Michael N. Jones's research while affiliated with United States University and other places

Publications (88)

Preprint
When concepts are retrieved from memory, this process occurs within a rich search space where multiple sources of information interact with each other. Although the mapping from wordform to meaning is generally considered to be arbitrary, there is recent evidence to suggest that form and meaning may be correlated in natural language, and semantic a...
Article
Measures of contextual diversity seek to replace word frequency by counting the number of different contexts that a word occurs in rather than the total raw number of occurrences (Adelman, Brown, & Quesada, 2006). It has repeatedly been shown that contextual diversity measures outperform word frequency on word recognition datasets (Adelman & Brown,...
Article
The dominant view in cognitive psychology is that memory includes several distinct and separate systems including episodic memory, semantic memory and associative learning, each with a different set of representations, explanatory principles and mechanisms. In opposition to that trend, there is a renewed effort to reconcile those distinctions in fa...
Article
Linguistic abnormalities can emerge early in the course of psychotic illness. Computational tools that quantify response similarity in standardized tasks such as the verbal fluency test could efficiently characterize the nature and functional correlates of these deficits. Participants with early-stage psychosis (n=20) and demographically matched co...
Article
Previous research has demonstrated that Distributional Semantic Models (DSMs) are capable of reconstructing maps from news corpora (Louwerse & Zwaan, 2009) and novels (Louwerse & Benesh, 2012). The capacity for reproducing maps is surprising since DSMs notoriously lack perceptual grounding . In this paper we investigate the statistical sources requ...
Article
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 ne...
Article
Recent research in the artificial grammar learning literature has shown that a simple instance model of memory can account for a wide variety of artificial grammar results (Jamieson & Mewhort, 2009, 2010, 2011), indicating that language processing may have more in common with episodic memory than previously thought. These results have been used to...
Article
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 com...
Article
Full-text available
The semantic memory literature has recently seen the emergence of predictive neural network models that use principles of reinforcement learning to create a “neural embedding” of word meaning when trained on a language corpus. These models have taken the field by storm, partially due to the resurgence of connectionist architectures, but also due to...
Preprint
Full-text available
Previous research has demonstrated that Distributional Semantic Models (DSMs) are capable of reconstructing maps from news corpora (Louwerse & Zwaan, 2009) and novels (Louwerse & Benesh, 2012). The capacity for reproducing maps is surprising since DSMs notoriously lack perceptual grounding (De Vega et al., 2012). In this paper we investigate the st...
Article
Full-text available
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 bet...
Preprint
The semantic memory literature has recently seen the emergence of predictive neural network models that use principles of reinforcement learning to create a “neural embedding” of word meaning when trained on a language corpus. These models have taken the field by storm, partially due to the resurgence of connectionist architectures, but also due to...
Article
Figure 9 in the original version of the article contained an error. The corrected Fig. 9 is presented below. Conclusions from the Instance Theory of Semantics (ITS) are preserved. However, conclusions from LSA and BEAGLE are not.
Article
Full-text available
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, incl...
Article
Full-text available
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, incl...
Article
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 a...
Preprint
Full-text available
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, incl...
Article
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...
Article
Distributional semantic models (DSMs) specify learning mechanisms with which humans construct a deep representation of word meaning from statistical regularities in language. Despite their remarkable success at fitting human semantic data, virtually all DSMs may be classified as prototype models in that they try to construct a single representation...
Article
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 repre...
Article
Full-text available
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...
Article
ion is a core principle of Distributional Semantic Models (DSMs) that learn semantic representations for words by applying dimensional reduction to statistical redundancies in language. Although the posited learning mechanisms vary widely, virtually all DSMs are prototype models in that they create a single abstract representation of a word’s meani...
Article
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...
Data
Topic-based decoding of 12 “cognitive components” reported in Yeo et al. [33]. (JPG)
Data
Full results for all topics learned by the GC-LDA model. Each row represents a single topic. For each topic, the word cloud displays the top semantic associates (the size of each term is roughly proportional to the strength of its loading, and the orthviews display all hard assignments of activations to that topic (each point represents a single ac...
Article
Full-text available
Author summary A central goal of cognitive neuroscience is to decode human brain activity—i.e., to be able to infer mental processes from observed patterns of whole-brain activity. However, existing approaches to brain decoding suffer from a number of important limitations—for example, they often work only in one narrow domain of cognition, and can...
Data
Topic-based decoding of 20 BrainMap-derived ICA components reported in Smith et al. [15]. (JPG)
Data
Topic-based reconstruction of whole-brain activity maps. Representative examples from (A) the set of 20 BrainMap ICA components reported in Smith et al. [15]. (B) the NeuroVault whole-brain image repository [2], and (C) single-subject contrast maps from the emotion processing task in the Human Connectome Project dataset (face vs. shape contrast). E...
Data
Reconstruction of 20 BrainMap ICA components reported in Smith et al. [15]. (JPG)
Data
Topic reconstruction of 12 “cognitive components” reported in Yeo et al. [33]. (JPG)
Data
Whole-brain image reconstruction using learned GC-LDA topics. (PDF)
Data
Topic reconstruction of 100 random maps extracted from the NeuroVault whole-brain image repository. Labels in white indicate human-annotated cognitive atlas paradigm, when available. (JPG)
Preprint
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 and day-out? The quantity and quality of words in a child’s input is typically measured in terms of total amoun...
Chapter
Full-text available
How is it that we know what a dog and a tree are, or, for that matter, what knowledge is? Our semantic memory consists of knowledge about the world, including concepts, facts, and beliefs. This knowledge is essential for recognizing entities and objects, and for making inferences and predictions about the world. In essence, our semantic knowledge d...
Article
Recent semantic space models learn vector representations for word meanings by observing statistical redundancies across a text corpus. A word's meaning is represented as a point in a high-dimensional semantic space, and semantic similarity between words is quantified by a function of their spatial proximity (typically the cosine of the angle betwe...
Conference Paper
Full-text available
Distributional models of semantics assume that the meaning of a given word is a function of the contexts in which it occurs. In line with this, prior research suggests that a word's semantic representation can be manipulated – pushed toward a target meaning, for example – by situating that word in distributional contexts frequented by the target. L...
Conference Paper
Full-text available
In the study of recognition memory, a mirror effect is commonly observed for word frequency, with low frequency items yielding both a higher hit rate and lower false alarm rate than high frequency items. The finding that LF items consistently outperform HF items in recognition was once termed the " frequency paradox " , as LF items are less well re...
Conference Paper
Full-text available
The principal aim of a cognitive model is to infer the process by which the human mind acts on some select set of environmental inputs such that it produces the observed set of behavioral outputs. In this endeavor, one of the central requirements is that the input to the model be represented as faithfully and accurately as possible. However, this i...
Article
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 prod...
Chapter
Full-text available
Classic accounts of lexical organization posit that humans are sensitive to environmental frequency, suggesting a mechanism for word learning based on repetition. However, a recent spate of evidence has revealed that it is not simply frequency but the diversity and distinctiveness of contexts in which a word occurs that drives lexical organization....
Chapter
Full-text available
The scientific study of discourse has witnessed major advances in recent years, with an exponential increase in the scale of mineable text and speech, and the advent of new technologies for mining and analyzing these resources. We review a few of the highlights of this wave of innovation and discuss the research possibilities opened up by data-driv...
Conference Paper
Full-text available
Human languages can be seen as socially evolved systems that have been structured to optimize information flow in communication. Communication appears to proceed both more efficiently and more smoothly when information is distributed evenly across the linguistic signal. In previous work (Ramscar et al., 2013), we used tools from information theory...
Preprint
Full-text available
A central goal of cognitive neuroscience is to decode human brain activity--i.e., 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, systemat...
Article
Full-text available
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 (Adel...
Article
Full-text available
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...
Article
We compared the ability of three different contextual models of lexical semantic memory (BEAGLE, Latent Semantic Analysis, and the Topic model) and of a simple associative model (POC) to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network pro...
Article
Full-text available
Young children learn language from the speech they hear. Previous work suggests that greater statistical diversity of words and of linguistic contexts is associated with better language outcomes. One potential source of lexical diversity is the text of picture books that caregivers read aloud to children. Many parents begin reading to their childre...
Article
In recent work exploring the semantic fluency task, we found evidence indicative of optimal foraging policies in memory search that mirror search in physical environments. We determined that a 2-stage cue-switching model applied to a memory representation from a semantic space model best explained the human data. Abbott, Austerweil, and Griffiths d...
Article
Full-text available
When searching for concepts in memory-as in the verbal fluency task of naming all the animals one can think of-people appear to explore internal mental representations in much the same way that animals forage in physical space: searching locally within patches of information before transitioning globally between patches. However, the definition of...
Article
Standard theories of language generally assume that some abstraction of linguistic input is necessary to create higher level representations of linguistic structures (e.g., a grammar). However, the importance of individual experiences with language has recently been emphasized by both usage-based theories (Tomasello, 2003) and grounded and situated...
Article
Full-text available
Recent studies of eye movements in world-situated language comprehension have demonstrated that rapid processing of morphosyntactic information - e.g., grammatical gender and number marking - can produce anticipatory eye movements to referents in the visual scene. We investigated how type of morphosyntactic information and the goals of language use...
Article
Full-text available
Encoding information about the order in which words typically appear has been shown to improve the performance of high-dimensional semantic space models. This requires an encoding operation capable of binding together vectors in an order-sensitive way, and efficient enough to scale to large text corpora. Although both circular convolution and rando...
Chapter
Full-text available
Meaning is a fundamental component of nearly all aspects of human cognition, but formal models of semantic memory have classically lagged behind many other areas of cognition. However, computational models of semantic memory have seen a surge progress in the last two decades, advancing our knowledge of how meaning is constructed from experience, ho...
Article
Full-text available
Semantic models play an important role in cognitive science. These models use statistical learning to model word meanings from co-occurrences in text corpora. A wide variety of semantic models have been proposed, and the literature has typically emphasized situations in which one model outperforms another. However, because these models often vary w...
Article
Groups of English monolingual and English–French bilingual participants completed letter and category fluency tasks, either only in English (monolinguals) or in English, French, free-switch and forced-switch conditions (bilinguals). Response patterns were modeled using a semantic space approach that estimates the weight of frequency and semantic si...
Article
Full-text available
We contrasted the predictive power of three measures of semantic richness-number of features (NFs), contextual dispersion (CD), and a novel measure of number of semantic neighbors (NSN)-for a large set of concrete and abstract concepts on lexical decision and naming tasks. NSN (but not NF) facilitated processing for abstract concepts, while NF (but...
Article
The relative abilities of word frequency, contextual diversity, and semantic distinctiveness to predict accuracy of spoken word recognition in noise were compared using two data sets. Word frequency is the number of times a word appears in a corpus of text. Contextual diversity is the number of different documents in which the word appears in that...
Article
Although many recent advances have taken place in corpus-based tools, the techniques used to guide exploration and evaluation of these systems have advanced little. Typically, the plausibility of a semantic space is explored by sampling the nearest neighbors to a target word and evaluating the neighborhood on the basis of the modeler's intuition. T...
Article
On June 7, 2012, a meeting was held at the University of Manitoba in Winnipeg, Canada to honour Professor Douglas John Kerr Mewhort, a recognised authority on human memory and computational cognitive modelling. This issue is a collection of articles from that meeting. Each article in this issue is written by a student or colleague. The common theme...
Article
Full-text available
Recent research has challenged the notion that word frequency is the organizing principle underlying lexical access, pointing instead to the number of contexts that a word occurs in (Adelman, Brown, & Quesada, 2006). Counting contexts gives a better quantitative fit to human lexical decision and naming data than counting raw occurrences of words. H...
Article
Full-text available
Do humans search in memory using dynamic local-to-global search strategies similar to those that animals use to forage between patches in space? If so, do their dynamic memory search policies correspond to optimal foraging strategies seen for spatial foraging? Results from a number of fields suggest these possibilities, including the shared structu...
Article
The literature contains a disconnect between accounts of how humans learn lexical semantic representations for words. Theories generally propose that lexical semantics are learned either through perceptual experience or through exposure to regularities in language. We propose here a model to integrate these two information sources. Specifically, th...
Article
Full-text available
Phenomena in a variety of verbal tasks--for example, masked priming, lexical decision, and word naming--are typically explained in terms of similarity between word-forms. Despite the apparent commonalities between these sets of phenomena, the representations and similarity measures used to account for them are not often related. To show how this ga...
Conference Paper
Full-text available
Many measures of human verbal behavior deal primarily with semantics (e.g., associative priming, semantic priming). Other measures are tied more closely to orthography (e.g., lexical decision time, visual word-form priming). Semantics and orthography are thus often studied and modeled separately. However, given that concepts must be built upon a fo...
Article
Abstract Since their inception, distributional models of semantics have been criticized as inadequate cognitive theories of human semantic learning and representation. A principal challenge is that the representations derived by distributional models are purely symbolic and are not grounded in perception and action; this challenge has led many to f...
Article
Full-text available
We studied contrast and assimilation in three tasks: an exemplar-production task, a categorization task, and a combined categorization-then-production task. On each trial of the first task, subjects produced a circle when prompted with a category label. In the second task, they classified lines that differed in length into one of four categories. O...
Article
Full-text available
Semantic space models of lexical semantics learn vector representations for words by observing statistical redundancies in a text corpus. A word's meaning is represented as a point in a high-dimensional semantic space. However, these spatial models have difficulty simulating human free association data due to the constraints placed upon them by met...
Article
Full-text available
Here we describe the Semantic Pictionary Project—a set of online games and tools designed to collect large amounts of structured data about the object characteristics and perceptual properties of word referents. The project hinges on the use of encoding-decoding games and a set of creation tools to capture data using online crowdsourcing. We descri...
Article
Full-text available
Text-analytic methods have become increasingly popular in cognitive science for understanding differences in semantic structure between documents. However, such methods have not been widely used in other disciplines. With the aim of disseminating these approaches, we introduce a text-analytic technique (Contrast Analysis of Semantic Similarity, CAS...
Article
A common assumption implicit in cognitive models is that lexical semantics can be approximated by using randomly generated representations to stand in for word meaning. However, the use of random representations contains the hidden assumption that semantic similarity is symmetrically distributed across randomly selected words or between instances w...
Article
Full-text available
Computational models of lexical semantics, such as latent semantic analysis, can automatically generate semantic similarity measures between words from statistical redundancies in text. These measures are useful for experimental stimulus selection and for evaluating a model's cognitive plausibility as a mechanism that people might use to organize m...