A boundedly rational model for category learning
The computational modeling of category learning is typically evaluated in terms of the model's accuracy. For a model to accurately infer category membership of stimuli, it has to have sufficient representational precision. Thus, many category learning models infer category representations that...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1477514/full |
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| author | Troy M. Houser Troy M. Houser |
| author_facet | Troy M. Houser Troy M. Houser |
| author_sort | Troy M. Houser |
| collection | DOAJ |
| description | The computational modeling of category learning is typically evaluated in terms of the model's accuracy. For a model to accurately infer category membership of stimuli, it has to have sufficient representational precision. Thus, many category learning models infer category representations that guide decision-making and the model's fitness is evaluated by its ability to accurately choose. Substantial decision-making research, however, indicates that noise plays an important role. Specifically, noisy representations are assumed to introduce an element of stochasticity to decision-making. Noise can be minimized at the cost of cognitive resource expenditure. Thus, a more biologically plausible model of category learning should balance representational precision with costs. Here, we tested an autoencoder model that learns categories (the six category structures introduced by Roger Shepard and colleagues) by balancing the minimization of error with minimization of resource usage. By incorporating the goal of reducing category complexity, the currently proposed model biases category decisions toward previously learned central tendencies. We show that this model is still able to account for category learning performance in a traditional category learning benchmark. The currently proposed model additionally makes some novel predictions about category learning that future studies can test empirically. The goal of this paper is to make progress toward development of an ecologically and neurobiologically plausible model of category learning that can guide future studies and theoretical frameworks. |
| format | Article |
| id | doaj-art-7dad4cad8a5d4a858b5ec28e389816ee |
| institution | Kabale University |
| issn | 1664-1078 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj-art-7dad4cad8a5d4a858b5ec28e389816ee2024-12-09T04:26:22ZengFrontiers Media S.A.Frontiers in Psychology1664-10782024-12-011510.3389/fpsyg.2024.14775141477514A boundedly rational model for category learningTroy M. Houser0Troy M. Houser1Department of Psychology, University of Oregon, Eugene, OR, United StatesInstitute of Neuroscience, University of Oregon, Eugene, OR, United StatesThe computational modeling of category learning is typically evaluated in terms of the model's accuracy. For a model to accurately infer category membership of stimuli, it has to have sufficient representational precision. Thus, many category learning models infer category representations that guide decision-making and the model's fitness is evaluated by its ability to accurately choose. Substantial decision-making research, however, indicates that noise plays an important role. Specifically, noisy representations are assumed to introduce an element of stochasticity to decision-making. Noise can be minimized at the cost of cognitive resource expenditure. Thus, a more biologically plausible model of category learning should balance representational precision with costs. Here, we tested an autoencoder model that learns categories (the six category structures introduced by Roger Shepard and colleagues) by balancing the minimization of error with minimization of resource usage. By incorporating the goal of reducing category complexity, the currently proposed model biases category decisions toward previously learned central tendencies. We show that this model is still able to account for category learning performance in a traditional category learning benchmark. The currently proposed model additionally makes some novel predictions about category learning that future studies can test empirically. The goal of this paper is to make progress toward development of an ecologically and neurobiologically plausible model of category learning that can guide future studies and theoretical frameworks.https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1477514/fullcategory learningautoencoder (AE) neural networksconcept learninggeneralization (psychology)RULEXrate distortion theory |
| spellingShingle | Troy M. Houser Troy M. Houser A boundedly rational model for category learning Frontiers in Psychology category learning autoencoder (AE) neural networks concept learning generalization (psychology) RULEX rate distortion theory |
| title | A boundedly rational model for category learning |
| title_full | A boundedly rational model for category learning |
| title_fullStr | A boundedly rational model for category learning |
| title_full_unstemmed | A boundedly rational model for category learning |
| title_short | A boundedly rational model for category learning |
| title_sort | boundedly rational model for category learning |
| topic | category learning autoencoder (AE) neural networks concept learning generalization (psychology) RULEX rate distortion theory |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1477514/full |
| work_keys_str_mv | AT troymhouser aboundedlyrationalmodelforcategorylearning AT troymhouser aboundedlyrationalmodelforcategorylearning AT troymhouser boundedlyrationalmodelforcategorylearning AT troymhouser boundedlyrationalmodelforcategorylearning |