A general mechanism for perceptual decision-making in the human brain

By Hauke R. Heekeren, Sean Marrett, Peter A. Bandettini, Leslie G. Ungerleider. Nature, 2004

763 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/04c295ed4a5f4672b05c64e356522207295e30d1

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In Cognitive Neuroscience

Citations in the corpus

Sensory-motor mechanisms in human parietal cortex underlie arbitrary visual decisions (2008, background): “First, are there other effector-specific regions that also show decision signals? Second, are there any brain regions that accumulate sensory evidence independently of motor plan or response type? In other words, is there evidence for a neural correlate of a general decision-making module? Two recen”
The speed-accuracy tradeoff: history, physiology, methodology, and behavior (2014, background): “Evidence supporting this claim derives from several sources, including human fMRI (Heekeren et al., 2004) and EEG (Ratcliff et al., 2009; O’Connell et al., 2012; van Vugt et al., 2012; Kelly and O’Connell, 2013), but by far the most convincing stems from single-neuron recordings in non-human…”
Symbols and quantities in parietal cortex: elements of a mathematical theory of number representation and manipulation (2007, background): “Most notably, when humans are asked to decide whether a noisy image depicts a face or a house, left dorsolateral prefrontal cortex activity is proportional to the difference between activation in the fusiform face area and in the parahippocampal place area and predicts behavioral performance (Heeker”
Analyzing Neuroimaging Data Through Recurrent Deep Learning Models (2018, neutral): “…gyrus, as well as the right fusiform gyrus for the body state, the fusiform gyrus (also known as the fusiform face area FFA; [Haxby et al., 2001, Heekeren et al., 2004]) and amygdala for the face state, the parahippocampal gyrus (or parahippocampal place area PPA; [Haxby et al., 2001, Heekeren…”
Interpretable LSTMs For Whole-Brain Neuroimaging Analyses (2018, methodology): “We restricted this analysis to the face and place stimulus classes, as the neural networks involved in processing face and place stimuli, respectively, have been widely characterized [57, 58].”
How the brain integrates costs and benefits during decision making (2010, methodology): “Note that the identified accumulator regions do not map onto neural systems associated with decision conflict (in anterior cingulate cortex or DLPFC) (30) or processing difficulty in general (31, 32).”
The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks (2008, neutral): “Also, studies using event-related potential (Philiastides, Ratcliff, & Sajda, 2006) and fMRI measures ( Heekeren, Marrett, Bandettini, & Ungerleider, 2004 ) are beginning to appear.”
Informatic parcellation of the network involved in the computation of subjective value. (2014, background): “Although the precise contribution of SFS to value-based choice is unknown, it is worth noting that the reported cluster in our IBMA closely neighbors the coordinates reported to correspond to sensory evidence in a perceptual decision-making task (Heekeren et al., 2004) and includes the SFS Fig.”
Decision making in recurrent neuronal circuits. (2008, background): “…in which the task was to discriminate whether an ambiguous image is a face or a house, and the BOLD signal in the dorsolateral prefrontal cortex was found to covary with the difference signal between the face- and house-selective regions in the ventral temporal cortex (Heekeren et al., 2004).”
The neural basis of decision making. (2007, methodology): “…in the brain that have rendered a decision, as opposed psychophysical represents a DV or even the outcome of the perceptual categorization (Sheinberg & Lo-gothetis 1997, 2001). were used to identify correlates of a DV that reads out object categorization evidence from IT (Heekeren et al. 2004).”