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Cited by 534Neural and Behavioral Psychology StudiesDecision-Making and Behavioral EconomicsNeurotransmitter Receptor Influence on Behavior

Confidence in value-based choice

Open access
De Martino, Benedetto, Fleming, Stephen M, Garrett, Neil, Dolan, Raymond J·2013·Nature Neuroscience
DOI 10.1038/nn.3279PMC PMC3786394
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Abstract

Decisions are never perfect, with confidence in one's choices fluctuating over time. How subjective confidence and valuation of choice options interact at the level of brain and behavior is unknown. Using a dynamic model of the decision process, we show that confidence reflects the evolution of a decision variable over time, explaining the observed relation between confidence, value, accuracy and reaction time. As predicted by our dynamic model, we show that a functional magnetic resonance imaging signal in human ventromedial prefrontal cortex (vmPFC) reflects both value comparison and confidence in the value comparison process. Crucially, individuals varied in how they related confidence to accuracy, allowing us to show that this introspective ability is predicted by a measure of functional connectivity between vmPFC and rostrolateral prefrontal cortex. Our findings provide a mechanistic link between noise in value comparison and metacognitive awareness of choice, enabling us both to want and to express knowledge of what we want.

AI snapshot

Written from the paper’s full text.

TL;DR

Confidence in value-based choices reflects the stochastic evolution of a value-comparison process, predicting choice accuracy and reaction time. The same vmPFC region represents both value difference and confidence, while rRLPFC and its functional coupling with vmPFC support metacognitive readout of confidence.

Key findings

  • Unsigned |DV| accounted for only 17.7% of variance in confidence ratings on average (r = 0.42 ± 0.19 SD), showing partial independence between confidence and value difference. Higher confidence was associated with increased choice accuracy; RT showed main effects of |DV| and confidence (both P < 0.001) but no interaction.
  • A logistic model with choice confidence as a modulator of DV (Model 2) fit best by BIC, with BIC differences relative to Model 2 of 214.6 for Model 1, 196.2 for Model 3, 251.7 for Model 4, and 111.9 for Model 5; Model 2 beat canonical Model 1 in 19/20 participants.
  • vmPFC activity was modulated by both |DV| and confidence ([12, 47, −11], p < 0.05 FWE cluster-level), with main effects of value F(1,19) = 5.1, p < 0.05 and confidence F(1,19) = 7.6, p < 0.05, but no interaction F(1,19) = 0.7, p > 0.5.
  • rRLPFC tracked confidence but not DV ([39, 41, 16], p < 0.005 SVC); confidence-modulated rRLPFC–vmPFC connectivity was found ([9, 50, −11], z = 3.05, p < 0.05 small-volume FWE), and connectivity strength predicted metacognitive accuracy (vmPFC 15, 56, −5; p < 0.05 SVC). Choice reversals occurred on 14.7% ± 5.7% of second presentations and were predicted by lower initial confidence.

Methods

  • 28 participants were recruited; 20 were included after exclusions for head motion (4), erratic choices (3), and insufficient confidence-rating range (1). Participants fasted 4 hours, made 340 binary choices among 19 snack items (each pair twice, counterbalanced left/right), rated choice confidence after each choice, then completed a post-scan BDM bidding task with bid confidence.
  • Behavioral analysis compared five logistic regression models predicting choice from DV, choice confidence, and bid confidence; a race model simulated value comparison, confidence, and RT. fMRI analysis used SPM8 with parametric GLMs for |DV| and confidence, factorial GLMs, and a psychophysiological interaction (PPI) analysis with rRLPFC as seed.
  • Statistical inference used whole-brain FWE correction at cluster level and small-volume correction for rRLPFC/vmPFC regions of interest.

Limitations

  • Stated: an alternative interpretation is that confidence is coded elsewhere and communicated to vmPFC, possibly via rRLPFC; resolving this requires high-temporal-resolution methods such as MEG. The authors also note that other functional forms linking model confidence to reported confidence are possible but were not investigated.
  • Stated: aside from |DV| and RT, other internal and external factors likely affect subjective confidence; only a limited set of factors with good experimental control is reported.
  • Evident from the text: the final fMRI sample is small (20 participants), and several key conclusions rely on individual-difference correlations and model comparisons within that sample.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1038/nn.3279

Cites

38 of 38 references verified0 citation errors · 0 not checkable

Statistics

4 tests recomputed · 0 with the reported and recomputed p on opposite sides of .05

Claims the paper makes

0 of 38 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    For 19 out of 20 subjects, there was a significant partial correlation (P < 0.05) between confidence ratings for repeated item pairs after controlling for |DV|, indicating stability in confidence for judgments of particular item pairs that cannot be accounted for by |DV| alone.

    “For 19 out of 20 subjects, there was a significant partial correlation (P < 0.05) between confidence ratings for repeated item pairs after controlling for the influence of ∣DV∣, indicating stability in confidence for judgments of particular item pairs that cannot be accounted for by ∣DV∣ alone.”
    Quote in the paperSubstantial claim
  • Findingown data

    Choices that would be subsequently reversed were associated with significantly lower initial confidence than those that would subsequently be repeated (reversal confidence = 210.6 ± 72.4 SD; repetition confidence = 340.2 ± 53.5 SD; t(19)=12.1, P<10−10).

    “Choices that would be subsequently reversed were associated with significantly lower initial confidence than those that would subsequently be repeated (reversal confidence (a.u.) = 210.6 ± 72.4 SD; repetition confidence = 340.2 ± 53.5 SD; t(19) = 12.1, P < 10−10).”
    Quote in the paperSubstantial claim
  • Findingown data

    Model 2 (DV*confidence) provided a significantly better account (lower BIC) of participants' choices than Models 1, 3, 4, and 5.

    “In accordance with our predictions, Model 2 provided a significantly better account (i.e. lower Bayesian information criterion – BIC) of participants’ choices than the other four models (Figure 2a) as shown by the difference in BIC relative to Model 2: Model 1, 214.6; Model 3, 196.2; Model 4, 251.7; Model 5, 111.9.”
    Quote in the paperSubstantial claim
  • Findingown data

    Unsigned |DV| accounted for an average of 17.7% of the variance in participants' confidence ratings (r = 0.42 ± 0.19 SD).

    “Importantly, we observed that unsigned ∣DV∣ only accounted for an average of 17.7% of the variance in participants’ confidence ratings (r =0.42 ±0.19 SD).”
    Quote in the paperSubstantial claim
  • Findingown data

    In a logistic regression predicting subsequent reversal from both |DV| and initial confidence, initial confidence was a significant negative predictor of choice reversal (mean standardised regression coefficient −0.0083 ± 0.0034 SD; t(19)=−10.9, P<10−9).

    “In a logistic regression model predicting subsequent reversal from both ∣DV∣ and initial confidence, initial confidence was a significant negative predictor of choice reversal (mean standardised regression coefficient −0.0083 ± 0.0034 SD; one-sample t-test t(19) = −10.9, P < 10−9).”
    Quote in the paperSubstantial claim
  • May combine two claims. 21
  • Classified as probably not a substantial claim. 8

  • May combine two claims.

    Findingcitation

    Difference in value (DV) is a reliable predictor of participants' choices, with the slope of the logistic regression being a measure of choice accuracy, or noise in the choice process.

    “In line with previous studies we show DV is a reliable predictor of participants’ choices2,19 with the slope of the logistic regression being a measure of choice accuracy, or noise in the choice process20.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [2] FitzGerald, T.H.B., Seymour, B. & Dolan, R.J. The role of human orbitofrontal cortex in value comparison for incommensurable objects. J. Neurosci. 29, 8388–8395 (2009). source

      Not checked in the free run.

    • cites [19] Boorman, E.D., Behrens, T.E.J., Woolrich, M.W. & Rushworth, M.F.S. How green is the grass on the other side? Frontopolar cortex and the evidence in favor of alternative courses of action. Neuron 62, 733–743 (2009). source

      Not checked in the free run.

    • cites [20] Sugrue, L.P., Corrado, G.S. & Newsome, W.T. Choosing the greater of two goods: neural currencies for valuation and decision making. Nat. Rev. Neurosci. 6, 363–375 (2005). source

      Not checked in the free run.

  • May combine two claims.

    Findingown data

    In vmPFC, there were main effects of |DV| and confidence but no interaction (2×2 ANOVA: main effect of value F(1,19)=5.1, p<0.05; main effect of confidence F(1,19)=7.6, p<0.05; interaction F(1,19)=0.7, p>0.5).

    “This analysis showed main effects of ∣DV∣ and confidence in vmPFC, but importantly no interaction (2×2 ANOVA with factors value, confidence: main effect of value: F(1,19) = 5.1, p<0.05; main effect of confidence: F(1,19) = 7.6, p<0.05; interaction: F(1,19) = 0.7, p>0.5) (Figure 3c).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    rRLPFC tracks changes in reported confidence but does not code for difference in value ([39, 41, 16], p<0.005 SVC).

    “We first established that rRLPFC tracks changes in reported confidence, but does not code for difference in value (Figure 4a, b, Supplementary Figure 4 and Supplementary Table 3, [39, 41, 16], p<0.005 SVC), as expected for a region providing a read-out of decision confidence.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Reaction time showed main effects of both |DV| and confidence (both P < 0.001), but no interaction.

    “This effect of confidence on choice was also reflected in reaction time (RT), with main effects of both ∣DV∣ and confidence (both P < 0.001), but no interaction (Figure 1d).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Confidence had a robust main effect on choice across sessions (F(1,19)=39.75; P<0.0001), with no significant main effect of session (F(3,57)=0.3; P=0.7) and no session-by-confidence interaction (F(3,57)=0.13; P=0.9).

    “Splitting the logistic regression analysis into separate sessions revealed a robust main effect of confidence (F(1,19) = 39.75; P < 0.0001) but a non-significant main effect of session (F(3,57) = 0.3; P = 0.7) and a lack of interaction between session and confidence (F(3,57) = 0.13; P = 0.9; Supplementary Figure 2).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    rRLPFC represented confidence, but not DV.

    “First, rRLPFC represented confidence, but not DV, as predicted for a brain region that has access to information about confidence but is not directly involved in value comparison.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Both confidence-related activity in rRLPFC and coupling between rRLPFC and vmPFC predicted the relationship between confidence and accuracy across individuals.

    “Second, both confidence-related activity in rRLPFC and coupling between rRLPFC and vmPFC predicted the relationship between confidence and accuracy across individuals.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Previous studies have reported neural correlates of decision confidence in brain regions associated with a value representation, for example firing rates in rat orbitofrontal cortex and fMRI signal in human vmPFC show graded changes as perceptual decisions become more difficult.

    “Previous studies have reported neural correlates of decision confidence in brain regions associated with a value representation. For example, firing rates in rat orbitofrontal cortex7 and fMRI signal in human vmPFC8 show graded changes as perceptual decisions become more difficult.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [7] Kepecs, A., Uchida, N., Zariwala, H.A. & Mainen, Z.F. Neural correlates, computation and behavioural impact of decision confidence. Nature 455, 227–231 (2008). source

      Not checked in the free run.

    • cites [8] Rolls, E.T., Grabenhorst, F. & Deco, G. Choice, difficulty, and confidence in the brain. Neuroimage 53, 694–706 (2010). source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Prior work has established that the ventromedial prefrontal cortex (vmPFC) plays a central role in computing the value of potential choice options, with activity in this region reflecting the dynamic evolution of a value comparison.

    “Prior work has established that the ventromedial prefrontal cortex (vmPFC) plays a central role in computing the value of potential choice options1-5, with activity in this region reflecting the dynamic evolution of a value comparison6.”
    Quote in the paperSubstantial claim0 of 6 sources checked
    • cites [1] Rangel, A. & Hare, T. Neural computations associated with goal-directed choice. Curr. Opin. Neurobiol. 20, 262–270 (2010). source

      Not checked in the free run.

    • cites [2] FitzGerald, T.H.B., Seymour, B. & Dolan, R.J. The role of human orbitofrontal cortex in value comparison for incommensurable objects. J. Neurosci. 29, 8388–8395 (2009). source

      Not checked in the free run.

    • cites [3] Kable, J.W. & Glimcher, P.W. The neural correlates of subjective value during intertemporal choice. Nat. Neurosci. 10, 1625–1633 (2007). source

      Not checked in the free run.

    • cites [4] Basten, U., Biele, G., Heekeren, H.R. & Fiebach, C.J. How the brain integrates costs and benefits during decision making. Proc. Natl. Acad. Sci. USA 107, 21767–21772 (2010). source

      Not checked in the free run.

    • cites [5] Smith, D.V. et al. Distinct value signals in anterior and posterior ventromedial prefrontal cortex. J. Neurosci. 30, 2490–2495 (2010). source

      Not checked in the free run.

    • cites [6] Hunt, L.T. et al. Mechanisms underlying cortical activity during value-guided choice. Nat. Neurosci. 15, 470–476 (2012). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    The pattern across conditions closely resembles that seen for RTs, providing convergent evidence that vmPFC activity is tightly linked to behavior.

    “Furthermore, the pattern across conditions closely resembles that seen for RTs (Figure 1d) providing convergent evidence that vmPFC activity is tightly linked to behavior.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Confidence can be measured independently of the choice process and is conceptualized as a second-order metacognitive evaluation.

    “In contrast, the field of perceptual decision-making has highlighted the fact that confidence can be measured independently of the choice process itself9,10, where it is conceptualized as reflecting a “second-order” metacognitive evaluation.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [9] Fleming, S.M., Weil, R.S., Nagy, Z., Dolan, R.J. & Rees, G. Relating introspective accuracy to individual differences in brain structure. Science 329, 1541–1543 (2010). source

      Not checked in the free run.

    • cites [10] Pleskac, T.J. & Busemeyer, J.R. Two-stage dynamic signal detection: A theory of choice, decision time, and confidence. Psychol. Rev. 117, 864–901 (2010). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    Decision confidence emerges from a value-comparison process in vmPFC, and this region is accessed by rRLPFC to enable a subjective assessment of confidence.

    “Here we show that decision confidence emerges from a value-comparison process in vmPFC, and that this region is in turn accessed by rRLPFC to enable a subjective assessment of confidence.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    The race model predicts a decrease in RT when either |DV| or Δe are increased, as seen in the behavioural data.

    “Furthermore this model predicts a decrease in RT when either ∣DV∣ or Δe are increased (Figure 2d), as seen in the behavioural data (Figure 1d).”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Evidence in favor of each option is accumulated over time and the decision is made on the basis of the first option to reach a threshold.
    • The authors state this as an assumption: In the race model, confidence is defined as the absolute difference between the two accumulators at decision time (Δe).

  • May combine two claims.

    Interpretationown data

    Both the level of activity in rRLPFC itself and its coupling strength with vmPFC influence the degree to which confidence is effectively read out for metacognitive report.

    “Thus, both the level of activity in rRLPFC itself and its coupling strength with vmPFC influences the degree to which confidence is effectively “read out” for metacognitive report.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    Neural activity in the same anatomical region (vmPFC) represents both value difference and confidence, suggesting that confidence and DV are separate behavioural manifestations of the same underlying decision variable.

    “In so doing we demonstrate that neural activity in the same anatomical region represents both variables, suggesting that confidence and DV are separate behavioural manifestations of the same underlying decision variable.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationcitation

    Choice confidence emerges from the dynamics of noisy accumulators in the race model, leading to dual effects of difference in value and reaction time on confidence.

    “Choice confidence can be seen to emerge from the dynamics of noisy accumulators in the race model7,21, leading to dual effects of difference in value and reaction time on confidence27.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [7] Kepecs, A., Uchida, N., Zariwala, H.A. & Mainen, Z.F. Neural correlates, computation and behavioural impact of decision confidence. Nature 455, 227–231 (2008). source

      Not checked in the free run.

    • cites [21] Vickers, D. Evidence for an accumulator model of psychophysical discrimination. Ergonomics 13, 37–58 (1970). source

      Not checked in the free run.

    • cites [27] Kiani, R. & Shadlen, M.N. Representation of confidence associated with a decision by neurons in the parietal cortex. Science 324, 759–764 (2009). source

      Not checked in the free run.

    • The authors state this as an assumption: Evidence in favor of each option is accumulated over time and the decision is made on the basis of the first option to reach a threshold.
    • The authors state this as an assumption: In the race model, confidence is defined as the absolute difference between the two accumulators at decision time (Δe).

  • May combine two claims.

    Interpretationown data

    The absence of an interaction at the neural level is consistent with a theoretical independence between value and noise in the choice process, such that one can have high confidence in a low value choice, and vice-versa.

    “The absence of an interaction at the neural level is consistent with a theoretical independence between value and noise in the choice process, such that one can have high confidence in a low value choice, and vice-versa.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    Confidence is a stable predictor of choice accuracy and does not reflect local changes in attention.

    “Together these results indicate that confidence is a stable predictor of choice accuracy, and does not reflect local changes in attention.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    Participants were included only if they used a sufficient range of confidence ratings (standard deviation > 0.8) to allow estimation of metacognitive ability.

    “Participants were only included if they used a sufficient range of confidence ratings (standard deviation > 0.8) to allow estimation of metacognitive ability.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    If reported confidence is a noisy facsimile of the confidence inherent in a decision process, the relationship between confidence and behavior will weaken, and metacognitive accuracy will decrease.

    “Intuitively, if reported confidence is a noisy facsimile of the confidence inherent in a decision process, the relationship between confidence and behavior will weaken, and metacognitive accuracy will decrease26.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [26] Maniscalco, B. & Lau, H. Comparing signal detection models of perceptual decision confidence. J. Vis. 10, 213 (2010). source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductcitation

    Models were compared via BIC scores using a fixed-effects analysis, where a difference in BIC of 5 indicates strong evidence for one model over another.

    “Models were compared via BIC scores using a fixed-effects analysis, where a difference in BIC of 5 indicates strong evidence for one model over another35.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [35] Kass, R. Bayes factor. J. Am. Stat. Assoc. 430, 773–795 (1995). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Confidence-related activity in rRLPFC is also seen in perceptual decision-making, together with a modulation of connectivity with visual cortex.

    “Notably, confidence-related activity in rRLPFC is also seen in perceptual decision-making14, together with a modulation of connectivity with visual cortex.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [14] Fleming, S.M., Huijgen, J. & Dolan, R.J. Prefrontal contributions to metacognition in perceptual decision making. J. Neurosci. 32, 6117–6125 (2012). source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    20 participants were included in the final analysis.

    “20 participants were included in the final analysis.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    Activations were reported as significant if they survived family wise error correction (FWE) for multiple comparisons across the whole brain at the cluster-level.

    “Activations were reported as significant if they survived family wise error correction (FWE) for multiple comparisons across the whole brain at the cluster-level.”
    Quote in the paperSubstantial claim
24 of 24

  • The authors state this as an assumption.

    Confidence reflects the degree of subjective certainty in having made the best choice, which equates to choosing the higher valued item.

    “Confidence here reflects the degree of subjective certainty in having made the best choice, which equates to choosing the higher valued item.”

  • The authors state this as an assumption.

    Evidence in favor of each option is accumulated over time and the decision is made on the basis of the first option to reach a threshold.

    “We adapted a race model7,21 wherein evidence in favor of each option (the snacks presented on the left and right side of the screen) is accumulated over time and the decision is made on the basis of the first option to reach a threshold (Figure 2b).”

  • The authors state this as an assumption.

    In the race model, confidence is defined as the absolute difference between the two accumulators at decision time (Δe).

    “In this model confidence is defined as the absolute difference between the two accumulators at decision time (Δe).”

  • The authors state this as an assumption.

    Reported confidence is drawn from a Gaussian distribution centred on model confidence Δe with noise σconf.

    “On each trial, reported confidence was drawn from a Gaussian distribution centred on Δe:conf=N(Δe,σconf).”

  • The authors state this as an assumption.

    The race model assumes separate decision variables accumulate evidence for distinct options, with the final decision determined by which accumulator reaches threshold first.

    “In the race model, separate decision variables accumulate evidence for distinct options, with the final decision determined by which accumulator reaches threshold first.”

  • The authors state this as an assumption.

    Decision confidence can be estimated from the race model as the distance between the two accumulators at the time the race is terminated.

    “An estimate of decision confidence, Δe, can be recovered from the race model as the distance between the two accumulators Rt and Lt at the time the race is terminated (Figure 2b; 7,12).”
6 of 6

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?

  • It is hard to define notions of accuracy for subjective decisions, such as value-based choices or aesthetic preferences, and yet we can still evaluate confidence in such decisions.

    “Theoretically, it is also hard to define notions of accuracy for subjective decisions, such as value-based choices or aesthetic preferences-and yet we can still evaluate confidence in such decisions (De Martino et al. 2013, Lebreton et al. 2015).”

    From Metacognition and Confidence: A Review and Synthesis (2024) · cites this paper as De Martino et al. 2013

    The Introduction and Results (pages not specified in our copy) show that the study collected confidence ratings for value-based choices and evaluated choice accuracy using participants’ subjective valuations. The paper does not say that accuracy is hard to define for subjective decisions, and it does not address aesthetic preferences. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • An aggregate measure of metacognitive sensitivity can be computed as the difference in slope between psychometric functions constructed from high and low confidence trials.

    “An alternative approach is to compute an aggregate measure of metacognitive sensitivity as the difference in slope between psychometric functions constructed from high and low confidence trials (e.g., De Martino et al., 2013; de Gardelle and Mamassian, 2014).”

    From How to measure metacognition (2014) · cites this paper as De Martino et al., 2013

  • The operations leading to confidence seem neither mysterious nor dissociable from the decision process.

    “Yet the operations leading to confidence seem neither mysterious nor dissociable from the decision process (cf. De Martino et al., 2013).”

    From A common mechanism underlies changes of mind about decisions and confidence (2016) · cites this paper as De Martino et al., 2013

  • Possible candidates for a meta-cognitive system for monitoring learning progress include frontal systems that encode uncertainty or confidence in humans and monkeys.

    “Possible candidates for such a system include frontal systems that encode the uncertainty or confidence in humans and monkeys (Fleming, Weil et al. 2010; Fleming, Huijgen et al. 2012; De Martino, Fleming et al. 2013)”

    From Information-seeking, curiosity, and attention: computational and neural mechanisms (2013) · cites this paper as De Martino, Fleming et al. 2013

Statistics that don’t add up

Each reported p-value recomputed from its test statistic and degrees of freedom.

  • Inconsistent p-value: F(1,19) = 0.7, p > .5

    Minor

    The paper reports p > .5 here; recomputing from the test statistic and its degrees of freedom gives p = .413. The check cannot tell whether the statistic or the p-value is the misprint.

    Reported p > .5 · recomputed p = .413

1 of 1

Statistics recomputed

Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.

  • Doesn’t add upF(1, 19) = 0.7reported p > .5 · recomputed p = .413

    “F(1,19) = 0.7, p > .5”

  • Consistentt(19) = -0.64reported p = .53 · recomputed p = .530

    “t(19) = −0.64, p = .53”

  • ConsistentF(1, 19) = 5.1reported p < .05 · recomputed p = .036

    “F(1,19) = 5.1, p < .05”

  • ConsistentF(1, 19) = 7.6reported p < .05 · recomputed p = .013

    “F(1,19) = 7.6, p < .05”

4 of 4

Self-check

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

Each contradiction between two passages listed here was confirmed by an independent check.

  • Reported rRLPFC peak lies outside its stated correction volume

    Major

    The reported peak [39, 41, 16] is about 12.7 mm from the stated correction-volume centre [36, 44, 28], so it cannot lie within an 8-mm-radius sphere centred there.

  • Nineteen items yield 171 pairs, not 170

    Minor

    Choosing every distinct pair from 19 items yields 19 × 18 ÷ 2 = 171 pairs. The stated 170 pairs therefore cannot include every combination.

  • Auction purchases can exceed the stated maximum willingness to pay

    Minor

    The bids are described as each participant's maximum willingness to pay, but the stated auction rule makes a participant buy when the market price is above their bid. That requires payment above their stated maximum.

3 of 3

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Authors
De Martino, B., Fleming, S. M., Garrett, N., & Dolan, R. J.
Affiliations
  • Wellcome Centre for Human Neuroimaging (GB)
  • University College London (GB)
Corresponding author
Fleming, Stephen M
ORCID
Venue
Nature Neuroscience
Publisher
Springer Science and Business Media LLC
Published
2012-12-09
Volume
16
Issue
1
Pages
105-110
ISSN
  • 1097-6256 (ISSN-L)
  • 1546-1726
DOI
10.1038/nn.3279
PMC
PMC3786394
OpenAlex
W1982490126
Semantic Scholar
fe1d742b6b3cca7a981478d6c541590eb27f9f60
Cited by
534
References
38
Citations by year
2026: 23 · 2025: 42 · 2024: 42 · 2023: 42 · 2022: 45 · 2021: 57 · 2020: 58 · 2019: 34 · 2018: 48 · 2017: 36 · 2016: 35 · 2015: 32 · 2014: 32 · 2013: 7
FWCI
11.62
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Nature Neuroscience (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • CaltechAUTHORS (California Institute of Technology) (submittedVersion)
  • UCL Discovery (University College London) (submittedVersion)
  • Max Planck Digital Library (submittedVersion, other-oa)
  • MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
  • UEA Digital Repository (University of East Anglia) (submittedVersion)
Funders
Wellcome Trust (091593, WT096185, 082674/Z/07/Z, 098362/Z/12/Z, 091593/Z/10/Z)
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 › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
Keywords
decision confidence, value-based choice, ventromedial prefrontal cortex, rostrolateral prefrontal cortex, metacognition, value comparison, dynamic decision model, functional connectivity, choice accuracy, reaction time
MeSH terms
Adult, Brain Mapping, Choice Behavior, Cognition, Computer Simulation, Female, Humans, Image Processing, Computer-Assisted, Judgment, Magnetic Resonance Imaging, Male, Models, Psychological, Psychophysics, Linear Models, Prefrontal Cortex, Young Adult

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.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.

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 ›

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