the properties of distributed network models, and support this account by demonstrating that an implemented simulation closely approximates the empirical findings despite the absence of expectancy-based processes and postlexical semantic matching. The results suggest that distributed network models can provide a viable single-mechanism account of lexical processing. Introduction It is well-established that people are faster and more accurate to read a word (e.g., BUTTER) when it is preceded by a related word (e.g., BREAD) compared with when it is preceded by an unrelated word (e.g., DOCTOR; The research was supported by an NIMH FIRST award (MH55628) to the first author and by NIMH Training Grant 5T32MH19102 and NICHD Grant 80258. The computational simulation was run using customized software written within the Xerion simulator (version 3.1) developed by Drew van Camp, Tony Plate, and Geoff Hinton at the Univers

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In a regression study of conversational speech, we show that frequency, contextual predictability and repetition have separate contributions to word duration, despite their substantial correlations. Moreover, content- and function-word durations are affected differently by their frequency and predictability. Content words are shorter when more frequent, and shorter when repeated, while function words are not so affected. Function words have shorter pronunciations, after controlling for frequency and predictability. While both content and function words are strongly affected by predictability from the word following them, sensitivity to predictability from the preceding word is largely limited to very frequent function words. The results support the view that content and function words are accessed differently in production. We suggest a lexical-access-based model of our results, in which frequency or repetition lead to shorter or longer word durations by causing faster or slower lexical access, mediated by a general mechanism that coordinates the pace of higher-level planning and the execution of the articulatory plan.

Investigations of decision making have typically assumed stationarity, even though commonly observed “context effects ” are dynamic by definition. Mirror effects are an important class of context effects that can be explained by changes in participants ’ decision criteria. When easy and difficult conditions are blocked alternately and a mirror effect is observed, participants must repeatedly change their decision criteria. The authors investigated the time course of these criterion changes and observed the buildup of mirror effects on a trial-by-trial basis. The data are consistent with slow, systematic changes in decision criteria that lag behind stimulus changes. The length of this lag is considerable: analysis of a simple dynamic signal-detection model suggests participants take an average of around 14 trials to adjust to new decision environments. This trial-level measurement of experimentally induced changes has implications for traditional blockwise analyses of data and for models of decision making.

Four pairs of connectionist simulations are presented in which quasi-regular mappings are computed using localist and distributed representations. In each simulation, a control parameter termed input gain was modulated over the only level of representation that mapped inputs to outputs. Input gain caused both localist and distributed models to shift between regularity-based and item-based modes of processing. Performance on irregular items was selectively impaired in the regularity-based modes, whereas performance on novel items was selectively impaired in the item-based modes. Thus, the models exhibited double dissociations without separable processing components. These results are discussed in the context of analogous dissociations found in language domains such as word reading and inflectional morphology.

Three different speech production paradigms are used to assess the hypothesis introduced by Kello, Plant, and MacWhinney (2000) according to...

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