How the brain integrates costs and benefits during decision making

By Ulrike Basten, Guido Biele, Hauke R. Heekeren, Christian J. Fiebach. 2010

333 citations recorded by the source corpus.

When we make decisions, the benefits of an option often need to be weighed against accompanying costs. Little is known, however, about the neural systems underlying such cost–benefit computations. Using functional magnetic resonance imaging and choice modeling, we show that decision making based on cost–benefit comparison can be explained as a stochastic accumulation of cost–benefit difference. Model-driven functional MRI shows that ventromedial and left dorsolateral prefrontal cortex compare costs and benefits by computing the difference between neural signatures of anticipated benefits and costs from the ventral striatum and amygdala, respectively. Moreover, changes in blood oxygen level dependent (BOLD) signal in the bilateral middle intraparietal sulcus reflect the accumulation of the difference signal from ventromedial prefrontal cortex. In sum, we show that a neurophysiological mechanism previously established for perceptual decision making, that is, the difference-based accumulation of evidence, is fundamental also in value-based decisions. The brain, thus, weighs costs against benefits by combining neural benefit and cost signals into a single, difference-based neural representation of net value, which is accumulated over time until the individual decides to accept or reject an option.

Subfields: Cognitive Neuroscience, Economics and Econometrics, General Decision Sciences

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Citations in the corpus

Orthogonal representations for robust context-dependent task performance in brains and neural networks (2022, neutral): “, 2008) and medial orbitofrontal cortex (Basten et al., 2010) (Figures S3C and S3D), and an encoding of choice value (Boorman et al.”
Collective decision-making (2017, background): “Conceptualising value-based decisions shows, however, that there are similarities between perceptual and valuebased decision making [17,18], although the usage of the term ‘value’ may vary with context [19]; value may refer to stimulus intensity, or to reward magnitude.”
Neurophysiology of performance monitoring and adaptive behavior. (2014, background): “Furthermore, in human neuroimaging studies, it appears that vmPFC/mOFC generally represents expected subjective value, independent of whether the outcome is appetitive or aversive (24, 418, 419, 514).”
Confidence in value-based choice (2012, background): “(c) Signal in vmPFC (6-mm sphere centered at the peak of the DV main effect (12, 56, 4)), showing main effects of DV and degree of confidence in the absence of an interaction.”
The Computation of Stimulus Values in Simple Choice (2014, methodology): “For behavioral and neural evidence related to the drift diffusion model, or DDM, see Chapter 19 and Basten et al. (2010), Hare et al. (2011b), and Krajbich et al. (2010).”
Informatic parcellation of the network involved in the computation of subjective value. (2014, background): “All voxels shown survive voxel-level significance of P < 0.001. coordinates reported for the comparisons of cost and benefit in a value-based choice task (Basten et al., 2010).”
Diffusion Decision Model: Current Issues and History (2016, background): “Many studies have established relationships between diffusion models and neurophysiological measures such as single cell recordings in rodents and monkeys [52,88,105,143–148], EEG [42,43,149,150], fMRI [42,43,139,151–155], transcranial magnetic stimulation (TMS) [156], and transcranial alternating c”
The root of all value: a neural common currency for choice (2012, background): “Basten et al. (2010) [28] even showed that when subjects must integrate information about both monetary gains (benefit) and monetary losses (cost), activity in this same medial frontal area is correlated with the integrated difference between these two properties.”
The Attentional Drift-Diffusion Model Extends to Simple Purchasing Decisions (2012, background): “More recently, it has been shown that these models also provide good accounts of value-based choice (Basten et al., 2010; Krajbich et al., 2010; Milosavljevic et al., 2010; Philiastides et al., 2010; Hare et al., 2011; Krajbich and Rangel, 2011).”