Perceptual confidence neglects decision-incongruent evidence in the brain

Abstract

Our perceptual experiences are accompanied by a subjective sense of certainty. These confidence judgements typically correlate meaningfully with the probability that the relevant decision is correct1,2,3,4,5,6, bolstering prevailing opinion that both perceptual decisions and confidence optimally reflect the probability of having made a correct decision6,7,8,9,10,11,12,13. However, recent behavioural reports suggest that confidence computations overemphasize information supporting a decision, while selectively down-weighting evidence for other possible choices14,15,16,17,18,19. This view remains controversial, and supporting neurobiological evidence has been lacking. Here we use intracranial electrophysiological recordings in humans together with machine-learning techniques to demonstrate that perceptual decisions and confidence rely on spatiotemporally separable neural representations in a face/house discrimination task. We then use normative computational models to show that confidence relies excessively on evidence supporting a decision (for example, face evidence for a ‘face’ decision), even while decisions themselves reflect the optimal balance of all evidence (for example, both face and house evidence). Thus, confidence may not reflect a readout of the probability of being correct; instead, observers may sacrifice optimality in favour of self-consistency20 in the face of limited neural and computational resources. Although seemingly suboptimal, this strategy may reflect the inference problem that perceptual systems are evolutionarily optimized to solve.

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Figure 1: Behavioural task and results.
Figure 2: Spatiotemporal dissociation between ‘decision’ and ‘confidence’ decoding.
Figure 3: Choice probability analyses show that confidence computations were insensitive to decision-incongruent evidence.
Figure 4: Violations of the normative model for confidence but not accuracy.

References

  1. 1

    Charles, L., King, J.-R. & Dehaene, S. Decoding the dynamics of action, intention, and error detection for conscious and subliminal stimuli. J. Neurosci. 34, 1158–1170 (2014).

  2. 2

    Fleming, S. M., Huijgen, J. & Dolan, R. J. Prefrontal contributions to metacognition in perceptual decision making. J. Neurosci. 32, 6117–6125 (2012).

  3. 3

    Fleming, S. M., Weil, R. S., Nagy, Z., Dolan, R. & Rees, G. Relating introspective accuracy to individual differences in brain structure. Science 329, 1541–1543 (2010).

  4. 4

    Kepecs, A., Uchida, N., Zariwala, H. A. & Mainen, Z. F. Neural correlates, computation and behavioural impact of decision confidence. Nature 455, 227–231 (2008).

  5. 5

    Kiani, R., Corthell, L. & Shadlen, M. N. Choice certainty is informed by both evidence and decision time. Neuron 84, 1329–1342 (2014).

  6. 6

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

  7. 7

    Fetsch, C. R., Kiani, R., Newsome, W. T. & Shadlen, M. N. Effects of cortical microstimulation on confidence in a perceptual decision. Neuron 83, 797–804 (2014).

  8. 8

    Pouget, A., Drugowitsch, J. & Kepecs, A. Confidence and certainty: distinct probabilistic quantities for different goals. Nat. Neurosci. 19, 366–374 (2016).

  9. 9

    Kepecs, A. & Mainen, Z. F. A computational framework for the study of confidence in humans and animals. Phil. Trans. R. Soc. B 367, 1322–1337 (2012).

  10. 10

    Meyniel, F., Schlunegger, D. & Dehaene, S. The sense of confidence during probabilistic learning: a normative account. PLoS Comput. Biol. 11, e1004305 (2015).

  11. 11

    Sanders, J. I., Hangya, B. & Kepecs, A. Signatures of a statistical computation in the human sense of confidence. Neuron 90, 499–506 (2016).

  12. 12

    Meyniel, F., Sigman, M. & Mainen, Z. F. Confidence as Bayesian probability: from neural origins to behavior. Neuron 88, 78–92 (2015).

  13. 13

    Gherman, S. & Philiastides, M. G. Neural representations of confidence emerge from the process of decision formation during perceptual choices. Neuroimage 106, 134–143 (2015).

  14. 14

    van den Berg, R. et al. A common mechanism underlies changes of mind about decisions and confidence. Elife 5, e12192 (2015).

  15. 15

    Koizumi, A., Maniscalco, B. & Lau, H. Does perceptual confidence facilitate cognitive control? Atten. Percept. Psychophys. 77, 1295–1306 (2015).

  16. 16

    Maniscalco, B., Peters, M. A. K. & Lau, H. Heuristic use of perceptual evidence leads to dissociation between performance and metacognitive sensitivity. Atten. Percept. Psychophys. 78, 923–937 (2016).

  17. 17

    Samaha, J., Barrett, J. J., Sheldon, A. D., Larocque, J. J. & Postle, B. R. Dissociating perceptual confidence from discrimination accuracy reveals no influence of metacognitive awareness on working memory. Front. Psychol. 7, 851 (2016).

  18. 18

    Zylberberg, A., Barttfeld, P. & Sigman, M. The construction of confidence in a perceptual decision. Front. Integr. Neurosci. 6, 79–79 (2012).

  19. 19

    Aitchison, L., Bang, D., Bahrami, B. & Latham, P. E. Doubly Bayesian analysis of confidence in perceptual decision-making. PLoS Comput. Biol. 11, e1004519 (2015).

  20. 20

    Stocker, A. A. & Simoncelli, E. P. A Bayesian model of conditioned perception . Adv. Neural Inf. Process. Syst. 20, 1409–1416 (2008).

  21. 21

    Ray, S., Crone, N. E., Niebur, E., Franaszczuk, P. J. & Hsiao, S. S. Neural correlates of high-gamma oscillations (60–200?Hz) in macaque local field potentials and their potential implications in electrocorticography. J. Neurosci. 28, 11526–11536 (2008).

  22. 22

    Ray, S. & Maunsell, J. H. R. Different origins of gamma rhythm and high-gamma activity in macaque visual cortex. PLoS Biol. 9, e1000610 (2011).

  23. 23

    Winawer, J. et al. Asynchronous broadband signals are the principal source of the bold response in human visual cortex. Curr. Biol. 23, 1145–1153 (2013).

  24. 24

    Mukamel, R. et al. Coupling between neuronal firing, field potentials, and FMRI in human auditory cortex. Science 309, 951–954 (2005).

  25. 25

    Kunii, N., Kamada, K., Ota, T., Kawai, K. & Saito, N. Characteristic profiles of high gamma activity and blood oxygenation level-dependent responses in various language areas. Neuroimage 65, 242–249 (2013).

  26. 26

    Esposito, F. et al. Cortex-based inter-subject analysis of iEEG and fMRI data sets: application to sustained task-related BOLD and gamma responses. Neuroimage 66, 457–468 (2013).

  27. 27

    Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001).

  28. 28

    Crone, N. E., Sinai, A. & Korzeniewska, A. High-frequency gamma oscillations and human brain mapping with electrocorticography. Prog. Brain Res. 159, 275–295 (2006).

  29. 29

    Crone, N. E., Boatman, D., Gordon, B. & Hao, L. Induced electrocorticographic gamma activity during auditory perception. (Brazier Award-winning article, 2001). Clin. Neurophysiol. 112, 565–582 (2001).

  30. 30

    Hermes, D., Miller, K. J., Wandell, B. A. & Winawer, J. Stimulus dependence of gamma oscillations in human visual cortex. Cereb. Cortex 25, 2951–2959 (2015).

  31. 31

    Hipp, J. F., Engel, A. K. & Siegel, M. Oscillatory synchronization in large-scale cortical networks predicts perception. Neuron 69, 387–396 (2011).

  32. 32

    Laczó, B., Antal, A., Niebergall, R., Treue, S. & Paulus, W. Transcranial alternating stimulation in a high gamma frequency range applied over V1 improves contrast perception but does not modulate spatial attention. Brain Stimul. 5, 484–491 (2012).

  33. 33

    Davidesco, I. et al. Exemplar selectivity reflects perceptual similarities in the human fusiform cortex. Cereb. Cortex 24, 1879–1893 (2014).

  34. 34

    Privman, E. et al. Antagonistic relationship between gamma power and visual evoked potentials revealed in human visual cortex. Cereb. Cortex 21, 616–624 (2011).

  35. 35

    Shum, J. et al. A brain area for visual numerals. J. Neurosci. 33, 6709–6715 (2013).

  36. 36

    Dastjerdi, M., Ozker, M., Foster, B. L., Rangarajan, V. & Parvizi, J. Numerical processing in the human parietal cortex during experimental and natural conditions. Nat. Commun. 4, 2528 (2013).

  37. 37

    Kubánek, J., Miller, K. J., Ojemann, J. G., Wolpaw, J. R. & Schalk, G. Decoding flexion of individual fingers using electrocorticographic signals in humans. J. Neural Eng. 6, 066001 (2009).

  38. 38

    Yu, S., Pleskac, T. J. & Zeigenfuse, M. D. Dynamics of postdecisional processing of confidence. J. Exp. Psychol. Gen. 144, 489–510 (2015).

  39. 39

    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).

  40. 40

    Maniscalco, B. & Lau, H. The signal processing architecture underlying subjective reports of sensory awareness. Neurosci. Conscious. 2016, niw002 (2016).

  41. 41

    Chen, J., Feng, T., Shi, J., Liu, L. & Li, H. Neural representation of decision confidence. Behav. Brain Res. 245, 50–57 (2013).

  42. 42

    Heereman, J., Walter, H. & Heekeren, H. R. A task-independent neural representation of subjective certainty in visual perception. Front. Hum. Neurosci. 9, 551 (2015).

  43. 43

    McCurdy, L. Y. et al. Anatomical coupling between distinct metacognitive systems for memory and visual perception. J. Neurosci. 33, 1897–1906 (2013).

  44. 44

    Schwiedrzik, C. M., Singer, W. & Melloni, L. Subjective and objective learning effects dissociate in space and in time. Proc. Natl Acad. Sci. USA 108, 4506–4511 (2011).

  45. 45

    Li, Q., Hill, Z. & He, B. J. Spatiotemporal dissociation of brain activity underlying subjective awareness, objective performance and confidence. J. Neurosci. 34, 4382–4395 (2014).

  46. 46

    Middlebrooks, P. G. & Sommer, M. A. Neuronal correlates of metacognition in primate frontal cortex. Neuron 75, 517–530 (2012).

  47. 47

    Fleming, S. M. & Dolan, R. J. The neural basis of metacognitive ability. Phil. Trans. R. Soc. B 367, 1338–1349 (2012).

  48. 48

    Rounis, E., Maniscalco, B., Rothwell, J. C., Passingham, R. E. & Lau, H. Theta-burst transcranial magnetic stimulation to the prefrontal cortex impairs metacognitive visual awareness. Cogn. Neurosci. 1, 165–175 (2010).

  49. 49

    Lau, H. & Passingham, R. E. Relative blindsight in normal observers and the neural correlate of visual consciousness. Proc. Natl Acad. Sci. USA 103, 18763–18768 (2006).

  50. 50

    Britten, K. H., Newsome, W. T., Shadlen, M. N., Celebrini, S. & Movshon, J. A. A relationship between behavioral choice and the visual responses of neurons in macaque MT. Vis. Neurosci. 13, 87–100 (1996).

  51. 51

    Green, D. M. & Swets, J. A. Signal Detection Theory and Psychophysics (Wiley, 1966).

  52. 52

    Macmillan, N. A. & Creelman, C. D. Detection Theory: A User’s Guide (Taylor & Francis, 2004).

  53. 53

    King, J.-R. & Dehaene, S. Characterizing the dynamics of mental representations: the temporal generalization method. Trends Cogn. Sci. 18, 203–210 (2014).

  54. 54

    Peters, M. A. K. & Lau, H. Human observers have optimal introspective access to perceptual processes even for visually masked stimuli. Elife 4, e09651 (2015).

  55. 55

    Vlassova, A., Donkin, C. & Pearson, J. Unconscious information changes decision accuracy but not confidence. Proc. Natl Acad. Sci. USA 111, 16214–16218 (2014).

  56. 56

    Lak, A. et al. Orbitofrontal cortex is required for optimal waiting based on decision confidence. Neuron 84, 190–201 (2014).

  57. 57

    Komura, Y., Nikkuni, A., Hirashima, N., Uetake, T. & Miyamoto, A. Responses of pulvinar neurons reflect a subject’s confidence in visual categorization. Nat. Neurosci. 16, 749–755 (2013).

  58. 58

    Zylberberg, A., Roelfsema, P. R. & Sigman, M. Variance misperception explains illusions of confidence in simple perceptual decisions. Conscious. Cogn. 27C, 246–253 (2014).

  59. 59

    Rahnev, D., Maniscalco, B., Luber, B., Lau, H. & Lisanby, S. H. Direct injection of noise to the visual cortex decreases accuracy but increases decision confidence. J. Neurophysiol. 107, 1556–1563 (2012).

  60. 60

    Rahnev, D. et al. Attention induces conservative subjective biases in visual perception. Nat. Neurosci. 14, 1513–1515 (2011).

  61. 61

    Peters, M.A.K. et al. Transcranial magnetic stimulation to visual cortex induces suboptimal introspection. Cortex 93, 119–132 (2017).

  62. 62

    Beck, J. M., Ma, W. J., Latham, P. E. & Pouget, A. Probabilistic population codes and the exponential family of distributions. Prog. Brain Res. 165, 509–519 (2007).

  63. 63

    Beck, J. M. et al. Probabilistic population codes for Bayesian decision making. Neuron 60, 1142–1152 (2008).

  64. 64

    Ma, W. J., Beck, J. M., Latham, P. & Pouget, A. Bayesian inference with probabilistic population codes. Nat. Neurosci. 9, 1432–1438 (2006).

  65. 65

    Fiser, J., Berkes, P., Orbán, G. & Lengyel, M. Statistically optimal perception and learning: from behavior to neural representations. Trends Cogn. Sci. 14, 119–130 (2010).

  66. 66

    Ma, W. J., Beck, J. M. & Pouget, A. Spiking networks for Bayesian inference and choice. Curr. Opin. Neurobiol. 18, 217–222 (2008).

  67. 67

    Berkes, P., Orban, G., Lengyel, M. & Fiser, J. Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science 331, 83–87 (2011).

  68. 68

    Maniscalco, B. & Lau, H. A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings. Conscious. Cogn. 21, 422–430 (2012).

  69. 69

    Fleming, S. M. & Lau, H. How to measure metacognition. Front. Hum. Neurosci. 8, 443 (2014).

  70. 70

    Wei, X. & Stocker, A. Efficient coding provides a direct link between prior and likelihood in perceptual Bayesian inference. Adv. Neural Inf. Process. Syst. 25, 1313–1321 (2012).

  71. 71

    Fleming, S. M., Maloney, L. T. & Daw, N. D. The irrationality of categorical perception. J. Neurosci. 33, 19060–19070 (2013).

  72. 72

    Jazayeri, M. & Movshon, J. A. A new perceptual illusion reveals mechanisms of sensory decoding. Nature 446, 912–915 (2007).

  73. 74

    Luu, L. & Stocker, A. A. Choice-induced biases in perception. Preprint at http://biorxiv.org/content/early/2016/04/01/043224(2016).

  74. 73

    Rutishauser, U. et al. Representation of retrieval confidence by single neurons in the human medial temporal lobe. Nat. Neurosci. 18, 1041–1050 (2015).

  75. 75

    Zawadzka, K., Higham, P. A. & Hanczakowski, M. Confidence in forced-choice recognition: what underlies the ratings? J. Exp. Psychol. Learn. Mem. Cogn. 43, 552–564 (2016).

  76. 76

    James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning, with Applications in R (Springer, 2015).

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Acknowledgements

This work is supported by funding from the Templeton Foundation (grant 21569 to H.L.) and the US National Institute of Neurological Disorders and Stroke (NIH R01 NS088628 to H.L.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank U. Maoz for discussion on some technical issues regarding analysis.

Author information

M.A.K.P. and H.L. together developed the key theoretical ideas behind the project, analysed the data and wrote the paper. H.L., T.T., E.H. and M.D. designed the behavioral paradigm and initiated project planning. T.T. and M.D. were primarily responsible for data collection. B.M., Y.D.K. and M.D. contributed to data analysis. W.D., R.K. and O.D. contributed to data collection and overcoming logistical challenges. T.T. oversaw the logistical issues and planning involved in the entire project.

Correspondence to Megan A. K. Peters.

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The authors declare no competing interests.

Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Results, Supplementary Figures 1–13, Supplementary Tables 1–8, and Supplementary Notes.

Supplementary Dataset

MNI coordinates of all electrodes.

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Peters, M., Thesen, T., Ko, Y. et al. Perceptual confidence neglects decision-incongruent evidence in the brain. Nat Hum Behav 1, 0139 (2017). https://doi.org/10.1038/s41562-017-0139

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