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VisioTracker, an Innovative Automated Approach to Oculomotor Analysis
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Perceived object trajectories during occlusion constrain visual statistical learning.

József Fiser1, Brian J Scholl, Richard N Aslin

  • 1Volen Center for Complex Systems, Brandeis University, Waltham, Massachusetts 02454, USA. fiser@brandeis.edu

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Visual statistical learning is influenced by perceptual biases. Our study shows that how we perceive moving shapes, like bouncing or streaming, affects how we learn object sequences, simplifying visual processing.

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Area of Science:

  • Cognitive Psychology
  • Visual Perception
  • Machine Learning

Background:

  • Visual statistical learning enables prediction of upcoming events based on observed regularities.
  • Object occlusion presents a challenge for tracking object identity and predicting subsequent appearances.
  • Perceptual interpretations, such as 'bouncing' or 'streaming,' may influence how visual sequences are processed.

Purpose of the Study:

  • To investigate whether perceptual biases (bouncing vs. streaming) constrain statistical learning of shape sequences during occlusion.
  • To determine if statistical learning equally associates pre- and post-occlusion objects, or if perception plays a role.
  • To examine how spatiotemporal perceptual biases impact the computational complexity of visual statistical learning.

Main Methods:

  • Participants viewed sequences of moving shapes with trajectories designed to elicit either a bouncing or streaming percept.
  • A learning phase involved shapes moving behind an occluder, followed by the emergence of two new shapes.
  • Familiarity judgments were used to assess participants' learning of shape transition associations, with a follow-up experiment controlling for eye movements.

Main Results:

  • Participants reliably selected shape pairs consistent with the bouncing or streaming perceptual bias established during learning.
  • The observed effect could not be attributed to differential eye movements, suggesting a cognitive rather than purely oculomotor mechanism.
  • Learning was biased towards shape transitions that aligned with the dominant perceptual interpretation (bouncing or streaming).

Conclusions:

  • Sequential statistical learning is constrained by emergent spatiotemporal perceptual biases that link objects moving through occlusion.
  • These perceptual constraints simplify visual statistical learning by reducing the number of potential associations to be learned.
  • Findings highlight the interplay between low-level perceptual processing and higher-level statistical learning mechanisms in vision.