Understanding interactions amongst cognitive control, learning and representation
- Sebastian Musslick, Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States
- Abigail Novick Hoskin, Princeton University, Princeton, New Jersey, United States
- Taylor Webb, Princeton University, Princeton, New Jersey, United States
- Steven Frankland, Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States
- Jonathan Cohen, Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States
- Rebecca Jackson, MRC Cognition and Brain Science Unit, Cambridge University, Cambridge, Cambridgeshire, United Kingdom
- Matthew Lambon Ralph, MRC Cognition and Brain Science Unit, Cambridge University, Cambridge, Cambridgeshire, United Kingdom
- Lang Chen, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, California, United States
- Timothy Rogers, UW-Madison, Madison, Wisconsin, United States
- Randall O'Reilly, Dept of Psych and Neuro, University of Colorado Boulder, Boulder, Colorado, United States
- Alexander Petrov, Department of Psychology, Ohio State University, Columbus, Ohio, United States
AbstractResearch in cognitive control investigates how cognition and behavior get tailored to suit behavioral goals in particular task contexts. The work often focuses on mechanisms that adjudicate competition amongst simultaneously active but mutually incompatible representations. The objects of control—the competing representations—are typically cast as fixed entities: control influences interactions among these but does not shape the representations themselves. Conversely, research into the origins of mental representations (perceptual, linguistic, semantic, etc.) often neglects questions central to theories of control: whether and how the acquired representations support flexible task-dependent behaviors, the degree to which learning produces representations that compete or cooperate within and across tasks, or the extent to which learned representations require task-dependent potentiation to operate effectively.