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. 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.

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. Participants preferred to sample the more efficient feature first, especially when feature information had a higher access cost (Experiment 1). When access costs were all but eliminated using eye-tracking (Experiment 2), participants' fixations still emphasized efficiency over high probability gain, though probability gain was shown to influence access patterns.

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. In Experiments 1 and 2, we recorded participants' eye movements while they performed three different categorization tasks. We extended previous research by demonstrating that not only are participants less likely to fixate irrelevant features, but also, when they do, these fixations are shorter than fixations to relevant features. We also found that participants' fixation patterns show increasingly consistent temporal patterns. Participants were faster, although no more accurate, when their fixation sequences followed a consistent temporal structure. In Experiment 3, we replicated these findings in a task where participants used mouse movements to uncover features. Overall, we showed that there are important temporal regularities in information sampling during category learning that cannot be accounted for by existing models. These can be used to supplement extant models for richer predictions of how information is attended to during the buildup to a categorization decision.

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. Precision, by contrast, refers to the consistency of repeated attempts to produce a pitch. A group of 45 non-musician participants was asked to vocally imitate unfamiliar 5-note melodies, as well as to sing a series of familiar melodies from memory (e.g., Happy Birthday). The results showed that singers were more accurate than they were precise, and that a majority of participants could justifiably be categorized as imprecise singers. Accuracy and precision measures were correlated with one another, and conditional-probability analyses suggested that accuracy predicted precision more so than the converse. Finally, performance differences across groups of singers were greater for the imitation of unfamiliar tone sequences than for the recall of familiar melodies.

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. It was found that participants optimized their fixation patterns in the absence of both performance errors and corrective external feedback. Optimization began immediately after each category was mastered and continued for many trials. These results demonstrate that error is neither necessary nor sufficient for all forms of attentional learning.