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People use prior knowledge to predict future event details. This study reveals that predicting both location and semantic category of images simultaneously creates a bottleneck, impacting eye movement predictions.

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Area of Science:

  • Cognitive Neuroscience
  • Visual Perception
  • Human Behavior

Background:

  • Humans leverage prior knowledge to anticipate future event features, including location and semantics.
  • Mechanistic understanding of how multi-dimensional predictions are implemented remains limited.
  • Few studies explore the interplay between location and semantic predictions in early visual processing.

Purpose of the Study:

  • To investigate the interaction between target-location and target-category predictions during early visual orientation.
  • To examine the mechanistic implementation of multi-dimensional predictions using eye-tracking.
  • To determine how predictability influences anticipatory eye movements.

Main Methods:

  • Utilized eye tracking to monitor saccade latencies and gaze positioning.
  • Employed stochastic series across four conditions: location prediction, category prediction, joint prediction, or no prediction.
  • Modeled saccade latencies with the ELATER (Explaining Latencies by Accumulation To a Threshold) model, analyzing accumulation rate (AR) and its variance.

Main Results:

  • Accumulation rate (AR) for saccades correlated with the surprise of target location.
  • Predicting semantic category slowed saccade latencies, indicating a bottleneck in joint predictions.
  • Semantically expected targets showed higher AR variance, suggesting richer early evaluation processes.
  • Target category predictability influenced pre-saccadic gaze positioning.

Conclusions:

  • Foreknowledge of object location and semantics interact significantly during stimulus-guided saccades.
  • Statistical regularities in visual input influence anticipatory, non-stimulus-guided processes.
  • A bottleneck exists when implementing simultaneous predictions of both location and semantic features.