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The Effect of Feedback on Attention Allocation in Category Learning: An Eye Tracking Study
Yael Arbel1, Emily Feeley1, Xinyi He1
1Department of Communication Sciences and Disorders, Massachusetts General Hospital (MGH) Institute of Health Professions, Boston, MA, United States.
Frontiers in Psychology
|November 27, 2020
Summary
Category learning involves shifts in attention. This study found that negative feedback significantly alters eye-gaze patterns, influencing attention allocation during classification tasks.
Area of Science:
- Cognitive Psychology
- Neuroscience
- Machine Learning
Background:
- Category learning is influenced by attention allocation based on feature relevance.
- Previous research (Rehder & Hoffman) used eye-gaze to study attention during category learning based on feature diagnosticity.
Purpose of the Study:
- To investigate how feature diagnosticity and feedback valence affect attention allocation during category learning.
- To extend previous findings by incorporating feedback valence into the analysis of eye-gaze behavior.
Main Methods:
- Modified Rehder and Hoffman's category learning paradigm.
- Utilized eye-gaze measures to track participants' attention.
- Analyzed eye-gaze patterns in relation to feature diagnosticity and feedback valence (positive/negative).
Main Results:
- Confirmed that attention (gaze) on low-diagnosticity features decreases over time.
- Observed greater changes in fixation probability following negative feedback compared to positive feedback.
Conclusions:
- Selective attention in category learning is modulated by feedback valence.
- Learners adjust their attention strategies based on the success or failure indicated by feedback.
