Cited by 167Neural and Behavioral Psychology StudiesDecision-Making and Behavioral EconomicsNeural dynamics and brain function

Building Bridges between Perceptual and Economic Decision-Making: Neural and Computational Mechanisms

Open access
Summerfield, Christopher, Tsetsos, Konstantinos·2012·Frontiers in Neuroscience

Abstract

Investigation into the neural and computational bases of decision-making has proceeded in two parallel but distinct streams. Perceptual decision-making (PDM) is concerned with how observers detect, discriminate, and categorize noisy sensory information. Economic decision-making (EDM) explores how options are selected on the basis of their reinforcement history. Traditionally, the sub-fields of PDM and EDM have employed different paradigms, proposed different mechanistic models, explored different brain regions, disagreed about whether decisions approach optimality. Nevertheless, we argue that there is a common framework for understanding decisions made in both tasks, under which an agent has to combine sensory information (what is the stimulus) with value information (what is it worth). We review computational models of the decision process typically used in PDM, based around the idea that decisions involve a serial integration of evidence, and assess their applicability to decisions between good and gambles. Subsequently, we consider the contribution of three key brain regions - the parietal cortex, the basal ganglia, and the orbitofrontal cortex (OFC) - to perceptual and EDM, with a focus on the mechanisms by which sensory and reward information are integrated during choice. We find that although the parietal cortex is often implicated in the integration of sensory evidence, there is evidence for its role in encoding the expected value of a decision. Similarly, although much research has emphasized the role of the striatum and OFC in value-guided choices, they may play an important role in categorization of perceptual information. In conclusion, we consider how findings from the two fields might be brought together, in order to move toward a general framework for understanding decision-making in humans and other primates.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3389/fnins.2012.00070

Cites

228 of 228 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The race model captures several features of the relationship between choice, reaction time and confidence in a value-based choice paradigm.

    “In keeping with recent research efforts that have incorporated dynamic models into the field of economic decision-making28, we find that this model captures several features of the relationship between choice, reaction time and confidence in a value-based choice paradigm.”

    From Confidence in value-based choice (2013) · cites this paper as [28]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. Dickinson A. Balleine B. (2002). “The role of learning in motivation,” in Stevens’ Handbook of Experimental Psychology Vol. 3: Learning, Motivation and Emotion, ed. Gallistel C. R. (New York: Wiley), 497– 533
  2. Krajbich I. Armel C. Rangel A. (2011). Visual fixations and the computation and comparison of value in simple choice. Nat. Neurosci. 14, 1217– 1217 10.1038/nn0911-1217e 20835253 · DOI

Cited by

Sorted by citations

Authors
Summerfield, C., & Tsetsos, K.
Affiliations
University of Oxford (GB)
Corresponding author
Summerfield, Christopher
Venue
Frontiers in Neuroscience
Published
2012
Volume
6
Pages
70-70
ISSN
  • 1662-453X (ISSN-L)
  • 1662-4548
OpenAlex
W2070685206
Cited by
167
References
228
Citations by year
2026: 2 · 2025: 9 · 2024: 6 · 2023: 9 · 2022: 12 · 2021: 10 · 2020: 16 · 2019: 19 · 2018: 10 · 2017: 21 · 2016: 15 · 2015: 17 · 2014: 10 · 2013: 9 · 2012: 2
FWCI
6.22
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Frontiers in Neuroscience (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Explore Bristol Research (publishedVersion)
  • BIROn (Birkbeck, University of London) (acceptedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
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
perceptual decision making, economic decision-making, evidence integration, parietal cortex, basal ganglia, striatum, orbitofrontal cortex, reward value, computational models of decision making

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review