CoChat
Sign Up
Cited by 44Visual perception and processing mechanismsNeural dynamics and brain functionNeural and Behavioral Psychology Studies

Tuned inhibition in perceptual decision-making circuits can explain seemingly suboptimal confidence behavior

Open access
Maniscalco, Brian, Odegaard, Brian, Grimaldi, Piercesare, Cho, Seong Hah, Basso, Michele A., Lau, Hakwan, Peters, Megan A. K.·2021·PLOS Computational Biology
DOI 10.1371/journal.pcbi.1008779PMC PMC8032199

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

Current dominant views hold that perceptual confidence reflects the probability that a decision is correct. Although these views have enjoyed some empirical support, recent behavioral results indicate that confidence and the probability of being correct can be dissociated. An alternative hypothesis suggests that confidence instead reflects the magnitude of evidence in favor of a decision while being relatively insensitive to the evidence opposing the decision. We considered how this alternative hypothesis might be biologically instantiated by developing a simple neural network model incorporating a known property of sensory neurons: tuned inhibition. The key idea of the model is that the level of inhibition that each accumulator unit receives from units with the opposite tuning preference, i.e. its inhibition ‘tuning’, dictates its contribution to perceptual decisions versus confidence judgments, such that units with higher tuned inhibition (computing relative evidence for different perceptual interpretations) determine perceptual discrimination decisions, and units with lower tuned inhibition (computing absolute evidence) determine confidence. We demonstrate that this biologically plausible model can account for several counterintuitive findings reported in the literature where confidence and decision accuracy dissociate. By comparing model fits, we further demonstrate that a full complement of behavioral data across several previously published experimental results—including accuracy, reaction time, mean confidence, and metacognitive sensitivity—is best accounted for when confidence is computed from units without, rather than units with, tuned inhibition. Finally, we discuss predictions of our results and model for future neurobiological studies. These findings suggest that the brain has developed and implements this alternative, heuristic theory of perceptual confidence computation by relying on the diversity of neural resources available.

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 · 10.1371/journal.pcbi.1008779

Cites

17 of 17 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?

  • Initial theoretical explanations proposed that the PEB may result from a bias in the broadcast or readout of propositional confidence estimates, or from a heuristic applied to evidence spaces that are often detection- rather than discrimination-like.

    “Initial theoretical explanations proposed that the PEB may result from a bias in the broadcast or readout of propositional confidence estimates, or from a heuristic applied to evidence spaces that are often detection-rather than discrimination-like (Maniscalco et al. 2021, Miyoshi & Lau 2020).”

    From Metacognition and Confidence: A Review and Synthesis (2024) · cites this paper as Maniscalco et al. 2021

    Split into 2 atoms — 1 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

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: 64

  1. [1]Sanders JI, Hangya B, Kepecs A. Signatures of a Statistical Computation in the Human Sense of Confidence. Neuron. 2016; 90: 499– 506. 10.1016/j.neuron.2016.03.025 27151640 PMC5350614 · DOI · Paper page
  2. [2]Pouget A, Drugowitsch J, Kepecs A. Confidence and certainty: distinct probabilistic quantities for different goals. Nat Neurosci. 2016; 19: 366– 374. 10.1038/nn.4240 26906503 PMC5378479 · DOI · Paper page
  3. [4]Fetsch CR, Kiani R, Newsome WT, Shadlen MN. Effects of Cortical Microstimulation on Confidence in a Perceptual Decision. Neuron. 2014; 1– 8. 28898624
  4. [5]Zylberberg A, Fetsch CR, Shadlen MN, Frank MJ. The influence of evidence volatility on choice, reaction time and confidence in a perceptual decision. eLife. 2016; 5: e17688. 10.7554/eLife.17688 27787198 PMC5083065 · DOI
  5. [6]Ratcliff R, McKoon G. The diffusion decision model: Theory and data for two-choice decision tasks. Neural Comput. 2008; 20: 873– 922. 10.1162/neco.2008.12-06-420 18085991 PMC2474742 · DOI · Paper page
  6. [8]Tsetsos K, Gao J, McClelland JL, Usher M. Using Time-Varying Evidence to Test Models of Decision Dynamics: Bounded Diffusion vs. the Leaky Competing Accumulator Model. Front Neurosci. 2012; 6. 10.3389/fnins.2012.00079 22701399 PMC3372959 · DOI
  7. [10]Koizumi A, Maniscalco B, Lau H. Does perceptual confidence facilitate cognitive control? Atten Percept Psychophys. 2015. 10.3758/s13414-015-0843-3 25737256 · DOI
  8. [11]Rahnev D, Maniscalco B, Luber B, Lau H, Lisanby SH. Direct injection of noise to the visual cortex decreases accuracy but increases decision confidence. J Neurophysiol. 2012; 107: 1556– 1563. 10.1152/jn.00985.2011 22170965 · DOI · Paper page
  9. [12]Peters MAK, Fesi J, Amendi N, Knotts JD, Lau H, Ro T. Transcranial magnetic stimulation to visual cortex induces suboptimal introspection. Cortex. 2017; 93: 119– 132. 10.1016/j.cortex.2017.05.017 28646672 PMC5541901 · DOI
  10. [13]Rahnev D, Maniscalco B, Graves T, Huang E, de Lange FP, Lau H. Attention induces conservative subjective biases in visual perception. Nat Neurosci. 2011; 14: 1513– 1515. 10.1038/nn.2948 22019729 · DOI · Paper page
  11. [14]Maniscalco B, Peters MAK, Lau H. Heuristic use of perceptual evidence leads to dissociation between performance and metacognitive sensitivity. Atten Percept Psychophys. 2016. 10.3758/s13414-016-1059-x 26791233 PMC4811689 · DOI · Paper page
  12. [15]Odegaard B, Grimaldi P, Cho SH, Peters MAK, Lau H, Basso MA. Superior colliculus neuronal ensemble activity signals optimal rather than subjective confidence. Proceedings of the National Academy of Sciences. 2018; 115: E1588– E1597. 10.1073/pnas.1711628115 29382765 PMC5816145 · DOI
  13. [16]Samaha J, Barrett JJ, Sheldon AD, LaRocque JJ, Postle BR. Dissociating Perceptual Confidence from Discrimination Accuracy Reveals No Influence of Metacognitive Awareness on Working Memory. Front Psychol. 2016; 7: 851. 10.3389/fpsyg.2016.00851 27375529 PMC4893488 · DOI
  14. [17]Maniscalco B, Castaneda OG, Odegaard B, Morales J, Rajananda S, Peters MAK. The metaperceptual function: Exploring dissociations between confidence and task performance with type 2 psychometric curves. 2020. 10.31234/osf.io/5qrjn · DOI
  15. [18]Samaha J, Iemi L, Postle BR. Prestimulus alpha-band power biases visual discrimination confidence, but not accuracy. Conscious Cogn. 2017. 10.1016/j.concog.2017.02.005 28222937 PMC5561529 · DOI
  16. [19]Stocker AA, Simoncelli EP. A Bayesian Model of Conditioned Perception. Adv Neural Inf Process Syst. 2008; 20: 1409– 1416. PMC4199208 25328364
  17. [20]Luu L, Stocker AA. Post-decision biases reveal a self-consistency principle in perceptual inference. eLife Sciences. 2018; 7: e33334. 10.7554/eLife.33334 29785928 PMC5963926 · DOI
  18. [21]Zylberberg A, Barttfeld P, Sigman M. The construction of confidence in a perceptual decision. Front Integr Neurosci. 2012; 6: 79– 79. 10.3389/fnint.2012.00079 23049504 PMC3448113 · DOI · Paper page
  19. [23]Peters MAK, Thesen T, Ko YD, Maniscalco B, Carlson C, Davidson M, et al. Perceptual confidence neglects decision-incongruent evidence in the brain. Nature Human Behaviour. 2017. PMC5675133 29130070
  20. [24]Ni AM, Ray S, Maunsell JHR. Tuned normalization explains the size of attention modulations. Neuron. 2012; 73: 803– 813. 10.1016/j.neuron.2012.01.006 22365552 PMC3292773 · DOI
  21. [25]Ruff DA, Alberts JJ, Cohen MR. Relating normalization to neuronal populations across cortical areas. J Neurophysiol. 2016; 4: jn.00017.2016-jn.00017.2016. 10.1152/jn.00017.2016 27358313 PMC5040386 · DOI
  22. [26]Verhoef B-E, Maunsell JHR. Attention-related changes in correlated neuronal activity arise from normalization mechanisms. Nat Neurosci. 2017. 10.1038/nn.4572 28553943 PMC5507208 · DOI
  23. [28]Reynolds JH, Heeger DJ. The normalization model of attention. Neuron. 2009; 61: 168– 185. 10.1016/j.neuron.2009.01.002 19186161 PMC2752446 · DOI · Paper page
  24. [29]Maniscalco B, Lau H. A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings. Conscious Cogn. 2012; 21: 422– 430. 10.1016/j.concog.2011.09.021 22071269 · DOI · Paper page
  25. [30]Ohshiro T, Angelaki DE, DeAngelis GC. A normalization model of multisensory integration. Nat Neurosci. 2011; 14: 775– 782. 10.1038/nn.2815 21552274 PMC3102778 · DOI · Paper page
  26. [31]Churchland AK. Normalizing relations between the senses. Nat Neurosci. 2011; 14: 672– 673. 10.1038/nn.2850 21613993 · DOI
  27. [32]Ling S, Blake R. Normalization regulates competition for visual awareness. Neuron. 2012; 75: 531– 540. 10.1016/j.neuron.2012.05.032 22884335 PMC3419498 · DOI
  28. [33]Nassi JJ, Gómez-Laberge C, Kreiman G, Born RT. Corticocortical feedback increases the spatial extent of normalization. Front Syst Neurosci. 2014; 8: 105– 105. 10.3389/fnsys.2014.00105 24910596 PMC4039070 · DOI
  29. [34]Simoncelli EP, Heeger DJ. A model of neuronal responses in visual area MT. Vision Res. 1998; 38: 743– 761. 10.1016/s0042-6989(97)00183-1 9604103 · DOI · Paper page
  30. [35]Usher M, McClelland JL. The time course of perceptual choice: the leaky, competing accumulator model. Psychol Rev. 2001; 108: 550– 592. 10.1037/0033-295x.108.3.550 11488378 · DOI · Paper page
  31. [36]Yu S, Pleskac TJ, Zeigenfuse MD. Dynamics of Postdecisional Processing of Confidence. J Exp Psychol Gen. 2015; 144: 489– 510. 10.1037/xge0000062 25844627 · DOI
  32. [37]Ratcliff R, Rouder JN. A diffusion model account of masking in two-choice letter identification. J Exp Psychol Hum Percept Perform. 2000; 26: 127– 140. 10.1037//0096-1523.26.1.127 10696609 · DOI
  33. [38]Morales J, Odegaard B, Maniscalco B. The Neural Substrates of Conscious Perception without Performance Confounds. philpapers.org. 2020. Available: https://philpapers.org/rec/MORTNS-4 36095104
  34. [40]Yeung N, Summerfield C. Metacognition in human decision-making: confidence and error monitoring. Philos Trans R Soc Lond B Biol Sci. 2012; 367: 1310– 1321. 10.1098/rstb.2011.0416 22492749 PMC3318764 · DOI · Paper page
  35. [43]Orbán G, Berkes P, Fiser J, Lengyel M. Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex. Neuron. 2016; 92: 530– 543. 10.1016/j.neuron.2016.09.038 27764674 PMC5077700 · DOI
  36. [44]Rahnev D, Bahdo L, de Lange FP, Lau H. Prestimulus hemodynamic activity in dorsal attention network is negatively associated with decision confidence in visual perception. J Neurophysiol. 2012; 108: 1529– 1536. 10.1152/jn.00184.2012 22723670 · DOI
  37. [45]Samaha J, Switzky M, Postle BR. Confidence boosts serial dependence in orientation estimation. J Vis. 2019; 590. 10.1167/19.4.25 31009526 PMC6690400 · DOI
  38. [46]Janssen P, Shadlen MN. A representation of the hazard rate of elapsed time in macaque area LIP. Nat Neurosci. 2005; 8: 234– 241. 10.1038/nn1386 15657597 · DOI · Paper page
  39. [47]Leon MI, Shadlen MN. Representation of time by neurons in the posterior parietal cortex of the macaque. Neuron. 2003; 38: 317– 327. 10.1016/s0896-6273(03)00185-5 12718864 · DOI · Paper page
  40. [48]Finnerty GT, Shadlen MN, Jazayeri M, Nobre AC, Buonomano DV. Time in Cortical Circuits. J Neurosci. 2015; 35: 13912– 13916. 10.1523/JNEUROSCI.2654-15.2015 26468192 PMC4604229 · DOI
  41. [50]Ma WJ, Beck JM, Pouget A. Spiking networks for Bayesian inference and choice. Curr Opin Neurobiol. 2008; 18: 217– 222. 10.1016/j.conb.2008.07.004 18678253 · DOI
  42. [51]Bays PM. A signature of neural coding at human perceptual limits. J Vis. 2016; 16: 4. 10.1167/16.11.4 27604067 PMC5024667 · DOI
  43. [52]Abrahamyan A, Silva LL, Dakin SC, Carandini M, Gardner JL. Adaptable history biases in human perceptual decisions. Proc Natl Acad Sci U S A. 2016; 113: E3548– 57. 10.1073/pnas.1518786113 27330086 PMC4922170 · DOI
  44. [53]Koster R, Duzel E, Dolan RJ. Action and valence modulate choice and choice-induced preference change. PLoS One. 2015; 10: e0119682. 10.1371/journal.pone.0119682 25747703 PMC4352030 · DOI
  45. [54]Festinger L. A theory of cognitive dissonance. Stanford University Press; 1957.
  46. [55]Brehm JW. Postdecision changes in the desirability of alternatives. J Abnorm Psychol. 1956; 52: 384– 389. 10.1037/h0041006 13318848 · DOI
  47. [56]Gerard HB, White GL. Post-Decisional Reevaluation of Choice Alternatives. Pers Soc Psychol Bull. 1983; 9: 365– 369.
  48. [58]Heine SJ, Lehman DR. Culture, Dissonance, and Self-Affirmation. Pers Soc Psychol Bull. 1997; 23: 389– 400.
  49. [59]Koriat A. The self-consistency model of subjective confidence. Psychol Rev. 2012; 119: 80– 113. 10.1037/a0025648 22022833 · DOI
  50. [60]Zawadzka K, Higham PA, Hanczakowski M. Confidence in Forced-Choice Recognition: What Underlies the Ratings? J Exp Psychol Learn Mem Cogn. 2016. 10.1037/xlm0000321 27685019 · DOI
  51. [61]Körding KP, Tenenbaum JB. Causal inference in sensorimotor integration. NIPS. 2007.
  52. [63]Lau H. Consciousness, Metacognition, & Perceptual Reality Monitoring. PsyArxiv. 2019. 10.31234/osf.io/ckbyf · DOI
  53. [64]Merten K, Nieder A. Active encoding of decisions about stimulus absence in primate prefrontal cortex neurons. Proc Natl Acad Sci U S A. 2012; 109: 6289– 6294. 10.1073/pnas.1121084109 22460793 PMC3341060 · DOI
  54. [66]Fleming SM, Huijgen J, Dolan RJ. Prefrontal contributions to metacognition in perceptual decision making. J Neurosci. 2012; 32: 6117– 6125. 10.1523/JNEUROSCI.6489-11.2012 22553018 PMC3359781 · DOI · Paper page
  55. [68]Fleming SM, Weil RS, Nagy Z, Dolan R, Rees G. Relating Introspective Accuracy to Individual Differences in Brain Structure. Science. 2010; 329. 10.1126/science.1197747 20847276 PMC3173849 · DOI · Paper page
  56. [69]Kao Y-C, Davis ES, Gabrieli JDE. Neural correlates of actual and predicted memory formation. Nat Neurosci. 2005; 8: 1776– 1783. 10.1038/nn1595 16286927 · DOI
  57. [70]Janowsky JS, Shimamura AP, Kritchevsky M, Squire LR. Cognitive impairment following frontal lobe damage and its relevance to human amnesia. Behav Neurosci. 1989; 103: 548– 560. 10.1037//0735-7044.103.3.548 2736069 · DOI
  58. [71]Pannu JK, Kaszniak AW. Metamemory experiments in neurological populations: a review. Neuropsychol Rev. 2005; 15: 105– 130. 10.1007/s11065-005-7091-6 16328731 · DOI · Paper page
  59. [72]Schnyer DM, Verfaellie M, Alexander MP, LaFleche G, Nicholls L, Kaszniak AW. A role for right medial prefontal cortex in accurate feeling-of-knowing judgements: evidence from patients with lesions to frontal cortex. Neuropsychologia. 2004; 42: 957– 966. 10.1016/j.neuropsychologia.2003.11.020 14998710 · DOI · Paper page
  60. [74]Middlebrooks PG, Sommer MA. Neuronal Correlates of Metacognition in Primate Frontal Cortex. Neuron. 2012; 75: 517– 530. 10.1016/j.neuron.2012.05.028 22884334 PMC3418516 · DOI · Paper page
  61. [76]Kim B, Basso M. Saccade target selection in the superior colliculus: a signal detection theory approach. J Neurosci. 2008; 28: 2991– 3007. 10.1523/JNEUROSCI.5424-07.2008 18354003 PMC6670709 · DOI
  62. [77]Smith PL, Ratcliff R. Psychology and neurobiology of simple decisions. Trends Neurosci. 2004; 27: 161– 168. 10.1016/j.tins.2004.01.006 15036882 · DOI · Paper page
  63. [78]Gold JI, Shadlen MN. Representation of a perceptual decision in developing oculomotor commands. Nature. 2000; 404: 390– 394. 10.1038/35006062 10746726 · DOI
  64. [79]Horwitz GD, Batista AP, Newsome WT. Representation of an abstract perceptual decision in macaque superior colliculus. J Neurophysiol. 2004; 91: 2281– 2296. 10.1152/jn.00872.2003 14711971 · DOI

Cited by

Sorted by citations

Authors
Maniscalco, B., Odegaard, B., Grimaldi, P., Cho, S. H., Basso, M. A., Lau, H., & Peters, M. A. K.
Affiliations
  • University of California, Riverside (US)
  • University of California, Los Angeles (US)
  • University of Hong Kong (HK)
Corresponding author
  • Maniscalco, Brian
  • Odegaard, Brian
  • Grimaldi, Piercesare
  • Cho, Seong Hah
  • Basso, Michele A.
  • Lau, Hakwan
  • Peters, Megan A. K.
ORCID
Venue
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2021-03-29
Volume
17
Issue
3
Pages
e1008779
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
DOI
10.1371/journal.pcbi.1008779
PMC
PMC8032199
OpenAlex
W3140711255
Cited by
44
References
17
Citations by year
2026: 4 · 2025: 7 · 2024: 8 · 2023: 13 · 2022: 4 · 2021: 6 · 2020: 2
FWCI
4.08
Type
journal article
Language
en
Update notices
new_version (2021-04-08)
Open access
Licence
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • Institutional Research Information System University of Turin (University of Turin) (submittedVersion)
Funders
  • Air Force Office of Scientific Research (FA9550-20-1-0106)
  • National Institutes of Health (R01NS088628-01, R01EY13692)
  • Canadian Institute for Advanced Research (Azrieli Global Scholars Program)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Keywords
perceptual confidence, perceptual decision making, metacognition, metacognitive sensitivity, neural network model, sensory neurons, reaction time
MeSH terms
Animals, Decision Making, Inhibition, Psychological, Macaca mulatta, Male, Models, Neurological, Perception, Reaction Time, Superior Colliculi, Neural Networks, Computer, Computational Biology

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