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.

Modeling Two-Channel Speech Processing With the EPIC Cognitive Architecture.

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. Although many aspects of human performance have been successfully modeled in this approach, accounting for multitalker speech task performance is a novel problem. This article presents a model for performance in a two-talker task that incorporates concepts from psychoacoustics, in particular, masking effects and stream formation.

Enhancing listener strategies using a payoff matrix in speech-on-speech masking experiments.

Speech recognition was measured as a function of the target-to-masker ratio (TMR) with syntactically similar speech maskers. In the first experiment, listeners were instructed to report keywords from the target sentence. Data averaged across listeners showed a plateau in performance below 0 dB TMR when masker and target sentences were from the same talker. In this experiment, some listeners tended to report the target words at all TMRs in accordance with the instructions, while others reported keywords from the louder of the sentences, contrary to the instructions. In the second experiment, stimuli were the same as in the first experiment, but listeners were also instructed to avoid reporting the masker keywords, and a payoff matrix penalizing masker keywords and rewarding target keywords was used. In this experiment, listeners reduced the number of reported masker keywords, and increased the number of reported target keywords overall, and the average data showed a local minimum at 0 dB TMR with same-talker maskers. The best overall performance with a same-talker masker was obtained with a level difference of 9 dB, where listeners achieved near perfect performance when the target was louder, and at least 80% correct performance when the target was the quieter of the two sentences.

Theoretical implications of articulatory duration, phonological similarity, and phonological complexity in verbal working memory.

The phonological-loop model provides a prominent theoretical description of verbal working memory. According to it, serial recall accuracy should be inversely related to the articulatory duration and phonological similarity of verbal items in memorized sequences. Initial tests of these predictions by A. D. Baddeley and colleagues (e.g., A. D. Baddeley, N. Thomson, & M. Buchanan, 1975) appeared to support the phonological-loop model, but subsequent researchers have obtained conflicting data that putatively disconfirm its assumptions. Such conflicts may have stemmed from less than ideal measurements of articulatory duration and phonological similarity. This article discusses these concerns and proposes new theoretically principled methods for measuring articulatory duration and phonological similarity. Two experiments that used these methods in the context of a verbal serial recall task are reported. The results of these experiments confirm and extend the predictions of the phonological-loop model while disarming previous criticisms of it.