Chris Westbury

Distinct Brain Systems for Processing Concrete and Abstract Concepts

Behavioral and neurophysiological effects of word imageability and concreteness remain a topic of central interest in cognitive neuroscience and could provide essential clues for understanding how the brain processes conceptual knowledge. We examined these effects using event-related functional magnetic resonance imaging while participants identified concrete and abstract words. Relative to nonwords, concrete and abstract words both activated a left-lateralized network of multimodal association areas previously linked with verbal semantic processing. Areas in the left lateral temporal lobe were equally activated by both word types, whereas bilateral regions including the angular gyrus and the dorsal prefrontal cortex were more strongly engaged by concrete words. Relative to concrete words, abstract words activated left inferior frontal regions previously linked with phonological and verbal working memory processes. The results show overlapping but partly distinct neural systems for processing concrete and abstract concepts, with greater involvement of bilateral association areas during concrete word processing, and processing of abstract concepts almost exclusively by the left hemisphere.

Neural Correlates of Lexical Access during Visual Word Recognition

People can discriminate real words from nonwords even when the latter are orthographically and phonologically word-like, presumably because words activate specific lexical and/or semantic information. We investigated the neural correlates of this identification process using event-related functional magnetic resonance imaging (fMRI). Participants performed a visual lexical decision task under conditions that encouraged specific word identification: Nonwords were matched to words on orthographic and phonologic characteristics, and accuracy was emphasized over speed. To identify neural responses associated with activation of nonsemantic lexical information, processing of words and nonwords with many lexical neighbors was contrasted with processing of items with no neighbors. The fMRI data showed robust differences in activation by words and word-like nonwords, with stronger word activation occurring in a distributed, left hemisphere network previously associated with semantic processing, and stronger nonword activation occurring in a posterior inferior frontal area previously associated with grapheme-to-phoneme mapping. Contrary to lexicon-based models of word recognition, there were no brain areas in which activation increased with neighborhood size. For words, activation in the left prefrontal, angular gyrus, and ventrolateral temporal areas was stronger for items without neighbors, probably because accurate responses to these items were more dependent on activation of semantic information. The results show neural correlates of access to specific word information. The absence of facilitatory lexical neighborhood effects on activation in these brain regions argues for an interpretation in terms of semantic access. Because subjects performed the same task throughout, the results are unlikely to be due to task-specific attentional, strategic, or expectancy effects.

Characterizing semantic space: Neighborhood effects in word recognition

A specification of the structural characteristics of the mental lexicon is a central goal in word recognition research. Of various word-level characteristics, semantics remains the most resistant to this endeavor. Although there are several theoretically distinct models of lexical semantics with fairly clear operational definitions (e.g., in terms of feature sharing, category membership, associations, or cooccurrences), attempts to empirically adjudicate between these different models have been scarce. In this paper, we present several experiments in which we examined the effects of semantic neighborhood size as defined by two models of lexical semantics—one that defines semantics in terms of associations, and another that defines it in terms of global co-occurrences. We present data that address the question of whether these measures can be fruitfully applied to examinations of lexical activation during visual word recognition. The findings demonstrate that semantic neighborhood can predict performance on both lexical decision and word naming.

Primary Progressive Aphasia: A Review of 112 Cases ☆

Primary progressive aphasia (PPA) was first recognized by Mesulam in 1982. Although dozens of cases have since been described, it has been difficult to place these cases into a coherent framework due to the wide variation in measures which have been reported. We review 170 contacts with 112 patients to provide a clinical, neuroanatomical, and neuropsychological profile of patients with the disorder. The progression of the disease is analyzed over a 10-year reporting period starting from symptom onset to show how progression affects five general linguistic skills: oral and written naming, reading, repetition, and general comprehension. The pattern of functional and neurological deficits in PPA is heterogeneous. Differences in the distribution of neurological anomalies between patients with bilateral and unilateral changes suggest that there may be two separate disease processes involved.

Exploring lexical co-occurrence space using HiDEx.

Hyperspace analog to language (HAL) is a high-dimensional model of semantic space that uses the global co-occurrence frequency of words in a large corpus of text as the basis for a representation of semantic memory. In the original HAL model, many parameters were set without any a priori rationale. We have created and publicly released a computer application, the High Dimensional Explorer (HiDEx), that makes it possible to systematically alter the values of these parameters to examine their effect on the co-occurrence matrix that instantiates the model. We took an empirical approach to understanding the influence of the parameters on the measures produced by the models, looking at how well matrices derived with different parameters could predict human reaction times in lexical decision and semantic decision tasks. New parameter sets give us measures of semantic density that improve the model’s ability to predict behavioral measures. Implications for such models are discussed.

The effect of semantic distance in yes/no and go/no-go semantic categorization tasks.

The effect of semantic distance (Lund & Burgess, 1996) was examined in three semantic categorization experiments. Experiment 1, a yes/no task that required participants to make animal/nonanimal judgments by responding to both sets of stimuli (Forster & Shen, 1996), revealed no effect of semantic distance. Experiment 2, a go/no-go task that required participants to respond to only the experimental (i.e., nonanimal) items, revealed a large effect of semantic distance. In addition, response latencies were longer and error rates were lower to the experimental items in Experiment 2 than to those in Experiment 1. These findings were replicated in Experiment 3, in which semantic distance and task condition were manipulated within subjects. We conclude that these results are consistent with (1) the view that the go/no-go tasks elicited more extensive processing of the experimental items and (2) a connectionist account of semantic activation, whereby processing is facilitated by the presence of semantic neighbors.

Semantics and semantic errors: Implicit access to semantic information from words and nonwords in deep dyslexia

In this paper we describe dissociations of implicit versus explicit access to semantic information in a patient with deep dyslexia. This acquired reading disorder is characterized by the production of morphological (e.g., SLEEP read as SLEEPING) and semantic errors (e.g., HEART read as BLOOD) and consequently provides a potential window into the operation of both aspects of the language system. The deep dyslexic patient in this study (JO) demonstrated implicit semantic access to items in a number of tasks despite the fact that she was unable to correctly read these items aloud. The findings from this study are consistent with a model of lexical deficits that distinguishes between explicit and implicit access to lexical representations on the basis of inhibitory processes.

The Probability of the Least Likely Non-Length-Controlled Bigram Affects Lexical Decision Reaction Times

The frequency effect, by which high frequency words are recognized with more ease than low frequency words, is one of the most robust effects in cognitive psychology. Frequency interacts with many word-level variables, to the extent that most effects reported in word recognition literature have an impact only on low frequency words. This has been taken as evidence that high frequency words are accessed in a special way, via either an addressed pathway as in the dual-route model or an assembled pathway as in a PDP model. Under either model, sublexical effects should have no bearing on the ease with which representations for high frequency words are accessed. In this article, however, we describe a series of studies that examine a sublexical effect (namely nonlength controlled minimal bigram frequency) that is only found for high frequency words, suggesting that sublexical processing must play a role in the recognition of even high frequency words.

Word frequency effects in high-dimensional co-occurrence models: A new approach.

The HAL (hyperspace analog to language) model of lexical semantics uses global word co-occurrence from a large corpus of text to calculate the distance between words in co-occurrence space. We have implemented a system called HiDEx (High Dimensional Explorer) that extends HAL in two ways: It removes unwanted influence of orthographic frequency from the measures of distance, and it finds the number of words within a certain distance of the word of interest (NCount, the number of neighbors). These two changes to the HAL model produce

Avoid violence, rioting, and outrage; approach celebration, delight, and strength: Using large text corpora to compute valence, arousal, and the basic emotions

Ever since Aristotle discussed the issue in Book II of his Rhetoric, humans have attempted to identify a set of “basic emotion labels”. In this paper we propose an algorithmic method for evaluating sets of basic emotion labels that relies upon computed co-occurrence distances between words in a 12.7-billion-word corpus of unselected text from USENET discussion groups. Our method uses the relationship between human arousal and valence ratings collected for a large list of words, and the co-occurrence similarity between each word and emotion labels. We assess how well the words in each of 12 emotion label sets—proposed by various researchers over the past 118 years—predict the arousal and valence ratings on a test and validation dataset, each consisting of over 5970 items. We also assess how well these emotion labels predict lexical decision residuals (LDRTs), after co-varying out the effects attributable to basic lexical predictors. We then demonstrate a generalization of our method to determine the most predictive “basic” emotion labels from among all of the putative models of basic emotion that we considered. As well as contributing empirical data towards the development of a more rigorous definition of basic emotions, our method makes it possible to derive principled computational estimates of emotionality—specifically, of arousal and valence—for all words in the language.