotti, G., 2013b. Laterality effects in normal subjects' recognition of familiar faces, voices and names. Perceptual and representational components. Neuropsychologia 51, 1151-1160.

Gainotti, G., Barbier, A., Marra, C., 2003. Slowly progressive defect in recognition of familiar people in a patient with right anterior temporal atrophy. Brain 126, 792-803.

Gainotti, G., Ferraccioli, M., Marra, C., 2010. The relation between person identity nodes, familiarity judgment and biographical information. Evidence from two patients with right and left anterior temporal atrophy. Brain Res. 1307, 103-114.

Gainotti, G., Ferraccioli, M., Quaranta, D., Marra, C., 2008. Cross-modal recognition disorders for persons and other unique entities in a patient with right fronto-temporal degeneration. Cortex 44, 238-248.

Galper, R.E., 1970. Recognition of faces in photographic negative. Psychonomic Sci. 19, 207-208.

Gao, X., Wen, M., Sun, M., Rossion, B., 2022. A genuine interindividual variability in number and anatomical localization of face-selective regions in the human brain. Cerebr. Cortex 32, 4834-4856.

Garrido, L., Furl, N., Draganski, B., Weiskopf, N., Stevens, J., Tan, G.C.-Y., Driver, J., Dolan, R.J., Duchaine, B., 2009. Voxel-based morphometry reveals reduced grey matter volume in the temporal cortex of developmental prosopagnosics. Brain 132, 3443-3455.
Gauthier, I., Behrmann, M., Tarr, M.J., 1999. Can face recognition really be dissociated from object recognition? J. Cognit. Neurosci. 11, 349-370.

Gauthier, I., Tarr, M.J., Moylan, J., Skudlarski, P., Gore, J.C., Anderson, A.W., 2000. The fusiform "face area" is part of a network that processes faces at the individual level. J. Cognit. Neurosci. 12, 495-504.

Gawrylowicz, J., Gabbert, F., Carson, D., Lindsay, W.R., Hancock, P.J.B., 2013. Face recognition and description abilities in people with mild intellectual disabilities. J. Appl. Res. Intellect. Disabil. 26, 435-446.

Gentileschi, V., Sperber, S., Spinnler, H., 2001. Crossmodal agnosia for familiar people as a consequence of right infero-polar temporal atrophy. Cogn. Neuropsychol. 18, 439-463.

Germaine, L.T., Duchaine, B., Nakayama, K., 2011. Where cognitive development and aging meet: face learning ability peaks after age 30. Cognition 118, 201-210.

Geskin, J., Behrmann, M., 2018. Congenital prosopagnosia without object agnosia? A literature review. Cogn. Neuropsychol. 35, 4-54.

Gignac, G.E., Shankaralingam, M., Walker, K., Kilpatrick, P., 2016. Short-term memory for faces relates to general intelligence moderately. Intelligence 57, 96-104.

Giovanello, K.S., Alexander, M., Verfaellie, M., 2003. Differential impairment of person-specific knowledge in a patient with semantic dementia. Neurocase 9, 15-26.

Glosser, G., Salvucci, A.E., Chiaravalli, N.D., 2003. Naming and recognizing famous faces in temporal lobe epilepsy. Neurology 61, 81-86.

Głowic, C., Violon, A., 1981. [A case of regressive prosopagnosia (author's transl)]. Acta Neurol. Belg. 81, 86-97.

Gomez, J., Pestilli, F., Witthoft, N., Golarai, G., Liberman, A., Poltoratski, S., Yoon, J., Grill-Spector, K., 2015. Functionally defined white matter reveals segregated pathways in human ventral temporal cortex associated with category-specific processing. Neuron 85, 216-227.

Goodale, M., Milner, D., 2013. Sight Unseen: an Exploration of Conscious and Unconscious Vision. OUP, Oxford.

Gorno-Tempini, M.L., Rankin, K.P., Woolley, J.D., Rosen, H.J., Phengrasamy, L., Miller, B.L., 2004. Cognitive and behavioral profile in a case of right anterior temporal lobe neurodegeneration. Cortex 40, 631-644.

Gray, K.L.H., Bird, G., Cook, R., 2017. Robust associations between the 20-item prosopagnosia index and the Cambridge Face Memory Test in the general population. R. Soc. Open Sci. 4.

Greene, J.D.W., Hodges, J.R., 1996. Identification of famous faces and famous names in early Alzheimer's disease: relationship to anterograde episodic and general semantic memory. Brain 119, 111-128.

Gregory, R.L., 1966. Eye and Brain: the Psychology of Seeing. McGraw-Hill Book Company, New York.

Griffin, J.W., 2020. Quantifying the face inversion effect in nonhuman primates: a phylogenetic meta-analysis. Anim. Cognit. 23, 237-249.

Griffin, J.W., Bauer, R., Scherf, K.S., 2021. A quantitative meta-analysis of face recognition deficits in autism: 40 years of research. Psychol. Bull. 147, 268-292.

Haeger, A., Pouzat, C., Luecken, V., N'Diaye, K., Elger, C., Kennerknecht, I., Axmacher, N., Dinkelacker, V., 2021. Face processing in developmental prosopagnosia: altered neural representations in the fusiform face area. Front. Behav. Neurosci. 15.

Hamsher, K. des., Levin, H.S., Benton, A.L., 1979. Facial recognition in patients with focal brain lesions. Arch. Neurol. 36, 837-839.

Hancock, P.J.B., Bruce, V., Burton, A.M., 2000. Recognition of unfamiliar faces. Trends Cogn. Sci. 4, 330-337.

Hanley, J.R., Young, A.W., Pearson, N.A., 1989. Defective recognition of familiar people. Cogn. Neuropsychol. 6, 179-210.

Haxby, J.V., Hoffman, E.A., Gobbini, I., 2000. The distributed human neural system for face perception. Trends Cognit. Sci. 4, 223-233.

Hécaen, H., 1972. Introduction à la neuropsychologie. Larousse, Paris.

Hécaen, H., 1981. The neuropsychology of face recognition. In: Davies, G., Ellis, H., Shepherd, J. (Eds.), Perceiving and Remembering Faces. Academic Press, London, pp. 39-54.

Hécaen, H., Angelergues, R., 1962. Agnosia for faces (prosopagnosia). Arch. Neurol. 7, 92-100.

Hécaen, H., Angelergues, René R., Bernhardt, C., Chiarelli, J., 1957. Essai de distinction des modalités cliniques de l'agnosie des physionomies. Rev. Neurol. 96, 125-144.

Hendel, R.K., Starrfelt, R., Gerlach, C., 2019. The good, the bad, and the average: characterizing the relationship between face and object processing across the face recognition spectrum. Neuropsychologia 124, 274-284.

Herbet, G., Zemmoura, I., Duffau, H., 2018. Functional anatomy of the inferior longitudinal fasciculus: from historical reports to current hypotheses. Front. Neuroanat. 12.

Herlitz, A., Lovén, J., 2013. Sex differences and the own-gender bias in face recognition: a meta-analytic review. Vis. Cognit. 21, 1306-1336.

Herzmann, G., Danthir, V., Schacht, A., Sommer, W., Wilhelm, O., 2008. Toward a comprehensive test battery for face cognition: assessment of the tasks. Behav. Res. Methods 40, 840-857.

Herzog, M.H., Thunell, E., Ogmen, H., 2016. Putting low-level vision into global context: why vision cannot be reduced to basic circuits. Vision Research, Quantitative Approaches in Gestalt Perception 126, 9-18.

Heutink, J., Indorf, D.L., Cordes, C., 2019. The neuropsychological rehabilitation of visual agnosia and Balint's syndrome. Neuropsychol. Rehabil. 29, 1489-1508.

Hildebrandt, A., Wilhelm, O., Schmiedek, F., Herzmann, G., Sommer, W., 2011. On the specificity of face cognition compared with general cognitive functioning across adult age. Psychol. Aging 26, 701-715.

Hills, P.J., Lewis, M.B., 2018. The development of face expertise: evidence for a qualitative change in processing. Cognit. Dev. 48, 1-18.

Hippolyte, L., Barisnikov, K., Van der Linden, M., 2008. Face processing and facial emotion recognition in adults with Down syndrome. Am. J. Ment. Retard. 113, 292-306.

Hirji, S.H., Hood, D.C., Liebmann, J.M., Blumberg, D.M., 2021. Association of patterns of glaucomatous blumar damage with contrast sensitivity and facial recognition in patients with glaucoma. JAMA Ophthalmology 139, 27-32.

Hodges, J.R., Patterson, K., 2007. Semantic dementia: a unique clinicopathological syndrome. Lancet Neurol. 6, 1004-1014.

Howard, D., Patterson, K., 1992. The Pyramids and Palm Trees Test: A Test for Semantic Access from Words and Pictures. Thames Valley Test Company.

Hsiao, J.H., Cottrell, G., 2008. Two fixations suffice in face recognition. Psychol. Sci. 19, 1008-1006.

Humble, D., Schweinberger, S.R., Mayer, A., Jesgarzewicz, T.L., Dobel, C., Zäske, R., 2022. The Jena Voice Learning and Memory Test (JVLMT): a standardized tool for assessing the ability to learn and recognize voices. Behav Res 55, 1352-1371.

Jacques, C., Jonas, J., Colnat-Coulbois, S., Maillard, L., Rossion, B., 2022. Low and high frequency intracranial neural signals match in the human associative cortex. Elife 11, e76544.

Jacques, C., Rossion, B., Volfart, A., Brissart, H., Colnat-Coulbois, S., Maillard, L.G., Jonas, J., 2020. The neural basis of rapid unfamiliar face individualization with human intracerebral recordings. Neuroimage 221, 117174.

Jaimes-Bautista, A.G., Rodríguez-Camacho, M., Martínez-Juárez, I.E., Rodríguez-Aguelo, Y., 2015. Semantic processing impairment in patients with temporal lobe epilepsy. Epilepsy Res Treat. 2015, 746745.

Jansari, A., Miller, S., Pearce, L., Cobb, S., Sagiv, N., Williams, A.L., Tree, J.J., Hanley, J. R., 2015. The man who mistook his neuropsychologist for a popstar: when configural processing fails in acquired prosopagnosia. Front. Hum. Neurosci. 9, 390.

Jemel, B., Mottron, L., Dawson, M., 2006. Impaired face processing in autism: fact or artifact? J. Autism Dev. Disord. 36, 91-106.

Jenkins, R., Dowsett, A.J., Burton, A.M., 2018. How many faces do people know? Proceedings of the Royal Society B 285, 20181319.

Jenkins, R., White, D., Van Montfort, X., Burton, A.M., 2011. Variability in photos of the same face. Cognition 121, 313-323.

Jenkins, R.E., Tsermenteli, S., Monks, C.P., Robertson, D.J., Stevenage, S.V., Symons, A. E., Davis, J.P., 2021. Are super-face-recognisers also super-voice-recognisers? Evidence from cross-modal identification tasks. Appl. Cognit. Psychol. 35, 590-605.

Jiahui, G., Yang, H., Duchaine, B., 2018. Developmental Prosopagnosias Have Widespread Selectivity Reductions across Category-Selective Visual Cortex, vol. 115. Proceedings of the National Academy of Sciences, pp. E6418-E6427.

Johnston, A., Hill, H., Carman, N., 1992. Recognising faces: effects of lighting direction, inversion, and brightness reversal. Perception 21, 365-375.

Johnstone, L.T., Karlsson, E.M., Carey, D.P., 2021. Left-handers are less lateralized than right-handers for both left and right hemispheric functions. Cerebr. Cortex 31, 3780-3787.

Jonas, J., Brissart, H., Hossu, G., Colnat-Coulbois, S., Vignal, J.-P., Rossion, B., Maillard, L., 2018. A face identity hallucination (palinopsia) generated by intracerebral stimulation of the face-selective right lateral fusiform cortex. Cortex 99, 296-310.

Jonas, J., Jacques, C., Liu-Shuang, J., Brissart, H., Colnat-Coulbois, S., Maillard, L., Rossion, B., 2016. A face-selective ventral occipito-temporal map of the human brain with intracerebral potentials. Proc. Natl. Acad. Sci. U.S.A. 113, 4088-4097.

Jonas, J., Rossion, B., 2021. Intracerebral electrical stimulation to understand the neural basis of human face identity recognition. Eur. J. Neurosci. 54, 4197-4211.

Jonas, J., Rossion, B., Brissart, H., Frismand, S., Jacques, C., Hossu, G., Colnat-Coulbois, S., Vespignani, H., Vignal, J.-P., Maillard, L., 2015. Beyond the core face-processing network: intracerebral stimulation of a face-selective area in the right anterior fusiform gyrus elicits transient prosopagnosia. Cortex 72, 140-155.

Jones, R.D., Tranel, D., 2001. Severe developmental prosopagnosia in a child with superior intellectual. J. Clin. Exp. Neuropsychol. 23, 265-273.

Joubert, S., Felician, O., Barbeau, E., Ranjeva, J.-P., Christophe, M., Didic, M., Poncet, M., Ceccaldi, M., 2006. The right temporal lobe variant of frontotemporal dementia. J. Neurol. 253, 1447-1458.

Kanwisher, N., McDermott, J., Chun, M.M., 1997. The fusiform face area: a module in human extrastriate cortex specialized for face perception. J. Neurosci. 17, 4302-4311.

Kaplan, E., Goodglass, H., Weintraub, S., 1983. The Boston Naming Test. Lea & Febiger, Philadelphia, PA.

Kapur, N., Pearson, D., 1983. Memory symptoms and memory performance of neurological patients. Br. J. Psychol. 74, 409-415.

Kennerknecht, I., Grueter, T., Welling, B., Wentzek, S., Horst, J., Edwards, S., Grueter, M., 2006. First report of prevalence of non-syndromic hereditary prosopagnosia (HPA). Am. J. Med. Genet. 140A, 1617-1622.

Kho, S.K., Leong, B.Q.Z., Keeble, D.R.T., Wong, H.K., Estudillo, A.J., 2023. A new asian version of the CFMT: the Cambridge face memory test - Chinese Malaysian (CFMT-MY). Behav. Res. Methods. https://doi.org/10.3758/s13428-023-02085-6.

Kolb, B., Milner, B., Taylor, L., 1983. Perception of faces by patients with localized cortical excisions. Can. J. Psychol./Rev. Canad. Psychol. 37, 8-18.

Kovács, G., 2020. Getting to know someone: familiarity, person recognition, and identification in the human brain. J. Cognit. Neurosci. 32, 2205-2225.

Kramer, R.S., Tree, J.J., 2023. Investigating people's metacognitive insight into their own face abilities. Q. J. Exp. Psychol., 17470218231218662

Lambon Ralph, M.A., Jefferies, E., Patterson, K., Rogers, T.T., 2017. The neural and computational bases of semantic cognition. Nat. Rev. Neurosci. 18, 42-55.

Lavallée, M.M., Gandini, D., Rouleau, I., Vallet, G.T., Joannette, M., Kergoat, M.-J., Busigny, T., Rossion, B., Joubert, S., 2016. A qualitative impairment in face
perception in Alzheimer's disease: evidence from a reduced face inversion effect. J Alzheimers Dis 51, 1225-1236.

Levine, D.N., Calvanio, R., 1989. Prosopagnosia: a defect in visual configural processing. Brain Cognit. 10, 149-170.

Lhermitte, F., Chain, F., Escourolle, R., Ducarne, B., Pillon, B., 1972. [Anatomoclinical study of a case of prosopagnosia]. Rev. Neurol. (Paris) 126, 329-346.

Lissauer, H., 1890. Ein Fall von Seelenblindheit nebst einem Beitrage zur Theorie derselben. Archiv für Psychiatrie und Nervenkrankheiten 21, 222-270.

Lissauer, H., Jackson, M., 1988. A case of visual agnosia with a contribution to theory. Cogn. Neuropsychol. 5, 157-192.

Liu-Shuang, J., Norcia, A.M., Rossion, B., 2014. An objective index of individual face discrimination in the right occipito-temporal cortex by means of fast periodic oddball stimulation. Neuropsychologia 52, 57-72.

Liu-Shuang, J., Torfs, K., Rossion, B., 2016. An objective electrophysiological marker of face individualisation impairment in acquired prosopagnosia with fast periodic visual stimulation. Neuropsychologia 83, 100-113.

Lochy, A., Van Belle, G., Rossion, B., 2015. A robust index of lexical representation in the left occipito-temporal cortex as evidenced by EEG responses to fast periodic visual stimulation. Neuropsychologia 66, 18-31.

Logan, A.J., Gordon, G.E., Loffler, G., 2020. The effect of age-related macular degeneration on components of face perception. Invest. Ophthalmol. Vis. Sci. 61, 38.

Logan, A.J., Wilkinson, F., Wilson, H.R., Gordon, G.E., Loffler, G., 2016. The Caledonian face test: a new test of face discrimination. Vis. Res. 119, 29-41.

Lohse, M., Garrido, L., Driver, J., Dolan, R.J., Duchaine, B., Furl, N., 2016. Effective connectivity from early visual cortex to posterior occipitotemporal face areas supports face selectivity and predicts developmental prosopagnosia. J. Neurosci. 36, 3821-3828.

Luzzi, S., Baldinelli, S., Ranaldi, V., Fabi, K., Cafazzo, V., Fringuelli, F., Silvestrini, M., Provinciali, L., Reverberi, C., Gainotti, G., 2017. Famous faces and voices: differential profiles in early right and left semantic dementia and in Alzheimer's disease. Neuropsychologia 94, 118-128.

Lykouras, L., Typaldou, M., Mourtzouchou, P., Oulis, P., Koutsaftis, C., Dokianaki, F., Michalopoulou, P.G., Havaki-Kontaxaki, M., Christodoulou, A., 2008. Neuropsychological relationships in paranoid schizophrenia with and without delusional misidentification syndromes. A comparative study. Prog. Neuro Psychopharmacol. Biol. Psychiatr. 32, 1445-1448.

Ma, D., Fetahu, I.S., Wang, M., Fang, R., Li, J., Liu, H., Gramyk, T., Iwanicki, I., Gu, S., Xu, W., Tan, L., Wu, F., Shi, Y.G., 2020. The fusiform gyrus exhibits an epigenetic signature for Alzheimer's disease. Clin. Epigenet. 12, 129.

Malone, D.R., Morris, H.H., Kay, M.C., Levin, H.S., 1982. Prosopagnosia: a double dissociation between the recognition of familiar and unfamiliar faces. J. Neurol. Neurosurg. Psychiatr. 45, 820-822.

Mandler, G., 1980. Recognizing: the judgment of previous occurrence. Psychol. Rev. 87, 252-271.

Manzanero, A.L., Contreras, M.J., Recio, M., Alemany, A., Martorell, A., 2012. Effects of presentation format and instructions on the ability of people with intellectual disability to identify faces. Res. Dev. Disabil. 33, 391-397.

Marschollek, K., Nowakowska-Kotas, M., Marschollek, P., Marschollek, J., Drozdź, G., Drozdź, J., Budrewicz, S., 2019. Developmental prosopagnosia in Poland: an analysis of online-conducted population based study. E-methodology 6, 57-64.

Matsuyoshi, D., Watanabe, K., 2021. People have modest, not good, insight into their face recognition ability: a comparison between self-report questionnaires. Psychol. Res. 85, 1713-1723.

Mayer, E., Fistarol, P., Valenza, N., 1999. Prise en charge neuropsychologique d'une patiente prosopagnosique. In: Azouvi, P., Perrier, D., Van der Linden, M. (Eds.), La reeducation en neuropsychologie: Etudes de cas. Solal, Marseille, pp. 249-264.

Mayer, E., Rossion, B., 2007. Prosopagnosia: In: The Behavioral and Cognitive Neurology of Stroke. Cambridge University Press, New York, NY, pp. 315-334.

Mayes, A., Montaldi, D., Migo, E., 2007. Associative memory and the medial temporal lobes. Trends Cognit. Sci. 11, 126-135.

Mayes, A.R., Holdstock, J.S., Isaac, C.I., Hunkin, N.m., Roberts, N., 2002. Relative sparing of item recognition memory in a patient with adult-onset damage limited to the hippocampus. Hippocampus 12, 325-340.

Mazard, A., Schiltz, C., Rossion, B., 2006. Recovery from adaptation to facial identity is larger for upright than inverted faces in the human occipito-temporal cortex. Neuropsychologia 44, 912-922.

McBain, R., Norton, D., Chen, Y., 2009. Females excel at basic face perception. Acta Psychol. 130, 168-173.

McConachie, H.R., 1976. Developmental prosopagnosia: a single case report. Cortex 12, 76-82.

McKone, E., Hall, A., Pidcock, M., Palermo, R., Wilkinson, R.B., Rivolta, D., Yovel, G., Davis, J.M., O'Connor, K.B., 2011. Face ethnicity and measurement reliability affect face recognition performance in developmental prosopagnosia: evidence from the Cambridge Face Memory Test-Australian. Cogn. Neuropsychol. 28, 109-146.

McKone, E., Stokes, S., Liu, J., Cohan, S., Fiorentini, C., Pidcock, M., Yovel, G., Broughton, M., Pelleg, M., 2012. A robust method of measuring other-race and other-ethnicity effects: the Cambridge Face Memory Test format. PLoS One 7, e49756.

McKone, E., Wan, L., Robbins, R., Crookes, K., Liu, J., 2017. Diagnosing prosopagnosia in east asian individuals: norms for the Cambridge face memory test-Chinese. Cogn. Neuropsychol. 34, 268-284.

Megreya, A.M., Bindemann, M., Havard, C., 2011. Sex differences in unfamiliar face identification: evidence from matching tasks. Acta Psychol. 137, 83-89.

Meissner, C.A., Brigham, J.C., 2001. Thirty years of investigating the own-race bias in memory for faces: a meta-analytic review. Psychol. Publ. Pol. Law 7, 3-35.

Mendez, M.F., Ringman, J.M., Shapira, J.S., 2015. Impairments in the face-processing network in developmental prosopagnosia and semantic dementia. Cognit. Behav. Neurol. 28, 188-197.

Metin, B., Arikan, M.K., Kalem, S.A., Tarhan, N., 2019. A case of Capgras syndrome with frontotemporal dementia. Cognit. Behav. Neurol. 32, 134-138.

Milner, B., 1968. Visual recognition and recall after right temporal-lobe excision in man. Neuropsychologia 6, 191-209.

Mina, C., Marianetti, M., Fratino, M., Montemurro, M., Vanacore, N., Amabile, G.A., 2010. Recognition and naming of famous buildings: Italian normative data. Neurol. Sci. 31, 441-447.

Mishra, M.V., Fry, R.M., Saad, E., Arizpe, J.M., Ohashi, Y.-G.B., DeGutis, J.M., 2021. Comparing the sensitivity of face matching assessments to detect face perception impairments. Neuropsychologia 163, 108067.

Mishra, M.V., Likitlersuang, J., Wilmer, J.B., Cohan, S., Germine, L., DeGutis, J.M., 2019. Gender differences in familiar face recognition and the influence of sociocultural gender inequality. Sci. Rep. 9, 1-12.

Montaldi, D., Kafkas, A., 2022. Neural mechanisms of familiarity. In: Kahana, M.J., Wagner, A.D. (Eds.), Oxford Handbook of Human Memory. Oxford University Press (PsyArXiv).

Muhammad, J., Wang, Y., Wang, C., Zhang, K., Sun, Z., 2021. CASIA-face-africa: a large-scale african face image database. IEEE Trans. Inf. Forensics Secur. 16, 3634-3646.

Mühl, C., Sheil, O., Jarutyté, L., Bestelmeyer, P.E.G., 2018. The Bangor Voice Matching Test: a standardized test for the assessment of voice perception ability. Behav. Res. Methods 50, 2184-2192.

Murphy, J., Gray, K.L.H., Cook, R., 2020. Inverted faces benefit from whole-face processing. Cognition 194, 104105.

Murray, E., Bate, S., 2019. Self-ratings of face recognition ability are influenced by gender but not prosopagnosia severity. Psychol. Assess. 31, 828-832.

Murray, E., Bate, S., 2020. Diagnosing developmental prosopagnosia: repeat assessment using the Cambridge Face Memory Test. R. Soc. Open Sci. 7, 200884.

Murray, E., Bennetts, R., Tree, J., Bate, S., 2022. An update of the Benton facial recognition test. Behav Res 54, 2318-2331.

Murray, E., Hills, P.J., Bennetts, R.J., Bate, S., 2018. Identifying hallmark symptoms of developmental prosopagnosia for non-experts. Sci. Rep. 8, 1690.

Neuner, F., Schweinberger, S.R., 2000. Neuropsychological impairments in the recognition of faces, voices, and personal names. Brain Cognit. 44, 342-366.

Newcombe, F., 1979. The processing of visual information in prosopagnosia and acquired dyslexia: functional versus physiological interpretation. Research in psychology and medicine 1, 315-322.

Noppeney, U., Patterson, K., Tyler, L.K., Moss, H., Stamatakis, E.A., Bright, P., Mummery, C., Price, C.J., 2007. Temporal lobe lesions and semantic impairment: a comparison of herpes simplex virus encephalitis and semantic dementia. Brain 130, 1138-1147.

Norcia, A.M., Appelbaum, L.G., Ales, J.M., Cottereau, B.R., Rossion, B., 2015. The steady-state visual evoked potential in vision research: a review. J. Vis. 15, 4.

Nørkær, E., Guðbjörnsdóttir, E., Roest, S.B., Shah, P., Gerlach, C., Starrfelt, R., 2023. The Danish version of the 20-item prosopagnosia (PI20): translation, validation and a link to face perception. Brain Sci. 13, 337.

O'Hearn, K., Schroer, E., Minshew, N., Luna, B., 2010. Lack of developmental improvement on a face memory task during adolescence in autism. Neuropsychologia 48, 3955-3960.

Olson, I.R., McCoy, D., Klobusicky, E., Ross, L.A., 2013. Social cognition and the anterior temporal lobes: a review and theoretical framework. Soc. Cognit. Affect Neurosci. 8, 123-133.

Orban de Vivry, J.-J., Ramon, M., Lefèvre, P., Rossion, B., 2008. Reduced fixation on the upper area of personally familiar faces following acquired prosopagnosia. J. Neuropsychol. 2, 245-268.

Oyebode, F., Edelstyn, N.M.J., Humphreys, G.W., Booker, E., 1998. Facial processing and the delusional misidentification syndromes. Cognit. Neuropsychiatry 3, 299-314.

Palejwala, A.H., O'Connor, K.P., Milton, C.K., Anderson, C., Pelargos, P., Briggs, R.G., Conner, A.K., O'Donoghue, D.L., Glenn, C.A., Sughrue, M.E., 2020. Anatomy and white matter connections of the fusiform gyrus. Sci. Rep. 10.

Palermo, R., Rossion, B., Rhodes, G., Laguesse, R., Tez, T., Hall, B., Albonico, A., Malaspina, M., Daini, F., Irons, J., Al-Janabi, S., Taylor, L.C., Rivolta, D., McKone, E., 2017. Do people have insight into their face recognition abilities? Q. J. Exp. Psychol. 70, 218-233.

Papagno, C., Pisoni, A., Gainotti, G., 2021. False alarms during recognition of famous people from faces and voices in patients with unilateral temporal lobe resection and normal participants tested after anodal tDCS over the left or right ATL. Neuropsychologia 159, 107926.

Parker, B.J., Voorhies, W.I., Jiahui, G., Miller, J.A., Willbrand, E., Hallock, T., Furl, N., Garrido, L., Duchaine, B., Weiner, K.S., 2023. Hominoid-specific sulcal variability is related to face perception ability. Brain Struct. Funct. 228, 677-685.

Patterson, K., Kopelman, M.D., Woollams, A.M., Brownsett, S.L.E., Geranmayeh, F., Wise, R.J.S., 2015. Semantic memory: which side are you on? Neuropsychologia, Special Issue: Semantic Cognition 76, 182-191.

Pesenti, M., Thioux, M., Samson, D., Bruyer, R., Seron, X., 2000. Number processing and calculation in a case of visual agnosia. Cortex 36, 377-400.

Petersen, L.A., Leue, A., 2021. Extraordinary face recognition performance in laboratory and online testing. Appl. Cognit. Psychol. 35, 579-589.

Petersen, L.A., Leue, A., 2022. Face memory and face matching. J. Indiv. Differ. 43, 152-159.

Petersen, L.M., Susilo, T., Clifford, C.W.G., Palmer, C.J., 2023. Discrimination of facial identity based on simple contrast patterns generated by shading and shadows. Vis. Res. 212, 108307.