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Related Experiment Video

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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Attentional mechanisms in learned predictiveness.

Chris J Mitchell1, Oren Griffiths, Joyce Seetoo

  • 1School of Psychology, University of New South Wales, Sydney, Australia. Christopher.Mitchell@plymouth.ac.uk

Journal of Experimental Psychology. Animal Behavior Processes
|February 29, 2012
PubMed
Summary
This summary is machine-generated.

Learned predictiveness speeds up learning for previously reliable cues, a nonnormative bias. This effect stems from strategic attention, not automatic processes, influencing how we learn from experience.

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Area of Science:

  • Cognitive Psychology
  • Learning Sciences
  • Neuroscience

Background:

  • Prior training influences subsequent learning, even when objective cue-outcome relationships are equal.
  • Reliable cues in initial training are learned faster in later phases than unreliable cues.
  • This phenomenon, termed learned predictiveness, represents a nonnormative bias in learning.

Purpose of the Study:

  • To confirm that learned predictiveness arises from attentional processes.
  • To investigate whether these attentional processes are voluntary (strategic) or automatic.
  • To elucidate the mechanisms underlying learned predictiveness.

Main Methods:

  • Two experiments were conducted to investigate learned predictiveness.
  • Participants underwent initial and second phases of cue-outcome training.
  • Outcome prediction accuracy and eye-gaze behavior were measured.

Main Results:

  • Learned predictiveness was confirmed, with faster learning for previously reliable cues.
  • Eye-gaze data indicated an involvement of top-down strategic attentional processes.
  • No evidence supported an automatic attentional bias in this task.

Conclusions:

  • Learned predictiveness is mediated by strategic, top-down attentional control.
  • The bias is not driven by automatic attentional capture.
  • Understanding these attentional mechanisms is key to explaining nonnormative learning biases.