Predicting the Future as Bayesian Inference: People Combine Prior Knowledge With Observations When Estimating Duration and Extent
redicting the future is a basic problem that people have to solve every day and a component of planning, decision making, memory, and causal reasoning. In this article, we present 5 experiments testing a Bayesian model of predicting the duration or extent of phenomena from their current state. This...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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American Psychological Association
2012
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| Online Access: | http://hdl.handle.net/1721.1/70990 https://orcid.org/0000-0002-1925-2035 |
| _version_ | 1826196945560928256 |
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| author | Griffiths, Thomas L. Tenenbaum, Joshua B. |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Griffiths, Thomas L. Tenenbaum, Joshua B. |
| author_sort | Griffiths, Thomas L. |
| collection | MIT |
| description | redicting the future is a basic problem that people have to solve every day and a component of planning, decision making, memory, and causal reasoning. In this article, we present 5 experiments testing a Bayesian model of predicting the duration or extent of phenomena from their current state. This Bayesian model indicates how people should combine prior knowledge with observed data. Comparing this model with human judgments provides constraints on possible algorithms that people might use to predict the future. In the experiments, we examine the effects of multiple observations, the effects of prior knowledge, and the difference between independent and dependent observations, using both descriptions and direct experience of prediction problems. The results indicate that people integrate prior knowledge and observed data in a way that is consistent with our Bayesian model, ruling out some simple heuristics for predicting the future. We suggest some mechanisms that might lead to more complete algorithmic-level accounts. |
| first_indexed | 2024-09-23T10:40:05Z |
| format | Article |
| id | mit-1721.1/70990 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:40:05Z |
| publishDate | 2012 |
| publisher | American Psychological Association |
| record_format | dspace |
| spelling | mit-1721.1/709902022-09-30T22:08:37Z Predicting the Future as Bayesian Inference: People Combine Prior Knowledge With Observations When Estimating Duration and Extent Griffiths, Thomas L. Tenenbaum, Joshua B. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Tenenbaum, Joshua B. Tenenbaum, Joshua B. redicting the future is a basic problem that people have to solve every day and a component of planning, decision making, memory, and causal reasoning. In this article, we present 5 experiments testing a Bayesian model of predicting the duration or extent of phenomena from their current state. This Bayesian model indicates how people should combine prior knowledge with observed data. Comparing this model with human judgments provides constraints on possible algorithms that people might use to predict the future. In the experiments, we examine the effects of multiple observations, the effects of prior knowledge, and the difference between independent and dependent observations, using both descriptions and direct experience of prediction problems. The results indicate that people integrate prior knowledge and observed data in a way that is consistent with our Bayesian model, ruling out some simple heuristics for predicting the future. We suggest some mechanisms that might lead to more complete algorithmic-level accounts. Mitsubishi Electronic Research Laboratories John Winthrop Hackett Studentship 2012-06-01T18:10:50Z 2012-06-01T18:10:50Z 2011-11 Article http://purl.org/eprint/type/JournalArticle 0022-1015 http://hdl.handle.net/1721.1/70990 Griffiths, Thomas L., and Joshua B. Tenenbaum. “Predicting the Future as Bayesian Inference: People Combine Prior Knowledge with Observations When Estimating Duration and Extent.” Journal of Experimental Psychology: General 140.4 (2011): 725–743. Web. https://orcid.org/0000-0002-1925-2035 en_US http://dx.doi.org/10.1037/a0024899 Journal of Experimental Psychology Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf American Psychological Association Other University Web Domain |
| spellingShingle | Griffiths, Thomas L. Tenenbaum, Joshua B. Predicting the Future as Bayesian Inference: People Combine Prior Knowledge With Observations When Estimating Duration and Extent |
| title | Predicting the Future as Bayesian Inference: People Combine Prior Knowledge With Observations When Estimating Duration and Extent |
| title_full | Predicting the Future as Bayesian Inference: People Combine Prior Knowledge With Observations When Estimating Duration and Extent |
| title_fullStr | Predicting the Future as Bayesian Inference: People Combine Prior Knowledge With Observations When Estimating Duration and Extent |
| title_full_unstemmed | Predicting the Future as Bayesian Inference: People Combine Prior Knowledge With Observations When Estimating Duration and Extent |
| title_short | Predicting the Future as Bayesian Inference: People Combine Prior Knowledge With Observations When Estimating Duration and Extent |
| title_sort | predicting the future as bayesian inference people combine prior knowledge with observations when estimating duration and extent |
| url | http://hdl.handle.net/1721.1/70990 https://orcid.org/0000-0002-1925-2035 |
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