Implicit knowledge of visual uncertainty guides decisions with asymmetric outcomes. Export

@article{citeulike:3129411, abstract = {Perception is an "inverse problem," in which the state of the world must be inferred from the sensory neural activity that results. However, this inference is both ill-posed (Helmholtz, 1856; Marr, 1982) and corrupted by noise (Green \& Swets, 1989), requiring the brain to compute perceptual beliefs under conditions of uncertainty. Here we show that human observers performing a simple visual choice task under an externally imposed loss function approach the optimal strategy, as defined by Bayesian probability and decision theory (Berger, 1985; Cox, 1961). In concert with earlier work, this suggests that observers possess a model of their internal uncertainty and can utilize this model in the neural computations that underlie their behavior (Knill \& Pouget, 2004). In our experiment, optimal behavior requires that observers integrate the loss function with an estimate of their internal uncertainty rather than simply requiring that they use a modal estimate of the uncertain stimulus. Crucially, they approach optimal behavior even when denied the opportunity to learn adaptive decision strategies based on immediate feedback. Our data thus support the idea that flexible representations of uncertainty are pre-existing, widespread, and can be propagated to decision-making areas of the brain.}, address = {Gatsby Computational Neuroscience Unit, University College London, London, UK. louisew@gatsby.ucl.ac.uk}, author = {Whiteley, Louise and Sahani, Maneesh}, citeulike-article-id = {3129411}, citeulike-linkout-0 = {http://dx.doi.org/10.1167/8.3.2}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18484808}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18484808}, doi = {10.1167/8.3.2}, issn = {1534-7362}, journal = {Journal of vision}, keywords = {bayesian\_inference, decision, uncertainty, unimodal, visual}, number = {3}, posted-at = {2009-08-06 08:55:55}, priority = {0}, title = {Implicit knowledge of visual uncertainty guides decisions with asymmetric outcomes.}, url = {http://dx.doi.org/10.1167/8.3.2}, volume = {8}, year = {2008} }

TY - JOUR ID - citeulike:3129411 L3 - citeulike-article-id:3129411 N2 - Perception is an "inverse problem," in which the state of the world must be inferred from the sensory neural activity that results. However, this inference is both ill-posed (Helmholtz, 1856; Marr, 1982) and corrupted by noise (Green & Swets, 1989), requiring the brain to compute perceptual beliefs under conditions of uncertainty. Here we show that human observers performing a simple visual choice task under an externally imposed loss function approach the optimal strategy, as defined by Bayesian probability and decision theory (Berger, 1985; Cox, 1961). In concert with earlier work, this suggests that observers possess a model of their internal uncertainty and can utilize this model in the neural computations that underlie their behavior (Knill & Pouget, 2004). In our experiment, optimal behavior requires that observers integrate the loss function with an estimate of their internal uncertainty rather than simply requiring that they use a modal estimate of the uncertain stimulus. Crucially, they approach optimal behavior even when denied the opportunity to learn adaptive decision strategies based on immediate feedback. Our data thus support the idea that flexible representations of uncertainty are pre-existing, widespread, and can be propagated to decision-making areas of the brain. IS - 3 JF - Journal of vision SN - 1534-7362 AD - Gatsby Computational Neuroscience Unit, University College London, London, UK. louisew@gatsby.ucl.ac.uk TI - Implicit knowledge of visual uncertainty guides decisions with asymmetric outcomes. VL - 8 KW - bayesian_inference KW - decision KW - uncertainty KW - unimodal KW - visual AU - Whiteley, Louise AU - Sahani, Maneesh PY - 2008/// UR - http://dx.doi.org/10.1167/8.3.2 ER -