Journal article
Symmetrical ‘super learning’: enhancing learning using a bidirectional probabilistic outcome
- Abstract:
- In a learning environment, with multiple predictive cues for a single outcome, cues interfere with or enhance each other during the acquisition process (e.g., Baker et al., 1993). Previous experiments have focussed on cues that signal the presence or absence of binary outcomes. This introduces a perceptual and perhaps motivational asymmetry between excitatory and inhibitory learning. Here using a bidirectional outcome, we asked whether learning about both generative (incremental positive outcome) and preventative (incremental negative outcome) causal cues show similar enhancement effects in opposite directions. In three experiments with humans using predictive learning tasks, participants (N = 133) were exposed to probabilistic predictive cues for opposite polarity events. Generative cues caused an increase in outcome likelihood, while preventative cues decreased it. An analysis of explicit predictive ratings found evidence for symmetrical learning and enhanced learning for both generative and preventative cues. The results are discussed in relation to super learning, an effect derived from theories of competitive learning based on error correction, and from theories of contrasting probability estimates.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 930.0KB, Terms of use)
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- Publisher copy:
- 10.1037/xan0000390
Authors
- Publisher:
- American Psychological Association
- Journal:
- Journal of Experimental Psychology: Animal Learning and Cognition More from this journal
- Volume:
- 51
- Issue:
- 1
- Pages:
- 1–12
- Publication date:
- 2025-01-16
- Acceptance date:
- 2024-11-17
- DOI:
- EISSN:
-
1939-2184
- ISSN:
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0097-7403
- Language:
-
English
- Keywords:
- Pubs id:
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2063916
- Local pid:
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pubs:2063916
- Deposit date:
-
2024-11-21
Terms of use
- Copyright holder:
- Castiello et al.
- Copyright date:
- 2025
- Rights statement:
- © 2025 The Author(s). Open Access funding provided by University of Oxford: This work is licensed under a Creative Commons Attribution 4.0 International License (CCBY4.0; https://creativecommons.org/licenses/by/4.0). This license permits copying and redistributing the work in any medium or format, as well as adapting the material for any purpose, even commercially.
- Notes:
- The author accepted manuscript (AAM) of this paper has been made available under the University of Oxford’s Open Access Publications Policy, and a CC BY public copyright licence has been applied.
- Licence:
- CC Attribution (CC BY)
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