A Bayesian latent mixture model approach to assessing performance in stock-flow reasoning
People often perform poorly on stock-flow reasoning tasks, with many (but not all) participants appearing to erroneously match the accumulation of the stock to the inflow – a response pattern attributed to the use of a “correlation heuristic”. Efforts to improve understanding of stock-flow systems h...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2017-09-01
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| Series: | Judgment and Decision Making |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S1930297500006471/type/journal_article |
| _version_ | 1797697919398182912 |
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| author | Arthur Kary Guy E. Hawkins Brett K. Hayes Ben R. Newell |
| author_facet | Arthur Kary Guy E. Hawkins Brett K. Hayes Ben R. Newell |
| author_sort | Arthur Kary |
| collection | DOAJ |
| description | People often perform poorly on stock-flow reasoning tasks, with many (but not all) participants appearing to erroneously match the accumulation of the stock to the inflow – a response pattern attributed to the use of a “correlation heuristic”. Efforts to improve understanding of stock-flow systems have been limited by the lack of a principled approach to identifying and measuring individual differences in reasoning strategies. We present a principled inferential method known as Hierarchical Bayesian Latent Mixture Models (HBLMMs) to analyze stock-flow reasoning. HBLMMs use Bayesian inference to classify different patterns of responding as coming from multiple latent populations. We demonstrate the usefulness of this approach using a dataset from a stock-flow drawing task which compared performance in a problem presented in a climate change context, a problem in a financial context, and a problem in which the financial context was used as an analogy to assist understanding in the climate problem. The hierarchical Bayesian model showed that the proportion of responses consistent with the “correlation heuristic” was lower in the financial context and financial analogy context than in the pure climate context. We discuss the benefits of HBLMMs and implications for the role of contexts and analogy in improving stock-flow reasoning. |
| first_indexed | 2024-03-12T03:46:51Z |
| format | Article |
| id | doaj.art-bfe17ca6464f4089af18ba0c3588076c |
| institution | Directory Open Access Journal |
| issn | 1930-2975 |
| language | English |
| last_indexed | 2024-03-12T03:46:51Z |
| publishDate | 2017-09-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Judgment and Decision Making |
| spelling | doaj.art-bfe17ca6464f4089af18ba0c3588076c2023-09-03T12:44:17ZengCambridge University PressJudgment and Decision Making1930-29752017-09-011243044410.1017/S1930297500006471A Bayesian latent mixture model approach to assessing performance in stock-flow reasoningArthur Kary0Guy E. Hawkins1Brett K. Hayes2Ben R. Newell3School of Psychology, University of New South Wales, Sydney 2052, AustraliaSchool of Psychology, University of New South Wales and School of Psychology, University of NewcastleSchool of Psychology, University of New South WalesSchool of Psychology, University of New South WalesPeople often perform poorly on stock-flow reasoning tasks, with many (but not all) participants appearing to erroneously match the accumulation of the stock to the inflow – a response pattern attributed to the use of a “correlation heuristic”. Efforts to improve understanding of stock-flow systems have been limited by the lack of a principled approach to identifying and measuring individual differences in reasoning strategies. We present a principled inferential method known as Hierarchical Bayesian Latent Mixture Models (HBLMMs) to analyze stock-flow reasoning. HBLMMs use Bayesian inference to classify different patterns of responding as coming from multiple latent populations. We demonstrate the usefulness of this approach using a dataset from a stock-flow drawing task which compared performance in a problem presented in a climate change context, a problem in a financial context, and a problem in which the financial context was used as an analogy to assist understanding in the climate problem. The hierarchical Bayesian model showed that the proportion of responses consistent with the “correlation heuristic” was lower in the financial context and financial analogy context than in the pure climate context. We discuss the benefits of HBLMMs and implications for the role of contexts and analogy in improving stock-flow reasoning.https://www.cambridge.org/core/product/identifier/S1930297500006471/type/journal_articlestock-flow reasoningclimate changeBayesian modelsmixture models |
| spellingShingle | Arthur Kary Guy E. Hawkins Brett K. Hayes Ben R. Newell A Bayesian latent mixture model approach to assessing performance in stock-flow reasoning Judgment and Decision Making stock-flow reasoning climate change Bayesian models mixture models |
| title | A Bayesian latent mixture model approach to assessing performance in stock-flow reasoning |
| title_full | A Bayesian latent mixture model approach to assessing performance in stock-flow reasoning |
| title_fullStr | A Bayesian latent mixture model approach to assessing performance in stock-flow reasoning |
| title_full_unstemmed | A Bayesian latent mixture model approach to assessing performance in stock-flow reasoning |
| title_short | A Bayesian latent mixture model approach to assessing performance in stock-flow reasoning |
| title_sort | bayesian latent mixture model approach to assessing performance in stock flow reasoning |
| topic | stock-flow reasoning climate change Bayesian models mixture models |
| url | https://www.cambridge.org/core/product/identifier/S1930297500006471/type/journal_article |
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