Integrated Neural Processes for Defining Potential Actions and Deciding between Them: A Computational Model

By Paul Cisek. 2006

310 citations recorded by the source corpus.

To successfully accomplish a behavioral goal such as reaching for an object, an animal must solve two related problems: to decide which object to reach and to plan the specific parameters of the movement. Traditionally, these two problems have been viewed as separate, and theories of decision making and motor planning have been developed primarily independently. However, neural data suggests that these processes involve the same brain regions and are performed in an integrated manner. Here, a computational model is described that addresses both the question of how different potential actions are specified and how the brain decides between them. In the model, multiple potential actions are simultaneously represented as continuous regions of activity within populations of cells in frontoparietal cortex. These representations engage in a competition for overt execution that is biased by modulatory influences from prefrontal cortex. The model neural populations exhibit activity patterns that correlate with both the spatial metrics of potential actions and their associated decision variables, in a manner similar to activities in parietal, prefrontal, and premotor cortex. The model therefore suggests an explanation for neural data that have been hard to account for in terms of serial theories that propose that decision making occurs before action planning. In addition to simulating the activity of individual neurons during decision tasks, the model also reproduces key aspects of the spatial and temporal statistics of human choices and makes a number of testable predictions.

Subfields: Cognitive Neuroscience, Social Psychology

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Citations in the corpus

Normalization as a canonical neural computation (2011, background): “— for example, in the mechanism that triggers eye and limb movement”
Intention, action planning, and decision making in parietal-frontal circuits. (2009, methodology): “It has been proposed that making decisions between two spatial locations may involve competition between potential plans (Cisek, 2006), not dissimilar to the competitive bias model for spatial attention (Desimone and Duncan, 1995).”
Human volition: towards a neuroscience of will (2008, background): “For example, Cisek has suggested that prefrontal-cortex signals bias the activation strength of a specific movement alternative that is coded in parietal–premotor circuit”
Computation Through Neural Population Dynamics (2020, background): “Classic theories suggested that preparatory activity was subthreshold (Tanji & Evarts 1976) or gated (Cisek 2006).”
Neural computations associated with goal-directed choice. (2010, background): “(b) Illustration of the main components of the urgency-gating model of Cisek [60].”
Physiologically Inspired Model for the Visual Recognition of Transitive Hand Actions (2013, unknown): “Detailed fMRI studies on action recognition that compare human and monkey cortex suggest a partial homology between the relevant areas in both species (Buccino et al., 2004; Nelissen et al., 2005, 2006; Jastorff et al., 2011).”
Decision making in recurrent neuronal circuits. (2008, methodology): “Indeed, a recurrent circuit approach has been used to build models for action selection and movement preparation (Wilimzig et al., 2006; Cisek, 2006; Heinzle et al., 2007).”
Neural mechanisms for interacting with a world full of action choices. (2010, methodology): “…potential targets are available, representations of potential actions emerge within several frontoparietal neural populations, each composed of a continuum of cells with different preferences for the potential parameters of movement (Cisek 2006, Erlhagen & Schöner 2002, Tipper et al. 2000).”
Cortical control of arm movements: a dynamical systems perspective. (2013, background): “Indeed, premovement firing is found to vary with every movement parameter studied so far (Cisek 2006b), including direction (e.g., Kurata 1989, Wise 1985), distance (e.g., Crammond & Kalaska 2000), speed (Churchland et al. 2006b), and curvature (Hocherman & Wise 1991); even apparently random…”
Evolvable Neuronal Paths: A Novel Basis for Information and Search in the Brain (2011, background): “learning [26,33], many reinforcement learning algorithms (in which the competing units are state-action pairs) [34], and other action [35,36] and attention selection [37] models.”