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Time interval reproduction in parietal cortex: tracking time using the state of a motor plan| old_uid | 10417 |
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| title | Time interval reproduction in parietal cortex: tracking time using the state of a motor plan |
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| start_date | 2011/11/22 |
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| schedule | 14h30 |
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| online | no |
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| location_info | salle de séminaire |
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| summary | Humans and animals infer temporal regularities, anticipate events, and plan their actions in time. The neural mechanisms that give rise to the measurement and production of time intervals are however unknown. I will summarize results from our psychophysical studies in humans and physiological studies in nonhuman primates in which we have begun to explore the behavioral and neural correlates of interval timing.
Our psychophysical studies in humans show that interval timing is remarkably plastic. We found that when subjects reproduce a sample time interval, their responses are biased toward the mean of the distribution from which the sample is drawn (prior distribution). These biases imply that knowledge about the prior distribution influences interval timing. Accordingly, we found that a performanceoptimizing Bayesian model that takes the underlying distribution of samples into account provided an accurate description of subjects’ performance, variability and bias. This finding suggests that the central nervous system uses temporal statistics of the environment to calibrate the internal mechanisms of interval timing.
Our physiological studies have focused on information processing at the level of single neurons in the parietal association cortex (area LIP) of awake, behaving monkeys. We trained monkeys to measure a sample interval drawn from a uniform distribution and to reproduced it immediately afterwards by a saccadic eye movement. LIP activity during the production epoch was associated with a linear rise whose slope was adjusted according to the previously measured sample interval. This activity profile was consistent with planning the timing of the upcoming saccadic eye movement. Surprisingly however, neural activity was also modulated during the measurement epoch, long before the sample interval was specified. This early modulation had a nonlinear profile with a remarkable feature: its slope changed with time so as to predict the slope of the linear rise during the ensuing production epoch. This finding suggests that sensorimotor neurons measure elapsed time by way of ‘preplanning’ an upcoming motor plan. Establishing dynamics that encode recent past in terms of near future may be a key principle in coordinating information processing in time. |
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| responsibles | Riehle |
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