Modulating Episodic Memory Alters Risk Preference during Decision-making

The effect of episodic specificity inductions on cognitive tasks involving episodic retrieval: A quantitative review

A growing body of research suggests that an episodic specificity induction (ESI), that is, training in recalled details of a (recent) past event, impacts performance on subsequent tasks that require episodic retrieval processes. The constructive episodic simulation hypothesis (Schacter and Addis, 2007) posits that various tasks which require, at least partially, episodic retrieval processes rely on a single, flexible episodic memory system. As such, a specificity induction activates that episodic memory system and improves subsequent performance on tasks that require use of that memory system. The present quantitative review analyzed the literature demonstrating that the Episodic Specificity Induction (ESI) improves performance on subsequence cognitive tasks that require (at least partial) episodic retrieval processes. Twenty-three studies met criteria for measuring the impact of ESI, compared to a non-specificity control induction(s), on subsequent tasks requiring edpisodic retrieval, including memory, imagination, problem solving, divergent thinking. The results of this review demonstrate a strong, positive effect of ESI on episodic memory, imagination, divergent thinking, and problem-solving tasks.

Biased confabulation in risky choice

When people make risky decisions based on past experience, they must rely on memory. The nature of the memory representations that support these decisions is not yet well understood. A key question concerns the extent to which people recall specific past episodes or whether they have learned a more abstract rule from their past experience. To address this question, we examined the precision of the memories used in risky decisions-from-experience. In three pre-registered experiments, we presented people with risky options, where the outcomes were drawn from continuous ranges (e.g., 100-190 or 500-590), and then assessed their memories for the outcomes experienced. In two preferential tasks, people were more risk seeking for high-value than low-value options, choosing as though they overweighted the outcomes from more extreme ranges. Moreover, in two preferential tasks and a parallel evaluation task, people were very poor at recalling the exact outcomes encountered, but rather confabulated outcomes that were consistent with the outcomes they had seen and were biased towards the more extreme ranges encountered. This common pattern suggests that the observed decision bias in the preferential task reflects a basic cognitive process to overweight extreme outcomes in memory. These results highlight the importance of the edges of the distribution in providing the encoding context for memory recall. They also suggest that episodic memory influences decision-making through gist memory and not through direct recall of specific instances.

Readiness to remember: predicting variability in episodic memory

Learning and remembering are fundamental to our lives, so what causes us to forget? Answers often highlight preparatory processes that precede learning, as well as mnemonic processes during the act of encoding or retrieval. Importantly, evidence now indicates that preparatory processes that precede retrieval attempts also have powerful influences on memory success or failure. Here, we review recent work from neuroimaging, electroencephalography, pupillometry, and behavioral science to propose an integrative framework of retrieval-period dynamics that explains variance in remembering in the moment and across individuals as a function of interactions among preparatory attention, goal coding, and mnemonic processes. Extending this approach, we consider how a 'readiness to remember' (R2R) framework explains variance in high-level functions of memory and mnemonic disruptions in aging.

Cognitive and neural principles of a memory bias on preferential choices

Value-based decisions depend on different forms of memory. However, the respective roles of memory and valuation processes that give rise to these decisions are often vaguely described and have rarely been investigated jointly. In this review article, we address the problem of memory-based decision making from a neuroeconomic perspective. We first describe the neural and cognitive processes involved in decisions requiring memory processes, with a focus on episodic memory. Based on the results of a systematic research program, we then spotlight the phenomenon of the memory bias, a general preference for choice options that can be retrieved from episodic memory more successfully. Our findings indicate that failed memory recall biases neural valuation processes as indicated by altered effective connectivity between the hippocampus and ventromedial prefrontal cortex. This bias can be attributed to meta-cognitive beliefs about the relationship between subjective value and memory as well as to uncertainty aversion. After summarizing the findings, we outline potential future research endeavors to integrate the two research traditions of memory and decision making.

Mixing memory and desire: How memory reactivation supports deliberative decision-making

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.

Pairing facts with imagined consequences improves pandemic-related risk perception

The COVID-19 pandemic reached staggering new peaks during a global resurgence more than a year after the crisis began. Although public health guidelines initially helped to slow the spread of disease, widespread pandemic fatigue and prolonged harm to financial stability and mental well-being contributed to this resurgence. In the late stage of the pandemic, it became clear that new interventions were needed to support long-term behavior change. Here, we examined subjective perceived risk about COVID-19 and the relationship between perceived risk and engagement in risky behaviors. In study 1 (n = 303), we found that subjective perceived risk was likely inaccurate but predicted compliance with public health guidelines. In study 2 (n = 735), we developed a multifaceted intervention designed to realign perceived risk with actual risk. Participants completed an episodic simulation task; we expected that imagining a COVID-related scenario would increase the salience of risk information and enhance behavior change. Immediately following the episodic simulation, participants completed a risk estimation task with individualized feedback about local viral prevalence. We found that information prediction error, a measure of surprise, drove beneficial change in perceived risk and willingness to engage in risky activities. Imagining a COVID-related scenario beforehand enhanced the effect of prediction error on learning. Importantly, our intervention produced lasting effects that persisted after a 1- to 3-wk delay. Overall, we describe a fast and feasible online intervention that effectively changed beliefs and intentions about risky behaviors.

Probing relationships between reinforcement learning and simple behavioral strategies to understand probabilistic reward learning

BACKGROUND

Reinforcement learning (RL) and win stay/lose shift model accounts of decision making are both widely used to describe how individuals learn about and interact with rewarding environments. Though mutually informative, these accounts are often conceptualized as independent processes and so the potential relationships between win stay/lose shift tendencies and RL parameters have not been explored.

NEW METHOD

We introduce a methodology to directly relate RL parameters to behavioral strategy. Specifically, by calculating a truncated multivariate normal distribution of RL parameters given win stay/lose shift tendencies from simulating these tendencies across the parameter space, we maximize the normal distribution for a given set of win stay/lose shift tendencies to approximate reinforcement learning parameters.

RESULTS

We demonstrate novel relationships between win stay/lose shift tendencies and RL parameters that challenge conventional interpretations of lose shift as a metric of loss sensitivity. Further, we demonstrate in both simulated and empirical data that this method of parameter approximation yields reliable parameter recovery.

COMPARISON WITH EXISTING METHOD

We compare this method against the conventionally used maximum likelihood estimation method for parameter approximation in simulated noisy and empirical data. For simulated noisy data, we show that this method performs similarly to maximum likelihood estimation. For empirical data, however, this method provides a more reliable approximation of reinforcement learning parameters than maximum likelihood estimation.

CONCLUSIONS

We demonstrate the existence of relationships between win stay/lose shift tendencies and RL parameters and introduce a method that leverages these relationships to enable recovery of RL parameters exclusively from win stay/lose shift tendencies.

Learning reward frequency over reward probability: A tale of two learning rules

Learning about the expected value of choice alternatives associated with reward is critical for adaptive behavior. Although human choice preferences are affected by the presentation frequency of reward-related alternatives, this may not be captured by some dominant models of value learning, such as the delta rule. In this study, we examined whether reward learning is driven more by learning the probability of reward provided by each option, or how frequently each option has been rewarded, and assess how well models based on average reward (e.g. the delta model) and models based on cumulative reward (e.g. the decay model) can account for choice preferences. In a binary-outcome choice task, participants selected between pairs of options that had reward probabilities of 0.65 (A) versus 0.35 (B) or 0.75 (C) versus 0.25 (D). Crucially, during training there were twice the number of AB trials as CD trials, such that option A was associated with higher cumulative reward, while option C gave higher average reward. Participants then decided between novel combinations of options (e.g., AC). Most participants preferred option A over C, a result predicted by the Decay model, but not the Delta model. We also compared the Delta and Decay models to both more simplified as well as more complex models that assumed additional mechanisms, such as representation of uncertainty. Overall, models that assume learning about cumulative reward provided the best account of the data.

Decision-making Increases Episodic Memory via Postencoding Consolidation

The ability for individuals to actively make decisions engages regions within the mesolimbic system and enhances memory for chosen items. In other behavioral contexts, mesolimbic engagement has been shown to enhance episodic memory by supporting consolidation. However, research has yet to investigate how consolidation may support interactions between decision-making and episodic memory. Across two studies, participants encoded items that were covered by occluder screens and could either actively decide which of two items to uncover or an item was preselected by the experimenter. In Study 1, we show that active decision-making reduces forgetting rates across an immediate and 24-hr memory test, a behavioral marker of consolidation. In Study 2, we use functional neuroimaging to characterize putative neural markers of memory consolidation by measuring interactions between the hippocampus and perirhinal cortex (PRC) during a postencoding period that reexposed participants to elements of the decision-making context without exposing them to memoranda. We show that choice-related striatal engagement is associated with increased postencoding hippocampal-PRC interactions. Finally, we show that a previous reported relationship between choice-related striatal engagement and long-term memory is accounted for by these postencoding hippocampal-PRC interactions. Together, these findings support a model by which actively deciding to encode information enhances memory consolidation to preserve episodic memory for outcomes, a process that may be facilitated by reexposure to the original decision-making context.