1
|
Kikumoto A, Shibata K, Nishio T, Badre D. Practice Reshapes the Geometry and Dynamics of Task-tailored Representations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.12.612718. [PMID: 39314386 PMCID: PMC11419051 DOI: 10.1101/2024.09.12.612718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Extensive practice makes task performance more efficient and precise, leading to automaticity. However, theories of automaticity differ on which levels of task representations (e.g., low-level features, stimulus-response mappings, or high-level conjunctive memories of individual events) change with practice, despite predicting the same pattern of improvement (e.g., power law of practice). To resolve this controversy, we built on recent theoretical advances in understanding computations through neural population dynamics. Specifically, we hypothesized that practice optimizes the neural representational geometry of task representations to minimally separate the highest-level task contingencies needed for successful performance. This involves efficiently reaching conjunctive neural states that integrate task-critical features nonlinearly while abstracting over non-critical dimensions. To test this hypothesis, human participants (n = 40) engaged in extensive practice of a simple, context-dependent action selection task over 3 days while recording EEG. During initial rapid improvement in task performance, representations of the highest-level, context-specific conjunctions of task-features were enhanced as a function of the number of successful episodes. Crucially, only enhancement of these conjunctive representations, and not lower-order representations, predicted the power-law improvement in performance. Simultaneously, over sessions, these conjunctive neural states became more stable earlier in time and more aligned, abstracting over redundant task features, which correlated with offline performance gain in reducing switch costs. Thus, practice optimizes the dynamic representational geometry as task-tailored neural states that minimally tesselate the task space, taming their high-dimensionality.
Collapse
Affiliation(s)
- Atsushi Kikumoto
- Department of Cognitive and Psychological Sciences, Brown University Providence, RI, U.S
- RIKEN Center for Brain Science, Wako, Saitama, Japan
| | | | | | - David Badre
- Department of Cognitive and Psychological Sciences, Brown University Providence, RI, U.S
- Carney Institute for Brain Science Brown University, Providence, RI, U.S
| |
Collapse
|
2
|
Yang G, Jiang J. Cost-benefit Tradeoff Mediates the Rule- to Memory-based Processing Transition during Practice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.13.580214. [PMID: 38405946 PMCID: PMC10888779 DOI: 10.1101/2024.02.13.580214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Practice not only improves task performance, but also changes task execution from rule- to memory-based processing by incorporating experiences from practice. However, how and when this change occurs is unclear. We tested the hypothesis that strategy transition in task learning results from cost-benefit analysis. Participants learned two task sequences and were then queried about the task type at a cued sequence and position. Behavioral improvement with practice can be accounted for by a computational model implementing cost-benefit analysis. Model-predicted strategy transition points are related to behavioral slowing and changes in fMRI activation patterns in the dorsolateral prefrontal cortex. Strategy transition is also related to increased pattern separation in the ventromedial prefrontal cortex. The cost-benefit analysis model outperforms alternative models (e.g., both strategies racing for being expressed in behavior) in accounting for empirical data. These findings support cost-benefit analysis as a mechanism of practice-induced strategy shift.
Collapse
Affiliation(s)
- Guochun Yang
- Cognitive Control Collaborative, University of Iowa, Iowa City, IA 52242, USA
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA 52242, USA
| | - Jiefeng Jiang
- Cognitive Control Collaborative, University of Iowa, Iowa City, IA 52242, USA
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA 52242, USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA 52242, USA
| |
Collapse
|
3
|
Wang X, Zwosta K, Hennig J, Böhm I, Ehrlich S, Wolfensteller U, Ruge H. The dynamics of functional brain network segregation in feedback-driven learning. Commun Biol 2024; 7:531. [PMID: 38710773 DOI: 10.1038/s42003-024-06210-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 04/17/2024] [Indexed: 05/08/2024] Open
Abstract
Prior evidence suggests that increasingly efficient task performance in human learning is associated with large scale brain network dynamics. However, the specific nature of this general relationship has remained unclear. Here, we characterize performance improvement during feedback-driven stimulus-response (S-R) learning by learning rate as well as S-R habit strength and test whether and how these two behavioral measures are associated with a functional brain state transition from a more integrated to a more segregated brain state across learning. Capitalizing on two separate fMRI studies using similar but not identical experimental designs, we demonstrate for both studies that a higher learning rate is associated with a more rapid brain network segregation. By contrast, S-R habit strength is not reliably related to changes in brain network segregation. Overall, our current study results highlight the utility of dynamic functional brain state analysis. From a broader perspective taking into account previous study results, our findings align with a framework that conceptualizes brain network segregation as a general feature of processing efficiency not only in feedback-driven learning as in the present study but also in other types of learning and in other task domains.
Collapse
Affiliation(s)
- Xiaoyu Wang
- Faculty of Psychology, Technische Universität Dresden, Dresden, Germany.
| | - Katharina Zwosta
- Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Julius Hennig
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Ilka Böhm
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Stefan Ehrlich
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
- Eating Disorder Treatment and Research Center, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Uta Wolfensteller
- Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Hannes Ruge
- Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| |
Collapse
|
4
|
Wards Y, Ehrhardt SE, Garner KG, Mattingley JB, Filmer HL, Dux PE. Stimulating prefrontal cortex facilitates training transfer by increasing representational overlap. Cereb Cortex 2024; 34:bhae209. [PMID: 38771242 DOI: 10.1093/cercor/bhae209] [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] [Received: 01/17/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/22/2024] Open
Abstract
A recent hypothesis characterizes difficulties in multitasking as being the price humans pay for our ability to generalize learning across tasks. The mitigation of these costs through training has been associated with reduced overlap of constituent task representations within frontal, parietal, and subcortical regions. Transcranial direct current stimulation, which can modulate functional brain activity, has shown promise in generalizing performance gains when combined with multitasking training. However, the relationship between combined transcranial direct current stimulation and training protocols with task-associated representational overlap in the brain remains unexplored. Here, we paired prefrontal cortex transcranial direct current stimulation with multitasking training in 178 individuals and collected functional magnetic resonance imaging data pre- and post-training. We found that 1 mA transcranial direct current stimulation applied to the prefrontal cortex paired with multitasking training enhanced training transfer to spatial attention, as assessed via a visual search task. Using machine learning to assess the overlap of neural activity related to the training task in task-relevant brain regions, we found that visual search gains were predicted by changes in classification accuracy in frontal, parietal, and cerebellar regions for participants that received left prefrontal cortex stimulation. These findings demonstrate that prefrontal cortex transcranial direct current stimulation may interact with training-related changes to task representations, facilitating the generalization of learning.
Collapse
Affiliation(s)
- Yohan Wards
- School of Psychology, The University of Queensland, McElwain Building, Campbell Road, St Lucia, Queensland 4072, Australia
| | - Shane E Ehrhardt
- School of Psychology, The University of Queensland, McElwain Building, Campbell Road, St Lucia, Queensland 4072, Australia
| | - Kelly G Garner
- School of Psychology, The University of Queensland, McElwain Building, Campbell Road, St Lucia, Queensland 4072, Australia
- Queensland Brain Institute, The University of Queensland, Building 79, Upland Road, St Lucia, Queensland 4072, Australia
- School of Psychology, University of New South Wales, Mathews Building, Gate 11, Botany Street, Randwick, New South Wales 2052, Australia
- School of Psychology, University of Birmingham, Hills Building, Edgbaston Park Rd, Birmingham B15 2TT, United Kingdom
| | - Jason B Mattingley
- School of Psychology, The University of Queensland, McElwain Building, Campbell Road, St Lucia, Queensland 4072, Australia
- Queensland Brain Institute, The University of Queensland, Building 79, Upland Road, St Lucia, Queensland 4072, Australia
- School of Psychology, University of Birmingham, Hills Building, Edgbaston Park Rd, Birmingham B15 2TT, United Kingdom
| | - Hannah L Filmer
- School of Psychology, The University of Queensland, McElwain Building, Campbell Road, St Lucia, Queensland 4072, Australia
| | - Paul E Dux
- School of Psychology, The University of Queensland, McElwain Building, Campbell Road, St Lucia, Queensland 4072, Australia
| |
Collapse
|