The Neural Basis of Decision Making
Abstract
The study of decision making spans such varied fields as neuroscience, psychology, economics, statistics, political science, and computer science. Despite this diversity of applications, most decisions share common elements including deliberation and commitment. Here we evaluate recent progress in understanding how these basic elements of decision formation are implemented in the brain. We focus on simple decisions that can be studied in the laboratory but emphasize general principles likely to extend to other settings.
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The role of the prefrontal cortex appears to shift through the stages of the addiction cycle, with ventral and medial prefrontal regions activated during intoxication and craving and decreased activity in these regions and dorsolateral prefrontal cortex during withdrawal.
“The role of the prefrontal cortex appears to shift through the stages of the addiction cycle, such that the ventral and medial prefrontal cortex, including the orbitofrontal cortex and the dor sal anterior cingulate cortex (regions involved with salience attri bution), are activated during the intoxication and craving stages. In contrast, the withdrawal stage is associated with a decreased activ”
From Substance use disorders: a comprehensive update of classification, epidemiology, neurobiology, clinical aspects, treatment and prevention (2023) · cites this paper as [62]
Internal decision variables are accumulated as ramping neural activity and transformed into a decision when a decision boundary is reached.
“Here, close to the top of the cortical hierarchy, internal decision variables (DV) are accumulated in a form of ramping neural activity (3–6) that is transformed into a decision when a decision boundary is reached (7).”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [4]
The neural activity ramp during epoch 4 is consistent with a bounded integration mechanism of decision-making.
“In what follows we argue the neural activity ramp during epoch 4 is consistent with a bounded integration mechanism of decision-making (3–5, 7).”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [4]
In the standard drift-diffusion model, a decision variable accumulates sensory evidence with stochastic noise and produces a decision at reaction time when it reaches either the upper or lower bound.
“A DV in standard drift-diffusion model (DDM) (3–5, 7) (Fig. 4A) accumulates sensory evidence (drift), accompanied by stochastic internal or sensory noise (diffusion) that, when reaches either upper (correct trials) or lower (error trials) bound, produces a decision at RT.”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [4]
The choice at initiation and reaction times as a function of task difficulty (coherence of dot motion) were explained by a bounded drift-diffusion model consistent with previous studies in humans and monkeys.
“The choice at initiation (initial hand trajectory) and reaction times as a function of task difficulty (coherence of dot motion) were explained by a bounded drift-diffusion model (Fig. 2, black curves) consistent with previous studies in humans and monkeys1,9,14.”
From Changes of mind in decision-making (2009) · cites this paper as [1]
Psychophysical and neural data support a model termed drift diffusion, random walk or race in which a decision is made when the accumulated noisy evidence reaches a criterion level, termed a decision bound.
“Psychophysical and neural data1 support a model termed drift diffusion6, random walk5,7 or race8 in which a decision is made when the accumulated noisy evidence (decision variable) reaches a criterion level, termed a decision bound.”
From Changes of mind in decision-making (2009) · cites this paper as [1]
The steeper buildup for stronger stimuli is consistent with the accumulation of stronger evidence, shorter decision times, and ultimately more accurate decisions.
“consistent with the accumulation of stronger evidence, shorter decision times, and ultimately more accurate decisions (13, 17).”
From Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex (2009) · cites this paper as [17]
The decision variable v(t) is represented by the firing rates of LIP neurons.
“This accumulation, which we call a decision variable, v(t), is represented by the firing rates of LIP neurons.”
From Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex (2009) · cites this paper as [17]
Left-right choices on this task are governed by the accumulation of evidence favoring one or the other option.
“As previously shown, the left-right choices on this task are governed by the accumulation of evidence favoring one or the other option (17).”
From Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex (2009) · cites this paper as [17]
The mechanism underlying the representation of certainty in LIP is linked to the same evidence accumulation that underlies choice and decision time.
“The mechanism underlying the representation of certainty in LIP is linked to the same evidence accumulation that underlies choice and decision time (17, 20).”
From Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex (2009) · cites this paper as [17]
In trained monkeys, MT and LIP play different but complementary roles in forming the direction decision: MT provides sensory evidence used to form the decision, whereas LIP accumulates and interprets the sensory evidence to form the categorical judgment that instructs behaviour.
“In trained monkeys, MT and LIP are thought to play different but complementary roles in forming the direction decision: MT (possibly in tandem with other motion-sensitive areas like the middle superior temporal area, or MST) provides sensory evidence used to form the decision, whereas LIP (possibly in tandem with other sensory-oculomotor areas like the FEF and superior colliculus) accumulates and”
From Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area (2008) · cites this paper as [23]
Near the saccade, the neural correlate of motion strength vanished.
“Near the saccade, however, this neural correlate of motion strength 1,2,15 vanished (Fig. 2a,c, right).”
From Decision-making with multiple alternatives (2008) · cites this paper as [1]
The pattern of LIP activity in the 4-choice task is similar to the process underlying 2-choice decisions.
“This pattern of LIP activity in the 4-choice task is similar to the process underlying 2-choice decisions1,2,16.”
From Decision-making with multiple alternatives (2008) · cites this paper as [1]
On a variety of 2-choice decision tasks, a threshold crossing is associated with termination of the decision.
“On a variety of 2-choice decision tasks, a threshold crossing is associated with termination of the decision 1,2,19.”
From Decision-making with multiple alternatives (2008) · cites this paper as [1]
In a well-known motion discrimination paradigm, the direction of motion of a sensory cue is thought to be discriminated by cells in the middle temporal area, while lateral intraparietal cells select the appropriate action (e.g., a specific saccade).
“For example in a well-known motion discrimination paradigm, the direction of motion of a sensory cue is thought to be discriminated by cells in the middle temporal area, while lateral intraparietal cells select the appropriate action (e.g., a specific saccade) (Gold and Shadlen, 2007).”
From Attention, learning, and the value of information (2012) · cites this paper as Gold and Shadlen, 2007
Reward or value signals modulate neural firing through synaptic inputs, just like sensory stimuli.
“Reward or value signals modulate neural firing through synaptic inputs, just like sensory stimuli, in consonance with the view of Gold and Shadlen (2007).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Gold and Shadlen, 2007
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- Authors
- Gold, J. I., & Shadlen, M. N.
- Affiliations
- University of Pennsylvania (US)
- Howard Hughes Medical Institute (US)
- Venue
- Annual Review of Neuroscience
- Publisher
- Annual Reviews
- Published
- 2007-07-01
- Volume
- 30
- Issue
- 1
- Pages
- 535-574
- ISSN
- 0147-006X (ISSN-L)
- 1545-4126
- OpenAlex
- W2169134378
- Semantic Scholar
- 68922506a267e6ffe51d63289934a50fcb6709bb
- Cited by
- 3,764
- References
- 247
- Citations by year
- 2026: 226 · 2025: 239 · 2024: 242 · 2023: 223 · 2022: 240 · 2021: 216 · 2020: 244 · 2019: 249 · 2018: 235 · 2017: 194 · 2016: 195 · 2015: 240 · 2014: 224 · 2013: 179 · 2012: 188
- FWCI
- 6.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annual Review of Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- decision-making, neural basis of decision making, deliberation, commitment, brain circuits
- MeSH terms
- Animals, Brain, Cognition, Decision Making, Humans, Models, Neurological, Movement, Perception, Psychomotor Performance, Psychophysics, Volition
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