Bayesian approximations to the theory of visual attention (TVA) in a foraging task.

Foraging as a natural visual search for multiple targets has increasingly been studied in humans in recent years. Here, we aimed to model the differences in foraging strategies between feature and conjunction foraging tasks found by Á. Kristjánsson et al. Bundesen proposed the theory of visual attention (TVA) as a computational model of attentional function that divides the selection process into filtering and pigeonholing. The theory describes a mechanism by which the strength of sensory evidence serves to categorise elements. We combined these ideas to train augmented Naïve Bayesian classifiers using data from Á. Kristjánsson et al. as input. Specifically, we attempted to answer whether it is possible to predict how frequently observers switch between different target types during consecutive selections (switches) during feature and conjunction foraging using Bayesian classifiers. We formulated 11 new parameters that represent key sensory and bias information that could be used for each selection during the foraging task and tested them with multiple Bayesian models. Separate Bayesian networks were trained on feature and conjunction foraging data, and parameters that had no impact on the model's predictability were pruned away. We report high accuracy for switch prediction in both tasks from the classifiers, although the model for conjunction foraging was more accurate. We also report our Bayesian parameters in terms of their theoretical associations with TVA parameters, πj (denoting the pertinence value), and ßi (denoting the decision-making bias).

The Role of Working Memory in Dual-Target Visual Search.

Visual search (VS) for multiple targets is especially error prone. One of these errors is called subsequent search misses (SSM) and represents a decrease in accuracy at detecting a second target after a first target has been found. One of the possible explanations of SSM errors is working memory (WM) resource depletion. Three experiments investigated the role of WM in SSM errors using a dual task paradigm. The first experiment investigated the role of object WM using a classical color change detection task. In the second and the third experiments, a modified change detection task was applied, using shape as the relevant feature. The results of our study revealed no effect of additional WM task on second target detection in dual-target VS. To this end, SSM errors are not related to WM resource depletion. On the contrary, WM task performance was violated by dual-target VS as compared to single-target VS, when the targets in VS task were defined by the same feature used in the WM task.