Perceptual Decision-Making as Probabilistic Inference by Neural Sampling

Neuron

Volumn 90, Issue 3, 2016, Pages 649-660

  Haefner, Ralf M ^a   Berkes, Pietro ^b   Fiser, József ^c  

^a UNIVERSITY OF ROCHESTER   (United States)

^b BRANDEIS UNIVERSITY   (United States)

^c CENTRAL EUROPEAN UNIVERSITY   (Hungary)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ATTENTION; CONFIRMATION BIAS; DECISION MAKING; LEARNING; NERVE CELL NETWORK; NEUROPHYSIOLOGY; PERCEPTION; POSITIVE FEEDBACK; PREDICTION; PRIORITY JOURNAL; PSYCHOPHYSICS; RESPONSE TIME; RETINA IMAGE; REWARD; SENSORY NERVE CELL; STIMULUS RESPONSE; STRIATE CORTEX; TASK PERFORMANCE; VISION; BIOLOGICAL MODEL; BRAIN CORTEX; COGNITION; HUMAN; PHYSIOLOGY; PSYCHOMOTOR PERFORMANCE;

CEREBRAL CORTEX; CHOICE BEHAVIOR; COGNITION; DECISION MAKING; HUMANS; MODELS, NEUROLOGICAL; PERCEPTION; PSYCHOMOTOR PERFORMANCE; SENSORY RECEPTOR CELLS;

EID: 84963576381     PISSN: 08966273     EISSN: 10974199     Source Type: Journal    
DOI: 10.1016/j.neuron.2016.03.020     Document Type: Article

References (63)

1. Abbott L.F., Dayan P. The effect of correlated variability on the accuracy of a population code. Neural Comput. 1999, 11:91-101.
2. Ahumada A.J. Classification image weights and internal noise level estimation. J. Vis. 2002, 2:121-131.
3. Averbeck B.B., Latham P.E., Pouget A. Neural correlations, population coding and computation. Nat. Rev. Neurosci. 2006, 7:358-366.
4. Beck J.M., Ma W.J., Kiani R., Hanks T., Churchland A.K., Roitman J., Shadlen M.N., Latham P.E., Pouget A. Probabilistic population codes for Bayesian decision making. Neuron 2008, 60:1142-1152.
5. Beck J.M., Latham P.E., Pouget A. Marginalization in neural circuits with divisive normalization. J. Neurosci. 2011, 31:15310-15319.
6. Beck J.M., Ma W.J., Pitkow X., Latham P.E., Pouget A. Not noisy, just wrong: the role of suboptimal inference in behavioral variability. Neuron 2012, 74:30-39.
7. Berkes P., Orbán G., Lengyel M., Fiser J. Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science 2011, 331:83-87.
8. Bonawitz E., Denison S., Griffiths T.L., Gopnik A. Probabilistic models, learning algorithms, and response variability: sampling in cognitive development. Trends Cogn. Sci. 2014, 18:497-500.
9. Bondy A., Cumming B.G. Top down signals influence the distribution of noise correlations amongst sensory neurons. 2013 Neuroscience Meeting Planner 2013, Society for Neuroscience.
10. Bosking W.H., Maunsell J.H.R. Effects of stimulus direction on the correlation between behavior and single units in area MT during a motion detection task. J. Neurosci. 2011, 31:8230-8238.
11. 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. 1996, 13:87-100.
12. Brunton B.W., Botvinick M.M., Brody C.D. Rats and humans can optimally accumulate evidence for decision-making. Science 2013, 340:95-98.
13. Buesing L., Bill J., Nessler B., Maass W. Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons. PLoS Comput. Biol. 2011, 7:e1002211.
14. Cohen M.R., Kohn A. Measuring and interpreting neuronal correlations. Nat. Neurosci. 2011, 14:811-819.
15. Cohen M.R., Newsome W.T. Context-dependent changes in functional circuitry in visual area MT. Neuron 2008, 60:162-173.
16. Cohen M.R., Newsome W.T. Estimates of the contribution of single neurons to perception depend on timescale and noise correlation. J. Neurosci. 2009, 29:6635-6648.
17. Doucet A. Sequential Monte Carlo Methods 2001, Wiley Online Library.
18. Drugowitsch J., Moreno-Bote R., Churchland A.K., Shadlen M.N., Pouget A. The cost of accumulating evidence in perceptual decision making. J. Neurosci. 2012, 32:3612-3628.
19. Ecker A.S., Berens P., Keliris G.A., Bethge M., Logothetis N.K., Tolias A.S. Decorrelated neuronal firing in cortical microcircuits. Science 2010, 327:584-587.
20. Ecker A.S., Berens P., Tolias A.S., Bethge M. The effect of noise correlations in populations of diversely tuned neurons. J. Neurosci. 2011, 31:14272-14283.
21. Ecker A.S., Berens P., Cotton R.J., Subramaniyan M., Denfield G.H., Cadwell C.R., Smirnakis S.M., Bethge M., Tolias A.S. State dependence of noise correlations in macaque primary visual cortex. Neuron 2014, 82:235-248.
22. Ecker A.S., Denfield G.H., Bethge M., Tolias A.S. On the structure of neuronal population activity under fluctuations in attentional state. J. Neurosci. 2016, 36:1775-1789.
23. Fiser J., Berkes P., Orbán G., Lengyel M. Statistically optimal perception and learning: from behavior to neural representations. Trends Cogn. Sci. 2010, 14:119-130.
24. Ganguli D., Simoncelli E.P. Implicit encoding of prior probabilities in optimal neural populations. Advances in Neural Information Processing Systems 23 2010, 6-9. Neural Information Processing Systems Foundation. J.D. Lafferty, C.K.I. Williams, J. Shawe-Taylor, R.S. Zemel, A. Culotta (Eds.).
25. Gold J.I., Shadlen M.N. The neural basis of decision making. Annu. Rev. Neurosci. 2007, 30:535-574.
26. Goldstone R.L. Perceptual learning. Annu. Rev. Psychol. 1998, 49:585-612.
27. Goris R.L., Simoncelli E.P., Movshon J.A. Origin and function of tuning diversity in macaque visual cortex. Neuron 2015, 88:819-831.
28. Grabska-Barwinska A., Beck J., Pouget A., Latham P.E. Demixing odors-fast inference in olfaction. Advances in Neural Information Processing Systems 26 2013, 1-9. Neural Information Processing Systems Foundation. C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, K.Q. Weinberger (Eds.).
29. Griffiths T.L., Vul E., Sanborn A.N. Bridging levels of analysis for probabilistic models of cognition. Curr. Dir. Psychol. Sci. 2012, 21:263-268.
30. Gu Y., Liu S., Fetsch C.R., Yang Y., Fok S., Sunkara A., DeAngelis G.C., Angelaki D.E. Perceptual learning reduces interneuronal correlations in macaque visual cortex. Neuron 2011, 71:750-761.
31. Haefner R.M. On the relationship between stimulus-evoked and choice-related responses and correlations during perceptual decision-making in a probabilistic inference framework. 2014 Neuroscience Meeting Planner 2014, Society for Neuroscience.
32. Haefner R.M., Gerwinn S., Macke J.H., Bethge M. Inferring decoding strategies from choice probabilities in the presence of correlated variability. Nat. Neurosci. 2013, 16:235-242.
33. Hoyer P.O.P., Hyvärinen A. Interpreting neural response variability as Monte Carlo sampling of the posterior. Advances in Neural Information Processing Systems 15 2003, 293-300. Neural Information Processing Systems Foundation. S. Becker, S. Thrun, K. Obermayer (Eds.).
34. Kersten D., Mamassian P., Yuille A. Object perception as Bayesian inference. Annu. Rev. Psychol. 2004, 55:271-304.
35. Krug K. A common neuronal code for perceptual processes in visual cortex? Comparing choice and attentional correlates in V5/MT. Philos. Trans. R. Soc. Lond. B Biol. Sci. 2004, 359:929-941.
36. Law C.-T.T., Gold J.I. Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area. Nat. Neurosci. 2008, 11:505-513.
37. Lee T.S., Mumford D. Hierarchical Bayesian inference in the visual cortex. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 2003, 20:1434-1448.
38. Lieder F., Griffiths T.L., Goodman N.D. Burn-in, bias, and the rationality of anchoring. Advances in Neural Information Processing Systems 25 2012, 2699-2707. Neural Information Processing Systems Foundation. F. Pereira, C.J.C. Burges, L. Bottou, K.Q. Weinberger (Eds.).
39. Ma W.J. Organizing probabilistic models of perception. Trends Cogn. Sci. 2012, 16:511-518.
40. Ma W.J., Beck J.M., Latham P.E., Pouget A. Bayesian inference with probabilistic population codes. Nat. Neurosci. 2006, 9:1432-1438.
41. Moreno-Bote R., Knill D.C., Pouget A. Bayesian sampling in visual perception. Proc. Natl. Acad. Sci. USA 2011, 108:12491-12496.
42. Moreno-Bote R., Beck J., Kanitscheider I., Pitkow X., Latham P., Pouget A. Information-limiting correlations. Nat. Neurosci. 2014, 17:1410-1417.
43. Mumford D. On the computational architecture of the neocortex. II. The role of cortico-cortical loops. Biol. Cybern. 1992, 66:241-251.
44. Neri P., Parker A.J., Blakemore C. Probing the human stereoscopic system with reverse correlation. Nature 1999, 401:695-698.
45. Nickerson R.S. Confirmation bias: a ubiquitous phenomenon in many guises. Rev. Gen. Psychol. 1998, 2:175-220.
46. Nienborg H., Cumming B.G. Decision-related activity in sensory neurons reflects more than a neuron's causal effect. Nature 2009, 459:89-92.
47. Nienborg H., Cumming B. Correlations between the activity of sensory neurons and behavior: how much do they tell us about a neuron's causality?. Curr. Opin. Neurobiol. 2010, 20:376-381.
48. Nienborg H., Cohen M.R., Cumming B.G. Decision-related activity in sensory neurons: correlations among neurons and with behavior. Annu. Rev. Neurosci. 2012, 35:463-483.
49. Olshausen B.A., Field D.J. Sparse coding with an overcomplete basis set: a strategy employed by V1?. Vision Res. 1997, 37:3311-3325.
50. Pecevski D., Buesing L., Maass W. Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons. PLoS Comput. Biol. 2011, 7:e1002294.
51. Pouget A., Beck J.M., Ma W.J., Latham P.E. Probabilistic brains: knowns and unknowns. Nat. Neurosci. 2013, 16:1170-1178.
52. Purushothaman G., Bradley D.C. Neural population code for fine perceptual decisions in area MT. Nat. Neurosci. 2005, 8:99-106.
53. Qamar A.T., Cotton R.J., George R.G., Beck J.M., Prezhdo E., Laudano A., Tolias A.S., Ma W.J. Trial-to-trial, uncertainty-based adjustment of decision boundaries in visual categorization. Proc. Natl. Acad. Sci. USA 2013, 110:20332-20337.
54. Rabinowitz N.C., Goris R.L., Cohen M., Simoncelli E.P. Attention stabilizes the shared gain of V4 populations. eLife 2015, 4:e08998.
55. Rao R.P., Ballard D.H. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 1999, 2:79-87.
56. Schwartz O., Simoncelli E.P. Natural signal statistics and sensory gain control. Nat. Neurosci. 2001, 4:819-825.
57. Shadlen M.N., Britten K.H., Newsome W.T., Movshon J.A. A computational analysis of the relationship between neuronal and behavioral responses to visual motion. J. Neurosci. 1996, 16:1486-1510.
58. Shamir M., Sompolinsky H. Implications of neuronal diversity on population coding. Neural Comput. 2006, 18:1951-1986.
59. Helmholtz H. Handbuch der physiologischen Optik 1867, Leopold Voss, Leipzig.
60. Vul E., Goodman N., Griffiths T.L., Tenenbaum J.B. One and done? Optimal decisions from very few samples. Cogn. Sci. 2014, 38:599-637.
61. Wei X.-X., Stocker A. Efficient coding provides a direct link between prior and likelihood in perceptual Bayesian inference. Advances in Neural Information Processing Systems 25 2012, 1-9. Neural Information Processing Systems Foundation. F. Pereira, C.J.C. Burges, L. Bottou, K.Q. Weinberger (Eds.).
62. Wimmer K., Compte A., Roxin A., Peixoto D., Renart A., de la Rocha J. Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT. Nat. Commun. 2015, 6:6177.
63. Zohary E., Shadlen M.N., Newsome W.T. Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 1994, 370:140-143.