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Shaoming Wang

Bio: Shaoming Wang is an academic researcher from New York University. The author has contributed to research in topics: Action selection & Cognition. The author has an hindex of 1, co-authored 1 publications receiving 8 citations.

Papers
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Posted ContentDOI
TL;DR: A model suggests a central role for the dynamics of memory reactivation in determining the influence of different kinds of memory in decisions and proposes that representation-specific dynamics can implement a bottom-up “product of experts” rule that integrates multiple sets of action-outcome predictions weighted on the basis of their uncertainty.
Abstract: Memories affect nearly every aspect of our mental life. They allow us to both resolve uncertainty in the present and to construct plans for the future. Recently, renewed interest in the role memory plays in adaptive behavior has led to new theoretical advances and empirical observations. We review key findings, with particular emphasis on how the retrieval of many kinds of memories affects deliberative action selection. These results are interpreted in a sequential inference framework, in which reinstatements from memory serve as "samples" of potential action outcomes. The resulting model suggests a central role for the dynamics of memory reactivation in determining the influence of different kinds of memory in decisions. We propose that representation-specific dynamics can implement a bottom-up "product of experts" rule that integrates multiple sets of action-outcome predictions weighted based on their uncertainty. We close by reviewing related findings and identifying areas for further research. This article is categorized under: Psychology > Reasoning and Decision Making Neuroscience > Cognition Neuroscience > Computation.

13 citations


Cited by
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Journal Article
TL;DR: The authors proposed a new model of free recall on the basis of M. Howard and M. J. McClelland's leaky-accumulator decision model, where recall decisions are controlled by a race between competitive leaky accumulators.
Abstract: The authors present a new model of free recall on the basis of M. W. Howard and M. J. Kahana's (2002a) temporal context model and M. Usher and J. L. McClelland's (2001) leaky-accumulator decision model. In this model, contextual drift gives rise to both short-term and long-term recency effects, and contextual retrieval gives rise to short-term and long-term contiguity effects. Recall decisions are controlled by a race between competitive leaky accumulators. The model captures the dynamics of immediate, delayed, and continual distractor free recall, demonstrating that dissociations between short- and long-term recency can naturally arise from a model in which an internal contextual state is used as the sole cue for retrieval across time scales.

252 citations

Journal ArticleDOI
TL;DR: In this article, the authors model the decision process for simple choice as an information sampling problem and approximate the optimal sampling policy, finding that it is optimal to sample from options whose value estimates are both high and uncertain.
Abstract: Simple choices (e.g., eating an apple vs. an orange) are made by integrating noisy evidence that is sampled over time and influenced by visual attention; as a result, fluctuations in visual attention can affect choices. But what determines what is fixated and when? To address this question, we model the decision process for simple choice as an information sampling problem, and approximate the optimal sampling policy. We find that it is optimal to sample from options whose value estimates are both high and uncertain. Furthermore, the optimal policy provides a reasonable account of fixations and choices in binary and trinary simple choice, as well as the differences between the two cases. Overall, the results show that the fixation process during simple choice is influenced dynamically by the value estimates computed during the decision process, in a manner consistent with optimal information sampling.

43 citations

Journal ArticleDOI
TL;DR: The benefits an agent can gain from replay that cannot be achieved through direct interactions with the world itself are summarized and include faster learning and data efficiency, less forgetting, 15 prioritizing important experiences, as well as improved planning and generalization.

21 citations

Posted ContentDOI
14 Feb 2021-bioRxiv
TL;DR: It is found that the ability to use learned value signals to selectively enhance memory for useful information strengthened throughout childhood and into adolescence, and developmental increases in the strategic engagement of the prefrontal cortex support the emergence of adaptive memory.
Abstract: Prioritizing memory for valuable information can promote adaptive behavior across the lifespan, but it is unclear how the neurocognitive mechanisms that enable the selective acquisition of useful knowledge develop. Here, using a novel task coupled with functional magnetic resonance imaging, we examined how children, adolescents, and adults (N = 90) learn from experience what information is likely to be rewarding, and modulate encoding and retrieval processes accordingly. We found that the ability to use learned value signals to selectively enhance memory for useful information strengthened throughout childhood and into adolescence. Encoding and retrieval of high- vs. low-value information was associated with increased activation in striatal and prefrontal regions implicated in value processing and cognitive control. Age-related increases in value-based lateral prefrontal cortex modulation mediated the relation between age and memory selectivity. Our findings demonstrate that developmental increases in the strategic engagement of the prefrontal cortex support the emergence of adaptive memory.

1 citations