Michael Jones

The authors present a computational model that builds a holographic lexicon representing both word meaning and word order from unsupervised experience with natural language. The model uses simple convolution and superposition mechanisms (cf. BB Murdock, 1982) to learn distributed holographic representations for words. The structure of the resulting lexicon can account for empirical data from classic experiments studying semantic typicality, categorization, priming, and semantic constraint in sentence completions. Furthermore, order information can be retrieved from the holographic representations, allowing the model to account for limited word transitions without the need for built-in transition rules. The model demonstrates that a broad range of psychological data can be accounted for directly from the structure of lexical representations learned in this way, without the need for complexity to be built into …

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 structure of the search problems—searching in patchy environments—and recent evidence supporting a domain-general cognitive search process. To investigate these questions directly, we asked participants to recover from memory as many animal names as they could in 3 min. Memory search was modeled over a representation of the semantic search space generated from the BEAGLE memory model of Jones and Mewhort (2007), via a search process similar to models of associative memory search (eg, Raaijmakers & Shiffrin, 1981). We found evidence for local …

A broad range of priming data has been used to explore the structure of semantic memory and to test between models of word representation. In this paper, we examine the computational mechanisms required to learn distributed semantic representations for words directly from unsupervised experience with language. To best account for the variety of priming data, we introduce a holographic model of the lexicon that learns word meaning and order information from experience with a large text corpus. Both context and order information are learned into the same composite representation by simple summation and convolution mechanisms (cf. Murdock, B.B. (1982). A theory for the storage and retrieval of item and associative information. Psychological Review, 89, 609–626). We compare the similarity structure of representations learned by the holographic model, Latent Semantic Analysis (LSA; Landauer, T.K., & …

Concepts and word meaning are fundamental to nearly all aspects of human cognition. People use this knowledge daily to recognize entities and objects in their environment, generate expectancies for upcoming events, and interpret language. In this chapter, we review contemporary research in semantic memory. Our discussion is restricted to the meaning of individual words, focusing on recent experimental results and theoretical trends. Over the past number of years, semantic memory research has blossomed for a number of reasons, and our goal is to provide the reader with a feel for the exciting research and theoretical approaches that have resulted. The chapter deals primarily with the following topics: implications of grounded cognition for semantic memory, neural organization of concepts, the importance of people’s knowledge of everyday events for semantic memory, distinctions among semantic and associative relations, research on abstract concepts, connectionist models of semantic computations, and distributional models of semantic representations.

An increasing number of results in sentence and discourse processing demonstrate that comprehension relies on rich pragmatic knowledge about real-world events, and that incoming words incrementally activate such knowledge. If so, then even outside of any larger context, nouns should activate knowledge of the generalized events that they denote or typically play a role in. We used short stimulus onset asynchrony priming to demonstrate that (1) event nouns prime people (sale–shopper) and objects (trip–luggage) commonly found at those events; (2) location nouns prime people/animals (hospital–doctor) and objects (barn–hay) commonly found at those locations; and (3) instrument nouns prime things on which those instruments are commonly used (key–door), but not the types of people who tend to use them (hose–gardener). The priming effects are not due to normative word association. On our account …

<h3 class="gsh_h3">Objective</h3> To investigate whether catheter ablation of atrial fibrillation (AF) reduces stroke rate or mortality.<h3 class="gsh_h3">Methods</h3> An international multicentre registry was compiled from seven centres in the UK and Australia for consecutive patients undergoing catheter ablation of AF. Long-term outcomes were compared with (1) a cohort with AF treated medically in the Euro Heart Survey, and (2) a hypothetical cohort without AF, age and gender matched to the general population. Analysis of stroke and death was carried out after the first procedure (including peri-procedural events) regardless of success, on an intention-to-treat basis.<h3 class="gsh_h3">Results</h3> 1273 patients, aged 58±11 years, 56% paroxysmal AF, CHADS₂ score 0.7±0.9, underwent 1.8±0.9 procedures. Major complications occurred in 5.4% of procedures, including stroke/TIA in 0.7%. Freedom from AF following the last procedure was 85% (76% off antiarrhythmic drugs) for …

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 meaning in memory. Although humans are exposed to enormous quantities of speech, practical constraints limit the amount of data that many current computational models can learn from. We follow up on previous work evaluating a simple metric of pointwise mutual information. Controlling for confounds in previous work, we demonstrate that this metric benefits from training on extremely large amounts of data and correlates more closely with human semantic similarity ratings than do publicly available implementations of several more complex models. We …

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 of progress in the last two decades, advancing our knowledge of how meaning is constructed from experience, how knowledge is represented and used, and what processes are likely to be culprit in disorders characterized by semantic impairment. This chapter provides an overview of several recent clusters of models and trends in the literature, including modern connectionist and distributional models of semantic memory, and contemporary advances in grounding semantic models with perceptual information and models of compositional semantics. Several common lessons have emerged from both the connectionist and distributional literatures, and we attempt to synthesize these themes to better focus future

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 favor feature‐based models of semantic representation. We argue that the amount of perceptual and other semantic information that can be learned from purely distributional statistics has been underappreciated. We compare the representations of three feature‐based and nine distributional models using a semantic clustering task. Several distributional models demonstrated semantic clustering comparable with clustering‐based on feature‐based representations. Furthermore, when trained on child‐directed speech, the same distributional models perform as well as sensorimotor …

We tabulated upper- and lowercase letter frequency using several large-scale English corpora (∼183 million words in total). The results indicate that the relative frequencies for upper- and lowercase letters are not equivalent. We report a letter-naming experiment in which uppercase frequency predicted response time to uppercase letters better than did lowercase frequency. Tables of case-sensitive letter and bigram frequency are provided, including common nonalphabetic characters. Because subjects are sensitive to frequency relationships among letters, we recommend that experimenters use case-sensitive counts when constructing stimuli from letters.

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 children shortly after birth, so this is potentially an important source of linguistic input for many children. We constructed a corpus of 100 children’s picture books and compared word type and token counts in that sample and a matched sample of child-directed speech. Overall, the picture books contained more unique word types than the child-directed speech. Further, individual picture books generally contained more unique word types than length-matched, child-directed conversations. The text of picture books may be an important source of vocabulary for young children, and these …

The alternative pathway of the complement cascade plays a role in the pathogenesis of dense deposit disease (DDD). Deficiency of complement factor H and mutations in CFH associate with the development of DDD, but it is unknown whether allelic variants in other complement genes also associate with this disease. We studied patients with DDD and identified previously unreported sequence alterations in several genes in addition to allelic variants and haplotypes common to patients with DDD. We found that the likelihood of developing DDD increases with the presence of two or more risk alleles in CFH and C3. To determine the functional consequence of this finding, we measured the activity of the alternative pathway in serum samples from phenotypically normal controls genotyped for variants in CFH and C3. Alternative pathway activity was higher in the presence of variants associated with DDD. Taken …

We contrasted the predictive power of three measures of semantic richness—number of features (NF), 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 not NSN) facilitated processing for the most concrete concepts, consistent with claims that linguistic information is more relevant for abstract concepts in early processing. Additionally, converging evidence from two datasets suggests that when NSN and CD are controlled for, the features that most facilitate processing are those associated with a concept’s physical characteristics and real-world contexts. These results suggest that rich linguistic contexts (many semantic neighbors) facilitate early activation of abstract concepts, whereas concrete concepts benefit more from rich physical contexts (many associated objects and locations).

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, the model uses the global structure of memory to exploit the redundancy between language and perception in order to generate inferred perceptual representations for words with which the model has no perceptual experience. We test the model on a variety of different datasets from grounded cognition experiments and demonstrate that this diverse set of results can be explained as perceptual simulation (cf. Barsalou, Simmons, Barbey, &amp; Wilson, 2003) within a global memory model.

People with the apolipoprotein-Eε4 (APOE-4) genetic risk for Alzheimer's disease show morphologic differences in medial temporal lobe regions when compared to non-carriers of the allele. Using a high-resolution MRI and cortical unfolding approach, our aim was to determine the rate of cortical thinning among medial temporal lobe subregions over the course of 2 years. We hypothesized that APOE-4 genetic risk would contribute to longitudinal cortical thickness change in the subiculum and entorhinal cortex, regions preferentially susceptible to Alzheimer's disease related pathology. Thirty-two cognitively intact subjects, mean age 61 years, 16 APOE-4 carriers, 16 non-carriers, underwent baseline and follow-up MRI scans. Over this relatively brief interval, we found significantly greater cortical thinning in the subiculum and entorhinal cortex of APOE-4 carriers when compared to non-carriers of the allele. Average …