Abstract
Learning how to allocate attention properly is essential for success at many categorization tasks. Advances in our understanding of learned attention are stymied by a chicken-and-egg problem: there are no theoretical accounts of learned attention that predict patterns of eye movements, making data collection difficult to justify, and there are not enough datasets to support the development of a rich theory of learned attention. The present work addresses this by reporting five measures relating to the overt allocation of attention across 10 category learning experiments: accuracy, probability of fixating irrelevant information, number of fixations to category features, the amount of change in the allocation of attention (using a new measure called Time Proportion Shift - TIPS), and a measure of the relationship between attention change and erroneous responses. Using these measures, the data suggest that eye-movements are not substantially connected to error in most cases and that aggregate trial-by-trial attention change is generally stable across a number of changing task variables. The data presented here provide a target for computational models that aim to account for changes in overt attentional behaviors across learning.
Highlights
The visual modality is a primary source of information from which our understanding of the world arises
For each of the experiments in this report, we provide a mixed effects logistic regression (LMER) for measures that yielded binary responses trial-to-trial; and a within-subjects analysis of variance (ANOVA) on the number of fixations and the Time Proportion Shift (TIPS) scores
As in Experiment 1, data are presented in Blocks, where each level of the Block factor represents 75 trials or 3.75 bins of Figure 2; we added Instruction Condition (Speed, Accuracy) as a between-subjects factor in the ANOVA analyses, and as a fixed effect in the LMER models constructed from the accuracy and probability of fixating irrelevant features
Summary
The visual modality is a primary source of information from which our understanding of the world arises. We learn to navigate through this world by prioritizing relevant sources of information to which we further allocate mental resources. The relevance of certain properties of the visual scene changes based on the goals of the observer; meaning that our visual-cognitive system must be able to respond to changes in both the task at hand and the scene itself. This question of how the human cognitive architecture is able to flexibly respond to a complex, dynamically changing visual environment is a defining problem for the psychological sciences [1,2]. The resulting data are made available to inform the advancement of theories of goal-directed attention and category learning
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