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Research Articles, Behavioral/Cognitive

A Postdecisional Neural Marker of Confidence Predicts Information-Seeking in Decision-Making

Kobe Desender, Peter Murphy, Annika Boldt, Tom Verguts and Nick Yeung
Journal of Neuroscience 24 April 2019, 39 (17) 3309-3319; DOI: https://doi.org/10.1523/JNEUROSCI.2620-18.2019
Kobe Desender
1Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg-Eppendorf 20251, Germany,
2Department of Experimental Psychology, Ghent University, Ghent 9000, Belgium,
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Peter Murphy
1Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg-Eppendorf 20251, Germany,
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Annika Boldt
3Institute of Cognitive Neuroscience, University College London, London WC1N 3AZ, United Kingdom, and
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Tom Verguts
2Department of Experimental Psychology, Ghent University, Ghent 9000, Belgium,
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Nick Yeung
4Department of Experimental Psychology, University of Oxford, Oxford OX2 6HG, United Kingdom
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  • Figure 1.
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    Figure 1.

    Timeline of an experimental trial. A stimulus was presented for 200 ms, and participants made a speeded response with the mouse, deciding whether the average color of the eight elements was red or blue. On free-choice trials (75%), participants subsequently used a vertical slider to choose either to see the stimulus again in an easier version (by moving the gray cursor toward S) or to give their response (by moving the gray cursor toward R). When the stimulus is shown again, the mean of the eight elements is more clearly red and the variance is smaller (note that the displayed change is exaggerated for illustration purposes). On no-choice trials (25%), participants could only choose to give their response. Finally, on all trials, participants jointly indicated their final response and level of confidence on a horizontal continuous response scale. Being accurate was rewarded (5 points), errors were punished (−5 points), and there was a small cost associated with sampling more information (−1 point).

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    Figure 2.

    Behavioral performance is modulated by mean and variance. A, Mean accuracy of the primary response (based on all data). B, Median reaction times of the primary response (based on all data). C, Mean accuracy of the final response (based on no-choice data). D, Mean standardized confidence (based on no-choice data). E, The number of trials on which participants waived the see again option (based on free-choice data).

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    Figure 3.

    ERPs as a function of confidence (A) and information-seeking choices (B). Head plots represent the difference in scalp distribution during the significant time periods for low-high confidence (A) and see again-respond (B). Gray horizontal lines indicate clusters during which both conditions significantly differ. Shadings represent SEM. High and low confidence is calculated from no-choice data and information-seeking choices from free-choice trials.

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    Figure 4.

    Time-resolved multivariate regression of EEG data by confidence and task difficulty. Horizontal lines indicate clusters significantly differing from 0. Shadings represent SEM.

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    Figure 5.

    Within-condition decoding of information-seeking choices (A–D) and confidence (E–H). Classifiers were trained and tested on all time points (on correct trials only; steps of 10 ms and a sliding window of 106 ms). Topographies represent the scalp projections obtained from the logistic regression classifier at the training time where classification is maximal. A, B, E, F, Prestimulus baseline (−100 ms until 0 ms). C, D, G, H, Preresponse baseline (−100 ms until 0 ms). Because of this difference in baseline, the regions of significant decoding in B, C and F, G are not identical. Solid black lines indicate significant clusters (p < 0.05). The training times of each panel correspond to the testing time of that panel; for example, t = 0 corresponds to stimulus, response, response, and information-seeking decisions in panels A–D and E–H, respectively.

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    Figure 6.

    Across-condition decoding of information-seeking choices by confidence, locked to the stimulus (A), the response (B, C) and the information-seeking choice (D). Classifiers are trained on high versus low confidence from no-choice data and tested on see again versus respond decisions from free-choice data (both on correct trials only). Above-chance decoding only occurs postresponse. The same conventions as in Figure 5A–D apply.

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    Table 1.

    Models of different complexity predicting information seeking

    ModeldfBICTestχ2p
    0A. Variance4−37———
    0B. Mean4−23———
    1. Variance + Mean5−431 vs 0A10.86<0.001
    1 vs 0B25.11<0.001
    2. Variance + Mean + RT + Accuracy12−1182 vs 1108.2<0.001
    3. Variance + Mean + RT + Accuracy + Confidencea13−1223 vs 29.130.002
    • ↵aWinning model.

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The Journal of Neuroscience: 39 (17)
Journal of Neuroscience
Vol. 39, Issue 17
24 Apr 2019
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A Postdecisional Neural Marker of Confidence Predicts Information-Seeking in Decision-Making
Kobe Desender, Peter Murphy, Annika Boldt, Tom Verguts, Nick Yeung
Journal of Neuroscience 24 April 2019, 39 (17) 3309-3319; DOI: 10.1523/JNEUROSCI.2620-18.2019

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A Postdecisional Neural Marker of Confidence Predicts Information-Seeking in Decision-Making
Kobe Desender, Peter Murphy, Annika Boldt, Tom Verguts, Nick Yeung
Journal of Neuroscience 24 April 2019, 39 (17) 3309-3319; DOI: 10.1523/JNEUROSCI.2620-18.2019
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Keywords

  • confidence
  • decision-making
  • error positivity
  • information sampling
  • information-seeking
  • metacognition

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