34
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Statistical Computations Underlying the Dynamics of Memory Updating

      research-article

      Read this article at

      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

          Psychophysical and neurophysiological studies have suggested that memory is not simply a carbon copy of our experience: Memories are modified or new memories are formed depending on the dynamic structure of our experience, and specifically, on how gradually or abruptly the world changes. We present a statistical theory of memory formation in a dynamic environment, based on a nonparametric generalization of the switching Kalman filter. We show that this theory can qualitatively account for several psychophysical and neural phenomena, and present results of a new visual memory experiment aimed at testing the theory directly. Our experimental findings suggest that humans can use temporal discontinuities in the structure of the environment to determine when to form new memory traces. The statistical perspective we offer provides a coherent account of the conditions under which new experience is integrated into an old memory versus forming a new memory, and shows that memory formation depends on inferences about the underlying structure of our experience.

          Author Summary

          When do we modify old memories, and when do we create new ones? We suggest that this question can be answered statistically: The parsing of experience into distinct memory traces corresponds to inferences about the underlying structure of the environment. When sensory data change gradually over time, the brain infers that the environment has slowly been evolving, and the current representation of the environment (an existing memory trace) is updated. In contrast, abrupt changes indicate transitions between different structures, leading to the formation of new memories. While these ideas fall naturally out of statistical models of learning, they have not yet been directly tested in the domain of human memory. In this paper, we describe a model of statistical inference that instantiates these ideas, and test the model by asking human participants to reconstruct previously seen visual objects that have since changed gradually or abruptly. The results of this experiment support our theory of how the statistical structure of sensory experiences shapes memory formation.

          Related collections

          Most cited references34

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

          Neural networks and physical systems with emergent collective computational abilities.

          J Hopfield (1982)
          Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Attractor dynamics in the hippocampal representation of the local environment.

            Memories are thought to be attractor states of neuronal representations, with the hippocampus a likely substrate for context-dependent episodic memories. However, such states have not been directly observed. For example, the hippocampal place cell representation of location was previously found to respond continuously to changes in environmental shape alone. We report that exposure to novel square and circular environments made of different materials creates attractor representations for both shapes: Place cells abruptly and simultaneously switch between representations as environmental shape changes incrementally. This enables study of attractor dynamics in a cognitive representation and may correspond to the formation of distinct contexts in context-dependent memory.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              An approximately Bayesian delta-rule model explains the dynamics of belief updating in a changing environment.

              Maintaining appropriate beliefs about variables needed for effective decision making can be difficult in a dynamic environment. One key issue is the amount of influence that unexpected outcomes should have on existing beliefs. In general, outcomes that are unexpected because of a fundamental change in the environment should carry more influence than outcomes that are unexpected because of persistent environmental stochasticity. Here we use a novel task to characterize how well human subjects follow these principles under a range of conditions. We show that the influence of an outcome depends on both the error made in predicting that outcome and the number of similar outcomes experienced previously. We also show that the exact nature of these tendencies varies considerably across subjects. Finally, we show that these patterns of behavior are consistent with a computationally simple reduction of an ideal-observer model. The model adjusts the influence of newly experienced outcomes according to ongoing estimates of uncertainty and the probability of a fundamental change in the process by which outcomes are generated. A prior that quantifies the expected frequency of such environmental changes accounts for individual variability, including a positive relationship between subjective certainty and the degree to which new information influences existing beliefs. The results suggest that the brain adaptively regulates the influence of decision outcomes on existing beliefs using straightforward updating rules that take into account both recent outcomes and prior expectations about higher-order environmental structure.
                Bookmark

                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                November 2014
                6 November 2014
                : 10
                : 11
                : e1003939
                Affiliations
                [1 ]Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachussetts, United States of America
                [2 ]Department of Psychology and Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
                Indiana University, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: SJG KAN YN. Performed the experiments: SJG AR. Analyzed the data: SJG. Contributed reagents/materials/analysis tools: SJG. Wrote the paper: SJG AR KAN YN.

                Article
                PCOMPBIOL-D-13-01468
                10.1371/journal.pcbi.1003939
                4222636
                25375816
                1faff617-407a-4913-83eb-7bffcd6a7d72
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 19 August 2013
                : 26 September 2014
                Page count
                Pages: 13
                Funding
                This research was supported in part by the National Institute Of Mental Health of the National Institutes of Health under Award Number R01MH098861, a graduate research fellowship from the National Science Foundation (SJG), and an Alfred P. Sloan Research Fellowship (YN). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This publication was made possible in part through the support of a grant from the John Templeton Foundation. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the John Templeton Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Computational Biology
                Computational Neuroscience
                Neuroscience
                Cognitive Science
                Cognition
                Memory
                Cognitive Psychology
                Learning and Memory
                Psychology
                Social Sciences

                Quantitative & Systems biology
                Quantitative & Systems biology

                Comments

                Comment on this article