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Abstract Humans exhibit remarkably complex cognitive abilities and adaptive behavior in daily life. Cognitive operation in the " mental workspace, " such as mentally rotating a piece of luggage to fit into fixed trunk space, helps us maintain and manipulate information on a moment-to-moment basis. Skill acquisition in the " sensorimotor workspace, " such as learning a new mapping between the magnitude of new vehicle movement and wheel turn, allows us to adjust our behavior to changing environmental or internal demands to maintain appropriate motor performance. While this cognitive and sensorimotor synergy is at the root of adaptive behavior in the real world, their interplay has been understudied due to a divide-and-conquer approach. We evaluated whether a separate domain-specific or common domain-general operation drives mental and sensorimotor rotational transformations. We observed that participants improved the efficiency of mental rotation speed after the visuomotor rotation training, and their learning rate for visuomotor adaptation also improved after their mental rotation training. Such bidirectional transfer between two widely different tasks highlights the remarkable reciprocal plasticity and demonstrates a common transformation mechanism between two intertwined workspaces. Our findings urge the necessity of an explicitly integrated approach to enhance our understanding of the dynamic interdependence between cognitive and sensorimotor mechanisms.
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A model for learning based on the joint estimation of stochasticity and volatility
Abstract Previous research has stressed the importance of uncertainty for controlling the speed of learning, and how such control depends on the learner inferring the noise properties of the environment, especially volatility: the speed of change. However, learning rates are jointly determined by the comparison between volatility and a second factor, moment-to-moment stochasticity. Yet much previous research has focused on simplified cases corresponding to estimation of either factor alone. Here, we introduce a learning model, in which both factors are learned simultaneously from experience, and use the model to simulate human and animal data across many seemingly disparate neuroscientific and behavioral phenomena. By considering the full problem of joint estimation, we highlight a set of previously unappreciated issues, arising from the mutual interdependence of inference about volatility and stochasticity. This interdependence complicates and enriches the interpretation of previous results, such as pathological learning in individuals with anxiety and following amygdala damage.
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- Award ID(s):
- 1822571
- PAR ID:
- 10401270
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41467-021-26731-9
