The Precision of Attention Selection during Reward Learning Influences the Mechanisms of Value-Driven Attention
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| Title: | The Precision of Attention Selection during Reward Learning Influences the Mechanisms of Value-Driven Attention |
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| Language: | English |
| Authors: | Oudeng Jia, Qingsong Tan, Sihan Zhang, Ke Jia, Mengyuan Gong |
| Source: | npj Science of Learning. 2025 10. |
| Availability: | Nature Portfolio. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://www.nature.com/npjscilearn/ |
| Peer Reviewed: | Y |
| Page Count: | 10 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Attention, Rewards, Interference (Learning), Coding, Training, Prediction, Learning Processes |
| DOI: | 10.1038/s41539-025-00342-1 |
| ISSN: | 2056-7936 |
| Abstract: | Reward-predictive items capture attention even when task-irrelevant. While value-driven attention typically generalizes to stimuli sharing critical reward-associated features (e.g., red), recent evidence suggests an alternative generalization mechanism based on feature relationships (e.g., redder). Here, we investigated whether relational coding of reward-associated features operates across different learning contexts by manipulating search mode and target-distractor similarity. Results showed that singleton search training induced value-driven relational attention regardless of target-distractor similarity (Experiments 1a-1b). In contrast, feature search training produced value-driven relational attention only when targets and distractors were dissimilar, but not when they were similar (Experiments 2a-2c). These findings indicate that coarse selection training (singleton search or feature search among dissimilar items) promotes relational coding of reward-associated features, while fine selection (feature search among similar items) engages precise feature coding. The precision of target selection during reward learning thus critically determines value-driven attentional mechanisms. |
| Abstractor: | As Provided |
| Notes: | https://osf.io/q7dsm |
| Entry Date: | 2025 |
| Accession Number: | EJ1478693 |
| Database: | ERIC |
| Abstract: | Reward-predictive items capture attention even when task-irrelevant. While value-driven attention typically generalizes to stimuli sharing critical reward-associated features (e.g., red), recent evidence suggests an alternative generalization mechanism based on feature relationships (e.g., redder). Here, we investigated whether relational coding of reward-associated features operates across different learning contexts by manipulating search mode and target-distractor similarity. Results showed that singleton search training induced value-driven relational attention regardless of target-distractor similarity (Experiments 1a-1b). In contrast, feature search training produced value-driven relational attention only when targets and distractors were dissimilar, but not when they were similar (Experiments 2a-2c). These findings indicate that coarse selection training (singleton search or feature search among dissimilar items) promotes relational coding of reward-associated features, while fine selection (feature search among similar items) engages precise feature coding. The precision of target selection during reward learning thus critically determines value-driven attentional mechanisms. |
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| ISSN: | 2056-7936 |
| DOI: | 10.1038/s41539-025-00342-1 |
