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How and where does the brain predict the when: a Bayesian approach to modeling temporal expectation

How and where does the brain predict the when: a Bayesian approach to modeling temporal expectation Malhotra and Daucé BMC Neuroscience 2011, 12(Suppl 1):P53 http://www.biomedcentral.com/1471-2202/12/S1/P53 POSTER PRESENTATION Open Access How and where does the brain predict the when: a Bayesian approach to modeling temporal expectation Gaurav Malhotra , Emmanuel Daucé From Twentieth Annual Computational Neuroscience Meeting: CNS*2011 Stockholm, Sweden. 23-28 July 2011 How does the brain learn to predict when an event is problem of prediction can then be translated to a Baye- going to occur? We know from studies that vary the sian inference problem and learning can be performed foreperiod – the time between a warning and a response through well known algorithms such as expectation max- stimulus – that people can model the temporal variabil- imization. The monitor updates predictions dynamically ity of stimulus onset, i.e. react faster when a stimulus is by moving through the sequence of temporal causes, a statistically more likely [1]. We also know that reaction process that can be modeled as a Hidden Markov Model time (RT) decreases with the passage of time, showing that progressively invalidates temporal causes with the that people dynamically update the temporal expectation passage of time. of the stimulus [2]. Recent neurophysiological investiga- Simulating this model reproduces the faster RTs with tions and brain lesion studies have also revealed that increasing foreperiods for a rectangular distribution of areas in the dorsolateral prefrontal cortex, inferior parie- foreperiods and the lack of change in RTs when using tal cortex and posterior cerebellum perform functionally an exponential distribution of foreperiods, a well known distinct roles in the generation of temporal expectations [3]. In the current study, we use both behavioral and neurophysiological findings to develop a theoretical account of the computational processes that underlie the generation of temporal expectations. We identify four independent processes that are critical to generating temporal expectation: (a) a pulse-generator (oscillator or other tonic activity) that relates the sensory stimulus to an internal signal (b) an integrator, that accu- mulates the tonic activity, generating a temporal percept, (c) a predictor, that forms a probabilistic model based on the combination of the sensory signal and its temporal percept, and (d) a process that monitors the temporal percept and dynamically updates predictions, generating a temporal expectation (see Figure 1). The novelty of our work lies in laying out the computational principles behind the processes (c) and (d). We propose that the brain learns to predict time by modeling sensory signals as a finite mixture of hidden temporal causes. The * Correspondence: gm.academic@gmail.com L’Institut des Sciences du Mouvement, Université de la Mediterranée, 13288 Figure 1 Independent stages in generation of temporal Marseille, France expectation. Full list of author information is available at the end of the article © 2011 Malhotra and Daucé; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Malhotra and Daucé BMC Neuroscience 2011, 12(Suppl 1):P53 Page 2 of 2 http://www.biomedcentral.com/1471-2202/12/S1/P53 behavioral finding [2]. In addition to these implicit tim- ing results, simulations on the model also reproduce explicit timing results where top-down, cue-based learn- ingisusedtoorientparticipants’ attentionintime [4]. Thus, our research identifies the distinct computational steps involved in the generation of temporal expectation. Prediction itself seems to require a forward modelling of sensory signals, a function typically attributed to the parietal cortex and the cerebellum [5], while dynamically updating these predictions seems to require maintaining and moving through a sequence of hypotheses, a high- level cognitive function typically attributed to the pre- frontal cortex. Thus, by laying out the computations underlying each process, our study paves the way for understanding the role of different regions of the brain in predicting the time at which to expect a stimulus. Published: 18 July 2011 References 1. Klemmer ET: Simple reaction time as a function of time uncertainty. J Exp Psychol 1957, 54:195-200. 2. Niemi P, Näätänen R: Foreperiod and simple reaction time. Psychol Bull 1981, 89:133-162. 3. Coull JT, Cheng RK, Meck WH: Neuroanatomical and neurochemical substrates of timing. Neuropsychopharmacology Reviews 2011, 36:3-25. 4. Coull JT, Nobre AC: Where and when to pay attention: The neural systems for directing attention to spatial locations and to time intervals as revealed by both pet and fMRI. J Neurosci 1998, 18:7426-7435. 5. Wolpert DM, Miall RC, Kawato M: Internal models in the cerebellum. Trends Cogn Sci 1998, 2:338-347. doi:10.1186/1471-2202-12-S1-P53 Cite this article as: Malhotra and Daucé: How and where does the brain predict the when: a Bayesian approach to modeling temporal expectation. BMC Neuroscience 2011 12(Suppl 1):P53. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png BMC Neuroscience Springer Journals

How and where does the brain predict the when: a Bayesian approach to modeling temporal expectation

Malhotra, Gaurav; Daucé, Emmanuel
BMC Neuroscience , Volume 12 (1) – Jul 18, 2011
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References (7)

Publisher
Springer Journals
Copyright
Copyright © 2011 by Malhotra and Daucé; licensee BioMed Central Ltd.
Subject
Biomedicine; Neurosciences; Neurobiology; Animal Models
eISSN
1471-2202
DOI
10.1186/1471-2202-12-S1-P53
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Abstract

Malhotra and Daucé BMC Neuroscience 2011, 12(Suppl 1):P53 http://www.biomedcentral.com/1471-2202/12/S1/P53 POSTER PRESENTATION Open Access How and where does the brain predict the when: a Bayesian approach to modeling temporal expectation Gaurav Malhotra , Emmanuel Daucé From Twentieth Annual Computational Neuroscience Meeting: CNS*2011 Stockholm, Sweden. 23-28 July 2011 How does the brain learn to predict when an event is problem of prediction can then be translated to a Baye- going to occur? We know from studies that vary the sian inference problem and learning can be performed foreperiod – the time between a warning and a response through well known algorithms such as expectation max- stimulus – that people can model the temporal variabil- imization. The monitor updates predictions dynamically ity of stimulus onset, i.e. react faster when a stimulus is by moving through the sequence of temporal causes, a statistically more likely [1]. We also know that reaction process that can be modeled as a Hidden Markov Model time (RT) decreases with the passage of time, showing that progressively invalidates temporal causes with the that people dynamically update the temporal expectation passage of time. of the stimulus [2]. Recent neurophysiological investiga- Simulating this model reproduces the faster RTs with tions and brain lesion studies have also revealed that increasing foreperiods for a rectangular distribution of areas in the dorsolateral prefrontal cortex, inferior parie- foreperiods and the lack of change in RTs when using tal cortex and posterior cerebellum perform functionally an exponential distribution of foreperiods, a well known distinct roles in the generation of temporal expectations [3]. In the current study, we use both behavioral and neurophysiological findings to develop a theoretical account of the computational processes that underlie the generation of temporal expectations. We identify four independent processes that are critical to generating temporal expectation: (a) a pulse-generator (oscillator or other tonic activity) that relates the sensory stimulus to an internal signal (b) an integrator, that accu- mulates the tonic activity, generating a temporal percept, (c) a predictor, that forms a probabilistic model based on the combination of the sensory signal and its temporal percept, and (d) a process that monitors the temporal percept and dynamically updates predictions, generating a temporal expectation (see Figure 1). The novelty of our work lies in laying out the computational principles behind the processes (c) and (d). We propose that the brain learns to predict time by modeling sensory signals as a finite mixture of hidden temporal causes. The * Correspondence: gm.academic@gmail.com L’Institut des Sciences du Mouvement, Université de la Mediterranée, 13288 Figure 1 Independent stages in generation of temporal Marseille, France expectation. Full list of author information is available at the end of the article © 2011 Malhotra and Daucé; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Malhotra and Daucé BMC Neuroscience 2011, 12(Suppl 1):P53 Page 2 of 2 http://www.biomedcentral.com/1471-2202/12/S1/P53 behavioral finding [2]. In addition to these implicit tim- ing results, simulations on the model also reproduce explicit timing results where top-down, cue-based learn- ingisusedtoorientparticipants’ attentionintime [4]. Thus, our research identifies the distinct computational steps involved in the generation of temporal expectation. Prediction itself seems to require a forward modelling of sensory signals, a function typically attributed to the parietal cortex and the cerebellum [5], while dynamically updating these predictions seems to require maintaining and moving through a sequence of hypotheses, a high- level cognitive function typically attributed to the pre- frontal cortex. Thus, by laying out the computations underlying each process, our study paves the way for understanding the role of different regions of the brain in predicting the time at which to expect a stimulus. Published: 18 July 2011 References 1. Klemmer ET: Simple reaction time as a function of time uncertainty. J Exp Psychol 1957, 54:195-200. 2. Niemi P, Näätänen R: Foreperiod and simple reaction time. Psychol Bull 1981, 89:133-162. 3. Coull JT, Cheng RK, Meck WH: Neuroanatomical and neurochemical substrates of timing. Neuropsychopharmacology Reviews 2011, 36:3-25. 4. Coull JT, Nobre AC: Where and when to pay attention: The neural systems for directing attention to spatial locations and to time intervals as revealed by both pet and fMRI. J Neurosci 1998, 18:7426-7435. 5. Wolpert DM, Miall RC, Kawato M: Internal models in the cerebellum. Trends Cogn Sci 1998, 2:338-347. doi:10.1186/1471-2202-12-S1-P53 Cite this article as: Malhotra and Daucé: How and where does the brain predict the when: a Bayesian approach to modeling temporal expectation. BMC Neuroscience 2011 12(Suppl 1):P53. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit

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BMC Neuroscience – Springer Journals

Published: Jul 18, 2011

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