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.Visual Search without Selective Attention calls into question the necessity of a covert selective attention mechanism by implementing a formal model that includes basic visual mechanisms, saccades, and simple task strategies. Across three search tasks, the model accounts for response times as well as the proportion of errors observed in human participants, including effects of item crowding in the visual stimulus.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147754/1/tops12406.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147754/2/tops12406_am.pd

Evaluation of Adaptive Systems

. Unambiguously, adaptive systems have to be evaluated empirically to guarantee that the adaptivity really works. Nevertheless, only few of the existing adaptive systems have been evaluated. One of the most important reasons for this lack is, that measures for adaptivity success have not been investigated systematically up to now. The aim of this PhD thesis is to explore a methodology for the empirical evaluation of adaptive systems, including validated criteria, experimental designs and procedures. It will be demonstrated that cognitive and behavioral factors provide important evidence for adaptivity success.