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Embodied Choice: How Action Influences Perceptual Decision Making
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Embodied Choice considers action performance as a proper part of the decision making process rather than merely as a means to report the decision. The central statement of embodied choice is the existence of bidirectional influences between action and decisions. This implies that for a decision expressed by an action, the action dynamics and its constraints (e.g. current trajectory and kinematics) influence the decision making process. Here we use a perceptual decision making task to compare three types of model: a serial decision-then-action model, a parallel decision-and-action model, and an embodied choice model where the action feeds back into the decision making. The embodied model incorporates two key mechanisms that together are lacking in the other models: action preparation and commitment. First, action preparation strategies alleviate delays in enacting a choice but also modify decision termination. Second, action dynamics change the prospects and create a commitment effect to the initially preferred choice. Our results show that these two mechanisms make embodied choice models better suited to combine decision and action appropriately to achieve suitably fast and accurate responses, as usually required in ecologically valid situations. Moreover, embodied choice models with these mechanisms give a better account of trajectory tracking experiments during decision making. In conclusion, the embodied choice framework offers a combined theory of decision and action that gives a clear case that embodied phenomena such as the dynamics of actions can have a causal influence on central cognition.
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“The expected precision or confidence about preferred outcomes reports on the progress towards the goal – like spatial proximity or sub-goals (Lepora and Pezzulo, 2015, Maisto et al., 2015) – and in accordance with early cybernetic models of purposive action such as the Test-Operate-Test-Exit (TOTE) model (Miller et al., 1960) precision can be updated (in a Bayes optimal fashion) during goal-direct”
From Active Inference, homeostatic regulation and adaptive behavioural control (2015) · cites this paper as Lepora and Pezzulo, 2015
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Metadata
- Authors
- Lepora, N. F., & Pezzulo, G.
- Affiliations
- University of the West of England (GB)
- National Research Council (IT)
- Corresponding author
- Pezzulo, Giovanni
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-04-07
- Volume
- 11
- Issue
- 4
- Pages
- e1004110
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC4388485
- OpenAlex
- W2060215462
- Cited by
- 209
- References
- 76
- Citations by year
- 2026: 16 · 2025: 23 · 2024: 22 · 2023: 22 · 2022: 26 · 2021: 25 · 2020: 9 · 2019: 15 · 2018: 12 · 2017: 18 · 2016: 9 · 2015: 11
- FWCI
- 10.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Bristol Research (University of Bristol) (submittedVersion, other-oa)
- Explore Bristol Research (submittedVersion, other-oa)
- Funders
- Nvidia
- European Commission (270108)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- perceptual decision making, action preparation, commitment effect, trajectory tracking, response latency, response accuracy
- MeSH terms
- Animals, Choice Behavior, Decision Making, Decision Support Techniques, Models, Neurological, Motivation, Perception, Task Performance and Analysis, Ecosystem, Mice
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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