Learning-induced changes in attentional allocation during categorization: a sizable catalog of attention change as measured by eye movements.

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.

Temporal characteristics of overt attentional behavior during category learning.

Many theories of category learning incorporate mechanisms for selective attention, typically implemented as attention weights that change on a trial-by-trial basis. This is because there is relatively little data on within-trial changes in attention. We used eye tracking and mouse tracking as fine-grained measures of attention in three complex visual categorization tasks to investigate temporal patterns in overt attentional behavior within individual categorization decisions.

Waiting and weighting: Information sampling is a balance between efficiency and error-reduction.

The current study investigates the relative extent to which information utility and planning efficiency guide information-sampling strategies in a classification task. Prior research has pointed to the importance of probability gain, the degree to which sampling a feature reduces the chance of error, in contexts where participants are restricted to one sample. We monitored participants as they sampled information in an unrestricted context and recorded whether they began their search with a high gain feature or an efficient feature that ultimately allowed for fewer samples per trial.

Imprecise singing is widespread.

There has been a recent surge of research on the topic of poor-pitch singing. However, this research has not addressed an important distinction in measurement: that between accuracy and precision. With respect to singing, accuracy refers to the average difference between sung and target pitches.

Errors, efficiency, and the interplay between attention and category learning.

Learning to identify objects as members of categories is an essential cognitive skill and learning to deploy attention effectively is a core component of that process. The present study investigated an assumption imbedded in formal models of categorization: error is necessary for attentional learning. Eye-trackers were used to record participants' allocation of attention to task relevant and irrelevant features while learning a complex categorization task.