Cognition 186 (2019) 42–49

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Cognition
journal homepage: www.elsevier.com/locate/cognit

Original Articles

Decisional space modulates visual categorization – Evidence from saccadic
reaction times

T⁎

Meike Ramon1, , Nayla Sokhn1, Roberto Caldara
Eye and Brain Mapping Laboratory (iBMLab), Department of Psychology, University of Fribourg, Switzerland

A R T I C LE I N FO

A B S T R A C T

Keywords:
Face processing
Visual categorization
Minimum saccadic reaction times
Personal familiarity
Face gender
Task demands

Manual and saccadic reaction times (SRTs) have been used to determine the minimum time required for diﬀerent
types of visual categorizations. Such studies have demonstrated extremely rapid detection of faces within naturalscenes, whereas increasingly complex decisions (i.e. levels of processing) require longer processing times. We
reasoned that visual categorization speed is not only dependent on the processing level, but is further aﬀected bydecisional space constraints. In the context of two diﬀerent tasks, observers performed choice saccades towards
female (gender categorization) or personally familiar (familiarity categorization) faces. Additionally, familiarity
categorizations were completed with stimulus sets that diﬀered in the number of individuals presented (3 vs. 7
identities) to investigate the eﬀect of decisional space constraints. We observe an inverse relationship between
visual categorization proﬁciency and decisional space. Observers were most accurate for categorization ofgender, which could be achieved in as little as 140 ms. Categorization of highly predictable targets was moreerror-prone and required an additional ∼100 ms processing time. Our ﬁndings add to an increasing body ofevidence demonstraing that pre-activation of identity-information can modulate early visual processing in a topdown manner. They also emphasize the importance of considering procedural aspects, as well as terminology
when aiming to characterize cognitive processes.

1. Introduction
Across sensory modalities, stimulus categorization can involve a
number of diﬀerent processes. Diﬀerent types of stimuli can be detected, discriminated, recognized as having been encountered before, or
identiﬁed on the exemplar level. The human visual system can perform
such categorizations with high proﬁciency despite large variations instimulus input. For instance, categorization of animals presented inimages of natural scenes presented for as little as 20 ms can be reliably
achieved in under 300 ms (ms) (e.g., Macé, Thorpe, & Fabre-Thorpe,
2005; Rousselet, Fabre-Thorpe, & Thorpe, 2002; Thorpe, Fize, & Marlot,
1996). The speed of such categorical responses can be exploited to
study the characteristics of visual processing. Speciﬁcally, the time required to perform accurate visual categorizations provides a valuable
source of information, which can be levered to constrain theories ofvisual processing (for a review see e.g., Fabre-Thorpe, 2011). However,a number of other aspects can inﬂuence visual categorization speed.
One important aspect that aﬀects visual categorization speed is theeﬀector from which responses are recorded, with certain responses requiring more time to be executed than others. Estimates of visual

categorizations have been derived from veriﬁcation and naming tasks,
which typically involve longer reaction times (RTs) (Tanaka & Taylor,1991), as compared to manual forced choice paradigms, where subjects
press (or release) one of two buttons (a button) to distinguish between
stimulus categories (e.g., Hasbroucq, Mouret, Seal, & Akamatsu, 1995).
To reduce response times, more sensitive Go/no-go paradigms havebeen developed that require subjects to respond to predeﬁned target
categories via ﬁnger-lift from an infra-red sensor (e.g., Bacon-Macé,
Macé, Fabre-Thorpe, & Thorpe, 2005; Rousselet et al., 2002; Thorpe
et al., 1996; VanRullen & Thorpe, 2001). Finally, other paradigms exploiting the very rapid responses of oculo-motor eﬀectors have beendeveloped. The saccadic reaction times (SRTs) measured in such paradigms provide a more precise description of the lower bound of speededcategorizations (Crouzet, Joubert, Thorpe, & Fabre-Thorpe, 2009;
Crouzet, Kirchner, & Thorpe, 2010; Kirchner & Thorpe, 2006). Together, these developments have served the goal of maximizing the
precision estimates of visual processing speed (for a direct comparisonof paradigms, see Bacon-Macé, Kirchner, Fabre-Thorpe, & Thorpe,2007). Moreover, aside from deriving mean or median RTs, theseparadigms have aimed to determine the minimum reaction time⁎

Corresponding author at: University of Fribourg, Department of Psychology, Faucigny 2, 1700 Fribourg, Switzerland.
E-mail address: meike.ramon@gmail.com (M. Ramon).
1
Both authors contributed equally to this workhttps://doi.org/10.1016/j.cognition.2019.01.019
Received 29 March 2018; Received in revised form 24 January 2019; Accepted 29 January 2019
0010-0277/ © 2019 Published by Elsevier B.V.

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(minRT). The minRT is deﬁned as the ﬁrst time-bin for which correct
responses signiﬁcantly outnumber incorrect ones (Fabre-Thorpe,
Richard, & Thorpe, 1998; VanRullen & Thorpe, 2001), and is considered to reﬂect the minimal processing time required for reliable responses (Rousselet, Macé, & Fabre-Thorpe, 2003).
A second aspect that aﬀects visual categorization speed is the natureof the stimulus processed. In the ﬁeld of visual cognition, numerous
studies have investigated the role of stimulus category on categorization proﬁciency. Such studies have e.g. demonstrated that human faces
can be rapidly detected within natural scenes within as little as100–110 ms (Crouzet et al., 2010), and are more eﬃciently processedcompared to other non-face objects (e.g., Caudek, 2013; for a review
see Yovel, 2016). These ﬁndings have supported the notion that faces
represent a “special” stimulus category, studied by a large and continuously growing body of research, which can generate insights into
general functioning principles of the human brain.
A third aspect that aﬀects visual processing speed is the process
engaged in, or processing level (cf. Mack & Palmeri, 2015). Faces are
ideal stimuli to study visual categorization, because they can be processed at multiple levels. For example, typical observers can accurately
and rapidly detect the presence of a face, determine its gender, age, andethnicity, discriminate between and recognize previously encounteredpersons, and ﬁnally identify unique individuals and recall associatedsemantic information. Psychophysical studies have demonstrated that
diﬀerent visual categorization processes are associated with importantdiﬀerences in information diagnosticity. For instance, the ability todetect a face, determine its gender, expression, or familiarity, relies on
diﬀerent facial information (Schyns, 1998; Gosselin & Schyns, 2001;
Schyns, Bonnar & Gosselin, 2002; Smith, Fries, Gosselin, Goebel &Schyns, 2009; Smith, Volna & Ewing, 2016). Importantly, these different categorization processes are associated with varied levels of behavioral proﬁciency. For example, superordinate human vs. animal
decisions can be performed manually with high precision (98%) in as
little as 285 ms, while famous vs. unfamiliar manual decisions are moreerror-prone and slower (75%, 468 ms); gender categorization, on theother hand, can be performed with high ﬁdelity and intermediate
minRTs (94%; ∼310 ms) (Barragan-Jason, Lachat, & Barbeau, 2012).
Adding to these ﬁndings, we recently reported processing level dependent categorization proﬁciency for both healthy observers and acase of acquired prosopagnosia (Ramon, Sokhn, Lao & Caldara, 2018).
Despite such evidence, the majority of studies of visual categorization do not systematically address or consider the impact of diﬀerent
processing levels. Additionally, and perhaps consequently, many studies lack terminological precision. For instance, some researchers use
the term “face identiﬁcation” for any task that deals with processing offacial identity, e.g. face matching (e.g., Balsdon, Summersby, Kemo &White, 2018, who also use the term “face recognition” interchangeably). Similarly, while “face recognition” refers to the ability toidentify stimuli as previously learned (e.g., Duchaine & Nakayama,
2006; Blais, Jack, Scheepers, Fiset & Caldara, 2008; Barton & Corrow,
2016; Ramon & Gobbini, 2018), others misleadingly use this term in thecontext of face matching tasks (e.g., Phillips, Yates, Hu, Hahn, Noyes,
et al., 2018). Both indiscriminate terminology, as well as lack of consideration of process(es) implied can generate seemingly contradictoryﬁndings, which can be detrimental to advancing our understanding ofvisual categorization (Ramon, 2018; Ramon, Bobak & White, in press).
However, type of eﬀector, stimulus category, and processing levelare not the only factors that ﬂexibly modulate visual categorization on
the neural and behavioral level (cf. Mack & Palmeri, 2015; Praβ,
Grimsen König & Fahle, 2013; Kim & McCarthy, 2016; Rotshtein,Schﬁeld, Funes & Humphreys, 2010; Bonte, Hausfeld, Scharke, Valente& Formisano, 2014). Additionally, observers’ experience, environmental regularities, and expectations determine processing proﬁciency(e.g., Palmeri & Mack, 2015; Hall, Mattingley & Dux, 2018; Manahova,
Mostert, Schoﬀelen & de Lange, 2018; Eger, Henson, Driver & Dolan,
2007; Sauvé, Harmand, Vanni & Brodeur, 2017; Kadel, Feldmann-

Wüstefeld & Schubö, 2017; for reviews see Summerﬁeld & Egner, 2009;
Lupyan, 2015; O'Callaghan, Kveraga, Shine, Adams & Bar, 2017; Seriès& Seitz, 2013). A simple way to demonstrate this is by varying the sizeof the stimulus set used, which (together with other procedural choices)
determines an item’s probability of being encountered.
A large body of work has demonstrated the important eﬀect that
stimulus set size can have on neural processing and overt behavior.
Several studies support the idea that higher (neural and behavioral)
search eﬃciency is found for smaller categories (cf. Wu, McGee,
Echiverri & Zinszer, 2018). Psychophysical studies involving target
search for simple stimuli in multi-item displays have demonstrated thatvisual search proﬁciency declines as the number of simultaneously
available distractors increases (e.g., Palmer, 1994; Busey & Palmer,2008). Such decreases in performance accuracy and RT increases observed with increasing set sizes, referred to as set size eﬀects, are foundacross sensory modalities, as well as for individually presented stimuli
of varied complexity (e.g., Pollack, 1952; Kent & Lamberts, 2005;
Lacouture & Marley, 2004). Concerning high-level vision, as investigated by face processing, set size (or attentional load; cf. Olk &Garay-Vado, 2011) has been reported to aﬀect visual categorization.
For example, interactions between invariant features (e.g., emotional
expression, gender, race) varies as a function of set size (Lipp,
Karnadewi, Craig & Cronin, 2015; Craig, Mallan & Lipp, 2012). Crucially, set size has been reported to account for “apparent inconsistency
in the literature on face categorization” (Lipp et al., 2015; p.1293).
As such, two experiments can involve the same stimulus category(e.g., faces) and task (e.g., pressing a button when an exemplar of atarget category is presented). Independent of these similarities, both
can involve distinct processes (e.g., detecting the occurrence of a female
face, or that of a familiar individual), for which potentially diﬀerent
sources of information are diagnostic, which are in turn associated with
diﬀerent levels of categorization proﬁciency. Performance will also be
determined by two additional factors: the probability of seeing an exemplar from a target category (i.e., the proportion of female to male,and unfamiliar to familiar faces); the total number of exemplars per
category, i.e. stimulus set size. Speciﬁcally, if individual level processing is required (e.g., in familiarity decisions and identiﬁcation tasks),
small set sizes should be associated with more proﬁcient performance.
Simply speaking, it is easier to determine whether an exemplar is one offew, as opposed to one of many.
Therefore, studying visual categorization requires careful consideration of a number of aspects: eﬀector type, stimulus category(accurate deﬁnition and terminology related to), processing level, andstimulus predictability, which is partly determined by the stimulus set
size. Neglecting any of these aspects can promote erroneous conclusions
and apparently discrepant ﬁndings. To illustrate, consider recent ﬁndings of process-dependent face categorization. Numerous ﬁndings suggest that face detection precedes familiarity recognition. For example,
Besson et al. (2017) assessed face categorization at three diﬀerent levels
through manual minRTs using extremely large stimulus set to avoid
image repetitions. The authors reported highly accurate performanceand very rapid minRTs for “human face categorization” (i.e., face detection ∼240 ms) and “individual face recognition” (i.e., searching fora single predeﬁned target identity; ∼260 ms), but more error prone andslower responses for “familiar face recognition” (i.e, deciding whether aface belonged to a large pool of famous individuals; ∼380 ms), as also
reported for personally familiar face recognition (Ramon, Caharel, &Rossion, 2011). Thus, 380 ms represents the lower bound for speeded
manual familiarity decisions that occur ca. 180 ms after the earliest
neural marker of familiarity (e.g., Barragan-Jason, Cauchoix, &Barbeau, 2015; Caharel, Ramon, & Rossion, 2014; Huang et al., 2017).
Seemingly contradictory evidence was reported by Visconti di Oleggio
Castello and Gobbini (2015). Measuring the minimum speed of choicesaccades expressed towards personally familiar (PF) faces, the authors
concluded that personally familiar face “detection” could be achievedin 180 ms, before “explicit recognition of identity” (p.1). These ﬁndings
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Fig. 1. Examples of photographs of personally familiar and unfamiliar stimuli taken for stimulus creation.

who were their colleagues for several years; this ﬁrst group completed
all three tasks described below. The second and third groups(nstudents_gender = 36; 31 females, mean age: 21, range: 20–24;
nstudents_familiarity = 27; 24 females, mean age: 22, range: 20–24) comprised students from the Department of Psychology who knew themembers of Department depicted in the stimulus material through theirteaching and mentoring activities. The second group of observers
completed the gender categorization task; the third group completed
both personal familiarity categorization tasks. Note that observers of
the second and third groups completed only one paradigm (gender orfamiliarity decisions), due to internal departmental regulations regarding the number of experimental hours completed by student participants. Groups’ performance (i.e., lab members vs. students) was
considered separately in the analyses due to the inherent diﬀerences inage and exposure to individuals whose likenesses were presented in theexperiments. All participants provided written informed consent; allprocedures were approved by the internal ethics committee of the
Department of Psychology at the University of Fribourg, Switzerland
and are in accordance with the Code of Ethics of the World Medical
Association (Declaration of Helsinki).

are incompatible with electrophysiological studies, which have reported the earliest familiarity-dependent diﬀerential response to faces(∼140–200 ms; e.g. Barragan-Jason et al., 2015; Caharel et al., 2014;
Huang et al., 2017).
In our opinion, the most parsimonious account for these apparently
discrepant ﬁndings lies in procedural diﬀerences and misleading terminology (cf. Ramon, 2018; Ramon et al., 2018). Visconti di Oleggio
Castello and Gobbini’s (2015) observers performed choice saccades
towards a set of familiar faces comprising merely three target identities(which diﬀered across observers). Thus, given this highly constrained
decisional space, the process that Visconti di Oleggio Castello andGobbini (2015) measured is much less “detection” (i.e. spotting one ofnumerous possible instances of a category). Rather, it represents athree-alternative “individual face recognition” (Besson et al., 2017)
task, which can be achieved in < 140 ms for a single target identity(Mathey et al., 2012). Despite repeating each image several hundreds oftimes, Visconti di Oleggio Castello and Gobbini’s (2015) observers’
performance in the three-alternative target search task was inferior tothat reported by Mathey et al. (2012) in their single target task (performance ranges: 49–69% vs. 60–75%).
Here, we sought to systematically investigate the extent to which
visual categorization speed is aﬀected by two factors: processing leveland decisional space. To this end, we employed a time-sensitive SRTparadigm, in the context of two diﬀerent processes performed on asingle stimulus category (i.e., faces): gender and familiarity decisions
expressed through two alternative forced-choice saccades (cf. Ramonet al., 2018). Additionally, in the context of familiarity decisions, wemanipulated the decisional space by varying the number of target exemplars, thereby creating a 1-of-n-target search task. This eﬀectively
manipulates observers’ expectation given the varied probability of exemplar presentation: both familiarity decision tasks diﬀered in that
their decisional space was either comparatively broad or narrow.
To anticipate our ﬁndings, in line with previous work (Ramon et al.,2018; Mathey et al., 2012 ) rapid minSRTs are observed under conditions of high predictability, where the search space is conﬁned to abinary, unambiguous category (gender decision task), or a very small
number of target items (familiarity decision with few identities). Theseﬁndings indicate that “detection of personal familiarity” requires signiﬁcantly more time than previously reported, unless the term “detection” is used loosely to describe responses executed towards an extremely restricted number of target identities (Visconti di Oleggio
Castello & Gobbini, 2015) – a process that should more accurately belabeled as n-alternative target search (Besson et al., 2017).

2.2. Stimuli
The full stimulus set comprised natural (uncropped, color) images of
14 facial identities (7 un/familiar) taken from three diﬀerent viewpoints (frontal, left, right). For each PF identity, images of a corresponding unfamiliar identity carefully matched for age, gender, andappearance (hair color and style, eye color) were taken (see Fig. 1 forstimulus examples). Image processing included placement on a uniform
grey background (630 × 630 pixels) and correction for low-levelproperties (luminance, contrast) using the SHINE toolbox (Willenbockel
et al., 2010), as well as additional ones kindly provided by V. Willenbockel to allow for equation of color stimuli.
2.3. Procedures
Prior to completing the experiments, subjects completed familiarity
ratings to determine their level of familiarity with each identity (PF andunfamiliar) presented. Each item of the familiarity questionnaire consisted of an image of each stimulus identity taken under varied, natural
conditions. Lab members’ images were taken from professional websites, unfamiliar identities’ images were taken from social media.
Observers had to indicate their self-reported degree of familiarity witheach individual on a scale from 1 (not at all familiar) to 5 (highly familiar). For all experiments, stimuli were presented on a 1920 × 1080
pixel VIEWPixx monitor. Subjects’ oculo-motor behavior was recorded
at a sampling rate of 1000 Hz with an SR Research Desktop-Mount
EyeLink 2 K eye tracker (with a chin and forehead rest; average gaze
position error ∼0.5, spatial resolution: ∼0.01). The eye-tracker had alinear output over the range of the monitor used. Although viewing wasbinocular, only the left eye was tracked; given the fully balanced

2. Methods
2.1. Participants
We tested three groups of subjects: the ﬁrst (nlab_members = 8; 4 females, mean age: 31, range: 24–37 comprised group members from theiBMLab. Group members were highly familiar with the target identities,
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stimulus presentation across visual ﬁelds, inter-individual diﬀerences inocular dominance were considered irrelevant. The experiment was
implemented in Matlab (R2009b, The MathWorks, Natick, MA), using
the Psychophysics toolbox (PTB-3) (Kleiner, Brainard, & Pelli, 2007;
Pelli, 1997) and EyeLink Toolbox extensions (Cornelissen, Peters, &Palmer, 2002; Kleiner et al., 2007). Calibrations of eye ﬁxations wereconducted at the beginning of the experiment using a nine-point ﬁxation procedure as implemented in the EyeLink API (see EyeLink
Manual) and using Matlab software. Afterwards, calibrations were validated with the EyeLink software, and repeated when necessary until
reaching an optimal calibration criterion. Drift correction was performed on each trial via central cross ﬁxation.
In the gender categorization task, subjects were instructed to performchoice saccades towards female faces. In this task three images (viewpoint changes) for each PF individual (6 identities, 3 females) and theirunfamiliar counterparts were presented. A trial began with a central
ﬁxation cross displayed between 800 and 1600 ms, followed by a
200 ms blank and subsequent presentation of the target/distractor pairpresented for 600 ms. After a saccade was registered, the next trial waspresented after a 1000 ms blank inter-trial interval. Stimuli subtended
14˚x14° (average face height was 11°), and stimulus eccentricity was
8.6˚ of visual angle. With all possible combinations and equal number ofpresentations per identity and visual ﬁeld, the total number of trials was
216; subjects took self-paced breaks after each block of 54 trials.
The two familiarity categorization tasks diﬀered in terms of thenumber of identities depicted, but both required observers to performchoice saccades towards personally familiar identities presented with
unfamiliar distractors. The low and high predictability variants involved presentation of 7 PF (3 females), or 3 PF (all male) identities,
respectively, as well as an equal number of well-matched UF distractors. Note that, before beginning the respective experiments, participants were not aware of the number of identities actually presented.
Presentation parameters were identical to those described for thegender categorization task (see above). The procedural parametersparalleled those used by Visconti di Oleggio Castello and Gobbini(2015), with exception of stimulus presentation duration (600 ms instead of 400 ms), as initial pilot testing revealed slightly longer presentation durations were necessary for acceptable performance levels.
On each trial, a PF identity was paired with a same-gender, same-orientation distractor, and appeared with equal probability in either visual ﬁeld. The total number of trials for the low predictability familiarity categorization task amounted to 150. To achieve a comparable
number of trials in the high predictability variant for comparison withthe low predictability variant, each unique stimulus × visual ﬁeld
combination was presented three times, leading to a total 162 trials
over three blocks. These trials/blocks were doubled to further determine potential eﬀects of repetition in the high predictability categorization task (see Analyses, Section 3). Subjects took self-paced
breaks after each block of 50 or 54 trials (low or high predictability
variant), respectively. Note that the order of the high and low predictability familiarity decision tasks was not randomized. Participants
completed the low predictability version and the gender decision tasks(order counterbalanced) on the same day; two months later the highpredictability variant was completed.

3.2. Statistical analyses
As mentioned above, across all experiments we considered only datafrom subjects whose performance exceeded chance level. For thegender categorization task this led to the exclusion of one departmental
member (nlab_members = 7/8); all student participants performed above
chance and were considered (nstudents_gender = 36/36). For the familiarity categorization tasks, only data from subjects who performed reliably across both low and high predictability variants were considered(nlab_members = 4/8, nstudents_familiarity = 14/27). Analyses performed todetermine the eﬀect of stimulus repetition were conducted on data fromsubjects who performed above chance across all blocks of the highpredictability familiarity categorization task (nlab_members = 7/8,
nstudents_familiarity = 20/27). Analyses of accuracy and mean SRTs were
performed in R (version 3.2.4; R Core Team, 2013) using the lme4
package (Bates, Maechler, Bolker, & Walker, 2014) and the lmerTest(Kuznetsova, Brockhoﬀ, & Christensen, 2015) to obtain p-values of theﬁxed predictors of the ﬁtted models. Note that given our researchquestion we were not interested in between-group diﬀerences, but rather those related to stimulus predictability, i.e. those observed withingroups.
3.2.1. Gender categorization
Accuracy, mean and minSRTs are reported descriptively for thegender categorization task for all participants (nlab_members,
nstudents_gender), as this task served only as a baseline to demonstrate
subjects’ ability to perform the SRT task.
3.2.2. Personal familiarity categorization: low vs. High stimulus
predictability
Accuracy. To investigate the eﬀect of stimulus predictability on
subjects’ accuracy, we performed generalized linear mixed models witha binomial family (Jaeger, 2008) for the data obtained in the experiments characterized by lower (7 identities), or higher stimulus predictability (3 identities), respectively. This was done separately pergroup tested given the unequal sample sizes available. In this model,
the main predictor is the variable ‘predictability’ (low and high for larger
and smaller number of identities presented) and the variable participant is a random factor. We performed the Log-likelihood Ratio Test tocompare the null and full model and assess the signiﬁcance of thepredictor.
Mean SRTs. To investigate the eﬀect of stimulus predictability on
SRTs, we performed a linear mixed model for the data obtained in theexperiments characterized by lower (7 identities), or higher stimulus
predictability (3 identities), respectively, considering only the correct
trials. Note that, due to the diﬀerent number of trials across these experiments, we considered all trials from the low predictability (1 5 0),
and those of ﬁrst 3 blocks for the high predictability (1 6 2) familiarity
categorization tasks. In this model, the main predictor is the variable‘predictability’ and the variable participant is a random factor. As for
accuracy scores, we performed the Log-likelihood Ratio Test to compare
the null and full model and assess the signiﬁcance of the predictor.
Minimum SRTs. We estimated minSRTs in two diﬀerent ways, here
(as for mean SRTs) also only considering the ﬁrst three blocks of trials.
In order to facilitate comparison across studies, we opted for the sameprocedures as reported by Besson et al. (2017) and Visconti di Oleggio
Castello and Gobbini (2015). First, across subjects’ trials (i.e., group
minSRTs) we performed a chi-square test using 10 ms time bins acrosstrials. We considered the ﬁrst bin where the number of correct trials
outperforms statistically the number of incorrect trials (p < .05), followed by at least three signiﬁcant consecutive bins (Besson et al.,2017). Second, we determined individuals’ minSRTs. To this end, we (i)considered only RTs of participants who performed above chance level,and (ii) opted for 40 ms time bins using the Fisher’s exact test
(p < .05). Using this procedure, some participants’ individual minSRT
could not be computed (due to proximal in/correct trial distributions).

3. Analyses
3.1. Pre-processing
We applied the adaptive velocity based algorithm developed by
Nyström and Holmqvist (2010) to ﬁnd the onset of the ﬁrst saccade (ifany) within each trial. We discarded trials in which the onset of the ﬁrst
saccade was lower than 80 ms (Visconti di Oleggio Castello andGobbini, 2015), as these were considered anticipatory saccades.

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Table 1
Accuracy (in %) and minimum saccadic reaction times (in ms) obtained acrossexperiments for all subjects tested, whose performance was above chance level.
Gender categorization

Accuracy
minSRT
Mean SRTMedian SRT
CI

Department members(nlab_members=7)1

Students (nstudents_gender=36)

81 [71, 90]
200 ms276
262[272, 281]

84 [81, 86]
140 ms252236[251, 254]

Personal familiarity categorization

Low predictability
Accuracy
minSRT
Mean SRTMedian SRT
CIHigh predictability
Accuracy
minSRT
Mean SRTMedian SRT
CI

Department members(nlab_members=4)Students(nstudents_familiarity=14)

74 [64, 81]
360
373
390
[370, 388]

68 [64, 72]
260
334
334
[331, 340]

82 [74, 89]
260
345
353
[343, 357]

76 [71, 79]
240
345
339
[343, 350]

Fig. 2. Individual subjects’ minSRTs plotted against performance accuracy.
Note that for 3 student subjects minSRTs could not be computed (cf. Table S4).

4.2. Personal familiarity categorization: lower vs. Higher stimulus
predictability
Accuracy. The groups’ SRT distributions for the low and high predictability familiarity categorization tasks are illustrated in Fig. 4. For
departmental members’ accuracy scores, the ﬁtted model (considering
the total of 1248 trials) revealed a signiﬁcant main eﬀect of stimulus set
size (i.e., number of identities depicted; X2(1) = 13.73, p = .0002);
performance given 7 target identities was signiﬁcantly lower than when
3 target identities were presented (74% vs. 82%; z = 3.69, p = .0002).
For students’ accuracy scores, the results from the ﬁtted model (basedon a total of 4361 trials) also showed a signiﬁcant main eﬀect of stimulus set size X2(1) = 33.53, p < .0001); again, performance for the
lower as compared to higher predictability task variant was signiﬁcantly lower (68% vs. 76%; z = 5.78, p < .0001). For parameter
estimates of the ﬁxed eﬀects for the generalized linear mixed modelwith the binomial family for each group tested see Table S2.
Mean SRTs. For departmental members’ SRTs the ﬁtted model(considering the total of 967 trials) revealed a signiﬁcant main eﬀect ofstimulus set size (X2 (1) = 29.70, p < .0001). Mean (373 vs 345;
t = −5.49, p < .0001). For students’ SRTs the results from the ﬁtted
model (based on a total of 3142 trials) showed a signiﬁcant main eﬀectof stimulus set size (X2(1) = 14.67, p = .0001). Contrary to the departmental members, the mean of RTs in Experiment 1 was signiﬁcantly
faster than in Experiment 2 (334 vs 345; t = 3.83, p = .0001). For
parameter estimates of the ﬁxed eﬀects for the Linear Mixed Model foreach group tested see Table S3.
MinSRTs. The Wilcoxon signed-rank test revealed no main eﬀect ofstimulus set size on the individual minSRTS (see Table S4) for student
participants in nstudents_familiarity (p = .11).

95% conﬁdence intervals are provided in brackets. Individual observers’ data
are reported in Table S1 and Table S4.
1
Considering only the four obsservers reported for personal familiarity categorizations yielded the following performance: Accuracy: 91% [84 94];MinSRT: 200 ms; Mean SRT: 305 ms; Median SRT: 291 ms; CI [299, 310].

Finally, we assessed the eﬀect of stimulus set size for individual subjectsof n3 using the Wilcoxon signed-rank test (p < .05). Note that thecomparison of minSRTs as a function of stimulus set size was not conducted for n1 due to the insuﬃcient statistical power, as only four
participants’ data were considered.

3.2.3. Eﬀect of stimulus repetition under conditions of high stimulus
predictability
To determine the eﬀect of stimulus repetition, we used the above
described procedure for all behavioral measures, however, taking into
account all correct trials (from all 6 blocks of the high predictability
familiarity categorization experiment; cf. above). We compared RT
associated with the ﬁrst presentation of a stimulus with each subsequent presentation.

4.3. Eﬀect of stimulus repetition under conditions of high stimulus
predictability

4. Results

Accuracy. The ﬁtted model (considering the total of 1944 trials)
revealed no signiﬁcant eﬀect of repetition on subjects accuracy scoresfor n1 (X2(5) = 2.14, p = .83). However a signiﬁcant main eﬀect ofrepetition was shown for n2 (X2(5) = 16.22, p < .05; based on 6480
trials in total). Performance in the ﬁrst image presentation (73%) was
signiﬁcantly lower than for the fourth image presentation (z = 2.61,
p < .05) and the ﬁfth image presentation (z = 3.38, p < .001) with aperformance of 78% and 79%, respectively (see Table 2). For parameter
estimates of the ﬁxed eﬀects for the generalized linear mixed modelwith the binomial family for each group tested see Table S5.
Mean SRTs. The ﬁtted model revealed a signiﬁcant eﬀect of repetition on mean RTs for n1 (X2(5) = 32.18, p < .0001) and n2(X2(5) = 126, p < .0001) (based on a total 1411 and 4834 trials, respectively). Both groups were slower in the ﬁrst image presentationcompared to the ﬁve other image presentations (see Table 2). For

Table 1 summarizes the results obtained for all subjects for gendercategorization (nlab_members, nstudents_gender), and personally familiar facerecognition (nlab_members, nstudents_familiarity) under low and high predictability conditions, respectively. Fig. 2 shows individuals’ minSRTs
plotted against accuracy scores across all experiments. For Individual
subjects’ data see Supplemental Tables.

4.1. Gender categorization
Both departmental members and student participants could reliably
perform the gender categorization task, achieving 81% and 84% on
average, and exhibiting minimum SRTs of 200 ms and 140 ms, respectively (see Fig. 3).

46

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M. Ramon et al.

Fig. 3. Distributions of participants’ SRTs expressed during the gender categorization task. Hits and false alarms per time bin are indicated as thick and thin lines,
respectively. Vertical lines indicate each group’s min SRTs; along with average accuracy.

Fig. 4. Distributions of participants’ SRTs expressed during the personal familiarity categorization task with low (blue) and high (red) predictability. Hits and false
alarms per time bin are indicated as thick and thin lines, respectively. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the
web version of this article.)parameter estimates of the ﬁxed eﬀects for the Linear Mixed Model foreach group tested see Table S6.

for the task at hand , have been deployed to constrain theories of visual
processing (for a review see e.g., Fabre-Thorpe, 2011). For the most,previous studies have focused on how visual categorization speed stimulus varies as a function of response eﬀector (e.g., Bacon-Macé et al.,2007), stimulus category (e.g., Crouzet et al., 2010), processing level ortask- dependent information and diagnosticity (e.g., Schyns, 1998).
In the present study, we sought to systematically investigate theextent to which visual categorization speed of complex visual stimuli is
aﬀected by processing level and observers’ expectations, which canvary depending on stimulus set size (cf., Ramon, 2018; Ramon et al.,2018; Ramon & Rossion, 2010; Ramon, Busigny, Gosselin & Rossion,
2017; Ruﬃeux et al., 2017 ). To address this, we measured the speed ofgender, and familiar face categorization in a SRT paradigm (cf. Ramonet al., 2018). Moreover, to address previously reported incompatible
ﬁndings regarding the speed of familiarity categorization (cf. Visconti

5. Discussion
Human vision is a sensory system that is extensively investigated toadvance our understanding of cognition and brain functioning in general. Similar to other sensory modalities, visual processing enables extremely robust categorization of external stimuli. Previous work has
demonstrated that the primate brain can for instance visually categorize
faces and animals in a highly proﬁcient and rapid manner (e.g. Crouzet
et al., 2010; Fabre-Thorpe, 2011; Kirchner & Thorpe, 2006; Thorpe
et al., 1996; Rousselet et al., 2003). Visual categorization paradigms,
which are considered to involve activation of representations that impact observers’ responses via maximal “presetting” of the visual system

Table 2
Accuracy, mean and min SRTs for choice saccades towards personally familiar faces as a function of stimulus repetition during familiarity decisions under highpredictability.
Department members (nlab_members)

Students (nstudents_familiarity)

Block

Accuracy in %Mean SRTsCI

minSRTs

Accuracy in %Mean SRTsCI

minSRTs

1st
2nd
3rd
4th
5th
6th

77
78
75
76
74
74

331
307
306
293
310
306

[277; 386]
[239; 375]
[238; 375]
[225; 361]
[241; 379]
[237; 374]

250
300
340
290
260
290

73
74
76
78
79
74

350
330
326
312
318
319

[325; 374]
[298; 361]
[294; 357]
[280; 343]
[286; 350]
[287; 351]

280
260
240
220
220
240

Note that per block each stimulus was shown three times.
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M. Ramon et al.

terminology that respects the level of processing or task type (Besson
et al., 2017), stimulus repetition (Lewis & Ellis, 2000; Ramon et al.,2011), and decisional space within which visual categorizations are
performed (Ramon et al., 2018; Ruﬃeux et al., 2017), which can account for contradictory ﬁndings and conclusions (Ramon, 2018; Lippet al., 2015; Ramon et al., 2018). To make this point, we demonstratethat categorization speed modulation, which was previously attributed
to real-life familiarity (Visconti di Oleggio Castello & Gobbini, 2015),
can in fact be more parsimoniously accounted for in terms of observers’
expectations. We advocate for careful consideration of procedural aspects when categorization proﬁciency serves to constrain theories ofvisual processing, and cognition more generally.

di Oleggio Castello & Gobbini, 2015; vs. Besson et al., 2017; Mathey
et al., 2012; Ramon et al., 2018), we varied the number of personallyfamiliar face target identities to be detected (3 vs. 7, along with anequal number of distractors). We reasoned that, beyond reconcilingthese apparent inconsistencies in the face literature, our anticipatedﬁndings of processing level and stimulus set size dependent categorization would be relevant for both visual categorization speciﬁcally,
and cognition in general.
In line with previous ﬁndings (Ramon et al., 2018; cf. also Besson
et al., 2017), visual categorization proﬁciency varied with processing
level. Gender categorizations were performed more accurately and rapidly than familiarity categorizations. Interestingly, gender categorization latencies reported here and elsewhere (Ramon et al., 2018) werecomparable to those expressed when observers have to saccade towards
only a single target identity (140 ms; Mathey et al., 2012). This is theﬁrst line of evidence suggesting that it is not just the processing level,task, or stimulus familiarity per se that determines visual categorization
speed.
Additionally, extending previous work (e.g., Miller, 1956; Palmer,
1994; Busey & Palmer, 2008; Kent & Lamberts, 2005; Lacouture &Marley, 1995; Karpiuk et al., 1997), stimulus set size aﬀected performance expressed in the context of the same processing level, i.e. during
familiarity categorizations. Speciﬁcally, categorization was faster and/or more accurate for smaller, as compared to larger stimulus sets.
Saccades could be directed towards more predictable targets (threefamiliar identities) within ∼250 ms, although lab members performed
more accurately than students. Increasing the number of targets did notaﬀect students’ mSRTs, but led to a decrease in their accuracy (76% vs.
68%). Lab members, on the other hand, responded slower (360 ms), andtheir performance was less accurate (82% vs. 74%).
The results reported here demonstrate that both processing level,and decisional space, or stimulus set size aﬀect categorization proﬁciency. Our ﬁndings demonstrate that it is not “detection of personalfamiliarity” (Visconti di Oleggio Castello & Gobbini, 2015) per se that
expedites categorization, but the experimental circumstances that enable the measured latencies. Note that individuals can be diﬀerently
aﬀected by such procedural choices, with some observers’ performancevarying more in terms of response latencies, or accuracy, respectively.
Therefore, in order to determine the boundaries of visual categorization
speed, simply establishing mSRTs is insuﬃcient.2 At least in the context
of categorization of complex visual stimuli, processing should not be
considered as ﬁxed, but rather ﬂexibly modulated through a combination of contextual and structural aspects, related to both procedural
choices (task, processing level, stimulus set) as well as the stimulus
category (e.g., Lipp et al., 2015; Craig et al., 2012; Schyns, 1998).
Disregarding such aspects can result in seemingly inconsistent ﬁndings(cf. Lipp et al., 2015), spur scientiﬁc debates, and promote erroneous
conclusions. The challenge is therefore to carefully consider the conditions under which categorization proﬁciency is observed, and adopt
the appropriate terminology to communicate the ﬁndings made (cf.
Ramon, 2018; Ramon et al., 2018; Ramon, in press; Ramon et al., inpress).

Acknowledgements
We thank our previous Bachelor students Nour Al-Khodairy,
Morgane Bulliard, Aurelie Christinaz, and Lysiane Constantin for theirinvaluable help during data collection.
Appendix A. Supplementary material
Supplementary data to this article can be found online at https://doi.org/10.1016/j.cognition.2019.01.019.
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