The persistent visual store as the locus of fixation memory in visual search tasks

Visual Search Without Selective Attention: A Cognitive Architecture Account

A key phenomenon in visual search experiments is the linear relation of reaction time (RT) to the number of objects to be searched (set size). The dominant theory of visual search claims that this is a result of covert selective attention operating sequentially to "bind" visual features into objects, and this mechanism operates differently depending on the nature of the search task and the visual features involved, causing the slope of the RT as a function of set size to range from zero to large values. However, a cognitive architectural model presented here shows these effects on RT in three different search task conditions can be easily obtained from basic visual mechanisms, eye movements, and simple task strategies. No selective attention mechanism is needed. In addition, there are little-explored effects of visual crowding, which is typically confounded with set size in visual search experiments. Including a simple mechanism for crowding in the model also allows it to account for significant effects on error rate (ER). The resulting model shows the interaction between visual mechanisms and task strategy, and thus it represents a more comprehensive and fruitful approach to visual search than the dominant theory.

Cognitive architecture enables comprehensive predictive models of visual search

With a simple demonstration model, Hulleman & Olivers (H&O) effectively argue that theories of visual search need an overhaul. We point to related literature in which visual search is modeled in even more detail through the use of computational cognitive architectures that incorporate fundamental perceptual, cognitive, and motor mechanisms; the result of such work thus far bolsters their arguments considerably.

A Cognitive Architectural Account of Two-Channel Speech Processing

An important application of cognitive architectures is to provide human performance models that capture psychological mechanisms in a form that can be “programmed” to predict task performance of human-machine system designs. While many aspects of human performance have been successfully modeled in this approach, accounting for multi-talker speech task performance is a novel problem. This paper presents a model for performance in a two-talker task that incorporates concepts from the psychoacoustic study of speech perception, in particular, masking effects and stream formation.