Efficient sampling and noisy decisions
Human decisions are based on finite information, which makes them inherently imprecise. But what determines the degree of such imprecision? Here, we develop an efficient coding framework for higher-level cognitive processes in which information is represented by a finite number of discrete samples....
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2020-09-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/54962 |
| _version_ | 1818028496309452800 |
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| author | Joseph A Heng Michael Woodford Rafael Polania |
| author_facet | Joseph A Heng Michael Woodford Rafael Polania |
| author_sort | Joseph A Heng |
| collection | DOAJ |
| description | Human decisions are based on finite information, which makes them inherently imprecise. But what determines the degree of such imprecision? Here, we develop an efficient coding framework for higher-level cognitive processes in which information is represented by a finite number of discrete samples. We characterize the sampling process that maximizes perceptual accuracy or fitness under the often-adopted assumption that full adaptation to an environmental distribution is possible, and show how the optimal process differs when detailed information about the current contextual distribution is costly. We tested this theory on a numerosity discrimination task, and found that humans efficiently adapt to contextual distributions, but in the way predicted by the model in which people must economize on environmental information. Thus, understanding decision behavior requires that we account for biological restrictions on information coding, challenging the often-adopted assumption of precise prior knowledge in higher-level decision systems. |
| first_indexed | 2024-12-10T05:04:43Z |
| format | Article |
| id | doaj.art-1b19a2458e81488b81507956b9240df5 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-12-10T05:04:43Z |
| publishDate | 2020-09-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-1b19a2458e81488b81507956b9240df52022-12-22T02:01:17ZengeLife Sciences Publications LtdeLife2050-084X2020-09-01910.7554/eLife.54962Efficient sampling and noisy decisionsJoseph A Heng0https://orcid.org/0000-0002-3643-4623Michael Woodford1Rafael Polania2https://orcid.org/0000-0002-6176-6806Department of Health Sciences and Technology, Federal Institute of Technology (ETH), Zurich, SwitzerlandDepartment of Economics, Columbia University, New York, United StatesDepartment of Health Sciences and Technology, Federal Institute of Technology (ETH), Zurich, SwitzerlandHuman decisions are based on finite information, which makes them inherently imprecise. But what determines the degree of such imprecision? Here, we develop an efficient coding framework for higher-level cognitive processes in which information is represented by a finite number of discrete samples. We characterize the sampling process that maximizes perceptual accuracy or fitness under the often-adopted assumption that full adaptation to an environmental distribution is possible, and show how the optimal process differs when detailed information about the current contextual distribution is costly. We tested this theory on a numerosity discrimination task, and found that humans efficiently adapt to contextual distributions, but in the way predicted by the model in which people must economize on environmental information. Thus, understanding decision behavior requires that we account for biological restrictions on information coding, challenging the often-adopted assumption of precise prior knowledge in higher-level decision systems.https://elifesciences.org/articles/54962decision makinginformation theoryresource limitationsmemorysamplingreward |
| spellingShingle | Joseph A Heng Michael Woodford Rafael Polania Efficient sampling and noisy decisions eLife decision making information theory resource limitations memory sampling reward |
| title | Efficient sampling and noisy decisions |
| title_full | Efficient sampling and noisy decisions |
| title_fullStr | Efficient sampling and noisy decisions |
| title_full_unstemmed | Efficient sampling and noisy decisions |
| title_short | Efficient sampling and noisy decisions |
| title_sort | efficient sampling and noisy decisions |
| topic | decision making information theory resource limitations memory sampling reward |
| url | https://elifesciences.org/articles/54962 |
| work_keys_str_mv | AT josephaheng efficientsamplingandnoisydecisions AT michaelwoodford efficientsamplingandnoisydecisions AT rafaelpolania efficientsamplingandnoisydecisions |