Matching Behavior and the Representation of Value in the Parietal Cortex

By L. Sugrue, G. Corrado, William T. Newsome. Science, 2004

1,053 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2ed6866784ae093c3a2a6cb9f8c7b8a11076ca0e

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In Cognitive Neuroscience

Citations in the corpus

Gaussian Processes for time-marked time-series data (2012, methodology): “Clipping models have often been used in experimental science (for example Sugrue et al. (2004); Churchland et al. (2006)).”
Value-based gain control: divisive normalization in parietal cortex (2011, background): “Previous studies have framed LIP activity in terms of gain ratio (Platt and Glimcher, 1999) or fractional income (Sugrue et al., 2004; Corrado et al., 2005), both of which assume a fractional reward representation based on V in / (Vin Vout), but no study has isolated the effect of manipulating…”
A neural circuit model of flexible sensorimotor mapping: learning and forgetting on multiple timescales. (2007, background): “In other words, response choices that are determined by the slow learning components are selected in a proportion that matches the relative reinforcement obtained on these choices, and thus the model behaves according to the so called ‘‘matching law’’ (Herrnstein, 1997; Sugrue et al., 2004; Corrad”
Bayesian Spiking Neurons I: Inference (2008, methodology): “It is compatible with some neurophysiological data, since firing rates in the lateral parietal areas have been reported to be proportional to the log probability ratio for appropriate eye movement responses (Mazurek et al., 2003) or probability of reward (Glimcher & Rustichini, 2004; Sugrue, Corrad”
One-dimensional dynamics of attention and decision making in LIP. (2008, background): “…(Platt and Glimcher, 1999; Dorris and Glimcher, 2004), and visual categorization (Freedman and Assad, 2006) as well as the representation of value (Sugrue et al., 2004), time (Leon and Shadlen, 2003; Janssen and Shadlen, 2005), and inferred motion (Assad and Maunsell, 1995; Eskandar and Assad,…”
Generalization of value in reinforcement learning by humans (2012, background): “values in the brain are normalized between options (Platt & Glimcher, 1999; Dorris & Glimcher, 2004; Sugrue et al., 2004), and previous fMRI work (Daw et al.”
Probabilistic population codes for Bayesian decision making (2008, background): “Recent experimental data suggest that LIP might indeed represent either expected reward for all actions or the probability that an action will maximize reward (Platt and Glimcher, 1999; Sugrue et al., 2004).”