Samuel David McDougle, PhD
Research & Publications
Biography
Research Summary
The Action, Computation, & Thinking (ACT) Lab is directed by Samuel McDougle at Yale's Department of Psychology. The goal of our research is to leverage behavioral, computational, and neuroscience approaches to better understand the nature of human motor skill learning.
Extensive Research Description
One of the defining characteristics of the human species is our massive repertoire of motor skills, and our astoundingly flexible capacity to learn new ones throughout life. From masters (Serena Williams, Mozart) to amateurs (the rest of us), learning and executing skilled actions requires a mix of high-level knowledge, attention, and repetitive practice.
In this lab we are interested in studying how humans learn and remember skilled actions. Skilled action is complex, and skill acquisition relies on a variety of psychological processes and learning algorithms. Here are some broad questions we are currently scratching our heads over:
• How do learned motor behaviors go from controlled and effortful to automatic?
• How do cognitive computations and abstract thoughts interact with movement control?
• How does the brain represent goals, actions, and the rewards that reinforce our actions? And how is this accomplished computationally?
• How does our sense of space influence how we move within it?
• Which aspects of motor memory are explicit, and which are implicit? Do these systems interact?
• How might different cognitive systems vie for control during the selection of movements?
• Do brain regions conventionally linked to motor behavior also play a role in cognition (e.g., the cerebellum)? What are these roles, and what can they tell us about the relationship between sensorimotor and cognitive processes?
Approaches
We leverage multiple methodological approaches for investigating the psychology and neuroscience of human learning and memory.
Behavior
The careful study of behavior is key to addressing psychological and neuroscientific questions. We use a variety of behavioral paradigms to study different aspects of skill learning, including sensorimotor learning (e.g., force-field learning, visuomotor adaptation, etc.), reinforcement learning (e.g., bandit tasks, probabilistic RL, stimulus-response map learning, etc.), and more traditional psychophysics (e.g., evidence accumulation). Our goal is to use behavioral experiments to test novel computational theories of learning.
Computational Modeling
Computational modeling has many functions for our research, from generating precise behavioral and neural predictions, to simply organizing our thoughts. Our modeling approach relies on various machine learning techniques (e.g., state-space modeling, reinforcement learning, simple neural networks) and cognitive psychological approaches (e.g., drift-diffusion models).
Neuroimaging and Neuropsychology
Functional Magnetic Resonance Imaging has made great progress as a neuroscience technique over the last two decades. We use a combination of model-driven and multivariate fMRI methods to characterize teaching signals and map the neural circuits underlying various forms of learning. We also work with populations with particular neural pathologies, such as spino-cerebellar ataxia, to study how different neural regions (e.g., the cerebellum, basal ganglia, etc.) contribute to different aspects of learning, memory, and motor control. We use neuropsychology both to address basic research questions, and to try and inspire improvements in neurorehabilitation protocols.
Coauthors
Research Interests
Basal Ganglia; Cerebellum; Learning; Memory; Motor Skills; Neuropsychology; Psychophysics; Reward; Cognitive Science; Cognitive Neuroscience
Selected Publications
- Motor learning without movementKim OA, Forrence AD, McDougle SD. Motor learning without movement. Proceedings Of The National Academy Of Sciences Of The United States Of America 2022, 119: e2204379119. PMID: 35858450, PMCID: PMC9335319, DOI: 10.1073/pnas.2204379119.
- Continuous manipulation of mental representations is compromised in cerebellar degenerationMcDougle SD, Tsay JS, Pitt B, King M, Saban W, Taylor JA, Ivry RB. Continuous manipulation of mental representations is compromised in cerebellar degeneration. Brain 2022, 145: 4246-4263. PMID: 35202465, PMCID: PMC10200308, DOI: 10.1093/brain/awac072.
- Revisiting the Role of the Medial Temporal Lobe in Motor LearningMcDougle SD, Wilterson SA, Turk-Browne NB, Taylor JA. Revisiting the Role of the Medial Temporal Lobe in Motor Learning. Journal Of Cognitive Neuroscience 2022, 34: 532-549. PMID: 34942649, PMCID: PMC8832157, DOI: 10.1162/jocn_a_01809.
- Going beyond primary motor cortex to improve brain–computer interfacesGallego JA, Makin TR, McDougle SD. Going beyond primary motor cortex to improve brain–computer interfaces. Trends In Neurosciences 2022, 45: 176-183. PMID: 35078639, DOI: 10.1016/j.tins.2021.12.006.
- Context is key for learning motor skillsCollins AGE, McDougle SD. Context is key for learning motor skills. Nature 2021, 600: 387-388. PMID: 34789883, DOI: 10.1038/d41586-021-03028-x.
- Dissociable cognitive strategies for sensorimotor learningMcDougle S, Taylor J. Dissociable cognitive strategies for sensorimotor learning. Nature Communications 2019, 10: 40. PMID: 30604759, PMCID: PMC6318272, DOI: 10.1038/s41467-018-07941-0.
- Executive Function Assigns Value to Novel Goal-Congruent OutcomesMcDougle SD, Ballard IC, Baribault B, Bishop SJ, Collins AGE. Executive Function Assigns Value to Novel Goal-Congruent Outcomes. Cerebral Cortex 2021, 32: 231-247. PMID: 34231854, PMCID: PMC8634563, DOI: 10.1093/cercor/bhab205.
- Credit assignment in movement-dependent reinforcement learningMcDougle S, Boggess M, Crossley M, Parvin D, Ivry R, Taylor J. Credit assignment in movement-dependent reinforcement learning. Proceedings Of The National Academy Of Sciences Of The United States Of America 2016, 113: 6797-6802. PMID: 27247404, PMCID: PMC4914179, DOI: 10.1073/pnas.1523669113.
- Contextual effects in sensorimotor adaptation adhere to associative learning rulesAvraham G, Taylor J, Breska A, Ivry R, McDougle S. Contextual effects in sensorimotor adaptation adhere to associative learning rules. ELife 2022, 11: e75801. PMID: 36197002, PMCID: PMC9635873, DOI: 10.7554/elife.75801.
- Independent Influences of Movement Distance and Visual Distance on Fitts’ LawAl-Fawakhiri N, McDougle S. Independent Influences of Movement Distance and Visual Distance on Fitts’ Law. Journal Of Experimental Psychology General 2024 PMID: 38934948, DOI: 10.1037/xge0001612.
- Oblique warping: A general distortion of spatial perceptionYousif S, McDougle S. Oblique warping: A general distortion of spatial perception. Cognition 2024, 247: 105762. PMID: 38552560, DOI: 10.1016/j.cognition.2024.105762.
- Dissociable Codes in Motor Working MemoryHillman H, Botthof T, Forrence A, McDougle S. Dissociable Codes in Motor Working Memory. Psychological Science 2024, 35: 150-161. PMID: 38236687, DOI: 10.1177/09567976231221756.
- A common format for representing spatial location in visual and motor working memoryYousif S, Forrence A, McDougle S. A common format for representing spatial location in visual and motor working memory. Psychonomic Bulletin & Review 2023, 31: 697-707. PMID: 37670158, DOI: 10.3758/s13423-023-02366-3.
- Metacognitive judgments during visuomotor learning reflect the integration of error historyHewitson C, Al-Fawakhiri N, Forrence A, McDougle S. Metacognitive judgments during visuomotor learning reflect the integration of error history. Journal Of Neurophysiology 2023, 130: 264-277. PMID: 37377281, DOI: 10.1152/jn.00022.2023.
- “Don't [ruminate], be happy”: A cognitive perspective linking depression and anhedoniaRutherford A, McDougle S, Joormann J. “Don't [ruminate], be happy”: A cognitive perspective linking depression and anhedonia. Clinical Psychology Review 2023, 101: 102255. PMID: 36871425, DOI: 10.1016/j.cpr.2023.102255.
- Common coordinate systems for perception and actionYousif S, McDougle S. Common coordinate systems for perception and action. Journal Of Vision 2022, 22: 4029. DOI: 10.1167/jov.22.14.4029.
- Distinct Neural Signatures of Outcome Monitoring After Selection and Execution ErrorsMushtaq F, McDougle SD, Craddock MP, Parvin DE, Brookes J, Schaefer A, Mon-Williams M, Taylor JA, Ivry RB. Distinct Neural Signatures of Outcome Monitoring After Selection and Execution Errors. Journal Of Cognitive Neuroscience 2022, 34: 748-765. PMID: 35104323, PMCID: PMC8969121, DOI: 10.1162/jocn_a_01824.
- Post-error Slowing During Instrumental Learning is Shaped by Working Memory-based Choice StrategiesMcDougle SD. Post-error Slowing During Instrumental Learning is Shaped by Working Memory-based Choice Strategies. Neuroscience 2021, 486: 37-45. PMID: 34695537, DOI: 10.1016/j.neuroscience.2021.10.016.
- Prolonged response time helps eliminate residual errors in visuomotor adaptationLangsdorf L, Maresch J, Hegele M, McDougle SD, Schween R. Prolonged response time helps eliminate residual errors in visuomotor adaptation. Psychonomic Bulletin & Review 2021, 28: 834-844. PMID: 33483935, PMCID: PMC8219572, DOI: 10.3758/s13423-020-01865-x.
- Behavioral, Physiological, and Neural Signatures of Surprise during Naturalistic Sports ViewingAntony JW, Hartshorne TH, Pomeroy K, Gureckis TM, Hasson U, McDougle SD, Norman KA. Behavioral, Physiological, and Neural Signatures of Surprise during Naturalistic Sports Viewing. Neuron 2020, 109: 377-390.e7. PMID: 33242421, DOI: 10.1016/j.neuron.2020.10.029.
- The role of executive function in shaping reinforcement learningRmus M, McDougle S, Collins A. The role of executive function in shaping reinforcement learning. Current Opinion In Behavioral Sciences 2020, 38: 66-73. PMID: 35194556, PMCID: PMC8859995, DOI: 10.1016/j.cobeha.2020.10.003.
- Modeling the influence of working memory, reinforcement, and action uncertainty on reaction time and choice during instrumental learningMcDougle SD, Collins AGE. Modeling the influence of working memory, reinforcement, and action uncertainty on reaction time and choice during instrumental learning. Psychonomic Bulletin & Review 2020, 28: 20-39. PMID: 32710256, PMCID: PMC7854965, DOI: 10.3758/s13423-020-01774-z.
- Visuomotor Adaptation Tasks as a Window into the Interplay between Explicit and Implicit Cognitive ProcessesTaylor J, McDougle S. Visuomotor Adaptation Tasks as a Window into the Interplay between Explicit and Implicit Cognitive Processes. 2020, 549-558. DOI: 10.7551/mitpress/11442.003.0060.
- Assessing explicit strategies in force field adaptationSchween R, McDougle S, Hegele M, Taylor J. Assessing explicit strategies in force field adaptation. Journal Of Neurophysiology 2020, 123: 1552-1565. PMID: 32208878, PMCID: PMC7191530, DOI: 10.1152/jn.00427.2019.
- Highlights from the 29th Annual Meeting of the Society for the Neural Control of MovementMathis A, Pack A, Maeda R, McDougle S. Highlights from the 29th Annual Meeting of the Society for the Neural Control of Movement. Journal Of Neurophysiology 2019, 122: 1777-1783. PMID: 31461364, PMCID: PMC6843106, DOI: 10.1152/jn.00484.2019.
- Neural Signatures of Prediction Errors in a Decision-Making Task Are Modulated by Action Execution FailuresMcDougle S, Butcher P, Parvin D, Mushtaq F, Niv Y, Ivry R, Taylor J. Neural Signatures of Prediction Errors in a Decision-Making Task Are Modulated by Action Execution Failures. Current Biology 2019, 29: 1606-1613.e5. PMID: 31056386, PMCID: PMC6535105, DOI: 10.1016/j.cub.2019.04.011.
- Credit Assignment in a Motor Decision Making Task Is Influenced by Agency and Not Sensory Prediction ErrorsParvin D, McDougle S, Taylor J, Ivry R. Credit Assignment in a Motor Decision Making Task Is Influenced by Agency and Not Sensory Prediction Errors. Journal Of Neuroscience 2018, 38: 4521-4530. PMID: 29650698, PMCID: PMC5943979, DOI: 10.1523/jneurosci.3601-17.2018.
- Implications of plan-based generalization in sensorimotor adaptationMcDougle S, Bond K, Taylor J. Implications of plan-based generalization in sensorimotor adaptation. Journal Of Neurophysiology 2017, 118: 383-393. PMID: 28404830, PMCID: PMC5501918, DOI: 10.1152/jn.00974.2016.
- Taking Aim at the Cognitive Side of Learning in Sensorimotor Adaptation TasksMcDougle S, Ivry R, Taylor J. Taking Aim at the Cognitive Side of Learning in Sensorimotor Adaptation Tasks. Trends In Cognitive Sciences 2016, 20: 535-544. PMID: 27261056, PMCID: PMC4912867, DOI: 10.1016/j.tics.2016.05.002.
- Explicit and Implicit Processes Constitute the Fast and Slow Processes of Sensorimotor LearningMcDougle S, Bond K, Taylor J. Explicit and Implicit Processes Constitute the Fast and Slow Processes of Sensorimotor Learning. Journal Of Neuroscience 2015, 35: 9568-9579. PMID: 26134640, PMCID: PMC4571499, DOI: 10.1523/jneurosci.5061-14.2015.
- Adaptive Timing of Motor Output in the Mouse: The Role of Movement Oscillations in Eyelid ConditioningChettih S, McDougle S, Ruffolo L, Medina J. Adaptive Timing of Motor Output in the Mouse: The Role of Movement Oscillations in Eyelid Conditioning. Frontiers In Integrative Neuroscience 2011, 5: 72. PMID: 22144951, PMCID: PMC3226833, DOI: 10.3389/fnint.2011.00072.