What I like is what I remember: Memory modulation and preferential choice.

Aka, Ada; Bhatia, Sudeep

doi:10.1037/xge0001034

Cited by 10 publications

(14 citation statements)

References 47 publications

(62 reference statements)

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“…By contrast, additional parameters in variants of the RCV model that aim to capture the maximal size of the internal menu or the effect of recall order do not generalize well from one category to another and should be seen as a descriptive component of these extended models. Future studies are necessary to investigate the determinants of these variables of interest-for example, how the size and the composition of the internal menu may be affected by internal (e.g., working memory capacity, category knowledge, and preference structure) and contextual (e.g., environmental memory cues) factors (51)(52)(53)(54), as well as their effects on subsequent choices.…”

Section: Discussionmentioning

confidence: 99%

Retrieval-constrained valuation: Toward prediction of open-ended decisions

Zhang

Wang

Good

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Real-world decisions are often open ended, with goals, choice options, or evaluation criteria conceived by decision-makers themselves. Critically, the quality of decisions may heavily rely on the generation of options, as failure to generate promising options limits, or even eliminates, the opportunity for choosing them. This core aspect of problem structuring, however, is largely absent from classical models of decision-making, thereby restricting their predictive scope. Here, we take a step toward addressing this issue by developing a neurally inspired cognitive model of a class of ill-structured decisions in which choice options must be self-generated. Specifically, using a model in which semantic memory retrieval is assumed to constrain the set of options available during valuation, we generate highly accurate out-of-sample predictions of choices across multiple categories of goods. Our model significantly and substantially outperforms models that only account for valuation or retrieval in isolation or those that make alternative mechanistic assumptions regarding their interaction. Furthermore, using neuroimaging, we confirm our core assumption regarding the engagement of, and interaction between, semantic memory retrieval and valuation processes. Together, these results provide a neurally grounded and mechanistic account of decisions with self-generated options, representing a step toward unraveling cognitive mechanisms underlying adaptive decision-making in the real world.

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Section: Discussionmentioning

confidence: 99%

Retrieval-constrained valuation: Toward prediction of open-ended decisions

Zhang

Wang

Good

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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“…Crucially, objects that are semantically similar, like bagel and pita, tend to have similar word distributions in text, and therefore end up with vector representations that are close to each other by metrics like cosine similarity or Euclidean distance. For this reason, similarity measurements in DSM vector spaces can be used to describe many psychological phenomena related to semantic similarity, and more generally semantic representation and memory retrieval (Bhatia et al, 2019; Günther et al, 2019), including semantic-congruence effects in free association, free recall from lists, semantic memory search, and memory-based decision making (Aka & Bhatia, 2021; Bhatia, 2019; Griffiths et al, 2007; Hills et al, 2012; Howard & Kahana, 2002; M. N. Jones & Mewhort, 2007; Mandera et al, 2017).…”

Section: Overview Of Approachmentioning

confidence: 99%

Limitations to optimal search in naturalistic active learning.

He,

Richie,

Bhatia

2024

Journal of Experimental Psychology: General

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Optimality in active learning is under intense debate in numerous disciplines. We introduce a new empirical paradigm for studying naturalistic active learning, as well as new computational tools for jointly modeling algorithmic and rational theories of information search. Participants in our task can ask questions and learn about hundreds of everyday items but must retrieve queried items from memory. To maximize information gain, participants need to retrieve sequences of dissimilar items. In eight experiments (N = 795), we find that participants are unable to do this. Instead, associative memory mechanisms lead to the successive retrieval of similar items, an established memory effect known as semantic congruence. The extent of semantic congruence (and thus suboptimality in question asking) is unaffected by task instructions and incentives, though participants can identify efficient query sequences when given a choice between query sequences. Overall, our results indicate that participants can distinguish between optimal and suboptimal search if explicitly asked to do so, but have difficulty implementing optimal search from memory. We conclude that associative memory processes may place critical restrictions on people's ability to ask good questions in naturalistic active learning tasks. Public Significance StatementThe ability to guide our own learning is one of the greatest developmental tools we have. A common theory of such self-guided or "active" learning is that people tend to ask questions that gather the most information. However, most work supporting this theory is based on research with simple domains where questions need not be generated from memory. We show that in more complex domains, where more realistic questions must be recalled, subjects are far less able to generate optimal queries. This suggests that optimal question asking may be more limited in naturalistic, everyday domains than previously thought.

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“…A large and growing body of research suggests that people are especially likely to remember things that are of high value [8][9][10][11]23]. Presumably this is because, all else being equal, the valuable things are the ones we profit most from being able to remember and reason about-i.e., because this makes rational use of a limited cognitive resource [24,25].…”

Section: The Role Of Value In Prioritized Memorymentioning

confidence: 99%

“…Second, even if people learn that an object is good only after the time that they observe it, comparable effects still arise, with people devoting more cognitive capacity to memory consolidation for objects they regard as good [8]. Finally, there is evidence for an effect of value on memory retrieval [9,25]. This effect is particularly strong in domains where valuable targets are more important to retrieve-e.g., when one retrieves memories of objects in order to use them, and higher-value objects have highvalue uses.…”

Section: The Role Of Value In Prioritized Memorymentioning

confidence: 99%

The common effect of value on prioritized memory and category representation

Cushman¹,

Knobe²

2023

Preprint

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The way we represent categories depends on both the frequency and value of the category’s members. Thus, for instance, the “prototype” of a category tends to have especially common and valuable features. Notably, recent research on memory suggests that we tend to prioritize memory for frequent and valuable things. Although work on conceptual representation and work on prioritized memory have thus far proceeded almost entirely independently, the patterns of existing findings provide evidence for a link between these two phenomena. In particular, these patterns provide evidence for the hypothesis that the impact of value on prioritized memory explains the impact of value on conceptual representation.

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What I like is what I remember: Memory modulation and preferential choice.

Abstract: Sloan Foundation, and Wharton Risk Management Center's Russell Ackoff Doctoral Student Fellowship. We thank Drs. Michael Kahana, Eric Bradlow, Christophe Van den Bulte, and members of the Computational Behavioral Sciences Lab for helpful discussion.

Cited by 10 publications

References 47 publications

Retrieval-constrained valuation: Toward prediction of open-ended decisions

Retrieval-constrained valuation: Toward prediction of open-ended decisions

Limitations to optimal search in naturalistic active learning.

The common effect of value on prioritized memory and category representation

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