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Löffler A, Zylberberg A, Shadlen MN, Wolpert DM. Judging the difficulty of perceptual decisions. eLife 2023; 12:RP86892. [PMID: 37975792 PMCID: PMC10656101 DOI: 10.7554/elife.86892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023] Open
Abstract
Deciding how difficult it is going to be to perform a task allows us to choose between tasks, allocate appropriate resources, and predict future performance. To be useful for planning, difficulty judgments should not require completion of the task. Here, we examine the processes underlying difficulty judgments in a perceptual decision-making task. Participants viewed two patches of dynamic random dots, which were colored blue or yellow stochastically on each appearance. Stimulus coherence (the probability, pblue, of a dot being blue) varied across trials and patches thus establishing difficulty, |pblue -0.5|. Participants were asked to indicate for which patch it would be easier to decide the dominant color. Accuracy in difficulty decisions improved with the difference in the stimulus difficulties, whereas the reaction times were not determined solely by this quantity. For example, when the patches shared the same difficulty, reaction times were shorter for easier stimuli. A comparison of several models of difficulty judgment suggested that participants compare the absolute accumulated evidence from each stimulus and terminate their decision when they differed by a set amount. The model predicts that when the dominant color of each stimulus is known, reaction times should depend only on the difference in difficulty, which we confirm empirically. We also show that this model is preferred to one that compares the confidence one would have in making each decision. The results extend evidence accumulation models, used to explain choice, reaction time, and confidence to prospective judgments of difficulty.
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Affiliation(s)
- Anne Löffler
- Zuckerman Mind Brain Behavior Institute, Columbia UniversityNew YorkUnited States
- Department of Neuroscience, Columbia UniversityNew YorkUnited States
- Kavli Institute for Brain Science, Columbia UniversityNew YorkUnited States
| | - Ariel Zylberberg
- Zuckerman Mind Brain Behavior Institute, Columbia UniversityNew YorkUnited States
- Department of Neuroscience, Columbia UniversityNew YorkUnited States
| | - Michael N Shadlen
- Zuckerman Mind Brain Behavior Institute, Columbia UniversityNew YorkUnited States
- Department of Neuroscience, Columbia UniversityNew YorkUnited States
- Kavli Institute for Brain Science, Columbia UniversityNew YorkUnited States
- Howard Hughes Medical Institute, Columbia UniversityNew YorkUnited States
| | - Daniel M Wolpert
- Zuckerman Mind Brain Behavior Institute, Columbia UniversityNew YorkUnited States
- Department of Neuroscience, Columbia UniversityNew YorkUnited States
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Zhu T, Gallivan JP, Wolpert DM, Flanagan JR. Interaction between decision-making and motor learning when selecting reach targets in the presence of bias and noise. PLoS Comput Biol 2023; 19:e1011596. [PMID: 37917718 PMCID: PMC10703408 DOI: 10.1371/journal.pcbi.1011596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 12/07/2023] [Accepted: 10/13/2023] [Indexed: 11/04/2023] Open
Abstract
Motor errors can have both bias and noise components. Bias can be compensated for by adaptation and, in tasks in which the magnitude of noise varies across the environment, noise can be reduced by identifying and then acting in less noisy regions of the environment. Here we examine how these two processes interact when participants reach under a combination of an externally imposed visuomotor bias and noise. In a center-out reaching task, participants experienced noise (zero-mean random visuomotor rotations) that was target-direction dependent with a standard deviation that increased linearly from a least-noisy direction. They also experienced a constant bias, a visuomotor rotation that varied (across groups) from 0 to 40 degrees. Critically, on each trial, participants could select one of three targets to reach to, thereby allowing them to potentially select targets close to the least-noisy direction. The group who experienced no bias (0 degrees) quickly learned to select targets close to the least-noisy direction. However, groups who experienced a bias often failed to identify the least-noisy direction, even though they did partially adapt to the bias. When noise was introduced after participants experienced and adapted to a 40 degrees bias (without noise) in all directions, they exhibited an improved ability to find the least-noisy direction. We developed two models-one for reach adaptation and one for target selection-that could explain participants' adaptation and target-selection behavior. Our data and simulations indicate that there is a trade-off between adaptation and selection. Specifically, because bias learning is local, participants can improve performance, through adaptation, by always selecting targets that are closest to a chosen direction. However, this comes at the expense of improving performance, through selection, by reaching toward targets in different directions to find the least-noisy direction.
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Affiliation(s)
- Tianyao Zhu
- Centre for Neuroscience Studies, Queen’s University, Kingston, Ontario, Canada
| | - Jason P. Gallivan
- Centre for Neuroscience Studies, Queen’s University, Kingston, Ontario, Canada
- Department of Psychology, Queen’s University, Kingston, Ontario, Canada
- Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, Ontario, Canada
| | - Daniel M. Wolpert
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York
- Department of Neuroscience, Columbia University, New York, New York
| | - J. Randall Flanagan
- Centre for Neuroscience Studies, Queen’s University, Kingston, Ontario, Canada
- Department of Psychology, Queen’s University, Kingston, Ontario, Canada
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Löffler A, Zylberberg A, Shadlen MN, Wolpert DM. Judging the difficulty of perceptual decisions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.13.528254. [PMID: 36824715 PMCID: PMC9949003 DOI: 10.1101/2023.02.13.528254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Deciding how difficult it is going to be to perform a task allows us to choose between tasks, allocate appropriate resources, and predict future performance. To be useful for planning, difficulty judgments should not require completion of the task. Here we examine the processes underlying difficulty judgments in a perceptual decision making task. Participants viewed two patches of dynamic random dots, which were colored blue or yellow stochastically on each appearance. Stimulus coherence (the probability, p blue , of a dot being blue) varied across trials and patches thus establishing difficulty, p blue - 0.5 . Participants were asked to indicate for which patch it would be easier to decide the dominant color. Accuracy in difficulty decisions improved with the difference in the stimulus difficulties, whereas the reaction times were not determined solely by this quantity. For example, when the patches shared the same difficulty, reaction times were shorter for easier stimuli. A comparison of several models of difficulty judgment suggested that participants compare the absolute accumulated evidence from each stimulus and terminate their decision when they differed by a set amount. The model predicts that when the dominant color of each stimulus is known, reaction times should depend only on the difference in difficulty, which we confirm empirically. We also show that this model is preferred to one that compares the confidence one would have in making each decision. The results extend evidence accumulation models, used to explain choice, reaction time and confidence to prospective judgments of difficulty.
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Affiliation(s)
- Anne Löffler
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
- Department of Neuroscience, Columbia University, New York, NY 10027, USA
- Kavli Institute for Brain Science, Columbia University, NY 10027, USA
| | - Ariel Zylberberg
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
- Department of Neuroscience, Columbia University, New York, NY 10027, USA
| | - Michael N Shadlen
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
- Department of Neuroscience, Columbia University, New York, NY 10027, USA
- Kavli Institute for Brain Science, Columbia University, NY 10027, USA
- Howard Hughes Medical Institute, Columbia University, NY 10027, USA
| | - Daniel M Wolpert
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
- Department of Neuroscience, Columbia University, New York, NY 10027, USA
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Parma JO, Bacelar MFB, Cabral DAR, Lohse KR, Hodges NJ, Miller MW. That looks easy! Evidence against the benefits of an easier criterion of success for enhancing motor learning. PSYCHOLOGY OF SPORT AND EXERCISE 2023; 66:102394. [PMID: 37665856 DOI: 10.1016/j.psychsport.2023.102394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 01/18/2023] [Accepted: 01/19/2023] [Indexed: 09/06/2023]
Abstract
OPTIMAL theory predicts providing learners with a relatively easier criterion of success during practice enhances motor learning through increased self-efficacy, perceptions of competence, and intrinsic motivation. However, mixed results in the literature suggest this enhancement effect may be moderated by the number of successes achieved by learners practicing with the difficult criterion. To investigate this possibility, we manipulated quantity of practice to affect the absolute number of successes achieved by learners practicing with different success criteria. Eighty participants were divided into four groups and performed 50 or 100 trials of a mini-shuffleboard task. Groups practiced with either a large or a small zone of success surrounding the target. Learning was assessed 24 h after acquisition with retention and transfer tests. In terms of endpoint accuracy and precision, there were no learning or practice performance benefits of practicing with an easier criterion of success, regardless of the number of trials. This absence of a criterion of success effect was despite the efficacy of our manipulation in increasing the number of trials stopping within the zone of success, self-efficacy, perceptions of competence, and, for participants with 100 trials, intrinsic motivation. An equivalence test indicated that the effect of criterion of success was small, if existent. Moreover, at the individual level, intrinsic motivation did not predict posttest or acquisition performance. There were no benefits of easing the criterion of success on pressure, effort, accrual of explicit knowledge, or conscious processing. These data challenge key tenets of OPTIMAL theory and question the efficacy of easing criterion of success for motor learning.
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Affiliation(s)
- Juliana O Parma
- School of Kinesiology, Auburn University, 301 Wire Road, Auburn, AL, 36849, USA.
| | - Mariane F B Bacelar
- Department of Kinesiology, Boise State University, 1404 Bronco Circle, Boise, ID, 83725, USA.
| | - Daniel A R Cabral
- School of Kinesiology, Auburn University, 301 Wire Road, Auburn, AL, 36849, USA.
| | - Keith R Lohse
- Program in Physical Therapy, Washington University School of Medicine in St. Louis, USA.
| | - Nicola J Hodges
- School of Kinesiology, The University of British Columbia, Canada.
| | - Matthew W Miller
- School of Kinesiology, Auburn University, 301 Wire Road, Auburn, AL, 36849, USA; Center for Neuroscience Initiative, Auburn University, USA.
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Ikegami T, Flanagan JR, Wolpert DM. Reach adaption to a visuomotor gain with terminal error feedback involves reinforcement learning. PLoS One 2022; 17:e0269297. [PMID: 35648778 PMCID: PMC9159621 DOI: 10.1371/journal.pone.0269297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/17/2022] [Indexed: 11/18/2022] Open
Abstract
Motor adaptation can be achieved through error-based learning, driven by sensory prediction errors, or reinforcement learning, driven by reward prediction errors. Recent work on visuomotor adaptation has shown that reinforcement learning leads to more persistent adaptation when visual feedback is removed, compared to error-based learning in which continuous visual feedback of the movement is provided. However, there is evidence that error-based learning with terminal visual feedback of the movement (provided at the end of movement) may be driven by both sensory and reward prediction errors. Here we examined the influence of feedback on learning using a visuomotor adaptation task in which participants moved a cursor to a single target while the gain between hand and cursor movement displacement was gradually altered. Different groups received either continuous error feedback (EC), terminal error feedback (ET), or binary reinforcement feedback (success/fail) at the end of the movement (R). Following adaptation we tested generalization to targets located in different directions and found that generalization in the ET group was intermediate between the EC and R groups. We then examined the persistence of adaptation in the EC and ET groups when the cursor was extinguished and only binary reward feedback was provided. Whereas performance was maintained in the ET group, it quickly deteriorated in the EC group. These results suggest that terminal error feedback leads to a more robust form of learning than continuous error feedback. In addition our findings are consistent with the view that error-based learning with terminal feedback involves both error-based and reinforcement learning.
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Affiliation(s)
- Tsuyoshi Ikegami
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States of America
- Department of Neuroscience, Columbia University, New York, NY, United States of America
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, Suita City, Osaka, Japan
- Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan
- * E-mail:
| | - J. Randall Flanagan
- Department of Psychology, Queen’s University, Kingston, Ontario, Canada
- Centre for Neuroscience Studies, Queen’s University, Kingston, Ontario, Canada
| | - Daniel M. Wolpert
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States of America
- Department of Neuroscience, Columbia University, New York, NY, United States of America
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