Richard Shillcock

Eye Movements Reveal the On-Line Computation of Lexical Probabilities During Reading

Skilled readers are able to derive meaning from a stream of visual input with remarkable efficiency. In this article, we present the first evidence that statistical information latent in the linguistic environment can contribute to an account of reading behavior. In two eye-tracking studies, we demonstrate that the transitional probabilities between words have a measurable influence on fixation durations, and using a simple Bayesian statistical model, we show that lexical probabilities derived by combining transitional probability with the prior probability of a words occurrence provide the most parsimonious account of the eye movement data. We suggest that the brain is able to draw upon statistical information in order to rapidly estimate the lexical probabilities of upcoming words: a computationally inexpensive mechanism that may underlie proficient reading.

Eye-fixation behavior, lexical storage, and visual word recognition in a split processing model.

Some of the implications of a model of visual word recognition in which processing is conditioned by the anatomical splitting of the visual field between the two hemispheres of the brain are explored. The authors investigate the optimal processing of visually presented words within such an architecture, and, for a realistically sized lexicon of English, characterize a computationally optimal fixation point in reading. They demonstrate that this approach motivates a range of behavior observed in reading isolated words and text, including the optimal viewing position and its relationship with the preferred viewing location, the failure to fixate smaller words, asymmetries in hemisphere-specific processing, and the priority given to the exterior letters of words. The authors also show that split architectures facilitate the uptake of all the letter-position information necessary for efficient word recognition and that this information may be less specific than is normally assumed. A split model of word recognition captures a range of behavior in reading that is greater than that covered by existing models of visual word recognition.

Low-level predictive inference in reading: the influence of transitional probabilities on eye movements.

We report the results of an investigation into the ability of transitional probability (word-to-word contingency statistics) to account for reading behaviour. Using a corpus of eye movements recorded during the reading of newspaper text, we demonstrate both the forward [P(n/n-1)] and backward [P(n/n+1)] transitional probability measures to be predictive of first fixation and gaze durations: the higher the transitional probability, the shorter the fixation time. Initial fixation position was also affected by the forward measure; we observed a small rightward shift for words that were highly predictable from the preceding word. Although transitional probability is sensitive to word class, with function words being generally more predictable from their context than content words, the measures accounted equally well for the data for both classes.

An Anatomically Constrained, Stochastic Model of Eye Movement Control in Reading

This article presents SERIF, a new model of eye movement control in reading that integrates an established stochastic model of saccade latencies (LATER; R. H. S. Carpenter, 1981) with a fundamental anatomical constraint on reading: the vertically split fovea and the initial projection of information in either visual field to the contralateral hemisphere. The novel features of the model are its simulation of saccade latencies as a race between two stochastic rise-to-threshold LATER units and its probabilistic selection of the target for the next saccade. The model generates simulated eye movement behavior that exhibits important characteristics of actual eye movements made during reading; specifically, simulations produce realistic saccade target distributions and replicate a number of critical reading phenomena, including the effects of word frequency on fixation durations, the inverted optimal viewing position effect, the trade-off between first and second fixation durations of refixated words, and the dependence of parafoveal preview benefit on eccentricity.

Rethinking the Word Frequency Effect: The Neglected Role of Distributional Information in Lexical Processing

Attempts to quantify lexical variation have produced a large number of theoretical and empirical constructs, such as Word Frequency, Concreteness, and Ambiguity, which have been claimed to predict between-word differences in lexical processing behavior. Models of word recognition that have been developed to account for the effects of these variables have typically lacked adequate semantic representations, and have dealt with words as if they exist in isolation from their environment. We present a new dimension of lexical variation that is addressed to this concern. Contextual Distinctiveness(CD), a corpus-derived summary measure of the frequency distribution of the contexts in which a word occurs, is naturally compatible with contextual theories of semantic representation and meaning. Experiment 1 demonstrates that CD is a significantly better predictor of lexical decision latencies than occurrence frequency, suggesting that CD is the more psychologically relevant variable. We additionally explore the relationship between CD and six subjectively-defined measures: Concreteness, Context Availability, Number of Contexts, Ambiguity, Age of Acquisition and Familiarity and find CD to be reliably related to Ambiguity only. We argue for the priority of immediate context in determining the representation and processing of language.

The recognition of words after their acoustic offsets in spontaneous speech: Effects of subsequent context

Three experiments are presented that investigated the recognition of wordsafter their acoustic offsets in conversational speech. Utterances rand omly selected from the speech of 24 individuals (totalN=288) were gated in one-word increments and heard by 12 listeners each. of the successful recognitions, 21% occurred after the acoustic offset of the word in question and in the presence of subsequent context. The majority of late recognitions implicate subsequent context in the recognition process. Late recognitions were distributed nonrand omly with respect to the characteristics of the stimulus word tokens. Control experiments demonstrated that late recognitions were not artifacts of eliminating discourse context, of imposing artificial word boundaries, or of repeating words within successive gated presentations. The effects could be replicated only if subsequent context was available. The implications are discussed for models of word recognition in continuous speech.

How arbitrary is language

It is a long established convention that the relationship between sounds and meanings of words is essentially arbitrary—typically the sound of a word gives no hint of its meaning. However, there are numerous reported instances of systematic sound–meaning mappings in language, and this systematicity has been claimed to be important for early language development. In a large-scale corpus analysis of English, we show that sound–meaning mappings are more systematic than would be expected by chance. Furthermore, this systematicity is more pronounced for words involved in the early stages of language acquisition and reduces in later vocabulary development. We propose that the vocabulary is structured to enable systematicity in early language learning to promote language acquisition, while also incorporating arbitrariness for later language in order to facilitate communicative expressivity and efficiency.

Evaluating a split processing model of visual word recognition: effects of word length

A new theory of visual word recognition is based on the fact that the fovea is split in humans. When a reader fixates the center of a written word, the initial letters of the word that are to the left of fixation are projected first to the right cerebral hemisphere (RH) while the final letters are projected to the left cerebral hemisphere (LH). This paper explores the possibility that this has consequences for the early processing of the beginning and ends of centrally fixated words: specifically that lexical decision RTs are affected by the number of letters to the left of fixation but not by the number of letters to the right of fixation. For centrally presented five- and eight-letter words, we manipulated number of letters presented to the right or to the left of a fixation point (Experiment 1). We found that longer latencies to longer letter strings characterised the processing of the initial letters of words while LH word recognition features characterised the ends of words. Experiment 2 was a lateralized version of Experiment 1, and revealed the well established visual field and word length interaction. The results supported the split fovea theory.

Impaired artificial grammar learning in agrammatism.

It is often assumed that language is supported by domain-specific neural mechanisms, in part based on neuropsychological data from aphasia. If, however, language relies on domain-general mechanisms, it would be expected that deficits in non-linguistic cognitive processing should co-occur with aphasia. In this paper, we report a study of sequential learning by agrammatic aphasic patients and control participants matched for age, socio-economic status and non-verbal intelligence. Participants were first exposed to strings derived from an artificial grammar after which they were asked to classify a set of new strings, some of which were generated by the same grammar whereas others were not. Although both groups of participants performed well in the training phase of the experiment, only the control participants were able to classify novel test items better than chance. The results show that breakdown of language in agrammatic aphasia is associated with an impairment in artificial grammar learning, indicating damage to domain-general neural mechanisms subserving both language and sequential learning.

The Computational Exploration of Visual Word Recognition in a Split Model

We argue that the projection of the visual field to the cortex constrains and informs the modeling of visual word recognition. On the basis of anatomical and psychological evidence, we claim that the higher-level cognition involved in word recognition does not completely transcend initial foveal splitting. We present a schematic connectionist model of word recognition that instantiates the precise splitting of the visual field and the contralateral projection of the two hemifields. We explore the special nature of the exterior (i.e., first and last) letters of words in reading. The model produces the correct behavior spontaneously and robustly. We analyze this behavior of the model with respect to words and random patterns and conclude that the systematic division of the visual input has predictable, general informational consequences and is chiefly responsible for the exterior letters effect.