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1. Introduction

Lexico-semantic processing refers to the set of mechanisms that store and retrieve our knowledge about the world, concepts, and word meanings (Binder & Desai, 2011; Patterson et al., 2007; Jones et al., 2015; Reilly et al., 2023). Following Whitworth et al. (2014), lexico-semantic processing requires the use of at least a semantic system (a store of meanings) and input/output lexica for written and spoken word forms. Several experimental paradigms have been used to understand and to assess the different components of lexico-semantic processing both in healthy people and people with different neurological disorders (Kumar, 2020). Here, we will describe some of the most common paradigms to assess lexico-semantic processing, as a prelude to systematically review and discuss an advent of paradigms enabling computational techniques from cognitive network science which can now be used for the same purposes. Specifically, the paradigms of interest to the present review are relatedness-based tasks: those which allow for an investigation of the association strength between distinct items in the semantic system, by estimating the relatedness in meaning between either task items or items produced by participants. The primary motivation behind this review is to understand which such paradigms have been employed for individuals with language disorders, to whom and how they have been used, and what the general findings of such studies have been. This work is relevant because, to the best of our knowledge, the number of experimental paradigms that have been applied to people with language disorders is much smaller than that employed in healthy people. Hence, this review aims to elucidate a gap in the literature and to offer ways forward for research.

The semantic system is a core component of linguistic processing, as information is retrieved from this system in response to auditory/written words, ideas or concepts (Whitworth et al., 2014; Binder et al., 2009). The semantic system is central to language processing (Patterson & Shewell, 1987; Ellis & Young, 1996; Whitworth et al., 2014), and fundamental to our understanding of the neuroarchitecture that supports linguistic processing (Binder et al., 2009; Chang et al., 2015). Indeed, semantic information is necessary for both the production and comprehension of language (Whitworth et al., 2014). Also, it is of particular clinical relevance to understand and preserve the semantic system, as damage may significantly reduce linguistic abilities and quality of life (Dvorak et al., 2021). 

Deficits to the semantic system are observed in people with a number of neurological conditions, including stroke, neurodegeneration, autoimmune neurological disease, and brain infection, among others (Reilly et al., 2011; Rogers & Friedman, 2008; Hodges et al., 2000; Lambon Ralph et al., 2010; Gorno-Tempini et al., 2011; Wilson et al., 2017; Rook et al., 2023; Rofes et al., 2021). Semantic deficits are also reported among individuals who have undergone awake brain surgery for the excision of primary brain tumours in the left hemisphere (Campanella et al., 2009; Rofes et al., 2018, Satoer et al., 2018). However, the semantic system cannot be observed directly. As a multifaceted and complex system, its properties and organizational structure can only be deduced through carefully designed experimental paradigms, with different tasks tapping into different aspects of lexical semantics (Kumar, 2020). As a result, a multitude of paradigms have been documented in the literature, each purporting to reveal varying aspects of the semantic system.

Among these paradigms, investigations of priming (also called “semantic priming”), in which the processing of a word is facilitated by a preceding related word (e.g., the “prime” word yellow might facilitate the processing of the “target” word banana), are perhaps the most well established in assessing lexico-semantic processing (Neely, 1991; McNamara, 2005). This is not without good reason: priming tasks have a relatively straightforward experimental design, are backed by decades of ongoing research (e.g., Meyer & Schvaneveldt, 1971; Hutchison, 2003; Hutchison et al., 2013), and can unveil crucial insights about the structure of the semantic system (for overviews see Kumar, 2020; McNamara, 2005). This type of study has provided support for several models of semantics, perhaps chief among them being spreading activation models (e.g., Collins & Loftus, 1975). Support has also been proposed for distributed network models, as well as both feature-based and associative network models, with elements of multiple models often being integrated for a more comprehensive understanding of the semantic system (e.g., McRae, 2004; Hutchison, 2003; Plaut & Booth, 2000). Advocating for one perspective or another is outside of the scope of this article. However, in general terms, these models agree on the concept of an interconnected semantic system and the role of activation in lexico-semantic processing, and disagree on the specific mechanisms underlying these processes and the nature of semantic representations (Kumar, 2020; Reilly et al., 2023). In other words, while these models collectively acknowledge an intricate interconnection of word meanings, akin to a vast network, they differ in their views on how this network operates and how our brains organize these connections.

At the clinical level, priming studies have been used to characterize lexico-semantic deficits in different neurological populations: while individuals with post-stroke aphasia with impaired lexical comprehension have shown evidence of significant priming effects (Blumstein et al., 1982), the opposite has been found for individuals with post-stroke aphasia with impaired lexical production (Del Toro, 2000). However, more recent research has conversely suggested that priming is normal and unimpaired in most people with post-stroke aphasia, with authors calling into question the utility of such tasks in assessing lexico-semantic impairment (Dyson et al., 2020). Additionally, drawing conclusions about the degree of relatedness between primes and targets can be unreliable at the item level (Heyman et al., 2018), which may impede the ability of priming studies to elucidate more fine-grained details of semantic structure. For example, while a priming study may indicate that the words “yellow” and “banana” are related, it is difficult to objectively gauge whether this relation is greater than that between “sweet” and “banana”. 

In contrast to priming studies, picture-word interference experiments (also known as “semantic interference” paradigms), where participants are asked to name pictures while ignoring potential distractor words, have also been conducted (e.g., Roelofs & Piai, 2017). Such studies can shed light on how lexico-semantic relationships impact both linguistic and attentional processes (e.g., Scaltritti et al., 2015), and when coupled with magnetoencephalography (MEG), have provided evidence for the neuroplasticity of language in brain tumour patients (Piai et al., 2020). Object and action naming tasks, in which participants produce nouns or verbs represented by the presented stimuli are an additional common paradigm (e.g., Deloche & Hannequin, 1997). Naming tasks rely on multiple aspects of language, including not only lexico-semantic access and retrieval, but also object recognition and articulation (Rofes & Mahon, 2021). Such naming paradigms have contributed considerably to neurolinguistic research and to perspectives on lexico-semantic processing (Bozeat et al., 2000; Duong et al., 2006; Piai & Eikelboom, 2023). They have also contributed to diagnostic and intraoperative procedures (Ntemou et al., 2023; Bozeat et al., 2000; Rofes & Miceli, 2014). 

An additional category of tasks tapping into semantic cognition are non-verbal association tasks, used frequently as clinical measures of semantic decline (Klein & Buchanan, 2009). In the Pyramids and Palm Trees Test (PPTT), participants are presented with triads of object images, and are asked to select which of the two lower objects is most closely associated with the test item above (Howard & Patterson, 1992). Performance on the PPTT relies on visual object recognition, semantic retrieval processes, and making associations between object representations (Klein & Buchanan, 2009; Callahan et al., 2010). The Kissing and Dancing Test (KDT) is analogous to the PPTT, but differs in that the images represent actions, rather than objects (Bak & Hodges, 2003). Such tasks have contributed to our understanding of the neural substrates of lexico-semantic processing: for example, pairing direct electrical stimulation with the PPTT has demonstrated the role of both the dorsolateral prefrontal cortex and the right inferior fronto-occipital fasciculus in non-verbal semantic cognition (Herbet et al., 2017; 2018). Comparable action-based association tasks been used to demonstrate a role in action-related semantic cognition for left-hemisphere regions including the inferior frontal gyrus, ventral precentral gyrus, anterior insula, ventral postcentral gyrus, supramarginal gyrus, and posterior middle temporal gyrus (Kemmerer et al., 2012). However, tasks like the PPTT may not be sensitive to mild semantic deficits (Adlam et al., 2010). Behavioural measures in these tasks are typically limited to accuracy and response times (Fergadiotis et al., 2010), with responses being inherently binary as predetermined by task design. These design features may restrict the capacity of these tasks to unveil subtle variations in item-level associations or more complex aspects of lexico-semantic organization (Klein & Buchanan, 2009; Ohman et al., 2022).

As alluded to in the opening paragraph, some authors have recently stressed that new methods are necessary to disentangle competing descriptions of the semantic system and input/output lexica, and to continue describing lexico-semantic deficits in clinical populations. The advent of computational techniques from fields such as cognitive network science has given rise to researchers using word networks (semantic, phonological, or both in the case of multiplex networks) and visualizations of semantic space (e.g., Bose et al., 2017; Castro & Stella, 2019; Kennet et al., 2016; Nevado et al., 2021; Nour et al., 2023; Vitevitch, 2022; Zemla & Austerweil, 2018). Within these types of studies, crucial elements of lexico-semantic processing can be investigated by either implicitly or explicitly asking participants to make associations between distinct concepts or words (e.g., how often is “yellow” mentioned when people are given the word “banana” or how similar are “yellow” and “banana” given a specific scale). This is done  by administering tasks which allow researchers to characterize or quantify those associations, for example, verbal fluency tasks (e.g., Zemla & Austerweil, 2019; Nour et al., 2023) and associative judgment tasks (e.g., Ovando-Tellez et al., 2022b; Cosgrove et al., 2023)

In the present review we will refer to these as relatedness-based tasks, operationalized as those tasks in which performance allows for an investigation of the degree of relatedness in meaning between either test items or items produced by participants. In other words, participant behaviour is an essential component in determining either the degree of subjective relatedness between items, or the items between which relatedness is to be investigated. These tasks offer the opportunity to investigate more directly the association strength that exists between distinct items within the semantic system and/or the lexica. While they are not necessarily superior, this is in a way different to the aforementioned paradigms of priming, picture-word interference, naming, and the PPTT which typically investigate effects that are secondary to association strength or relatedness (e.g., reaction time, accuracy; Bürki & Madec, 2022; McNamara, 2005; Moritz-Gasser et al., 2012; Higby et al., 2019; Klein & Buchanan, 2009).

For example, one category of relatedness-based tasks are associative judgement tasks (AJT). In a task such as the AJT of Bernard et al. (2019), participants are presented with pairs of words and are asked to indicate a subjective association rating on a scale from 0 to 100, indicating the degree to which they perceive the two words to be related in meaning. For example, the thematic association between nose and flower may elicit a higher rating than that which would be elicited by nose and existence, where a clear association may not be present. From these tasks, semantic networks can be generated with the aim of reflecting the semantic system’s underlying structure, allowing for both the detailed modelling of individual networks as well as comparisons between them (Castro & Siew, 2020; De Deyne et al., 2017; He et al., 2021; Siew et al., 2019). Such networks consist of nodes representing concepts or words in the semantic system, and edges representing the semantic similarity between nodes (Siew et al., 2019; Collins & Loftus, 1975). These networks have been used to show structural differences in the lexico-semantic organization of several neurological populations, including people with AD, Asperger syndrome, and schizophrenia, as well as in late talker children and healthy ageing (Stam et al., 2007; Kenett et al., 2016; Zeev-Wolf et al., 2014, Beckage et al., 2011; Cosgrove et al., 2023). 

At a more implicit level, category fluency tasks demand the production of as many words from a particular semantic category as possible (e.g., animals) within a predefined time limit (e.g., Rofes et al., 2023; Lezak et al., 2012; Stolwyk et al., 2015). Aside from asking that the words fall into the animal category, participants are not explicitly told to produce words with any further level of association to one another. However, highly related words are typically produced in a sequential manner, grouped by association strength (i.e., “clusters”, e.g., farm animals, insects, aquatic animals, etc.) (Troyer et al., 1997; Sung et al., 2012; Thiele et al., 2016). Associative search models such as the optimal foraging theory describe how these clusters and other related sequence-level aspects of fluency task responses can reflect exploration within the lexico-semantic space (Hills et al., 2009, 2015), and the implicit associations revealed by differing fluency task responses can thus reflect differences in lexico-semantic access or organization (Hills et al., 2012; Ovando-Tellez et al., 2022a). Category fluency tasks are also suitable for the generation of semantic networks (for an overview of prominent methods, see Zemla, 2022). In contrast to associative judgement tasks, where nodes in the network are predetermined by task design and edges are derived from participant behaviour (e.g., Benedek et al., 2017), nodes and the associations between them can instead be determined based on the order and co-occurrence of items in fluency responses (e.g., Cosgrove et al., 2021; Goñi et al., 2011; Kennet et al., 2013, Zemla & Austerweil, 2018). However, while relatedness-based tasks may be an invaluable tool in informing our knowledge about the lexico-semantic system, making use of novel computational techniques, and making clinical assessments in cases of impairment, we are unaware of any reviews to date which have attempted to provide an overview of such tasks. 

To the best of our knowledge there has not yet been an effort to compile a unified knowledge base or set of terminology surrounding relatedness-based tasks, their respective contributions to our understanding of the semantic system, or their applicability in different neurological populations. It is the aim of the present review to address this gap, particularly for work published over the last decade. It is hoped that the conclusions drawn from this review will aid in informing future research, provide a concise overview to researchers and clinicians, and highlight critical gaps that are worth exploring in future research. 

1.1 The present study

We aim to provide a comprehensive overview of relatedness-based tasks as they are used in populations with language disorders, and will seek to describe the experimental paradigms, test items, and item-selection methods used in each. Due to the heterogeneity of terms referring to relatedness-based tasks in contemporary literature, the present review, as stated previously, will operationalize these tasks as those which investigate the degree of relatedness in meaning between either test items or items produced by participants.

Specifically we will address the following research questions:
What types of relatedness-based tasks have been reported in adults with language disorders?
What type of language disorders are discussed? 
What types of items are used in these tasks, and how are those items selected?
What analysis methods are used in these tasks?

We expect that studies employing relatedness measures between items produced by participants will largely comprise category fluency tasks and comparable verbal generation paradigms, while studies investigating relatedness in meaning between task items will be similar in design to the aforementioned associative judgement tasks. We additionally expect that studies will be found whose relatedness-based paradigms may not yet be familiar to the authors. We anticipate category fluency studies with relatedness measures to be common among those populations who frequently perform such tasks clinically (e.g., post-stroke aphasia, neurodegeneration). In the case of tasks where relatedness in meaning between task items is assessed, we expect to find information about how these items were selected, in particular the linguistic variables for which task conditions and stimulus lists may have been balanced (e.g., frequency, age of acquisition, concreteness, relatedness). We anticipate analytical techniques comparable to those that have been applied in healthy populations (e.g., Ovando-Tellez et al., 2022b; Benedek et al., 2017) to also be present in clinical linguistic research, and that novel computational measures for verbal fluency will be revealed.

2. Methods

A systematic review of the literature was conducted using the following databases: PubMed, Embase, PsycInfo, and Web of Science (Core collection). Searches were conducted in October of 2023, with queries and search strategies formulated for each database. The search strategy as applied in PubMed is available in Table 1, while comparable search strings for each of the databases can be found in Supplementary Materials. A protocol for the systematic review following the PRISMA-P guidelines (Moher et al., 2015) was published on PROSPERO (https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024508094).

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The selection process for article inclusion is illustrated in Figure 1. The criteria for inclusion in the present review was as follows: (1) original experimental studies published within the 10 years prior to the review (October 2013-September 2023); (2) studies for which the participants were adults with linguistic deficits and/or neurological disorders (e.g., stroke, brain tumours, brain infections, neurodegeneration, autoimmune neurological diseases); (3) studies which investigate the degree of relatedness in meaning between either task items or items produced by the participants (e.g., category verbal fluency, semantic association tasks, and other relatedness-based tasks); and (4) studies published in English, French, German, and any other language for which a proficient member of the research team could be found (e.g., Catalan, Dutch, Italian, Spanish, Portuguese). Reviews, meta-analyses, case studies, and non-experimental studies were excluded. Studies were also excluded if they included tasks in which participants produced related items, but the relationships between these items were not considered in the analysis (e.g., where category fluency tasks were used as part of a larger cognitive battery and only the total score was considered). Similarly, studies were excluded if they included tasks in which participants made relatedness judgements, but either these judgements were not considered in the analysis, or performance on this task was not the focus of any part of the article. Studies where inter-item relatedness was determined prior to performance on the task (i.e., the dependent variables of interest were measures secondary to relatedness) were also excluded. This included synonym judgement tasks (e.g., Dubé  et al., 2014), priming tasks (e.g., Howells & Cardell, 2015), and tasks which used predetermined multiplex network properties to predict performance on other tasks (e.g., Castro & Stella, 2019). Such tasks would be included if they were analyzed in such a way that inter-item relatedness could be investigated based on participant behaviour.

It should be noted that the search strategy was not specifically designed to capture relevant studies in psychiatric populations. However, due to an apparent tendency for novel computational techniques to be used in this field (e.g., Nour et al., 2023), and ongoing debate regarding the distinction between psychiatric and neurological disorders (Crossley et al., 2015; David & Nicholson, 2015), it was decided that relevant papers featuring psychiatric populations and captured by the present search strategy would be included. Likewise, studies in other populations characterized by linguistic differences, and not necessarily deficits, would be included if they appeared among the search results (e.g., autism).

In addition to electronic database searches, the reference lists of those articles whose full texts were selected for inclusion were screened for further relevant studies. Study authors were to be contacted by email in the case that otherwise inaccessible papers passed the title and abstract screening phase, with data extraction proceeding without these papers only in the case that no response was received after two weeks. This was not the case for any of the identified studies.

Titles and abstracts retrieved from database searches were exported into the reference manager Endnote. Following deduplication, titles and abstracts were uploaded to the collaborative systematic review platform Rayyan (Ouzzani et al., 2016; available at https://www.rayyan.ai/).  Two independent reviewers (i.e., LG and LR) assessed the titles and abstracts for eligibility. The independent reviewers were blinded to one another’s decisions, with disagreements regarding eligibility being resolved through further discussion and examination relative to the pre-established inclusion and exclusion criteria. Any persisting disagreements were resolved by the decision of a third reviewer (i.e., AR; as senior author), and the full texts of all remaining articles following this screening phase were reviewed by the first author (i.e., LG). 

The following information was extracted from all full-text articles selected for inclusion: general publication information (e.g., journal, publication date, authors), participant characteristics (e.g., neurological population, sample size, age, education in years), experimental groups (e.g., mild vs. severe aphasia, patient vs. control), type of task used (e.g., category fluency, associative judgement task), task paradigm details (e.g., method of presentation, data recorded, inter-stimulus intervals, task length, presence of filler stimuli), analytical methods applied, type and number of items used in the task (e.g., images, text, auditory stimuli), method of test item pre-selection (if applicable), and type of neuroimaging information either concurrent with, or analyzed in relation to the task (if applicable).

3. Results

A total of 34 relevant studies were identified in the present review (see Figure 1). A summary of the identified papers, along with their populations, relatedness-based tasks, and analytical measures are presented in Table 2. 

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[Please add Table 2 about here]

3.1 What types of relatedness-based tasks have been reported in adults with language disorders?

All eligible studies (N=34) included in the present review made use of the category fluency task, paired with analytical techniques which investigated relatedness in meaning between the items produced by participants. These relatedness measures consisted largely of sequence-level fluency measures such as clustering and switching (described in more detail in section 3.4). Two studies among those additionally made use of measures of relatedness in meaning between items on phonological or letter fluency (Leimbach et al., 2020; Rofes et al., 2019). Two further studies made use of a clustering method which allows for either semantically or phonologically defined clusters in both category and letter fluency (Ledoux et al., 2014), without explicitly mentioning the degree to which each association type was identified among responses to the two tasks (Faroqi-Shah & Milman, 2018; van den Berg et al., 2017).

No studies were identified in which performance on a task led to an investigation of relatedness in meaning between items presented as part of the experimental paradigm, or in which participants gave relatedness judgements between items.

3.2 What type of language disorders are discussed?

The most commonly discussed language disorder among identified studies was post-stroke aphasia, with 7 studies (20.6%) focusing on this clinical population (Bose et al., 2017, 2022; Carpenter et al., 2017; Faroqi-Shah & Milman, 2018; Kiran et al., 2014; Pagliarin et al., 2021; Patra et al., 2020). Of these, 3 (8.8%) specifically focus on bilingual post-stroke aphasia (Carpenter et al., 2017; Kiran et al., 2014; Patra et al., 2020), and 2 (5.9%) additionally include participants with aphasia following TBI (Carpenter et al., 2017; Kiran et al., 2014). Similarly, there were 7 studies (20.6%) which involved individuals with mild cognitive impairment (MCI, Johns et al., 2018; Lopez-Higes et al., 2014; Mueller et al., 2015; Nevado et al., 2021; Quaranta et al., 2021; Tröger et al., 2019; Weakley et al., 2013), and 7 studies (20.6%) which involved individuals with schizophrenia, schizoaffective disorder, or psychosis (Grimes et al., 2021; Ku et al., 2021; Lundin et al., 2022a; Nicodemus et al., 2014; Pauselli et al., 2018; Piras et al., 2021; Voorspoels et al., 2014). Five studies (14.7%) involved individuals with Alzheimer’s disease (AD, Reverberi et al., 2014; Rofes et al., 2020; Tröger et al., 2019; Weakley & Schmitter-Edgecombe, 2014; Zemla & Austerweil, 2019). Three studies (8.8%) involved individuals with variants of primary progressive aphasia (PPA, Reverberi et al., 2014; Rofes et al., 2019; van den Berg et al., 2017). Two studies (5.9%) involved individuals with Parkinson’s disease (Jaywant et al., 2014; Leimbach et al., 2020). One study each (2.9%) was identified for individuals with specific learning disability (SLD, Hall et al., 2017), Down syndrome (Del Hoyo et al., 2015), autism (Ehlen et al., 2020), subjective cognitive decline (Nikolai et al., 2018), unilateral post-stroke brain damage without aphasia (Pagliarin et al., 2021), dyslexia (Smith-Spark et al., 2017), and behavioural-variant frontotemporal dementia (bvFTD, van den Berg et al., 2017). Note that the percentages above do not add to 100 as several studies investigated more than one of the stated populations.

3.3 What types of items are used in these tasks, and how are those items selected?

As no studies were identified for which relatedness was investigated between predetermined items, there were likewise no item selection methods revealed by the present review. While the identified studies made use of various semantic constraints in category fluency (e.g., animals, vegetables, furniture), item-level selection methods were not applicable to any of the identified relatedness-based tasks.

3.4 What analysis methods are used in these tasks?

Of the 34 category fluency studies identified, 27 (79.4%) made use of relatedness measures related to clustering and switching behaviour (Bose et al., 2017; 2022; Carpenter et al., 2021; Del Hoyo et al., 2015; Ehlen et al., 2020; Faroqi-Shah & Milman, 2018; Grimes et al., 2021; Hall et al., 2017; Jaywant et al., 2014; Kiran et al., 2014; Leimbach et al., 2020; Lopez-Higes et al., 2014; Lundin et al., 2022; Mueller et al., 2015; Nevado et al., 2021; Nikolai et al., 2018; Pagliarin et al., 2021; Patra et al., 2020; Piras et al., 2019; Quaranta et al., 2019; Rofes et al., 2020; Smith-Spark et al., 2017, Tröger et al., 2019; van den Berg et al., 2017; Weakley & Schmitter-Edgecombe, 2014; Weakley et al., 2013). Seven studies (20.6%) did not make use of clustering or switching measures (Johns et al., 2018; Ku et al., 2021; Nicodemus et al., 2014; Piras et al., 2019; Rofes et al., 2019; Voorspoels et al., 2014; Zemla & Austerweil, 2019).

A number of different semantic clustering methods were used in determining clusters of related items. The method established by Troyer and colleagues (1997) for animal fluency was most common, being used by 19 of the 27 studies which used clustering and switching measures (70.4%, Bose et al., 2017; 2022; Carpenter et al., 2021; Del Hoyo et al., 2015; Grimes et al., 2021; Hall et al., 2017; Jaywant et al., 2014; Leimbach et al., 2020; Lopez-Higes et al., 2014; Mueller et al., 2015; Nikolai et al., 2018; Pagliarin et al., 2021; Patra et al., 2020; Piras et al., 2019; Rofes et al., 2020; Smith-Spark et al., 2017; Tröger et al., 2019; Weakley & Schmitter-Edgecombe, 2014; Weakley et al., 2013). Two of the 27 studies which used cluster analysis (7.4%) used the method proposed by Ledoux and colleagues (2014), in which either semantic or phonological clusters in the style of Troyer and colleagues (1997) are allowable in both animal and letter fluency (Faroqi-Shah & Milman, 2018; van den Berg et al., 2017). Four further studies of the 27 which used cluster analysis (14.8%) used other subcategory-based clustering methods analogous to those of Troyer and colleagues (1997), primarily for use in categories other than animals (Hall et al., 2017; Kiran et al., 2014; Nikolai et al., 2018; Reverberi et al., 2014). 

Semantic clustering methods were also identified which did not rely on categories or predetermined lists of classifiers. Ehlen and colleagues (2020) made use of a temporal curve fitting model established in an earlier paper by the same group (Ehlen et al., 2016). A similarity-based method derived from word embedding models was used by two studies (Lundin et al., 2022a; Tröger et al., 2019). Nevado and colleagues (2021) generated semantic networks from fluency data using a method based on co-occurrence (Goñi et al., 2011), defining clusters based on subdivisions within this network, while Quaranta and colleagues (2019) based clusters on participant-specific thresholds for Path Length and Extended Gloss Overlap, metrics derived from WordNet (Fellbaum, 1998, 2005). 

Two of the 27 studies which used cluster analysis (7.4%) compared multiple clustering methods within the same task (Tröger et al., 2019; Leimbach et al., 2020). Tröger and colleagues (2019) used the method defined by Troyer and colleagues (1997) in addition to clusters based on both semantic association thresholds and temporal thresholds, while Leimbach and colleagues (2020) analyzed both animal and letter fluency using both the semantic and phonological clustering methods defined by Troyer and colleagues (1997). 

Among the studies for which clustering and switching were not considered, a number of analytical methods were employed to measure relatedness in meaning between items. To classify individuals with amnestic MCI relative to controls, Johns et al. (2018) used a maximum likelihood estimation model considering parameters for context similarity and order similarity between consecutive words (both from the BEAGLE semantic space, Jones & Mewhort, 2007), perceptual similarity (Johns & Jones, 2012), and frequency from a Wikipedia corpus (Jones & Mewhort, 2007). CoVec, an automated tool (Covington, 2016) was used by two studies to differentiate psychosis patients with different speech patterns, considering sliding 5 and 10-word windows to establish measures of coherence (Coherence-5 and Coherence-10, Ku et al., 2021; Pauselli et al., 2018). Nicodemus et al. (2014) differentiated schizophrenia patients from controls, and established correlations with specific genetic mutations, through the use of latent semantic analysis (LSA) derived fluency measures (Landauer et al., 1998). Voorspoels et al. (2014) explored the use of geometric representations of group-level and subsample-level semantic relations based on both proximity and co-occurrence of items in schizophrenic patients’ category fluency lists. Rofes et al. (2019) used random forests to classify PPA variants based on total score, 6 error types, and 9 word properties, including LSA-derived semantic association measures (with similar methodology to Rofes et al. (2020), which did consider cluster and switch behaviour). Zemla & Austerweil (2018) generated semantic networks from fluency lists based on a censored random walk methodology, differentiating the networks of individuals with Alzheimer’s from those of controls on six network metrics: number of nodes, diameter (D), density, average shortest path length (ASPL), smallworldness (S), and node degree.

Network methodologies were additionally employed by three of the studies which did consider clusters and switches. Quaranta et al. (2019) investigated the mean path length between consecutive fluency items based on their node proximity in WordNet, a lexical database of semantic relations (Fellbaum, 1998, 2005). Weakley & Schmitter-Edgecombe (2014) used multidimensional scaling to generate group-level geometric representations of semantic relations between words based on their proximity in fluency lists. Nevado and colleagues (2021) generated semantic networks for individuals with MCI and controls based on the methods of Goñi et al. (2011), comparing network metrics such as modularity (Q), clustering coefficient (CC), betweenness centrality (BC), degree, ASPL, and D. 

Lastly, while all identified studies made use of relatedness-based measures, 7 (20.6%) supplemented these measures with additional word-level differences between items (e.g., word properties, orthographic distances; Ehlen et al., 2020; Faroqi-Shah & Milman, 2018; Johns et al., 2018; Leimbach et al., 2020; Reverberi et al., 2014; Rofes et al., 2019, 2020). For an overview of all relatedness measures derived from verbal fluency in the identified studies, in addition to those for which significant between-group differences, significant correlations, or high importance in predictive models were found, please refer to Table 2.

Discussion

The goal of this systematic review was to comprehensively explore the landscape of relatedness-based tasks as they are utilized in populations with language disorders. We sought to address several key questions, namely the types of tasks employed, the language disorders under investigation, the types of items used, and the analytical methods applied. This work is significant because, to date, no extensive review or overview of relatedness-based tasks in these populations has been presented. These tasks provide a unique opportunity to leverage novel computational methods from network science, potentially enabling more in-depth investigations of mild language impairments and the underlying nature of lexico-semantic organization.

The results of this review revealed a predominant use of category fluency as a relatedness-based task in linguistic populations, with all identified studies (N=34) making use of this experimental paradigm. On one hand, this finding underscores the utility of category fluency in assessing lexico-semantic processing across various clinical populations, and likely reflects its ease of use, short administration time, and frequent inclusion in both neuropsychological and linguistic assessment batteries (Rabin et al., 2005; Strauss et al., 2006; Rofes et al., 2017, 2021; Rook et al., 2023). However, the exclusive reliance on category fluency tasks has the potential to limit the scope of insights into lexico-semantic deficits. Indeed, fluency tasks also require non-linguistic aspects, such as executive functioning (Rofes et al., 2023, Shao et al., 2014). This raises the issue of "task impurity", which highlights the fact that tasks might not depend on individual cognitive skills (Rabbitt, 1997). Hence, the results obtained with these tasks can be to a certain extent affected, particularly if patients had impairments in executive processes of shifting, updating, and monitoring (Rofes et al., 2023). From this perspective, it is notable that among the identified category fluency studies, several complemented relatedness-based measures with additional word-level differences between items (i.e., Ehlen et al., 2020; Faroqi-Shah & Milman, 2018; Johns et al., 2018; Leimbach et al., 2020; Reverberi et al., 2014; Rofes et al., 2019, 2020). This may offer the opportunity to disentangle the degree to which presumed lexico-semantic relationships between items are influenced by additional factors.

Another aspect is that, while category fluency tasks capture semantic relatedness indirectly through clustering, switching, and other semantic-search related behaviors (Hills et al., 2012; Ovando-Tellez et al., 2022a), other paradigms explicitly probing relatedness, such as associative judgment tasks (e.g., Bernard et al., 2019), were notably absent. Semantic networks derived from associative judgment tasks have been utilized in studies involving healthy populations, particularly in research focusing on creativity (e.g., Benedek et al., 2017, He et al., 2021), and the lack of comparable paradigms in clinical populations may present a notable void in the literature. Preliminary findings by the authors have indicated that network metrics obtained from associative judgment tasks may exhibit significant disparities between individuals who have undergone awake brain surgery and healthy controls (Gaudet et al., 2023), which may indicate mild lexico-semantic impairments beyond the detection of traditional tests. The lack of tasks in which relatedness between predetermined task items was investigated also precluded this review from addressing the types of stimuli used, as well as their selection methods.

A diverse range of clinical populations were addressed across the identified studies, with post-stroke aphasia, MCI, and schizophrenia being the most frequently studied conditions (N=7 each). Along with the inclusion of studies addressing conditions such as autism, Down syndrome, and specific learning disabilities, this diversity may highlight not only the multifaceted nature of lexico-semantic processing deficits, but the importance of investigating relatedness-based task performance across different clinical populations. The prevalence of novel methodologies within psychiatric research, particularly in the study of schizophrenia, schizoaffective disorder, and psychosis is also of particular interest. Of the 7 such studies retrieved, 5 (71.4%) made use of either cosine similarities, co-occurrence data, or fluency-derived network metrics (Ku et al., 2021; Lundin et al., 2022; Nicodemus et al., 2014; Pauselli et al., 2018; Voorspoels et al., 2014), while no such methods were found in the literature for post-stroke aphasia. While this may in part be due to the more severe nature of linguistic deficits in post-stroke aphasia relative to schizophrenia (Little et al., 2019), computational techniques that rely on vector space models and network science offer a level of automation not possible with traditional scoring methods, as well as a potential solution to issues of inter-rater reliability (Kim et al., 2019). As such, their use in post-stroke aphasia research may offer an expedited, computationally robust, and more reliable analysis of fluency responses.

Of particular note is the lack of relatedness-based tasks being used for individuals with brain tumours, as just one study was returned for which an individual with a brain tumour was included (among a bilingual aphasic study population, Carpenter et al., 2021). Lexico-semantic deficits are prevalent among individuals who have undergone surgery for brain tumours, especially those with high-grade gliomas (Campanella et al., 2009), and often manifest as difficulties in retrieving words, anomia, semantic paraphasias, and neologisms (Goodglass & Wingfield, 1997; Whitworth et al., 2014, Satoer et al., 2018). It is possible that studies employing relatedness-based tasks paired with novel computational methods in individuals with brain tumours could offer additional insights into the nature of not only these deficits, but the neurocognitive mechanisms which underlie them.

The analysis methods employed in the identified studies varied widely, ranging from the more prevalent traditional measures of clustering and switching (e.g., Mueller et al., 2015) to advanced computational techniques such as semantic network analysis and measures of inter-item coherence (e.g., Zemla & Austerweil, 2019; Ku et al., 2021). While traditional measures like clustering and switching provide valuable insights into semantic organization and cognitive processes (Thiele et al., 2016), the integration of computational approaches may offer novel avenues for understanding complex relationships between items in semantic memory. Notably, some studies employed multiple analysis methods within the same task, highlighting the potential for complementary insights from different analytical approaches (e.g., Tröger et al., 2019; Nevado et al., 2021).

We also initially sought to collect data on the neural correlates of performance on relatedness-based tasks in clinical linguistic populations, but such findings were absent in the identified studies, with the potential exception of studies whose experimental groups were divided based on laterality (e.g., Pagliarin et al., 2021; Jaywant et al., 2014). Tasks which investigate the level of relatedness in meaning between distinct concepts or words can provide unique insight into not only the organization of items, but the strength of the relationships between these items, and the processes involved in making those associations (e.g., Ovando-Tellez et al., 2022a; Nevado et al., 2021; Okruszek et al., 2013). When paired with neuroimaging techniques such as functional magnetic resonance imaging (fMRI), they can also inform our understanding of the lexico-semantic neural substrates. For example, judgements based on both taxonomic and thematic associations were found in an fMRI study to equivalently engage the same core network, consisting of the anterior temporal lobe, superior temporal sulcus, and ventral prefrontal cortex (Jackson et al., 2015). In an investigation of search behaviour, fMRI concurrent with a category fluency task has further shown both hippocampal and cerebellar activation during the switch from one cluster of related responses to another (Lundin et al., 2022b), while others propose the involvement of executive frontal lobe processes, the occipital lobe, and the interaction of multiple large-scale brain networks in similar semantic search behaviour (Troyer et al., 1998; Birn et al., 2010; Li et al., 2017; Ovando-Tellez et al., 2022a). Assessing the neural correlates of performance on relatedness-based tasks might further elucidate the neuroarchitecture supporting critical elements of linguistic cognition, particularly in populations where neuroimaging data may already exist.

One of the limitations in this study is the lack of search terms specifically targeting psychiatric research, despite the inclusion of more general terms which could be expected to encapsulate such articles. This omission may have resulted in the oversight of some studies involving populations with non-linguistic disorders characterized by differences in language, which is particularly relevant given the abundance of innovative techniques in this field. The review was also restricted to studies published within a ten-year timeframe. This was primarily due to the recency of the novel methodologies of interest, and also due to the broad search strategy found to be necessary in capturing all relevant studies. It remains possible that this could have excluded earlier notable works in the field. Lastly, the heterogeneity of methodologies and outcome measures across studies limits the comparability of findings and generalizability of conclusions. Future research in populations with language disorders should address the aforementioned gaps in the literature regarding paradigms that explicitly probe relatedness between items (e.g., associative judgement tasks), the use of relatedness-based tasks in individuals with brain tumours, and the continued development of novel computational and automated techniques.

Conclusion

This systematic review sought to provide a comprehensive synthesis of relatedness-based tasks in populations with language disorders, shedding light on commonly used paradigms and analytical methods over the last decade. Relatedness-based tasks hold promise as valuable tools for assessing and understanding lexico-semantic deficits in clinical populations.