The DOI matches its Crossref record; 75 of the 76 references it cites were found in a registry or an index, and 1 are not checked yet.
Embodied Choice: How Action Influences Perceptual Decision Making
Open accessAbstract
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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Cites
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The Neural Basis of Decision Making
Gold, J. I., & Shadlen, M. N. (2007) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.113038 · Cited by 3,764
Optimal feedback control as a theory of motor coordination
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The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks
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The Senses Considered as Perceptual Systems
Kensler, G. L., & Gibson, J. J. (1969) · Studies in Art Education · doi:10.2307/1319633 · Cited by 2,773
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Active Inference, homeostatic regulation and adaptive behavioural control
Pezzulo, G., Rigoli, F., & Friston, K. (2015) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2015.09.001 · Cited by 650
“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)…”
Self-citationNavigating the Affordance Landscape: Feedback Control as a Process Model of Behavior and Cognition
Pezzulo, G., & Cisek, P. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.03.013 · Cited by 419
Self-citationHierarchical active inference: A theory of motivated control
Pezzulo, G., Rigoli, F., & Friston, K. J. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.01.009 · Cited by 334
Self-citationHow Mouse-tracking Can Advance Social Cognitive Theory
Stillman, P. E., Shen, X., & Ferguson, M. J. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.03.012 · Cited by 168
Multistable Perception and the Role of the Frontoparietal Cortex in Perceptual Inference
Brascamp, J. W., Sterzer, P., Blake, R., & Knapen, T. H. J. (2017) · Annual Review of Psychology · doi:10.1146/annurev-psych-010417-085944 · Cited by 173
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
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