Error-driven learning in statistical summary perception

Judith E. Fan, Nicholas B. Turk-Browne, Jordan A. Taylor

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

We often interact with multiple objects at once, such as when balancing food and beverages on a dining tray. The success of these interactions relies upon representing not only individual objects, but also statistical summary features of the group (e.g., center-of-mass). Although previous research has established that humans can readily and accurately extract such statistical summary features, how this ability is acquired and refined through experience currently remains unaddressed. Here we ask if training and task feedback can improve summary perception. During training, participants practiced estimating the centroid (i.e., average location) of an array of objects on a touchscreen display. Before and after training, they completed a transfer test requiring perceptual discrimination of the centroid. Across 4 experiments, we manipulated the information in task feedback and how participants interacted with the objects during training. We found that vector error feedback, which conveys error both in terms of distance and direction, was the only form of feedback that improved perceptual discrimination of the centroid on the transfer test. Moreover, this form of feedback was effective only when coupled with reaching movements toward the visual objects. Taken together, these findings suggest that sensory-prediction error-signaling the mismatch between expected and actual consequences of an action-may play a previously unrecognized role in tuning perceptual representations.

Original languageEnglish (US)
Pages (from-to)266-280
Number of pages15
JournalJournal of Experimental Psychology: Human Perception and Performance
Volume42
Issue number2
DOIs
StatePublished - Feb 1 2016

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)
  • Behavioral Neuroscience

Keywords

  • Ensemble statistics
  • Feedback
  • Perception and action
  • Perceptual learning
  • Sensory-prediction error

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