162
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Temporal context calibrates interval timing

      research-article
      1 , 2 , 2
      Nature neuroscience

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          We use our sense of time to identify temporal relationships between events and to anticipate actions. How well we can exploit temporal contingencies depends on the variability of our measurements of time. We asked humans to reproduce time intervals drawn from different underlying distributions. As expected, production times were more variable for longer intervals. Surprisingly however, production times exhibited a systematic regression towards the mean. Consequently, estimates for a sample interval differed depending on the distribution from which it was drawn. A performance-optimizing 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 incorporates knowledge about temporal uncertainty to adapt internal timing mechanisms to the temporal statistics of the environment.

          Related collections

          Most cited references46

          • Record: found
          • Abstract: found
          • Article: not found

          Bayesian integration in sensorimotor learning.

          When we learn a new motor skill, such as playing an approaching tennis ball, both our sensors and the task possess variability. Our sensors provide imperfect information about the ball's velocity, so we can only estimate it. Combining information from multiple modalities can reduce the error in this estimate. On a longer time scale, not all velocities are a priori equally probable, and over the course of a match there will be a probability distribution of velocities. According to bayesian theory, an optimal estimate results from combining information about the distribution of velocities-the prior-with evidence from sensory feedback. As uncertainty increases, when playing in fog or at dusk, the system should increasingly rely on prior knowledge. To use a bayesian strategy, the brain would need to represent the prior distribution and the level of uncertainty in the sensory feedback. Here we control the statistical variations of a new sensorimotor task and manipulate the uncertainty of the sensory feedback. We show that subjects internally represent both the statistical distribution of the task and their sensory uncertainty, combining them in a manner consistent with a performance-optimizing bayesian process. The central nervous system therefore employs probabilistic models during sensorimotor learning.
            Bookmark
            • Record: found
            • Abstract: not found
            • Book: not found

            Bayesian Theory

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Object perception as Bayesian inference.

              We perceive the shapes and material properties of objects quickly and reliably despite the complexity and objective ambiguities of natural images. Typical images are highly complex because they consist of many objects embedded in background clutter. Moreover, the image features of an object are extremely variable and ambiguous owing to the effects of projection, occlusion, background clutter, and illumination. The very success of everyday vision implies neural mechanisms, yet to be understood, that discount irrelevant information and organize ambiguous or noisy local image features into objects and surfaces. Recent work in Bayesian theories of visual perception has shown how complexity may be managed and ambiguity resolved through the task-dependent, probabilistic integration of prior object knowledge with image features.
                Bookmark

                Author and article information

                Journal
                9809671
                21092
                Nat Neurosci
                Nature neuroscience
                1097-6256
                1546-1726
                2 June 2010
                27 June 2010
                August 2010
                1 February 2011
                : 13
                : 8
                : 1020-1026
                Affiliations
                [1 ] Helen Hay Whitney Foundation
                [2 ] HHMI, NPRC, Department of Physiology and Biophysics, University of Washington, Seattle, Washington
                Author notes
                Correspondence: Mehrdad Jazayeri, Department of Physiology and Biophysics, University of Washington, Box 357290, Seattle, WA 98195, Telephone: 206.616.3308, Fax: 206.543.1196, mjaz@ 123456u.washington.edu
                Article
                nihpa209209
                10.1038/nn.2590
                2916084
                20581842
                82fa04bc-0160-448a-b572-144ee07a0013

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                History
                Funding
                Funded by: National Eye Institute : NEI
                Funded by: National Center for Research Resources : NCRR
                Funded by: Howard Hughes Medical Institute
                Award ID: R01 EY011378-15 ||EY
                Funded by: National Eye Institute : NEI
                Funded by: National Center for Research Resources : NCRR
                Funded by: Howard Hughes Medical Institute
                Award ID: P51 RR000166-476531 ||RR
                Funded by: National Eye Institute : NEI
                Funded by: National Center for Research Resources : NCRR
                Funded by: Howard Hughes Medical Institute
                Award ID: ||HHMI_
                Categories
                Article

                Neurosciences
                Neurosciences

                Comments

                Comment on this article