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Cited by 209Embodied and Extended CognitionMotor Control and AdaptationNeural and Behavioral Psychology Studies

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Embodied Choice: How Action Influences Perceptual Decision Making

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Lepora, Nathan F., Pezzulo, Giovanni·2015·PLOS Computational Biology
DOI 10.1371/journal.pcbi.1004110PMC PMC4388485
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Abstract

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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Crossref · 10.1371/journal.pcbi.1004110

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Fig 1 Schematic illustration of alternative ways to link decision and action systems: (A) serial models, (B) parallel models, and (C) embodied choice models. Note that here decision, choice, and action are shown as separate and modular systems for illustrative purposes only.
Fig 1 Schematic illustration of alternative ways to link decision and action systems: (A) serial models, (B) parallel models, and (C) embodied choice models. Note that here decision, choice, and action are shown as separate and modular systems for illustrative purposes only.CC BY · PubMed Central
Fig 2 Mouse-tracking data. An action is made to either target T1 or target T2 to indicate the decision. This action has a trajectory beginning at a start position equidistant from both targets. The red line indicated the mean trajectory. Experiment: 10 trajectories randomly chosen from the data in ref. [16].
Fig 2 Mouse-tracking data. An action is made to either target T1 or target T2 to indicate the decision. This action has a trajectory beginning at a start position equidistant from both targets. The red line indicated the mean trajectory. Experiment: 10 trajectories randomly chosen from the data in ref. [16].CC BY · PubMed Central
Fig 3 Simulated 2AFC task with action dynamics (trajectory from Model 3). For simplicity, movement along the trajectory is at constant velocity. The trajectory depends on the interaction between the decision and action systems, driven by information for the alternatives. An action focus is used to link the decision and action systems, which here drifts from target T2 to T1 with the accumulated information for each alternative.
Fig 3 Simulated 2AFC task with action dynamics (trajectory from Model 3). For simplicity, movement along the trajectory is at constant velocity. The trajectory depends on the interaction between the decision and action systems, driven by information for the alternatives. An action focus is used to link the decision and action systems, which here drifts from target T2 to T1 with the accumulated information for each alternative.CC BY · PubMed Central
Fig 4 Accumulated information and motor response during the decision process. The top plot shows the accumulated information z(t) plotted against time, with decision bounds also shown and the times of crossing (dashed lines). The bottom plots show the trajectory of the focus on the line between the two targets. Model 1 (action initiation after decision) and Model 2 (action initiation and changes of mind) begin when the information first crosses threshold, with Model 2 changing its mind. Model 3 (action preparation) and Model 4 (preparation and commitment) begin at time zero, with the focus moving continually throughout the decision process.
Fig 4 Accumulated information and motor response during the decision process. The top plot shows the accumulated information z(t) plotted against time, with decision bounds also shown and the times of crossing (dashed lines). The bottom plots show the trajectory of the focus on the line between the two targets. Model 1 (action initiation after decision) and Model 2 (action initiation and changes of mind) begin when the information first crosses threshold, with Model 2 changing its mind. Model 3 (action preparation) and Model 4 (preparation and commitment) begin at time zero, with the focus moving continually throughout the decision process.CC BY · PubMed Central
Fig 5 Individual and average trajectories. Models 1–4: 10 trajectories randomly taken from the analysis for Figs 3, 4. Trajectories were selected to complete at target 1 to the left. The decision bound b was chosen for Models 1–3 to have a mean response time of 1.5 sec; whereas (1 sec for model 4 to be in range for this model). The average trajectory (red plot) in each case is taken over 150 trajectories, and computed parallel to the x-axis.
Fig 5 Individual and average trajectories. Models 1–4: 10 trajectories randomly taken from the analysis for Figs 3, 4. Trajectories were selected to complete at target 1 to the left. The decision bound b was chosen for Models 1–3 to have a mean response time of 1.5 sec; whereas (1 sec for model 4 to be in range for this model). The average trajectory (red plot) in each case is taken over 150 trajectories, and computed parallel to the x-axis.CC BY · PubMed Central
Fig 6 Trajectory dependence on noise. The ‘normal’ average trajectories are taken from Fig. 5. A second average trajectory was computed with increased noise σ = 4 (red curve).
Fig 6 Trajectory dependence on noise. The ‘normal’ average trajectories are taken from Fig. 5. A second average trajectory was computed with increased noise σ = 4 (red curve).CC BY · PubMed Central
Fig 7 Comparison of 4 decision making with action models and a Baseline Model without action. Each model is characterized by a speed-accuracy curve obtained from simulating (100000 samples) over a range of decision boundaries, with mean decision error plotted against mean response time. The minimal time-to-target T target for action completion is also shown (dotted line). Note that curves for Model 3 (green line) and Model 4 (red line) do not overlap, but span different response times.
Fig 7 Comparison of 4 decision making with action models and a Baseline Model without action. Each model is characterized by a speed-accuracy curve obtained from simulating (100000 samples) over a range of decision boundaries, with mean decision error plotted against mean response time. The minimal time-to-target T target for action completion is also shown (dotted line). Note that curves for Model 3 (green line) and Model 4 (red line) do not overlap, but span different response times.CC BY · PubMed Central

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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
ORCID
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
DOI
10.1371/journal.pcbi.1004110
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
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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