Information search with situation-specific reward functions
The goal of obtaining information to improve classification accuracy can strongly conflict with the goal of obtaining information for improving payoffs. Two environments with such a conflict were identified through computer optimization. Three subsequent experiments investigated people’s search beha...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2012-03-01
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| Series: | Judgment and Decision Making |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S1930297500002977/type/journal_article |
| _version_ | 1827826967274061824 |
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| author | Björn Meder Jonathan D. Nelson |
| author_facet | Björn Meder Jonathan D. Nelson |
| author_sort | Björn Meder |
| collection | DOAJ |
| description | The goal of obtaining information to improve classification accuracy can strongly conflict with the goal of obtaining information for improving payoffs. Two environments with such a conflict were identified through computer optimization. Three subsequent experiments investigated people’s search behavior in these environments. Experiments 1 and 2 used a multiple-cue probabilistic category-learning task to convey environmental probabilities. In a subsequent search task subjects could query only a single feature before making a classification decision. The crucial manipulation concerned the search-task reward structure. The payoffs corresponded either to accuracy, with equal rewards associated with the two categories, or to an asymmetric payoff function, with different rewards associated with each category. In Experiment 1, in which learning-task feedback corresponded to the true category, people later preferentially searched the accuracy-maximizing feature, whether or not this would improve monetary rewards. In Experiment 2, an asymmetric reward structure was used during learning. Subjects searched the reward-maximizing feature when asymmetric payoffs were preserved in the search task. However, if search-task payoffs corresponded to accuracy, subjects preferentially searched a feature that was suboptimal for reward and accuracy alike. Importantly, this feature would have been most useful, under the learning-task payoff structure. Experiment 3 found that, if words and numbers are used to convey environmental probabilities, neither reward nor accuracy consistently predicts search. These findings emphasize the necessity of taking into account people’s goals and search-and-decision processes during learning, thereby challenging current models of information search. |
| first_indexed | 2024-03-12T03:20:01Z |
| format | Article |
| id | doaj.art-427184aaabf84d9dabb865527dd15f5a |
| institution | Directory Open Access Journal |
| issn | 1930-2975 |
| language | English |
| last_indexed | 2024-03-12T03:20:01Z |
| publishDate | 2012-03-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Judgment and Decision Making |
| spelling | doaj.art-427184aaabf84d9dabb865527dd15f5a2023-09-03T14:02:45ZengCambridge University PressJudgment and Decision Making1930-29752012-03-01711914810.1017/S1930297500002977Information search with situation-specific reward functionsBjörn Meder0Jonathan D. Nelson1Max Planck Institute for Human Development, Center for Adaptive Behavior and Cognition (ABC), Lentzeallee 94, 14195 Berlin, GermanyMax Planck Institute for Human Development, Center for Adaptive Behavior and Cognition (ABC), Lentzeallee 94, 14195 Berlin, GermanyThe goal of obtaining information to improve classification accuracy can strongly conflict with the goal of obtaining information for improving payoffs. Two environments with such a conflict were identified through computer optimization. Three subsequent experiments investigated people’s search behavior in these environments. Experiments 1 and 2 used a multiple-cue probabilistic category-learning task to convey environmental probabilities. In a subsequent search task subjects could query only a single feature before making a classification decision. The crucial manipulation concerned the search-task reward structure. The payoffs corresponded either to accuracy, with equal rewards associated with the two categories, or to an asymmetric payoff function, with different rewards associated with each category. In Experiment 1, in which learning-task feedback corresponded to the true category, people later preferentially searched the accuracy-maximizing feature, whether or not this would improve monetary rewards. In Experiment 2, an asymmetric reward structure was used during learning. Subjects searched the reward-maximizing feature when asymmetric payoffs were preserved in the search task. However, if search-task payoffs corresponded to accuracy, subjects preferentially searched a feature that was suboptimal for reward and accuracy alike. Importantly, this feature would have been most useful, under the learning-task payoff structure. Experiment 3 found that, if words and numbers are used to convey environmental probabilities, neither reward nor accuracy consistently predicts search. These findings emphasize the necessity of taking into account people’s goals and search-and-decision processes during learning, thereby challenging current models of information search.https://www.cambridge.org/core/product/identifier/S1930297500002977/type/journal_articleinformation searchclassificationoptimal experimental designpayoffsdecisions from experience |
| spellingShingle | Björn Meder Jonathan D. Nelson Information search with situation-specific reward functions Judgment and Decision Making information search classification optimal experimental design payoffs decisions from experience |
| title | Information search with situation-specific reward functions |
| title_full | Information search with situation-specific reward functions |
| title_fullStr | Information search with situation-specific reward functions |
| title_full_unstemmed | Information search with situation-specific reward functions |
| title_short | Information search with situation-specific reward functions |
| title_sort | information search with situation specific reward functions |
| topic | information search classification optimal experimental design payoffs decisions from experience |
| url | https://www.cambridge.org/core/product/identifier/S1930297500002977/type/journal_article |
| work_keys_str_mv | AT bjornmeder informationsearchwithsituationspecificrewardfunctions AT jonathandnelson informationsearchwithsituationspecificrewardfunctions |