TY - JOUR
T1 - Bayesian inference with incomplete knowledge explains perceptual confidence and its deviations from accuracy
AU - Khalvati, Koosha
AU - Kiani, Roozbeh
AU - Rao, Rajesh P.N.
N1 - Funding Information:
We thank Saleh Esteki, Christina Hatch, Gouki Okazawa, John Sakon, Long Sha, Michael Waskom, and Mike Shadlen for helpful discussions. R.K. acknowledges support from the Simons Collaboration on the Global Brain (542997), National Institutes of Mental Health (R01 MH109180-01), Alfred P. Sloan Foundation, the McKnight Foundation, and a Pew Scholarship in the Biomedical Sciences. R.P.N.R. acknowledges support from the National Institutes of Mental Health (CRCNS 5R01MH112166-03), National Science Foundation (EEC-1028725), Templeton World Charity Foundation, and a CJ and Elizabeth Hwang Endowed Professorship.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12/1
Y1 - 2021/12/1
N2 - In perceptual decisions, subjects infer hidden states of the environment based on noisy sensory information. Here we show that both choice and its associated confidence are explained by a Bayesian framework based on partially observable Markov decision processes (POMDPs). We test our model on monkeys performing a direction-discrimination task with post-decision wagering, demonstrating that the model explains objective accuracy and predicts subjective confidence. Further, we show that the model replicates well-known discrepancies of confidence and accuracy, including the hard-easy effect, opposing effects of stimulus variability on confidence and accuracy, dependence of confidence ratings on simultaneous or sequential reports of choice and confidence, apparent difference between choice and confidence sensitivity, and seemingly disproportionate influence of choice-congruent evidence on confidence. These effects may not be signatures of sub-optimal inference or discrepant computational processes for choice and confidence. Rather, they arise in Bayesian inference with incomplete knowledge of the environment.
AB - In perceptual decisions, subjects infer hidden states of the environment based on noisy sensory information. Here we show that both choice and its associated confidence are explained by a Bayesian framework based on partially observable Markov decision processes (POMDPs). We test our model on monkeys performing a direction-discrimination task with post-decision wagering, demonstrating that the model explains objective accuracy and predicts subjective confidence. Further, we show that the model replicates well-known discrepancies of confidence and accuracy, including the hard-easy effect, opposing effects of stimulus variability on confidence and accuracy, dependence of confidence ratings on simultaneous or sequential reports of choice and confidence, apparent difference between choice and confidence sensitivity, and seemingly disproportionate influence of choice-congruent evidence on confidence. These effects may not be signatures of sub-optimal inference or discrepant computational processes for choice and confidence. Rather, they arise in Bayesian inference with incomplete knowledge of the environment.
UR - http://www.scopus.com/inward/record.url?scp=85116390133&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85116390133&partnerID=8YFLogxK
U2 - 10.1038/s41467-021-25419-4
DO - 10.1038/s41467-021-25419-4
M3 - Article
C2 - 34588440
AN - SCOPUS:85116390133
SN - 2041-1723
VL - 12
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 5704
ER -