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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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Publisher copy:
10.1037/xan0000390

Authors


More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Experimental Psychology
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Experimental Psychology
Role:
Author
ORCID:
0000-0002-3672-1366


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:
0097-7403


Language:
English
Keywords:
Pubs id:
2063916
Local pid:
pubs:2063916
Deposit date:
2024-11-21

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