How the brain decides when to modify old memories and when to carve new memories is revealed in a study published this week in PLOS Computational Biology.
MIT researcher Dr Samuel Gershman and his collaborators at Princeton propose a theoretical framework for understanding how the brain (to quote Plato) "carves nature at its joints".
This study has implications for how we understand the fundamental mechanisms of memory, as well as our view of how these mechanisms go awry after brain damage.
The authors use recent ideas from machine learning to formalize the mechanisms underlying memory formation and modification.
Human participants were presented with a sequence of simple stimuli (lines varying in length and orientation) and then asked to reconstruct one of the stimuli from memory. Some stimuli contained a "jump" which generated a large 'prediction error'. The jump appeared to induce a new memory, thereby protecting the memories of the early stimuli from interference by the later stimuli.
The theoretical framework asserts that large prediction errors will lead to memory formation, while small prediction errors will lead to memory modification.
The authors suggest that one brain area in particular--the hippocampus--plays an important role in this process. Damage to the hippocampus appears to severely impair the ability of humans and animals to carve their experience into different latent causes.
The authors commented: "Understanding how the hippocampus, in concert with other brain regions, allows us to acquire a rich internal model of the world is an important task for cognitive neuroscience."
All works published in PLOS Computational Biology are Open Access, which means that all content is immediately and freely available. Use this URL in your coverage to provide readers access to the paper upon publication: http://www.
Contact: Samuel Gershman
Address: Massachusetts Institute of Technology
Department of Brain and Cognitive Sciences
77 Massachusetts Ave.
Cambridge, MA 02139
Citation: Gershman SJ, Radulescu A, Norman KA, Niv Y (2014) Statistical Computations Underlying the Dynamics of Memory Updating. PLoS Comput Biol 10(11): e1003939. doi:10.1371/journal.pcbi.1003939
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.
Competing Interests: The authors have declared that no competing interests exist.
About PLOS Computational Biology
PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. All works published in PLOS Computational Biology are Open Access. All content is immediately available and subject only to the condition that the original authorship and source are properly attributed. Copyright is retained. For more information follow @PLOSCompBiol on Twitter or contact email@example.com.
PLOS is a nonprofit publisher and advocacy organization founded to accelerate progress in science and medicine by leading a transformation in research communication. For more information, visit http://www.