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Statistical computations underlying the dynamics of memory updating.

Gershman SJ, Radulescu A, Norman KA, Niv Y - PLoS Comput. Biol. (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachussetts, United States of America.

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.

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Experimental results and model predictions.(A) Euclidean distance between participants' reconstructions and the observed (true) first and last lines in a block. Error bars represent within-subject standard error of the mean. The results show that participants were more accurate in their reconstructions in the jump condition as compared to the gradual condition. (B) Stationary Kalman filter (KF) model predictions. Data in (A) are represented by black circles. (C) Non-stationary KF model predictions. (D) Stationary Dirichlet process Kalman filter (DP-KF) model predictions. (E) Non-stationary DP-KF predictions.
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pcbi-1003939-g004: Experimental results and model predictions.(A) Euclidean distance between participants' reconstructions and the observed (true) first and last lines in a block. Error bars represent within-subject standard error of the mean. The results show that participants were more accurate in their reconstructions in the jump condition as compared to the gradual condition. (B) Stationary Kalman filter (KF) model predictions. Data in (A) are represented by black circles. (C) Non-stationary KF model predictions. (D) Stationary Dirichlet process Kalman filter (DP-KF) model predictions. (E) Non-stationary DP-KF predictions.

Mentions: To test our hypothesis, for each sequence we calculated the Euclidean distance between the participant's reconstruction and the true line observed at the beginning of the block, as well as the distance from the line observed at the end of that block. The results, presented in Figure 4A, show that participants' reconstructions were closer to the last line (), and farther from the first line () in the gradual condition as compared to the jump condition. A two-way (first/last × gradual/jump) ANOVA confirmed that the interaction was significant (). We interpret this result as showing that, in the gradual condition, participants inferred one mode, thereby causing lines from the second half to influence memory for the lines from the first half; by contrast, in the jump condition participants inferred separate pre-jump and post-jump modes, thereby protecting their memory of the pre-jump lines from being distorted by the post-jump lines.


Statistical computations underlying the dynamics of memory updating.

Gershman SJ, Radulescu A, Norman KA, Niv Y - PLoS Comput. Biol. (2014)

Experimental results and model predictions.(A) Euclidean distance between participants' reconstructions and the observed (true) first and last lines in a block. Error bars represent within-subject standard error of the mean. The results show that participants were more accurate in their reconstructions in the jump condition as compared to the gradual condition. (B) Stationary Kalman filter (KF) model predictions. Data in (A) are represented by black circles. (C) Non-stationary KF model predictions. (D) Stationary Dirichlet process Kalman filter (DP-KF) model predictions. (E) Non-stationary DP-KF predictions.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4222636&req=5

pcbi-1003939-g004: Experimental results and model predictions.(A) Euclidean distance between participants' reconstructions and the observed (true) first and last lines in a block. Error bars represent within-subject standard error of the mean. The results show that participants were more accurate in their reconstructions in the jump condition as compared to the gradual condition. (B) Stationary Kalman filter (KF) model predictions. Data in (A) are represented by black circles. (C) Non-stationary KF model predictions. (D) Stationary Dirichlet process Kalman filter (DP-KF) model predictions. (E) Non-stationary DP-KF predictions.
Mentions: To test our hypothesis, for each sequence we calculated the Euclidean distance between the participant's reconstruction and the true line observed at the beginning of the block, as well as the distance from the line observed at the end of that block. The results, presented in Figure 4A, show that participants' reconstructions were closer to the last line (), and farther from the first line () in the gradual condition as compared to the jump condition. A two-way (first/last × gradual/jump) ANOVA confirmed that the interaction was significant (). We interpret this result as showing that, in the gradual condition, participants inferred one mode, thereby causing lines from the second half to influence memory for the lines from the first half; by contrast, in the jump condition participants inferred separate pre-jump and post-jump modes, thereby protecting their memory of the pre-jump lines from being distorted by the post-jump lines.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachussetts, United States of America.

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.

Show MeSH
Related in: MedlinePlus