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Connectionist models of consistency effects

Before the DO2000 model, there were already a number of computational models of consistency effects.  All of these models can be classified as specific instances of either the generic response selection model or the generic dimensional overlap model.  Interestingly, the response selection models are each designed to account for performance in only one kind of task: Cohen (Cohen, Dunbar & McClelland, 1990; Cohen & Huston, 1994) and Phaf, van der Heijden, and Hudson (1990) have described response selection models of performance in the Stroop task;  Servan-Schreiber (1990; Cohen, Servan-Schreiber, & McClelland, 1992) has described a response selection model of performance in the Eriksen task; and Zorzi and Umilta (1995) has described a model of performance in the Simon task (which could be classified as either a dimensional overlap model or a response selection model, since both models agree on their explanation of the S-R consistency effect).

The three models that have specifically been designed as general models of consistency effects, on the other hand, are all dimensional overlap models: Barber and O’Leary (1993; O’Leary & Barber, 1997) have described a dimensional overlap model of performance in Simon and Stroop tasks, and their variants; and both Zhang and Kornblum (1998) and Kornblum et al. (1999) have described dimensional overlap models of performance in consistency tasks in general, including Eriksen, Simon, Stroop and Stroop-like tasks, and their variants and factorial combinations.

All of these models also have a common computational heritage, and so therefore also share a number of common assumptions, as well as a common descriptive language.  They can generally be classified as connectionist network models (Quinlan, 1991; Rumelhart, McCelland, et al., 1986).  Connectionist models consist of a network of interconnected processing units, where each unit is very simple, usually involving a single variable (called the unit’s “activation”) that changes as a function of input to the unit, and determines the output of the unit to be transferred to other connected units.

More specifically, these models are localist connectionist models (see, e.g. Grainger & Jacobs, 1998; Page, in press).  This means that each unit in the network represents a mental code.  In models of performance in classification tasks, the units in the network can be divided into three groups or modules: a relevant stimulus module, containing units corresponding to each of the elements of the relevant stimulus set; an irrelevant stimulus module, containing units corresponding to each of the elements in the irrelevant stimulus set; and a response module, containing units corresponding to each of the elements in the response set.  Some models also include modules of units representing executive cognitive functions, such as “task demand units,” which represent mental codes that specify which of the two stimulus sets is relevant (e.g. Cohen & Huston, 1994).

 
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