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Related Experiment Video

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Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking
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On the feature specificity of value-driven attention.

Brian A Anderson1

  • 1Texas A&M University, College Station, Texas, United States of America.

Plos One
|May 10, 2017
PubMed
Summary

Learned reward associations automatically capture visual attention. This value-driven attentional bias extends to similar, non-rewarding features, showing imprecise attention to learned stimulus properties.

Area of Science:

  • Cognitive psychology
  • Neuroscience
  • Visual attention

Background:

  • Learned attentional biases are captured by stimuli predicting reward.
  • These biases are not limited to specific objects but extend to stimuli sharing features with reward cues.
  • Value-driven attentional biases can transfer to new contexts and encompass different exemplars of a stimulus category.

Purpose of the Study:

  • To investigate the precision of value-driven attentional biases.
  • To examine whether similarity to a reward-predictive feature is sufficient to elicit attentional bias.
  • To determine if non-rewarding colors are affected by learned associations with rewarding colors.

Main Methods:

  • Associative reward learning with red and green stimuli.
  • Assessing the impact on processing of other colors during a subsequent test phase.

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  • Analyzing the effect of color similarity on target identification speed.

    • Main Results:

    • Non-rewarding colors experienced during training were processed differently based on their similarity to the high-value color.
    • The speed of identifying targets with non-rewarding colors was influenced by their similarity to the reward-associated color.
    • This indicates that value-driven attentional biases for stimulus features are imprecise.

    Conclusions:

    • Value-driven attentional biases are not strictly limited to reward-predictive features but extend to similar, non-predictive features.
    • The findings support a sensory gain model, suggesting imprecise attentional prioritization based on learned value.
    • Attentional biases demonstrate a degree of generalization and tolerance for feature similarity.