The neural basis of decision making.

By Joshua I. Gold, Michael N. Shadlen. Annual Review of Neuroscience, 2007

3,612 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Sensory Systems

View Paper: https://www.semanticscholar.org/paper/68922506a267e6ffe51d63289934a50fcb6709bb

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Citations in the corpus

Reinforcement learning: the good, the bad and the ugly. (2008, positive): “The idea that subjects are trying to optimize their longrun rates of acquisition of reward has become important in studies of time-sensitive sensory decision-making [11,69].”
Artificial Neural Networks for Neuroscientists: A Primer (2020, methodology): “Progress was also made in developing models of basic cognitive functions, such as simple decision making (Gold and Shadlen, 2007; Wang, 2008).”
Speed/Accuracy Trade-Off between the Habitual and the Goal-Directed Processes (2011, background): “A similar idea has been taken by neuroscientists to explain the speed/accuracy tradeoff, using signal detection theory (see [58] for review).”
Intention, action planning, and decision making in parietal-frontal circuits. (2009, background): “…functions, it has also been shown in different contexts to be important for aspects of action, including movement intention and decision making (Mountcastle et al., 1975; Andersen, 1987; Andersen and Buneo, 2002; Gold and Shadlen, 2007; Rizzolatti et al., 1997; Kalaska et al., 1997; Johnson et. al.”
Attention, learning and the value of information (2012, background): “One line of research describes target selection in motor decision terms, as the integration of evidence toward, and eventual commitment to a shift of gaze (Gold and Shadlen, 2007; Kable and Glimcher, 2009).”
What cognitive processes drive response biases? A diffusion model analysis (2011, methodology): “In the emerging field of neuroeconomics, this type of manipulation is an example of a value-based decision (for reviews, Glimcher, 2005; Gold & Shadlen, 2007; Sanfey, Loewenstein, McClure, & Cohen, 2006; Sugrue, Corrado, & Newsome, 2005).”
Reward-based training of recurrent neural networks for cognitive and value-based tasks (2016, background): “Such policies can also be compared more directly to “optimal” solutions when they are known, for instance to the signal detection theory account of perceptual decision-making (Gold & Shadlen, 2007).”
Methodological and empirical developments for the Ratcliff diffusion model of response times and accuracy (2009, background): “Moreover, recent work has started to connect the diffusion model to a range of findings from neuroscience, mainly through the analysis of single cell recordings in monkeys, but also through mathematical modelling (eg, Bogacz & Gurney, 2007 ; for reviews see Gold & Shadlen, 2007 ; Ratcliff & McKoon”
Do People Make Decisions Under Risk Based on Ignorance? An Empirical Test of the Priority Heuristic Against Cumulative Prospect Theory (2008, background): “Considering the findings that individuals are able quickly to integrate information in a weighted compensatory manner and that decision time increases with the increasing similarity of the expected utilities of the gambles, evidence accumulation models (Busemeyer & Townsend, 1993; Busemeyer & Johnso”
Activation in the neural network responsible for categorization and recognition reflects parameter changes (2011, background): “S1, the maps also pointed to other brain regions that past work suggests are associated with criterion setting and evidence accumulation in decision making, including areas of posterior parietal cortex as well as the caudate (27, 28, 31, 32, 34, 35).”
Probabilistic population codes for Bayesian decision making (2008, methodology): “When accompanied by a termination rule, our model, like a number of others (Ditterich et al., 2003; Gold and Shadlen, 2007; Laming, 1968; Link, 1992; Link and Heath, 1975; Mazurek et al., 2003; Ratcliff and Rouder, 1998; Reddi and Carpenter, 2000; Smith and Ratcliff, 2004; Stone, 1960; Usher and…”
An Introduction to Model-Based Cognitive Neuroscience (2015, methodology): “As part of addressing this problem, in previous work, Donkin and colleagues ran extensive parameter recovery simulations for the diffusion and LBA models [12].”
The Noisy Brain: Stochastic Dynamics as a Principle of Brain Function (2010, neutral): “2004 , Smith and Ratcliff 2004 , Sugrue , Corrado and Newsome 2005 , Gold and Shadlen 2007 ) .”