Artículo

Estamos trabajando para incorporar este artículo al repositorio
Consulte el artículo en la página del editor
Consulte la política de Acceso Abierto del editor

Abstract:

Models that integrate sensory evidence to a threshold can explain task accuracy, response times and confidence, yet it is still unclear how confidence is encoded in the brain. Classic models assume that confidence is encoded in some form of balance between the evidence integrated in favor and against the selected option. However, recent experiments that measure the sensory evidence's influence on choice and confidence contradict these classic models. We propose that the decision is taken by many loosely coupled modules each of which represent a stochastic sample of the sensory evidence integral. Confidence is then encoded in the dispersion between modules. We show that our proposal can account for the well established relations between confidence, and stimuli discriminability and reaction times, as well as the fluctuations influence on choice and confidence.

Registro:

Documento: Artículo
Título:Confidence through consensus: A neural mechanism for uncertainty monitoring
Autor:Paz, L.; Insabato, A.; Zylberberg, A.; Deco, G.; Sigman, M.
Filiación:Integrative Neuroscience Laboratory, IFIBA, CONICET, Physics Department, FCEyN, UBA, Buenos Aires, Argentina
Universidad Pompeu Fabra, Barcelona, Spain
Department of Neuroscience, Howard Hughes Medical Institute, Columbia University, New York, NY 10032, United States
Universidad Torcuato di Tella, Buenos Aires, Argentina
Palabras clave:consensus; nervous system; stimulus; stochastic model; uncertainty; algorithm; biological model; computer simulation; consensus; decision making; human; perception; reaction time; Algorithms; Computer Simulation; Consensus; Decision Making; Humans; Models, Neurological; Perception; Reaction Time; Uncertainty
Año:2016
Volumen:6
DOI: http://dx.doi.org/10.1038/srep21830
Título revista:Scientific Reports
Título revista abreviado:Sci. Rep.
ISSN:20452322
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_20452322_v6_n_p_Paz

Referencias:

  • Usher, M., McClelland, J.L., The time course of perceptual choice: The leaky, competing accumulator model (2001) Psychological Review, 108, pp. 550-592
  • Smith, P.L., Ratcliff, R., Psychology and neurobiology of simple decisions (2004) Trends in Neurosciences, 27, pp. 161-168
  • Bogacz, R., Brown, E., Moehlis, J., Holmes, P., Cohen, J.D., The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks (2006) Psychological Review, 113, pp. 700-765
  • Brown, S.D., Heathcote, A., The simplest complete model of choice response time: Linear ballistic accumulation (2008) Cognitive Psychology, 57, pp. 153-178
  • Smith, P.L., McKenzie, C.R.L., Diffusive information accumulation by minimal recurrent neural models of decision making (2011) Neural Computation, 23, pp. 2000-2031
  • Audley, R.J., A stochastic model for individual choice behavior (1960) Psychological Review, 67, pp. 1-15
  • Vickers, D., Burt, J., Smith, P., Brown, M., Experimental paradigms emphasising state or process limitations: I effects on speedaccuracy tradeoffs (1985) Acta Psychol, 59, pp. 129-161
  • Kepecs, A., Uchida, N., Zariwala, H.A., Mainen, Z.F., Neural correlates, computation and behavioural impact of decision confidence (2008) Nature, 455, pp. 227-231
  • Fetsch, C.R., Kiani, R., Newsome, W.T., Shadlen, M.N., Effects of cortical microstimulation on confidence in a perceptual decision (2014) Neuron, 83, pp. 797-804
  • Vickers, D., Burt, J., Smith, P., Brown, M., Experimental paradigms emphasising state or process limitations: II effects on confidence (1985) Acta Psychol, 59, pp. 163-193
  • Kiani, R., Shadlen, M.N., Representation of confidence associated with a decision by neurons in the parietal cortex (2009) Science (New York, N.Y.), 324, pp. 759-764
  • Kiani, R., Corthell, L., Shadlen, M.N., Choice certainty is informed by both evidence and decision time (2014) Neuron, 84, pp. 1329-1342
  • Moreno-Bote, R., Decision confidence and uncertainty in diffusion models with partially correlated neuronal integrators (2010) Neural Computation, 22, pp. 1786-1811
  • Pleskac, T.J., Busemeyer, J.R., Two-stage dynamic signal detection: A theory of choice, decision time, and confidence (2010) Psychological Review, 117, pp. 864-901
  • Rolls, E.T., Grabenhorst, F., Deco, G., Choice, difficulty, and confidence in the brain (2010) NeuroImage, 53, pp. 694-706
  • Garret, H.E., A study of the relation of accuracy to speed (1922) Archs Psychol., 56, pp. 1-105
  • Johnson, D.M., Confidence and speed in the two-category judgment (1939) Archs Psychol., 34, pp. 1-53
  • Festinger, L., Studies in decision: I. Decision-time, relative frequency of judgment and subjective confidence (1943) J Exp Psychol, 32, pp. 291-306
  • Vickers, D., (1979) Decision Processes in Visual Perception, , (Academic Press, New York)
  • Kornell, N., Rhodes, M.G., Castel, A.D., Tauber, S.K., The ease-of-processing heuristic and the stability bias: Dissociating memory, memory beliefs, and memory judgments (2011) Psychol Sci, 22, pp. 787-794
  • Henmon, V.A.C., The relation of the time of a judgment to its accuracy (1911) Psychol Rev, 18, p. 186
  • Volkmann, J., The relation of time of judgment to certainty of judgment (1934) Psychol Bull, 31, pp. 672-673
  • Reed, J.B., The speed and accuracy in discriminating differences in hue, brilliance, area and shape (1951) The Psychology of Thought and Judgment, pp. 371-372. , Johnson, D. M. (ed.) (Harper, New York)
  • Zylberberg, A., Barttfeld, P., Sigman, M., The construction of confidence in a perceptual decision (2012) Frontiers in Integrative Neuroscience, 6, p. 79
  • Ahumada, A.J.J., Perceptual classification images from vernier acuity masked by noise (1996) Perception, p. 25
  • Kahneman, D., Tversky, A., Variants of uncertainty (1982) Cognition, 11, pp. 143-157
  • Meyniel, F., Sigman, M., Mainen, Z., Confidence as Bayesian probability: From neural origins to behavior (2015) Neuron, 88, pp. 78-92
  • Gardiner, C.W., (1985) Handbook of Stochastic Methods: For Physics, Chemistry and the Natural Sciences, , (Springer-Verlag Berlin Heidelberg New York)
  • Gold, J.I., Shadlen, M.N., The neural basis of decision making (2007) Annual Review of Neuroscience, 30, pp. 535-574
  • Wang, X.-J., Neural dynamics and circuit mechanisms of decision-making (2012) Current Opinion in Neurobiology, 22, pp. 1039-1046
  • Brunton, B.W., Botvinick, M.M., Brody, C.D., Rats and humans can optimally accumulate evidence for decision-making (2013) Science, 340, pp. 95-98
  • Hanks, T.D., Distinct relationships of parietal and prefrontal cortices to evidence accumulation (2015) Nature, 520, pp. 220-223
  • Lafuente, V.D., Jazayeri, M., Shadlen, M.N., Representation of accumulating evidence for a decision in two parietal areas (2015) Journal of Neuroscience, 35, pp. 4306-4318
  • Koriat, A., The self-consistency model of subjective confidence (2012) Psychological Review, 119, pp. 80-113
  • Wang, X.-J., Probabilistic decision making by slow reverberation in cortical circuits (2002) Neuron, 36, pp. 955-968
  • Wong, K.-F., Wang, X.-J., A recurrent network mechanism of time integration in perceptual decisions (2006) The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 26, pp. 1314-1328
  • Wang, X.-J., Decision making in recurrent neuronal circuits (2008) Neuron, 60, pp. 215-234
  • Mart, D., Deco, G., Mattia, M., Gigante, G., Del Giudice, P., A fluctuation-driven mechanism for slow decision processes in reverberant networks (2008) PloS One, 3, p. e2534
  • Churchland, A.K., Kiani, R., Shadlen, M.N., Decision-making with multiple alternatives (2008) Nature Neuroscience, 11, pp. 693-702
  • Bogacz, R., Wagenmakers, E.-J., Forstmann, B.U., Nieuwenhuis, S., The neural basis of the speed-accuracy tradeoff (2010) Trends in Neurosciences, 33, pp. 10-16
  • Thura, D., Beauregard-Racine, J., Fradet, C.-W., Cisek, P., Decision making by urgency gating: Theory and experimental support (2012) Journal of Neurophysiology, 108, pp. 2912-2930
  • Hanks, T.D., Kiani, R., Shadlen, M.N., A neural mechanism of speed-accuracy tradeoff in macaque area lip (2014) ELife, 2014, pp. 1-17
  • Swensson, R.G., Edwards, W., Response strategies in a two-choice reaction task with a continuous cost for time (1971) Journal of Experimental Psychology, 88, pp. 67-81
  • Ratcliff, R., Rouder, J.N., Modeling response times for two-choice decisions (1998) Psychological Science, 9, pp. 347-356
  • Lo, C.-C., Wang, X.-J., Cortico-basal ganglia circuit mechanism for a decision threshold in reaction time tasks (2006) Nature Neuroscience, 9, pp. 956-963
  • Chevalier, G., Deniau, J.M., Disinhibition as a basic process of striatal functions (1990) Trends in Neurosciences, 13, pp. 277-280
  • Letzkus, J.J., A disinhibitory microcircuit for associative fear learning in the auditory cortex (2011) Nature, 480, pp. 331-335
  • Cecchi, G.A., Noise in neurons is message dependent (2000) Proceedings of the National Academy of Sciences of the United States of America, 97, pp. 5557-5561
  • Self, M.W., Kooijmans, R.N., Supèr, H., Lamme, V.A., Roelfsema, P.R., Different glutamate receptors convey feedforward and recurrent processing in macaque v1 (2012) Proceedings of the National Academy of Sciences, 109, pp. 11031-11036
  • Wang, M., Nmda receptors subserve persistent neuronal firing during working memory in dorsolateral prefrontal cortex (2013) Neuron, 77, pp. 736-749
  • Vickers, D., Packer, J., Effects of alternating set for speed or accuracy on response time, accuracy and confidence in a unidimensional discrimination task (1982) Acta Psychologica, 50, pp. 179-197
  • Maniscalco, B., Lau, H., A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings (2012) Consciousness and Cognition, 21, pp. 422-430
  • Wei, Z., Wang, X.-J., Confidence estimation as a stochastic process in a neural dynamical system of decision making (2015) Journal of Neurophysiology, 114, pp. 99-113
  • Ma, W.J., Beck, J.M., Latham, P.E., Pouget, A., Bayesian inference with probabilistic population codes (2006) Nature Neuroscience, 9, pp. 1432-1438
  • Beck, J.M., Probabilistic population codes for Bayesian decision making (2008) Neuron, 60, pp. 1142-1152
  • Kahneman, D., Tversky, A., On the psychology of prediction (1973) Psychological Review, 80, pp. 237-251
  • Nickerson, R.S., Confirmation bias: A ubiquitous phenomenon in many guises (1998) Review of General Psychology, 2, pp. 175-220
  • Irwin, F.W., Smith, W.A.S., Mayfield, J.F., Tests of two theories of decision in an "expanded judgment" situation (1956) J Exp Psychol, 51, pp. 261-268
  • Hansen, N., Niederberger, A.S.P., Guzzella, L., Koumoutsakos, P., A method for handling uncertainty in evolutionary optimization with an application to feedback control of combustion (2009) Ieee Transactions on Evolutionary Computation, 13, pp. 180-197
  • Plackett, R.L., Karl pearson and the chi-squared test (1983) International Statistical Review, 51, pp. 59-72

Citas:

---------- APA ----------
Paz, L., Insabato, A., Zylberberg, A., Deco, G. & Sigman, M. (2016) . Confidence through consensus: A neural mechanism for uncertainty monitoring. Scientific Reports, 6.
http://dx.doi.org/10.1038/srep21830
---------- CHICAGO ----------
Paz, L., Insabato, A., Zylberberg, A., Deco, G., Sigman, M. "Confidence through consensus: A neural mechanism for uncertainty monitoring" . Scientific Reports 6 (2016).
http://dx.doi.org/10.1038/srep21830
---------- MLA ----------
Paz, L., Insabato, A., Zylberberg, A., Deco, G., Sigman, M. "Confidence through consensus: A neural mechanism for uncertainty monitoring" . Scientific Reports, vol. 6, 2016.
http://dx.doi.org/10.1038/srep21830
---------- VANCOUVER ----------
Paz, L., Insabato, A., Zylberberg, A., Deco, G., Sigman, M. Confidence through consensus: A neural mechanism for uncertainty monitoring. Sci. Rep. 2016;6.
http://dx.doi.org/10.1038/srep21830