Determining the optimal environmental information for training computational models of lexical semantics and lexical organization.

Experiential theories of cognition propose that the external environment shapes cognitive processing, shifting emphasis from internal mechanisms to the learning of environmental structure. Computational modelling, particularly distributional models of lexical semantics (e.g.

Determining the Relativity of Word Meanings Through the Construction of Individualized Models of Semantic Memory.

Distributional models of lexical semantics are capable of acquiring sophisticated representations of word meanings. The main theoretical insight provided by these models is that they demonstrate the systematic connection between the knowledge that people acquire and the experience that they have with the natural language environment. However, linguistic experience is inherently variable and differs radically across people due to demographic and cultural variables.

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.

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.

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).

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.

Determining the importance of frequency and contextual diversity in the lexical organization of multiword expressions.

Corpus-based models of lexical strength have called into question the role of word frequency as an organizing principle of the lexicon, revealing that contextual and semantic diversity measures provide a closer fit to lexical behavior data (Adelman et al., 2006; Jones et al., 2012).

Accounting for item-level variance in recognition memory: Comparing word frequency and contextual diversity.

Contextual diversity modifies word frequency by ignoring the repetition of words in context (Adelman, Brown, & Quesada,  2006, Psychological Science, 17(9), 814-823). Semantic diversity modifies contextual diversity by taking into account the uniqueness of the contexts that a word occurs in when calculating lexical strength (Jones, Johns, & Recchia,  2012, Canadian Journal of Experimental Psychology, 66, 115-124). Recent research has demonstrated that measures based on contextual and semantic diversity provide a considerable improvement over word frequency when accounting for lexical organization data (Johns, 2021, Psychological Review, 128, 525-557; Johns, Dye, & Jones, 2020a, Quarterly Journal of Experimental Psychology, 73, 841-855).

Distributional social semantics: Inferring word meanings from communication patterns.

Distributional models of lexical semantics have proven to be powerful accounts of how word meanings are acquired from the natural language environment (Günther, Rinaldi, & Marelli, 2019; Kumar, 2020). Standard models of this type acquire the meaning of words through the learning of word co-occurrence statistics across large corpora. However, these models ignore social and communicative aspects of language processing, which is considered central to usage-based and adaptive theories of language (Tomasello, 2003; Beckner et al.

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.

Disentangling contextual diversity: Communicative need as a lexical organizer.

Contextual diversity (CD; Adelman, Brown, & Quesada, 2006) modifies word frequency by ignoring word repetition in context. It has been repeatedly found that a CD count provides a better fit to lexical organization data than does word frequency (e.g.

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.

Semantic diversity in paired-associate learning: Further evidence for the information accumulation perspective of cognitive aging.

Normal aging is often associated with a performance decline on various cognitive tests, including paired associate learning (PAL), where participants are asked to learn and recall arbitrary word pairs. While many studies have taken this as evidence to support the notion of age-related deficits in cognitive processing, Ramscar, Hendrix, Shaoul, Milin, and Baayen (Topics in Cognitive Science, 6(1), 5-42) and Ramscar, Sun, Hendrix, and Baayen (Psychological Science, 28(8), 1171-1179, 2017) posit that the decline in performance on various cognitive tasks can be explained by the accumulation of linguistic knowledge over time. To demonstrate this, Ramscar et al.

The influence of place and time on lexical behavior: A distributional analysis.

We measured and documented the influence of corpus effects on lexical behavior. Specifically, we used a corpus of over 26,000 fiction books to show that computational models of language trained on samples of language (i.e.

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.

Gender bias at scale: Evidence from the usage of personal names.

Recent research within the computational social sciences has shown that when computational models of lexical semantics are trained on standard natural-language corpora, they embody many of the implicit biases that are seen in human behavior (Caliskan, Bryson, & Narayanan, 2017). In the present study, we aimed to build on this work and demonstrate that there is a large and systematic bias in the use of personal names in the natural-language environment, such that male names are much more prevalent than female names. This bias holds over an analysis of billions of words of text, subcategorized into different genres within fiction novels, nonfiction books, and subtitles from television and film.

Mining a Crowdsourced Dictionary to Understand Consistency and Preference in Word Meanings.

Big data approaches to psychology have become increasing popular (Jones, 2017). Two of the main developments of this line of research is the advent of distributional models of semantics (e.g.

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.

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.

A Large-Scale Analysis of Variance in Written Language.

The collection of very large text sources has revolutionized the study of natural language, leading to the development of several models of language learning and distributional semantics that extract sophisticated semantic representations of words based on the statistical redundancies contained within natural language (e.g., Griffiths, Steyvers, & Tenenbaum, ; Jones & Mewhort, ; Landauer & Dumais, ; Mikolov, Sutskever, Chen, Corrado, & Dean, ).

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.

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).

Applying an exemplar model to an implicit rule-learning task: Implicit learning of semantic structure.

Studies of implicit learning often examine peoples' sensitivity to sequential structure. Computational accounts have evolved to reflect this bias. An experiment conducted by Neil and Higham [Neil, G.

The influence of contextual diversity on word learning.

In a series of analyses over mega datasets, Jones, Johns, and Recchia (Canadian Journal of Experimental Psychology, 66(2), 115-124, 2012) and Johns et al. (Journal of the Acoustical Society of America, 132:2, EL74-EL80, 2012) found that a measure of contextual diversity that takes into account the semantic variability of a word's contexts provided a better fit to both visual and spoken word recognition data than traditional measures, such as word frequency or raw context counts. This measure was empirically validated with an artificial language experiment (Jones et al.