JOURNAL ARTICLE

The effects of viewpoint, motion, and affordance priming on perceptual learning of feelies.

  • Published In: Perception, 2025, v. 54, n. 4. P. 279 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Dowell, Catherine; Gunter, McKenzie; Hajnal, Alen 3 of 3

Abstract

This article investigates visual perceptual learning using novel, unfamiliar objects called "feelies," focusing on how motion, viewpoint, and task relevance influence learning. Two experiments tested whether viewing feelies statically or rotating (motion) from either a side (canonical) or top (noncanonical) perspective affected participants' ability to discriminate and learn these objects without feedback. Experiment 1 found that although static presentations led to faster attainment of perfect accuracy, the combination of motion and side view produced a more stable and efficient learning pattern. Experiment 2 introduced functional priming tasks—having participants generate or be provided with potential uses (affordances) of the objects—and demonstrated that active generation of affordances combined with viewing rotating objects from the side yielded the most accurate and stable learning, supporting the ecological theory that perceptual learning is enhanced by active exploration and functional relevance. The study concludes that perceptual learning benefits from active sampling through movement and meaningful engagement with object affordances, with implications for understanding perception as a goal-directed, action-oriented process.

Additional Information

  • Source:Perception. 2025/04, Vol. 54, Issue 4, p279
  • Document Type:Article
  • Subject Area:Education
  • Publication Date:2025
  • ISSN:0301-0066
  • DOI:10.1177/03010066251320575
  • Accession Number:184443071
  • Copyright Statement:Copyright of Perception is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.