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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
Published on: January 23, 2017
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Reward history modulates attention based on feature relationship
Yilin Chen1, Shikai Chen1, Xiaodong Zhang1
1Department of Psychology and Behavioral Sciences, Zhejiang University.
Journal of Experimental Psychology. General
|February 23, 2023
Summary
Attention can adapt to focus on items based on their relative reward value, not just absolute features. This adaptive attention is crucial for navigating complex environments and prioritizing survival-relevant cues.
Area of Science:
- Cognitive Psychology
- Neuroscience
- Visual Perception
Background:
- Prioritizing attention to reward-predictive cues is vital for survival.
- Attention selection is often challenged by varying item features and environments.
- The adaptive tuning of attention to context-dependent relative features of rewarded items is not well understood.
Purpose of the Study:
- To investigate if attention can be adaptively tuned to items matching the context-dependent, relative feature of previously rewarded items.
- To explore the flexibility of value-driven attention through feature relationships.
Main Methods:
- Four experiments trained participants on color-reward associations.
- Visual search tasks were employed using singleton distractors.
- Distractor colors matched either the feature value or feature relationship of previously rewarded colors.
Main Results:
- Enhanced attentional capture by distractors relationally matched to high-reward contexts was observed.
- A typical effect of learned value on distractors matching previously rewarded colors was also found.
- Findings suggest attention can be flexibly tuned via feature relationships.
Conclusions:
- Value-driven attention is flexible and can be guided by feature relationships.
- This adaptive attention mechanism is beneficial for real-world searches with changing sensory inputs.
- The study provides novel evidence for adaptive attentional control based on relative reward value.

