Bayes beyond the predictive distribution
Binz et al. argue that meta-learned models offer a new paradigm to study human cognition. Meta-learned models are proposed as alternatives to Bayesian models based on their capability to learn identical posterior predictive distributions. In our commentary, we highlight several arguments that reach...
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| Veröffentlicht in: | The Behavioral and brain sciences 2024-09, Vol.47, p.e166, Article e166 |
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| container_title | The Behavioral and brain sciences |
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| creator | Székely, Anna Orbán, Gergő |
| description | Binz et al. argue that meta-learned models offer a new paradigm to study human cognition. Meta-learned models are proposed as alternatives to Bayesian models based on their capability to learn identical posterior predictive distributions. In our commentary, we highlight several arguments that reach beyond a predictive distribution-based comparison, offering new perspectives to evaluate the advantages of these modeling paradigms. |
| doi_str_mv | 10.1017/S0140525X24000086 |
| format | Article |
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| language | eng |
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| source | MEDLINE; Cambridge University Press Journals Complete |
| subjects | Bayes Theorem Bayesian analysis Cognition Cognition - physiology Humans Hypotheses Learning - physiology Mathematical models Models, Psychological Open Peer Commentary |
| title | Bayes beyond the predictive distribution |
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