Hold your horses: A dynamic computational role for the subthalamic nucleus in decision making
By Michael J Frank. Neural Networks, 2006
731 citations recorded by the source corpus.
Subfields: Cellular and Molecular Neuroscience, Neurology
View Paper: https://www.semanticscholar.org/paper/b3e71f57cf5a1582d9cfe608fe840db7de92f30b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cellular and Molecular Neuroscience
- Michael J Frank
- Thomas V. Wiecki
- A. Diamond
- Matthew Botvinick
- A. G. Collins
- Adam R. Aron
- Henning Schroll
- Fred Henrik Hamker
In Neurology
- Adam R. Aron
- Michael J Frank
- Thomas V. Wiecki
- Matthew Botvinick
- Fred Henrik Hamker
- Henning Schroll
- A. G. Collins
- Randall C. O’Reilly
Citations in the corpus
- Conflict monitoring and decision making: Reconciling two perspectives on anterior cingulate function (2007, background): “Gehring and Willoughby (2002), for example, reported ACC engagement when subjects were informed of the outcomes of their decisions in a gambling task.”
- Towards an executive without a homunculus: computational models of the prefrontal cortex/basal ganglia system (2007, background): “The STN (subthalamic nucleus) provides an additional dynamic background of inhibition (NoGo) by exciting the SNr (Frank (2006) for computational advantages of this global NoGo signal for action selection).”
- Methodological and empirical developments for the Ratcliff diffusion model of response times and accuracy (2009, background): “In contrast, neurocomputational accounts such as those proposed by Brown, Bullock, and Grossberg (2004), Frank (2006), Lo and Wang (2006) do use results from neuroanatomy to inform their modelling.”
- Striatum and pre-SMA facilitate decision-making under time pressure (2008, background): “The key hypothesis that is shared by recent neuro-computational models of decisionmaking [8, 9, 10, 11, 12] is that when people have to make decisions under time pressure, the basal ganglia lessen their inhibitory control over the brain in a nonspecific fashion, thereby generally facilitating fast b”
- Triangulating a Cognitive Control Network Using Diffusion-Weighted Magnetic Resonance Imaging (MRI) and Functional MRI (2007, background): “The hypothesis that a preSMAbased conflict function may interact with a response control system was also motivated by computational modeling work, which suggests that the same putative STN mechanism that supports outright response inhibition could be recruited to slow responses in highconflict situa”
- Executive Functions (2012, positive): “The subthalamic nucleus appears to play a critical role in preventing such impulsive or premature responding (Frank 2006).”
- Cognitive Control Predicts Use of Model-based Reinforcement Learning (2014, background): “Because the latter process takes time, it is naturally matched to control theories envisioning a race to suppress a prepotent response while a more controlled process builds up (Frank, 2006).”
- Neurophysiology of performance monitoring and adaptive behavior. (2014, background): “It has been suggested that evaluative signals stem from pMFC via a hyperdirect pathway and that STN then implements motor inhibition to slow down reactions on conflict-laden decisions (183), thereby allowing to accumulate additional information useful for more specific adaptation.”
- The Neural Basis of Inhibition in Cognitive Control (2007, background): “Computational simulations suggest that active inhibition over motor output could be useful not just to cancel motor output (Aron and Poldrack 2006) but also to hold it in abeyance in the service of decision making (Frank 2006).”
- Anatomy of a decision: striato-orbitofrontal interactions in reinforcement learning, decision making, and reversal. (2006, positive): “…length of persistence of activity can theoretically be shorter depending on subsequent input (i.e., if the contents of working memory are updated; Frank et al., 2001 ; O'Reilly & Frank, 2006) or longer (if the working memory representations are refreshed because they continue to be task relevant).”
- A computational model of inhibitory control in frontal cortex and basal ganglia. (2011, positive): “While neural network models with a more detailed representation of PFC exist (e.g., O’Reilly & Frank, 2006) in which rule-like representations can develop through experience, how exactly the necessary computations can be implemented dynamically is as-of-today a still unresolved question.”
- Progress in Executive-Function Research (2008, background): “This is important because many requirements for control, e.g., in Stroop and other interference tasks, are not so much about stopping outright as about, ostensibly, braking an inappropriate response tendency until a discrepancy can be resolved (Frank, 2006).”
- Subcortical structures and cognition (2009, background): “In addition, this pathway is important for preventing premature responding (Frank, 2006).”