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Published on: January 7, 2019
Cancelling cancellation? Sensorimotor control, agency, and prediction
Clare Press1, Emily R Thomas2, Daniel Yon3
1Department of Psychological Sciences, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK; Wellcome Centre for Human Neuroimaging, UCL, 12 Queen Square, London WC1N 3AR, UK.
Classic theories of action control suggest the brain cancels predicted sensory input. New research questions this, but we argue core action theories can be preserved by considering quasi-predictive or non-canceling predictive processes.
Area of Science:
- Cognitive Neuroscience
- Neuroscience of Action Control
- Perception and Action
Background:
- Classic theories posit predictive cancellation of sensory consequences of actions.
- Recent research challenges the necessity of predictive cancellation for action control.
- This raises questions about the validity of established action theories.
Purpose of the Study:
- To re-evaluate the role of predictive cancellation in action control and awareness.
- To determine if existing action control theories need revision in light of new findings.
- To propose alternative mechanisms for explaining action-related perception.
Main Methods:
- Theoretical analysis and synthesis of existing research on action control and predictive processing.
- Conceptual framework development to accommodate new findings.
- Literature review of studies on predictive coding and action awareness.
Main Results:
- Doubts about predictive cancellation do not invalidate core theories of action control.
- Adaptive functions of predictive cancellation can be achieved via quasi-predictive or non-canceling predictive processes.
- Perception is influenced by predictions without necessarily requiring active cancellation.
Conclusions:
- Established theories of action control and awareness can be largely maintained.
- Rethinking prediction's role in perception is necessary, but not to the extent of discarding existing frameworks.
- Alternative predictive and quasi-predictive mechanisms offer pathways to understand agent-environment interactions.
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