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Suboptimality in perceptual decision making
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Abstract
Human perceptual decisions are often described as optimal. Critics of this view have argued that claims of optimality are overly flexible and lack explanatory power. Meanwhile, advocates for optimality have countered that such criticisms single out a few selected papers. To elucidate the issue of optimality in perceptual decision making, we review the extensive literature on suboptimal performance in perceptual tasks. We discuss eight different classes of suboptimal perceptual decisions, including improper placement, maintenance, and adjustment of perceptual criteria; inadequate tradeoff between speed and accuracy; inappropriate confidence ratings; misweightings in cue combination; and findings related to various perceptual illusions and biases. In addition, we discuss conceptual shortcomings of a focus on optimality, such as definitional difficulties and the limited value of optimality claims in and of themselves. We therefore advocate that the field drop its emphasis on whether observed behavior is optimal and instead concentrate on building and testing detailed observer models that explain behavior across a wide range of tasks. To facilitate this transition, we compile the proposed hypotheses regarding the origins of suboptimal perceptual decisions reviewed here. We argue that verifying, rejecting, and expanding these explanations for suboptimal behavior – rather than assessing optimality per se – should be among the major goals of the science of perceptual decision making.
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“when behavioral data fail to conform to the predictions of Bayesian optimality, researchers frequently appeal to auxiliary assumptions—such as “suboptimal” priors, cognitive capacity limitations, or the use of approximation algorithms—to account for these deviations (Rahnev and Denison 2018; Sanborn and Chater 2016).”
From The myth of the Bayesian brain (2025) · cites this paper as Rahnev and Denison 2018
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“Moreover, alternative non-Bayesian models—grounded in heuristics, dynamical systems, or adaptation mechanisms—can often account for the same neural patterns with equal or even greater parsimony and explanatory clarity (Rahnev and Denison 2018).”
From The myth of the Bayesian brain (2025) · cites this paper as Rahnev and Denison 2018
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“In tightly controlled tasks where prior probabilities and likelihoods are explicitly defined, persistent deviations from Bayesian-optimal behavior—unexplainable by noise, heuristics, or approximation—would falsify normative claims (Rahnev and Denison 2018).”
From The myth of the Bayesian brain (2025) · cites this paper as Rahnev and Denison 2018
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“More generally, different suboptimalities may coexist, and the same kind of computational constraints that affect first-order cognition are likely to affect the suboptimality of metacognition (Rahnev & Denison 2018).”
From Metacognition and Confidence: A Review and Synthesis (2024) · cites this paper as Rahnev & Denison 2018
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“However, the meaningfulness of a Bayes-optimal result is questionable [48] and the use of Bayesian modelling is disputed [49–53].”
From A model of cue integration as vector summation in the insect brain (2023) · cites this paper as [52]
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Cites
Items 201–320 of 320
Failure to handle more than one internal representation in visual detection tasks
Goréa, A., & Sagi, D. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.22.12380 · Cited by 93
Economic Value Biases Uncertain Perceptual Choices in the Parietal and Prefrontal Cortices
Summerfield, C., & Koechlin, É. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00208 · Cited by 93
Optimal Predictions in Everyday Cognition: The Wisdom of Individuals or Crowds?
Mozer, M. C., Pashler, H., & Homaei, H. (2008) · Cognitive Science · doi:10.1080/03640210802353016 · Cited by 90
Starns, J. J., & Ratcliff, R. (2011) · Psychonomic Bulletin & Review · doi:10.3758/s13423-011-0189-3 · Cited by 89
Computations underlying confidence in visual perception.
Spence, M. L., Dux, P. E., & Arnold, D. H. (2016) · Journal of Experimental Psychology: Human Perception and Performance · doi:10.1037/xhp0000179 · Cited by 89
How the brain codes intimacy: The neurobiological substrates of romantic touch
Kreuder, A.-K., Scheele, D., Wassermann, L., Wollseifer, M., Stoffel-Wagner, B., Lee, M. R., Hennig, J., Maier, W., & Hurlemann, R. (2017) · Human Brain Mapping · doi:10.1002/hbm.23679 · Cited by 89
Intensity perception. IV. Resolution in roving-level discrimination
Berliner, J. E., & Durlach, N. I. (1973) · The Journal of the Acoustical Society of America · doi:10.1121/1.1913465 · Cited by 88
Prevalence of Abnormalities Influences Cytologists' Error Rates in Screening for Cervical Cancer
Evans, K. K., Tambouret, R., Evered, A., et al. (2011) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2010-0739-oa · Cited by 87
Auditory phase and frequency discrimination: A comparison of nine procedures.
Creelman, C. D., & Macmillan, N. A. (1979) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.5.1.146 · Cited by 86
Prior expectations facilitate metacognition for perceptual decision
Sherman, M. T., Seth, A. K., Barrett, A. B., & Kanai, R. (2015) · Consciousness and Cognition · doi:10.1016/j.concog.2015.04.015 · Cited by 85
Neural Mechanisms of Human Perceptual Choice Under Focused and Divided Attention
Wyart, V., Myers, N. E., & Summerfield, C. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.3276-14.2015 · Cited by 85
Attention makes moving objects be perceived to move faster
Turatto, M., Vescovi, M., & Valsecchi, M. (2006) · Vision Research · doi:10.1016/j.visres.2006.10.002 · Cited by 84
Reference Repulsion When Judging the Direction of Visual Motion
Rauber, H.-J., & Treue, S. (1998) · Perception · doi:10.1068/p270393 · Cited by 83
Fechner’s law in metacognition: A quantitative model of visual working memory confidence.
van den Berg, R., Yoo, A. H., & Ma, W. J. (2017) · Psychological Review · doi:10.1037/rev0000060 · Cited by 83
Visual-proprioceptive interaction under large amounts of conflict.
Warren, D. H., & Cleaves, W. T. (1971) · Journal of Experimental Psychology · doi:10.1037/h0031545 · Cited by 82
Rosas, P., Wagemans, J. P., Ernst, M. O., & Wichmann, F. A. (2005) · Journal of the Optical Society of America A · doi:10.1364/josaa.22.000801 · Cited by 82
Weighting Mean and Variability during Confidence Judgments
de Gardelle, V., & Mamassian, P. (2015) · PLoS ONE · doi:10.1371/journal.pone.0120870 · Cited by 82
Intensity and frequency discrimination in one- and two-interval paradigms
Jesteadt, W., & Bilger, R. C. (1974) · The Journal of the Acoustical Society of America · doi:10.1121/1.1914696 · Cited by 81
Tuning the speed-accuracy trade-off to maximize reward rate in multisensory decision-making
Drugowitsch, J., DeAngelis, G. C., Angelaki, D. E., & Pouget, A. (2015) · eLife · doi:10.7554/elife.06678 · Cited by 79
Effects of Degree of Visual Association and Angle of Displacement on the “Ventriloquism” Effect
Jack, C. E., & Thurlow, W. R. (1973) · Perceptual and Motor Skills · doi:10.1177/003151257303700360 · Cited by 79
Varey, C. A., Mellers, B. A., & Birnbaum, M. H. (1990) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.16.3.613 · Cited by 77
Disentangling signal from noise in visual contrast discrimination
Goréa, A., & Sagi, D. (2001) · Nature Neuroscience · doi:10.1038/nn741 · Cited by 73
Testing Transitivity of Preferences on Two-Alternative Forced Choice Data
Regenwetter, M., Dana, J. D., & Davis‐Stober, C. P. (2010) · Frontiers in Psychology · doi:10.3389/fpsyg.2010.00148 · Cited by 73
Voluntary attention increases perceived spatial frequency
Abrams, J., Barbot, A., & Carrasco, A. (2010) · Attention Perception & Psychophysics · doi:10.3758/app.72.6.1510 · Cited by 73
Relation between belief and performance in perceptual decision making
Drugowitsch, J., Moreno-Bote, R., & Pouget, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0096511 · Cited by 72
Realism of confidence in sensory discrimination
Baranski, J. V., & Petrusic, W. M. (1999) · Perception & Psychophysics · doi:10.3758/bf03206187 · Cited by 72
Petrini, K., Remark, A., Smith, L., & Marko Nardini, A. (2014) · Developmental Science · doi:10.1111/desc.12127 · Cited by 72
Rahnev, D., Kok, P., Munneke, M. A. M., Bahdo, L., de Lange, F. P., & Lau, H. C. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00209.2013 · Cited by 71
Self-citationRahnev, D., Bahdo, L., de Lange, F. P., & Lau, H. C. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00184.2012 · Cited by 71
Self-citationUnconscious information changes decision accuracy but not confidence
Vlassova, A., Donkin, C., & Pearson, J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1403619111 · Cited by 70
Decisions reduce sensitivity to subsequent information
Bronfman, Z. Z., Brezis, N., Moran, R., Tsetsos, K., Donner, T. H., & Usher, M. (2015) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2015.0228 · Cited by 69
Emotion as a boost to metacognition: How worry enhances the quality of confidence
Massoni, S. (2014) · Consciousness and Cognition · doi:10.1016/j.concog.2014.08.006 · Cited by 66
Late Development of Cue Integration Is Linked to Sensory Fusion in Cortex
Dekker, T. M., Ban, H., van der Velde, B., Sereno, M. I., Welchman, A. E., & Marko Nardini, A. (2015) · Current Biology · doi:10.1016/j.cub.2015.09.043 · Cited by 66
Approach and Avoidance as Organizing Structures for Motivated Distance Perception
Balcetis, E. (2015) · Emotion Review · doi:10.1177/1754073915586225 · Cited by 66
The Development of Audio-Visual Integration for Temporal Judgements
Adams, W. J. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004865 · Cited by 66
Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models
Eberhardt, F., & Danks, D. J. (2011) · Minds and Machines · doi:10.1007/s11023-011-9241-3 · Cited by 66
Priming by the variability of visual information
Michael, E. L., de Gardelle, V., & Summerfield, C. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1308674111 · Cited by 65
Maniscalco, B., & Lau, H. C. (2015) · Neuroscience of Consciousness · doi:10.1093/nc/niv002 · Cited by 65
A decisional account of subjective inflation of visual perception at the periphery
Solovey, G., Graney, G. G., & Lau, H. (2015) · Attention, Perception, & Psychophysics · doi:10.3758/s13414-014-0769-1 · Cited by 63
Cue contrast modulates the effects of exogenous attention on appearance
Fuller, S., Park, Y., & Carrasco, M. (2009) · Vision Research · doi:10.1016/j.visres.2009.04.019 · Cited by 61
Hammett, S. T., Champion, R. A., Thompson, P., & Morland, A. B. (2006) · Vision Research · doi:10.1016/j.visres.2006.08.013 · Cited by 61
The Size-Weight Illusion is not anti-Bayesian after all: a unifying Bayesian account
Peters, M. A. K., Ma, W. J., & Shams, L. B. (2016) · PeerJ · doi:10.7717/peerj.2124 · Cited by 61
Signal detection theory, uncertainty, and Poisson-like population codes
Ma, W. J. (2010) · Vision Research · doi:10.1016/j.visres.2010.08.035 · Cited by 60
Interval bias in 2AFC detection tasks: sorting out the artifacts
García-Pérez, M. Á., & Alcalá‐Quintana, R. (2011) · Attention Perception & Psychophysics · doi:10.3758/s13414-011-0167-x · Cited by 59
How previous experience shapes perception in different sensory modalities
Snyder, J., Schwiedrzik, C., Vitela, A. D., & Melloni, L. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00594 · Cited by 59
How Haptic Size Sensations Improve Distance Perception
Battaglia, P., Kersten, D., & Schrater, P. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002080 · Cited by 58
Optimality of perceptual decision criteria.
Ulehla, Z. J. (1966) · Journal of Experimental Psychology · doi:10.1037/h0023007 · Cited by 58
Confidence measurement in the light of signal detection theory
Massoni, S., Gajdos, T., & Vergnaud, J.-C. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.01455 · Cited by 58
Birnbaum, M. H. (2011) · Psychological Review · doi:10.1037/a0023852 · Cited by 56
Attention in a Bayesian Framework
Whiteley, L., & Sahani, M. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00100 · Cited by 56
On the Calibration of Knowledge and Perception.
Baranski, J. V., & Petrusic, W. M. (1995) · Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · doi:10.1037/1196-1961.49.3.397 · Cited by 53
Category discriminability, base-rate, and payoff effects in perceptual categorization
Bohil, C. J., & Maddox, W. T. (2001) · Perception & Psychophysics · doi:10.3758/bf03194476 · Cited by 52
Irrational time allocation in decision-making
Oud, B., Krajbich, I. M., Miller, K. J., Cheong, J. H., Botvinick, M., & Fehr, E. (2016) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2015.1439 · Cited by 51
Intensity discrilnination: Performance in three paradigms
Viemeister, N. F. (1970) · Perception & Psychophysics · doi:10.3758/bf03207037 · Cited by 51
Signal recognition as influenced by presentation schedules
Tanner, T. A., Haller, R. W., & Atkinson, R. C. (1967) · Perception & Psychophysics · doi:10.3758/bf03210070 · Cited by 49
Cortical Correlates of Human Motion Perception Biases
Vintch, B., & Gardner, J. L. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.2809-13.2014 · Cited by 47
Calibration of sensory and cognitive judgments: Two different accounts
Winman, A., & Juslin, P. (1993) · Scandinavian Journal of Psychology · doi:10.1111/j.1467-9450.1993.tb01109.x · Cited by 47
Robust versus optimal strategies for two-alternative forced choice tasks
Zacksenhouse, M., Bogacz, R., & Holmes, P. J. (2010) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2009.12.004 · Cited by 46
A detailed comparison of optimality and simplicity in perceptual decision making.
Shen, S., & Ma, W. J. (2016) · Psychological Review · doi:10.1037/rev0000028 · Cited by 46
Apparent speed increases at low luminance
Vaziri-Pashkam, M., & Cavanagh, P. E. (2008) · Journal of Vision · doi:10.1167/8.16.9 · Cited by 45
Independent Effects of Adaptation and Attention on Perceived Speed
Anton-Erxleben, K., Herrmann, K., & Carrasco, M. (2012) · Psychological Science · doi:10.1177/0956797612449178 · Cited by 44
Limitations of Proposed Signatures of Bayesian Confidence
Adler, W. T., & Ma, W. J. (2018) · Neural Computation · doi:10.1162/neco_a_01141 · Cited by 44
Probabilistic vs. non-probabilistic approaches to the neurobiology of perceptual decision-making
Drugowitsch, J., & Pouget, A. (2012) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.07.007 · Cited by 43
Suboptimal decision criteria are predicted by subjectively weighted probabilities and rewards
Ackermann, J. F., & Landy, M. S. (2014) · Attention Perception & Psychophysics · doi:10.3758/s13414-014-0779-z · Cited by 43
Operating Characteristics from Yes-No and Forced-Choice Procedures
Schulman, A. I., & Mitchell, R. R. (1966) · The Journal of the Acoustical Society of America · doi:10.1121/1.1910098 · Cited by 42
Bohil, C. J., & Maddox, W. T. (2003) · Memory & Cognition · doi:10.3758/bf03194378 · Cited by 41
A Confidence Scale Defined in Terms of Expected Percentages
Adams, J. K. (1957) · The American Journal of Psychology · doi:10.2307/1419580 · Cited by 39
García-Pérez, M. Á., & Alcalá‐Quintana, R. (2010) · Journal of Sensory Studies · doi:10.1111/j.1745-459x.2010.00310.x · Cited by 39
Neural signatures of intransitive preferences
Kalenscher, T. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00049 · Cited by 38
Landy, M. S., Goutcher, R., Trommershäuser, J., & Mamassian, P. (2007) · Journal of Vision · doi:10.1167/7.6.4 · Cited by 38
A Bayesian Framework for Sensory Adaptation
Grzywacz, N. M., & Balboa, R. M. (2002) · Neural Computation · doi:10.1162/089976602317250898 · Cited by 38
Is that what Bayesians believe? Reply to Griffiths, Chater, Norris, and Pouget (2012).
Bowers, J. S., & Davis, C. J. (2012) · Psychological Bulletin · doi:10.1037/a0027750 · Cited by 38
The Irrationality of Categorical Perception
Fleming, S. M., Maloney, L. T., & Daw, N. D. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.1263-13.2013 · Cited by 36
Base-rate effects in multidimensional perceptual categorization.
Maddox, W. T. (1995) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037//0278-7393.21.2.288 · Cited by 36
Reckless, G. E., Ousdal, O. T., Server, A., Walter, H., Andreassen, O. A., & Jensen, J. (2014) · Brain and Behavior · doi:10.1002/brb3.223 · Cited by 34
The Paradox of the Universal Triangle: Concepts, Language, and Prototypes
Lupyan, G. (2016) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470218.2015.1130730 · Cited by 34
Do perceptual biases emerge early or late in visual processing? Decision-biases in motion perception
Zamboni, E., Ledgeway, T., McGraw, P. V., & Schluppeck, D. (2016) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2016.0263 · Cited by 34
Holmes, P. J., & Cohen, J. D. (2014) · Topics in Cognitive Science · doi:10.1111/tops.12084 · Cited by 33
Low attention impairs optimal incorporation of prior knowledge in perceptual decisions
Morales, J., Solovey, G., Maniscalco, B., Rahnev, D., de Lange, F. P., & Lau, H. C. (2015) · Attention Perception & Psychophysics · doi:10.3758/s13414-015-0897-2 · Cited by 33
Self-citationUnreliable Evidence: 2 Sources of Uncertainty During Perceptual Choice
Michael, E., de Gardelle, V., Nevado‐Holgado, A. J., & Summerfield, C. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht287 · Cited by 33
Covert visual search: prior beliefs are optimally combined with sensory evidence.
Vincent, B. (2011) · Journal of Vision · doi:10.1167/11.13.25 · Cited by 31
On the relation between base-rate and cost-benefit learning in simulated medical diagnosis.
Maddox, W. T., & Dodd, J. L. (2001) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037//0278-7393.27.6.1367 · Cited by 31
Robust sampling of decision information during perceptual choice
Vandormael, H., Castañón, S. H., Balaguer, J., Li, V., & Summerfield, C. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1613950114 · Cited by 31
Maddox, W. T., & Bohil, C. J. (2003) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/0278-7393.29.2.307 · Cited by 30
Heterogeneity and parsimony in intertemporal choice.
Regenwetter, M., Cavagnaro, D. R., Popova, A., Guo, Y., Zwilling, C. E., Lim, S. H., & Stevens, J. R. (2017) · Decision · doi:10.1037/dec0000069 · Cited by 30
Goodman, N. D., Frank, M. C., Griffiths, T. L., Tenenbaum, J. B., Battaglia, P., & Hamrick, J. B. (2015) · Psychological Science · doi:10.1177/0956797614559544 · Cited by 29
Rationalizable Irrationalities of Choice
Dayan, P. (2014) · Topics in Cognitive Science · doi:10.1111/tops.12082 · Cited by 29
Shading and Stereo in Early Perception of Shape and Reflectance
Sun, J., & Perona, P. (1997) · Perception · doi:10.1068/p260519 · Cited by 27
Overestimation of base-rate differences in complex perceptual categories
Maddox, W. T., & Bohil, C. J. (1998) · Perception & Psychophysics · doi:10.3758/bf03206047 · Cited by 25
Maddox, W. T., & Bohil, C. J. (2004) · Perception & Psychophysics · doi:10.3758/bf03194865 · Cited by 25
Complexity and specificity of experimentally-induced expectations in motion perception
Gekas, N., Chalk, M., Seitz, A. R., & Seriès, P. (2013) · Journal of Vision · doi:10.1167/13.4.8 · Cited by 24
Maddox, W. T., & Bohil, C. J. (2005) · Memory & Cognition · doi:10.3758/bf03195319 · Cited by 23
Costs and benefits in perceptual categorization
Maddox, W. T., & Bohil, C. J. (2000) · Memory & Cognition · doi:10.3758/bf03201250 · Cited by 23
Stimulation in the Dorsolateral Prefrontal Cortex Changes Subjective Evaluation of Percepts
Chiang, T.-C., Lu, R., Hsieh, S., Chang, Y.-H., & Yang, Y. K. (2014) · PLoS ONE · doi:10.1371/journal.pone.0106943 · Cited by 23
Suboptimal Criterion Learning in Static and Dynamic Environments
Norton, E. H., Fleming, S. M., Daw, N. D., & Landy, M. S. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005304 · Cited by 23
Motivation alters response bias and neural activation patterns in a perceptual decision-making task
Reckless, G. E., Bolstad, I., Nakstad, P., Andreassen, O. A., & Jensen, J. (2013) · Neuroscience · doi:10.1016/j.neuroscience.2013.02.015 · Cited by 22
Chen, C., & Tyler, C. W. (2015) · PLoS ONE · doi:10.1371/journal.pone.0132658 · Cited by 21
Interactions between spatially tuned mechanisms: converging evidence
Olzak, L. A. (1985) · Journal of the Optical Society of America A · doi:10.1364/josaa.2.001551 · Cited by 21
Perceptual biases are inconsistent with Bayesian encoding of speed in the human visual system
Hassan, O., & Hammett, S. T. (2015) · Journal of Vision · doi:10.1167/15.2.9 · Cited by 21
Feedback effects on cost-benefit learning in perceptual categorization
Maddox, W. T., & Bohil, C. J. (2001) · Memory & Cognition · doi:10.3758/bf03200461 · Cited by 21
Reported violations of rationality may be aggregation artifacts
Davis‐Stober, C. P., Park, S., Brown, N., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1606997113 · Cited by 21
Still Searching for Principles
Marcus, G., & Davis, E. (2015) · Psychological Science · doi:10.1177/0956797614568433 · Cited by 19
Criteria interactions across visual attributes
Goréa, A., Caetta, F., & Sagi, D. (2005) · Vision Research · doi:10.1016/j.visres.2005.03.018 · Cited by 19
Action Planning and the Timescale of Evidence Accumulation
Tsetsos, K., Pfeffer, T. A., Jentgens, P., & Donner, T. H. (2015) · PLoS ONE · doi:10.1371/journal.pone.0129473 · Cited by 18
Intensity Perception. III. Resolution in Small-Range Identification
Pynn, C., Braida, L. D., & Durlach, N. I. (1972) · The Journal of the Acoustical Society of America · doi:10.1121/1.1912878 · Cited by 18
Zhang, H., Morvan, C., & Maloney, L. T. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001023 · Cited by 16
Integration Trumps Selection in Object Recognition
Saarela, T., & Landy, M. S. (2015) · Current Biology · doi:10.1016/j.cub.2015.01.068 · Cited by 16
Comparison of ROC Curves from One- and Two-Interval Rating-Scale Procedures
Leshowitz, B. H. (1969) · The Journal of the Acoustical Society of America · doi:10.1121/1.1911703 · Cited by 16
Non-Bayesian Contour Synthesis
Anderson, B. L., O'Vari, J., & Barth, H. C. (2011) · Current Biology · doi:10.1016/j.cub.2011.02.011 · Cited by 15
Maddox, W. T., Bohil, C. J., & Dodd, J. L. (2003) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/0278-7393.29.6.1174 · Cited by 15
A test of the optimal classifier’s independence assumption in perceptual categorization
Bohil, C. J., & Maddox, W. T. (2003) · Perception & Psychophysics · doi:10.3758/bf03194577 · Cited by 15
Biases and optimality of sensory-motor and cognitive decisions
Trommershäuser, J. (2009) · Progress in brain research · doi:10.1016/s0079-6123(09)01321-1 · Cited by 14
Exploration mode affects visuohaptic integration of surface orientation
Plaisier, M. A., van Dam, L. C. J., Glowania, C., & Ernst, M. O. (2014) · Journal of Vision · doi:10.1167/14.13.22 · Cited by 13
Decision criteria in dual discrimination tasks estimated using external-noise methods
Zak, I., Katkov, M., Goréa, A., et al. (2012) · Attention Perception & Psychophysics · doi:10.3758/s13414-012-0269-0 · Cited by 12
Dissociation in decision bias mechanism between probabilistic information and previous decision
Kaneko, Y., & Sakai, K. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00261 · Cited by 9
Error patterns in judgment and production of horizontal and vertical scale positions
Brooke, J. B., & MacRae, A. W. (1980) · Perception & Psychophysics · doi:10.3758/bf03206117 · Cited by 9
Garcia, S. E., Jones, P. R., Reeve, E. I., Michaelides, M., Rubin, G. S., & Marko Nardini, A. (2017) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/xhp0000344 · Cited by 9
Schurger, A., Kim, M., & Cohen, J. D. (2015) · PLoS ONE · doi:10.1371/journal.pone.0125278 · Cited by 8
Dynamics of visibility, confidence, and choice during eye movements.
Navajas, J., Sigman, M., & Kamienkowski, J. E. (2014) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/a0036321 · Cited by 7
Fidelity of the representation of value in decision-making
Bays, P. M., & Dowding, B. A. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005405 · Cited by 7
Research works printed but not linked: 45
- Wohlgemuth, 1911, On the after-effect of seen movement
- 10.1167/10.7.213 · DOI · Paper page
- 10.3758/BF03199483 · DOI
- 10.1016/S1007-0214(11)70019-0 · DOI
- Osgood, 1953, Method and theory in experimental psychology
- Ramachandran, 1990, Vision: Coding and efficiency, 346
- 10.1093/acprof:oso/9780195387247.003.0002 · DOI
- Barlow, 1961, Sensory communication, 217
- Treisman, 1984, The setting and maintenance of criteria representing levels of confidence, Journal of Experimental Psychology: Human Perception and Performance, 10, 119
- Wei, X.-X. & Stocker, A. A. (2013) Efficient coding provides a direct link between prior and likelihood in perceptual Bayesian inference. In: Advances in neural information processing systems 25 (proceedings from the conference, Neural Information Processing Systems 2012), ed. Pereira, F. , Burges, C. J. C. , Bottou, L. & Weinberger, K. Q. . Available at: https://papers.nips.cc/paper/4489-efficient-coding-provides-a-direct-link-between-prior-and-likelihood-in-perceptual-bayesian-inference.
- 10.1093/acprof:oso/9780195387247.003.0001 · DOI
- Jaynes, 1957, Probability theory: The logic of science
- 10.1037/0096-3445.121.2.177 · DOI
- Cowan, 2005, Working memory capacity
- Sabra, 1989, The optics of Ibn Al-Haytham, Books I–III: On direct vision
- 10.7551/mitpress/1654.001.0001 · DOI
- 10.1016/S0166-4115(08)62776-4 · DOI
- 10.1016/0010-0285(92)90013-R · DOI
- Barlow, 1990, Vision: Coding and efficiency, 363
- Dawes, 1980, Similarity and choice: Papers in honor of Clyde Coombs, 327
- 10.1037/a0025291 · DOI
- 10.1093/acprof:oso/9780195387247.001.0001 · DOI
- 10.1037/h0034736 · DOI
- Swets, 1961, Information theory: Proceedings of the fourth London symposium, 177
- 10.1121/1.1908504 · DOI
- 10.1016/0004-3702(90)90060-D · DOI
- 10.1037/h0028949 · DOI
- Fechner, 1860, Elemente der psychophysik
- 10.1007/978-1-4757-2076-1 · DOI
- Healy, 1981, Probability matching and the formation of conservative decision rules in a numerical analog of signal detection, Journal of Experimental Psychology: Human Learning and Memory, 7, 344
- 10.1093/acprof:oso/9780199688890.003.0005 · DOI
- 10.1093/acprof:oso/9780195387247.003.0008 · DOI
- Vickers, 1979, Decision processes in visual perception
- Luu, 2016, Choice-induced biases in perception, bioRxiv
- Simon, 1957, Models of man, social and rational: Mathematical essays on rational human behavior in a social setting, 99
- 10.1080/13506280143000638 · DOI
- Macmillan, 2005, Detection theory: A user's guide
- Turnbull, 1961, The correspondence of Isaac Newton. Vol. 3, 1688–1694
- Green, 1966, Signal detection theory and psychophysics
- Helmholtz, 1856, Treatise on physiological optics
- 10.1037/rev0000045 · DOI · Paper page
- Geisler, 2013, Optimal and non-optimal fixation selection in visual search, Perception ECVP Abstract, 42, 226
- Stocker, A. A. & Simoncelli, E. P. (2006b) Sensory adaptation within a Bayesian framework for perception. In: Advances in neural information processing systems 18 (proceedings from the conference, Neural Information Processing Systems 2005), ed. Weiss, Y. & Schölkopf, B. & Platt, J. C. . Available at: https://papers.nips.cc/book/advances-in-neural-information-processing-systems-18-2005.
- Maloney, 1989, Proceedings of Society of Photo-Optical Instrumentation Engineers (SPIE) 1119, Visual Communications and Image Processing IV, 1154
- Jastrow, 1892, American Journal of Psychology, 5, 214
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Fleming, S. M. (2024) · Annual Review of Psychology · doi:10.1146/annurev-psych-022423-032425 · Cited by 256
Rational use of cognitive resources in human planning
Callaway, F., van Opheusden, B., Gul, S., Das, P., Krueger, P. M., Griffiths, T. L., & Lieder, F. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01332-8 · Cited by 121
If perception is probabilistic, why does it not seem probabilistic?
Block, N. (2018) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2017.0341 · Cited by 91
Consensus Goals in the Field of Visual Metacognition
Rahnev, D., Balsdon, T., Charles, L., de Gardelle, V., Denison, R., Desender, K., Faivre, N., Filevich, E., Fleming, S. M., Jehee, J., et al. (2022) · Perspectives on Psychological Science · doi:10.1177/17456916221075615 · Cited by 63
Self-citation
Metadata
- Authors
- Rahnev, D., & Denison, R. N.
- Affiliations
- Georgia Institute of Technology (US)
- New York University (US)
- Venue
- Behavioral and Brain Sciences
- Publisher
- Cambridge University Press (CUP)
- Published
- 2018
- Volume
- 41
- Pages
- e223-e223
- ISSN
- 0140-525X (ISSN-L)
- 1469-1825
- PMC
- PMC6110994
- OpenAlex
- W2790916085
- Cited by
- 342
- References
- 320
- Citations by year
- 2026: 23 · 2025: 36 · 2024: 34 · 2023: 62 · 2022: 45 · 2021: 42 · 2020: 37 · 2019: 48 · 2018: 12
- FWCI
- 20.70
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Behavioral and Brain Sciences (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Eye Institute (F32EY025533, T32EY007136)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- perceptual decision making, suboptimal performance, principle of optimality, speed-accuracy trade-off, confidence ratings, cue combination, perceptual illusions, perceptual bias, model observer
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 ›
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