forager: a Python package and web interface for modeling mental search.

Analyzing data from the verbal fluency task (e.g., "name all the animals you can in a minute") is of interest to both memory researchers and clinicians due to its broader implications for memory search and retrieval. Recent work has proposed several computational models to examine nuanced differences in search behavior, which can provide insights into the mechanisms underlying memory search. A prominent account of memory search within the fluency task was proposed by Hills et al. (2012), where mental search is modeled after how animals forage for food in physical space. Despite the broad potential utility of these models to scientists and clinicians, there is currently no open-source program to apply and compare existing foraging models or clustering algorithms without extensive, often redundant programming. To remove this barrier to studying search patterns in the fluency task, we created forager, a Python package ( https://github.com/thelexiconlab/forager ) and web interface ( https://forager.research.bowdoin.edu/ ). forager provides multiple automated methods to designate clusters and switches within a fluency list, implements a novel set of computational models that can examine the influence of multiple lexical sources (semantic, phonological, and frequency) on memory search using semantic embeddings, and also enables researchers to evaluate relative model performance at the individual and group level. The package and web interface cater to users with various levels of programming experience. In this work, we introduce forager's basic functionality and use cases that demonstrate its utility with pre-existing behavioral and clinical data sets of the semantic fluency task.

Neural evidence of switch processes during semantic and phonetic foraging in human memory.

Humans may retrieve words from memory by exploring and exploiting in "semantic space" similar to how nonhuman animals forage for resources in physical space. This has been studied using the verbal fluency test (VFT), in which participants generate words belonging to a semantic or phonetic category in a limited time. People produce bursts of related items during VFT, referred to as "clustering" and "switching." The strategic foraging model posits that cognitive search behavior is guided by a monitoring process which detects relevant declines in performance and then triggers the searcher to seek a new patch or cluster in memory after the current patch has been depleted. An alternative body of research proposes that this behavior can be explained by an undirected rather than strategic search process, such as random walks with or without random jumps to new parts of semantic space. This study contributes to this theoretical debate by testing for neural evidence of strategically timed switches during memory search. Thirty participants performed category and letter VFT during functional MRI. Responses were classified as cluster or switch events based on computational metrics of similarity and participant evaluations. Results showed greater hippocampal and posterior cerebellar activation during switching than clustering, even while controlling for interresponse times and linguistic distance. Furthermore, these regions exhibited ramping activity which increased during within-patch search leading up to switches. Findings support the strategic foraging model, clarifying how neural switch processes may guide memory search in a manner akin to foraging in patchy spatial environments.

Comparing word frequency, semantic diversity, and semantic distinctiveness in lexical organization.

Word frequency (WF) is a strong predictor of lexical behavior. However, much research has shown that measures of contextual and semantic diversity offer a better account of lexical behaviors than WF (Adelman et al., 2006; Jones et al., 2012). In contrast to these previous studies, Chapman and Martin (see record 2022-14138-001) recently demonstrated that WF seems to account for distinct and greater levels of variance than measures of contextual and semantic diversity across a variety of datatypes. However, there are two limitations to these findings. The first is that Chapman and Martin (2022) compared variables derived from different corpora, which makes any conclusion about the theoretical advantage of one metric over another confounded, as it could be the construction of one corpus that provides the advantage and not the underlying theoretical construct. Second, they did not consider recent developments in the semantic distinctiveness model (SDM; Johns, 2021a; Johns et al., 2020; Johns & Jones, 2022). The current paper addressed the second limitation. Consistent with Chapman and Martin (2022), our results showed that the earliest versions of the SDM were less predictive of lexical data relative to WF when derived from a different corpus. However, the later versions of the SDM accounted for substantially more unique variance than WF in lexical decision and naming data. The results suggest that context-based accounts provide a better explanation of lexical organization than repetition-based accounts. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Scalable cognitive modelling: Putting Simon's (1969) ant back on the beach.

A classic goal in cognitive modelling is the integration of process and representation to form complete theories of human cognition (Estes, 1955). This goal is best encapsulated by the seminal work of Simon (1969) who proposed the parable of the ant to describe the importance of understanding the environment that a person is embedded within when constructing theories of cognition. However, typical assumptions in accounting for the role of representation in computational cognitive models do not accurately represent the contents of memory (Johns & Jones, 2010). Recent developments in machine learning and big data approaches to cognition, referred to as scaled cognitive modelling here, offer a potential solution to the integration of process and representation. This article will review standard practices and assumptions that take place in cognitive modelling, how new big data and machine learning approaches modify these practices, and the directions that future research should take. The goal of the article is to ground big data and machine learning approaches that are emerging in the cognitive sciences within classic cognitive theoretical principles to provide a constructive pathway towards the integration of cognitive theory with advanced computational methodology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Contextual dynamics in lexical encoding across the ageing spectrum: A simulation study.

The field of psycholinguistics has recently questioned the primacy of word frequency (WF) in influencing word recognition and production, instead focusing on the importance of a word's contextual diversity (CD). WF is operationalised by counting the number of occurrences of a word in a corpus, while a word's CD is a count of the number of contexts that a word occurs in, with repetitions within a context being ignored. Numerous studies have converged on the conclusion that CD is a better predictor of word recognition latency and accuracy than frequency. These findings support a cognitive mechanism based on the principle of likely need over the principle of repetition in lexical organisation. In the current study, we trained the semantic distinctiveness model on communication patterns in social media platforms consisting of over 55-billion-word tokens and examined the ability of theoretically distinct models to explain word recognition latency and accuracy data from over 1 million participants from the Mandera et al. English Crowdsourding Project norms, consisting of approximately 59,000 words across six age bands ranging from ages 10 to 60 years. There was a clear quantitative trend across the age bands, where there is a shift from a social environment-based attention mechanism in the "younger" models, to a clear dominance for a discourse-based attention mechanism as models "aged." This pattern suggests that there is a dynamical interaction between the cognitive mechanisms of lexical organisation and environmental information that emerges across ageing.

Determining Whether Older Adults Use Similar Strategies to Young Adults in Theory of Mind Tasks.

OBJECTIVES: Theory of mind-the ability to infer others' mental states-declines over the life span, potentially due to cognitive decline. However, it is unclear whether deficits emerge because older adults use the same strategies as young adults, albeit less effectively, or use different or no strategies. The current study compared the similarity of older adults' theory of mind errors to young adults' and a random model. METHODS: One hundred twenty older adults (MAge = 74.68 years; 64 female) and 111 young adults (MAge = 19.1; 61 female) completed a novel theory of mind task (clips from an episode of the sitcom The Office®), and a standard measure of cognitive function (Logical Memory II). Monte Carlo resampling estimated the likelihood that older adults' error patterns were more similar to young adults' or a random distribution. RESULTS: Age deficits emerged on the theory of mind task. Poorer performance was associated with less similarity to young adults' response patterns. Overall, older adults' response patterns were ~2.7 million times more likely to match young adults' than a random model. Critically, one fourth of older adults' errors were more similar to the random distribution. Poorer memory ability contributed to this relationship. DISCUSSION: Age deficits in theory of mind performance may be driven by a subset of older adults and be related to disparities in strategy use. A certain amount of cognitive ability may be necessary for older adults to engage similar strategies to young adults' during theory of mind.

Semantic and phonetic similarity of verbal fluency responses in early-stage psychosis.

Linguistic abnormalities can emerge early in the course of psychotic illness. Computational tools that quantify similarity of responses in standardized language-based tasks such as the verbal fluency test could efficiently characterize the nature and functional correlates of these disturbances. Participants with early-stage psychosis (n=20) and demographically matched controls without a psychiatric diagnosis (n=20) performed category and letter verbal fluency. Semantic similarity was measured via predicted context co-occurrence in a large text corpus using Word2Vec. Phonetic similarity was measured via edit distance using the VFClust tool. Responses were designated as clusters (related items) or switches (transitions to less related items) using similarity-based thresholds. Results revealed that participants with early-stage psychosis compared to controls had lower fluency scores, lower cluster-related semantic similarity, and fewer switches; mean cluster size and phonetic similarity did not differ by group. Lower fluency semantic similarity was correlated with greater speech disorganization (Communication Disturbances Index), although more strongly in controls, and correlated with poorer social functioning (Global Functioning: Social), primarily in the psychosis group. Findings suggest that search for semantically related words may be impaired soon after psychosis onset. Future work is warranted to investigate the impact of language disturbances on social functioning over the course of psychotic illness.

A continuous source reinstatement model of true and false recollection.

In studies of false recognition, subjects not only endorse items that they have never seen, but they also make subjective judgments that they remember consciously experiencing them. This is a difficult problem for most models of recognition memory, as they propose that false memories should be based on familiarity, not recollection. We present a new computational model of recollection, based on the Recognition through Semantic Synchronization (RSS) model of Johns, Jones, & Mewhort (Cognitive Psychology, 2012, 65, 486), and fuzzy trace theory (Brainerd & Reyna, Current Directions in Psychological Science, 2002, 11, 164), that offers a solution to this problem. In addition to standard true and false recognition results, the model successfully extends to explain multiple studies on both true and false recollection. This work suggests that recollection does not have to be thought of as a separate process from recognition, but instead as one that is reliant upon different information sources. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Semantic Search in Psychosis: Modeling Local Exploitation and Global Exploration.

Impairments in category verbal fluency task (VFT) performance have been widely documented in psychosis. These deficits may be due to disturbed "cognitive foraging" in semantic space, in terms of altered salience of cues that influence individuals to search locally within a subcategory of semantically related responses ("clustering") or globally between subcategories ("switching"). To test this, we conducted a study in which individuals with schizophrenia (n = 21), schizotypal personality traits (n = 25), and healthy controls (n = 40) performed VFT with "animals" as the category. Distributional semantic model Word2Vec computed cosine-based similarities between words according to their statistical usage in a large text corpus. We then applied a validated foraging-based search model to these similarity values to obtain salience indices of frequency-based global search cues and similarity-based local cues. Analyses examined whether diagnosis predicted VFT performance, search strategies, cue salience, and the time taken to switch between vs search within clusters. Compared to control and schizotypal groups, individuals with schizophrenia produced fewer words, switched less, and exhibited higher global cue salience, indicating a selection of more common words when switching to new clusters. Global cue salience negatively associated with vocabulary ability in controls and processing speed in schizophrenia. Lastly, individuals with schizophrenia took a similar amount of time to switch to new clusters compared to control and schizotypal groups but took longer to transition between words within clusters. Findings of altered local exploitation and global exploration through semantic memory provide preliminary evidence of aberrant cognitive foraging in schizophrenia.

A Large-Scale Semantic Analysis of Verbal Fluency Across the Aging Spectrum: Data From the Canadian Longitudinal Study on Aging.

OBJECTIVES: The present study aimed to characterize changes in verbal fluency performance across the lifespan using data from the Canadian Longitudinal Study on Aging (CLSA). METHODS: We examined verbal fluency performance in a large sample of adults aged 45-85 (n = 12,686). Data are from the Tracking cohort of the CLSA. Participants completed a computer-assisted telephone interview that included an animal fluency task, in which they were asked to name as many animals as they could in 1 min. We employed a computational modeling approach to examine the factors driving performance on this task. RESULTS: We found that the sequence of items produced was best predicted by their semantic neighborhood, and that pairwise similarity accounted for most of the variance in participant analyses. Moreover, the total number of items produced declined slightly with age, and older participants produced items of higher frequency and denser semantic neighborhood than younger adults. DISCUSSION: These findings indicate subtle changes in the way people perform this task as they age. The use of computational models allowed for a large increase in the amount of variance accounted for in this data set over standard assessment types, providing important theoretical insights into the aging process.

Estimating the prevalence and diversity of words in written language.

Recently, a new crowd-sourced language metric has been introduced, entitled word prevalence, which estimates the proportion of the population that knows a given word. This measure has been shown to account for unique variance in large sets of lexical performance. This article aims to build on the work of Brysbaert et al. and Keuleers et al. by introducing new corpus-based metrics that estimate how likely a word is to be an active member of the natural language environment, and hence known by a larger subset of the general population. This metric is derived from an analysis of a newly collected corpus of over 25,000 fiction and non-fiction books and will be shown that it is capable of accounting for significantly more variance than past corpus-based measures.

New Perspectives on the Aging Lexicon.

The field of cognitive aging has seen considerable advances in describing the linguistic and semantic changes that happen during the adult life span to uncover the structure of the mental lexicon (i.e., the mental repository of lexical and conceptual representations). Nevertheless, there is still debate concerning the sources of these changes, including the role of environmental exposure and several cognitive mechanisms associated with learning, representation, and retrieval of information. We review the current status of research in this field and outline a framework that promises to assess the contribution of both ecological and psychological aspects to the aging lexicon.

The Role of Negative Information in Distributional Semantic Learning.

Distributional models of semantics learn word meanings from contextual co-occurrence patterns across a large sample of natural language. Early models, such as LSA and HAL (Landauer & Dumais, 1997; Lund & Burgess, 1996), counted co-occurrence events; later models, such as BEAGLE (Jones & Mewhort, 2007), replaced counting co-occurrences with vector accumulation. All of these models learned from positive information only: Words that occur together within a context become related to each other. A recent class of distributional models, referred to as neural embedding models, are based on a prediction process embedded in the functioning of a neural network: Such models predict words that should surround a target word in a given context (e.g., word2vec; Mikolov, Sutskever, Chen, Corrado, & Dean, 2013). An error signal derived from the prediction is used to update each word's representation via backpropagation. However, another key difference in predictive models is their use of negative information in addition to positive information to develop a semantic representation. The models use negative examples to predict words that should not surround a word in a given context. As before, an error signal derived from the prediction prompts an update of the word's representation, a procedure referred to as negative sampling. Standard uses of word2vec recommend a greater or equal ratio of negative to positive sampling. The use of negative information in developing a representation of semantic information is often thought to be intimately associated with word2vec's prediction process. We assess the role of negative information in developing a semantic representation and show that its power does not reflect the use of a prediction mechanism. Finally, we show how negative information can be efficiently integrated into classic count-based semantic models using parameter-free analytical transformations.

Decision contamination in the wild: Sequential dependencies in online review ratings.

Current judgments are systematically biased by prior judgments. Such biases occur in ways that seem to reflect the cognitive system's ability to adapt to statistical regularities within the environment. These cognitive sequential dependencies have primarily been evaluated in carefully controlled laboratory experiments. In this study, we used these well-known laboratory findings to guide our analysis of two datasets, consisting of over 2.2 million business review ratings from Yelp and 4.2 million movie and television review ratings from Amazon. We explored how within-reviewer ratings are influenced by previous ratings. Our findings suggest a contrast effect: Current ratings are systematically biased away from prior ratings, and the magnitude of this bias decays over several reviews. This work is couched within a broader program that aims to use well-established laboratory findings to guide our understanding of patterns in naturally occurring and large-scale behavioral data.

Using experiential optimization to build lexical representations.

To account for natural variability in cognitive processing, it is standard practice to optimize a model's parameters by fitting it to behavioral data. Although most language-related theories acknowledge a large role for experience in language processing, variability reflecting that knowledge is usually ignored when evaluating a model's fit to representative data. We fit language-based behavioral data using experiential optimization, a method that optimizes the materials that a model is given while retaining the learning and processing mechanisms of standard practice. Rather than using default materials, experiential optimization selects the optimal linguistic sources to create a memory representation that maximizes task performance. We demonstrate performance on multiple benchmark tasks by optimizing the experience on which a model's representation is based.

Quantity and Diversity: Simulating Early Word Learning Environments.

The words in children's language learning environments are strongly predictive of cognitive development and school achievement. But how do we measure language environments and do so at the scale of the many words that children hear day in, day out? The quantity and quality of words in a child's input are typically measured in terms of total amount of talk and the lexical diversity in that talk. There are disagreements in the literature whether amount or diversity is the more critical measure of the input. Here we analyze the properties of a large corpus (6.5 million words) of speech to children and simulate learning environments that differ in amount of talk per unit time, lexical diversity, and the contexts of talk. The central conclusion is that what researchers need to theoretically understand, measure, and change is not the total amount of words, or the diversity of words, but the function that relates total words to the diversity of words, and how that function changes across different contexts of talk.

Cognitive modeling as an interface between brain and behavior: Measuring the semantic decline in mild cognitive impairment.

Mild cognitive impairment (MCI) is characterised by subjective and objective memory impairment in the absence of dementia. MCI is a strong predictor for the development of Alzheimer's disease, and may represent an early stage in the disease course in many cases. A standard task used in the diagnosis of MCI is verbal fluency, where participants produce as many items from a specific category (e.g., animals) as possible. Verbal fluency performance is typically analysed by counting the number of items produced. However, analysis of the semantic path of the items produced can provide valuable additional information. We introduce a cognitive model that uses multiple types of lexical information in conjunction with a standard memory search process. The model used a semantic representation derived from a standard semantic space model in conjunction with a memory searching mechanism derived from the Luce choice rule (Luce, 1977). The model was able to detect differences in the memory searching process of patients who were developing MCI, suggesting that the formal analysis of verbal fluency data is a promising avenue to examine the underlying changes occurring in the development of cognitive impairment. (PsycINFO Database Record

Evidence of disturbances of deep levels of semantic cohesion within personal narratives in schizophrenia.

Since initial conceptualizations, schizophrenia has been thought to involve core disturbances in the ability to form complex, integrated ideas. Although this has been studied in terms of formal thought disorder, the level of involvement of altered latent semantic structure is less clear. To explore this question, we compared the personal narratives of adults with schizophrenia (n=200) to those produced by an HIV+ sample (n=55) using selected indices from Coh-Metrix. Coh-Metrix is a software system designed to compute various language usage statistics from transcribed written and spoken language documents. It differs from many other frequency-based systems in that Coh-Metrix measures a wide range of language processes, ranging from basic descriptors (e.g., total words) to indices assessing more sophisticated processes within sentences, between sentences, and across paragraphs (e.g., deep cohesion). Consistent with predictions, the narratives in schizophrenia exhibited less cohesion even after controlling for age and education. Specifically, the schizophrenia group spoke fewer words, demonstrated less connection between ideas and clauses, provided fewer causal/intentional markers, and displayed lower levels of deep cohesion. A classification model using only Coh-Metrix indices found language markers correctly classified participants in nearly three-fourths of cases. These findings suggest a particular pattern of difficulties cohesively connecting thoughts about oneself and the world results in a perceived lack of coherence in schizophrenia. These results are consistent with Bleuler's model of schizophrenia and offer a novel way to understand and measure alterations in thought and speech over time.

Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition.

A central goal of cognitive neuroscience is to decode human brain activity-that is, to infer mental processes from observed patterns of whole-brain activation. Previous decoding efforts have focused on classifying brain activity into a small set of discrete cognitive states. To attain maximal utility, a decoding framework must be open-ended, systematic, and context-sensitive-that is, capable of interpreting numerous brain states, presented in arbitrary combinations, in light of prior information. Here we take steps towards this objective by introducing a probabilistic decoding framework based on a novel topic model-Generalized Correspondence Latent Dirichlet Allocation-that learns latent topics from a database of over 11,000 published fMRI studies. The model produces highly interpretable, spatially-circumscribed topics that enable flexible decoding of whole-brain images. Importantly, the Bayesian nature of the model allows one to "seed" decoder priors with arbitrary images and text-enabling researchers, for the first time, to generate quantitative, context-sensitive interpretations of whole-brain patterns of brain activity.

The Role of Semantic Diversity in Word Recognition across Aging and Bilingualism.

Frequency effects are pervasive in studies of language, with higher frequency words being recognized faster than lower frequency words. However, the exact nature of frequency effects has recently been questioned, with some studies finding that contextual information provides a better fit to lexical decision and naming data than word frequency (Adelman et al., 2006). Recent work has cemented the importance of these results by demonstrating that a measure of the semantic diversity of the contexts that a word occurs in provides a powerful measure to account for variability in word recognition latency (Johns et al., 2012, 2015; Jones et al., 2012). The goal of the current study is to extend this measure to examine bilingualism and aging, where multiple theories use frequency of occurrence of linguistic constructs as central to accounting for empirical results (Gollan et al., 2008; Ramscar et al., 2014). A lexical decision experiment was conducted with four groups of subjects: younger and older monolinguals and bilinguals. Consistent with past results, a semantic diversity variable accounted for the greatest amount of variance in the latency data. In addition, the pattern of fits of semantic diversity across multiple corpora suggests that bilinguals and older adults are more sensitive to semantic diversity information than younger monolinguals.