Memory-based Decisions Research Articles

The coupling effect between the environment and strategies drives the emergence of group cooperation

The emergence of cooperation is a central issue in understanding collective behavior and evolution. The eco-evolutionary game model introduces a human–environment coupling mechanism, revealing that the feedback between strategies and the relevant environment is a key element in sustaining long-term cooperation. Previous theoretical studies have observed periodic oscillations between cooperative and defective actions under certain conditions. However, such investigations assume cooperators hold a benefit advantage over defectors, which does not fundamentally illuminate how cooperation emerges. Our paper emphasizes that understanding this issue requires considering inherent human memory characteristics. We refine the eco-evolutionary game model using reinforcement learning, constructing a multi-agent system that couples environment and memory-based decision-making. Comprehensive analyses encompass collective and individual perspectives. Our findings show that with the memory mechanism, oscillations between collective cooperation and defection can still occur, even if defection remains a strict Nash equilibrium. Cooperation emerges from the group’s random exploratory actions in depleted environments, altering the environment’s trends. A positive feedback loop forms among the environment, individual rewards, and actions, stabilizing cooperation as a favorable individual strategy at that point. However, established group cooperation leads individuals seeking optimal behavior to transition from cooperators to defectors through exploration, resulting in cooperation collapse. Subsequently, the memory mechanism reengages, diluting defectors’ expected payoffs and initiating a new round of exploratory behavior within the group. Our results unveil the micro-level mechanisms driving cyclic oscillations, enhancing our understanding of the environment-strategy interplay.

Nucleus reuniens transiently synchronizes memory networks at beta frequencies

Episodic memory-based decision-making requires top-down medial prefrontal cortex and hippocampal interactions. This integrated prefrontal-hippocampal memory state is thought to be organized by synchronized network oscillations and mediated by connectivity with the thalamic nucleus reuniens (RE). Whether and how the RE synchronizes prefrontal-hippocampal networks in memory, however, remains unknown. Here, we recorded local field potentials from the prefrontal-RE-hippocampal network while rats engaged in a nonspatial sequence memory task, thereby isolating memory-related activity from running-related oscillations. We found that synchronous prefrontal-hippocampal beta bursts (15–30 Hz) dominated during memory trials, whereas synchronous theta activity (6–12 Hz) dominated during non-memory–related running. Moreover, RE beta activity appeared first, followed by prefrontal and hippocampal synchronized beta, suggesting that prefrontal-hippocampal beta could be driven by the RE. To test whether the RE is capable of driving prefrontal-hippocampal beta synchrony, we used an optogenetic approach (retroAAV-ChR2). RE activation induced prefrontal-hippocampal beta coherence and reduced theta coherence, matching the observed memory-driven network state in the sequence task. These findings are the first to demonstrate that the RE contributes to memory by driving transient synchronized beta in the prefrontal-hippocampal system, thereby facilitating interactions that underlie memory-based decision-making.

BYOB of wine, but which one? Unveiling new boundary conditions and moderating effects for restaurant patrons' consideration set formation

PurposeDeparting from conflicting findings on the role of involvement in the formation of the consideration set, the authors of this study seek to shed light to the wine consumer behaviour and expand previous findings in the bring-your-own-bottle (BYOB) of wine restaurant industry. The authors seek to determine the contradictory effect of involvement on the consideration set size and variety.Design/methodology/approachThree empirical studies were conducted. In Study 1, the relationships were tested in a personal consumption situation and in Study 2 in a gift-giving context. Finally, in Study 3, inconsistencies in the intensity of the hypothesised relationships were explored by testing the triple interaction amongst the three variables (i.e. involvement, decision-making context and decision domain).FindingsAccording to the authors' findings BYOB of wine consumers form larger considerations sets in memory-based decision contexts. Involvement's effect on wine consideration set size is stronger in memory-based decisions. BYOB restaurant patrons form smaller sets of alternatives for personal consumption. BYOB restaurant patrons form more heterogeneous sets of alternatives in wine gift-giving. BYOB of wine restaurants should facilitate consumers' wine-selection process.Originality/valueThe authors make an effort to explain and determine the up-to-date contradictory effect of restaurant patrons' involvement on the BYOB of wine consideration set size and the amount of variety contained therein. The study offers new insights, by unfolding the moderating effect of decision-making contexts (i.e. memory-based versus stimuli-based) and decision domains (i.e. personal consumption versus gift-giving) on this effect of involvement on the properties of consideration sets.

Alpha rhythm modulations in the intraparietal sulcus reflect decision signals during item recognition

Theoretical work and empirical observations suggest a contribution of regions along the intraparietal sulcus to the process of evidence accumulation during episodic memory retrieval. In the present study, we recorded magnetoencephalographic signals in a group of healthy human participants to test whether the pattern of oscillatory modulations in the lateral parietal lobe is consistent with the mnemonic accumulator hypothesis. To this aim, the dynamic properties and the spatial distribution of MEG oscillatory power modulations were investigated during an item recognition task in which the amount of evidence for old vs. new memory decisions was manipulated across three levels. A data-driven approach was employed to identify brain nodes where oscillatory activity was sensitive to both retrieval success and the amount of evidence for old decisions. The analysis identified three nodes in the left lateral parietal lobe where the event-related desynchronization (ERD) in the alpha frequency band showed both effects. Further analyses revealed that the alpha ERD in the intraparietal sulcus, but not in other parietal nodes: i. showed modulation of duration in response to the amount of evidence for both old and new decisions, ii. was behaviorally significant, and iii. more accurately tracked the subjective memory judgment rather than the objective memory status. The present findings provide support for a recent anatomical-functional model of the parietal involvement in episodic memory retrieval and suggest that the alpha ERD in the intraparietal sulcus might represent a neural signature of the evidence accumulation process during simple memory-based decisions.

Process and content in decisions from memory.

Information stored in memory influences the formation of preferences and beliefs in most everyday decision tasks. The richness of this information, and the complexity inherent in interacting memory and decision processes, makes the quantitative model-driven analysis of such decisions very difficult. In this article we present a general framework that can address the theoretical and methodological barriers to building formal models of naturalistic memory-based decision making. Our framework implements established theories of memory search and decision making within a single integrated cognitive system, and uses computational language models to quantify the thoughts over which memory and decision processes operate. It can thus describe both the content of the information that is sampled from memory, as well as the processes involved in retrieving and evaluating this information in order to make a decision. Furthermore, our framework is tractable, and the parameters that characterize memory-based decisions can be recovered using thought listing and choice data from existing experimental tasks, and in turn be used to make quantitative predictions regarding choice probability, length of deliberation, retrieved thoughts, and the effects of decision context. We showcase the power and generality of our framework by applying it to naturalistic binary choices from domains such as risk perception, consumer behavior, financial decision making, ethical decision making, legal decision making, food choice, and social judgment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Hippocampal sharp-wave ripples in cognitive map maintenance versus episodic simulation

Hippocampal sharp-wave ripples (SWRs) have been proposed to support memory-based decision-making. A new study by Gillespie etal. (in this issue of Neuron) provides important new insights on how past experiences and future choices are reflected in neuronal activity during SWRs.

The influence of visual attention on memory-based preferential choice

Many decisions rely on past experiences. Recent research indicates that people's choices are biased towards choosing better-remembered options, even if these options are comparatively unattractive (i.e., a memory bias). In the current study, we used eye tracking to compare the influence of visual attention on preferential choice between memory-based and non-memory-based decisions. Participants completed the remember-and-decide task. In this task, they first learned associations between screen locations and snack items. Then, they made binary choices between snack items. These snacks were either hidden and required recall (memory-based decisions), or they were visible (non-memory-based decisions). Remarkably, choices were more strongly influenced by attention in memory-based compared to non-memory-based decisions. However, visual attention did not mediate the memory bias on preferential choices. Finally, we adopt and expand a recently proposed computational model to provide a comprehensive description of the role of attention in memory-based decisions. In sum, the present work elucidates how visual attention interacts with episodic memory and preference formation in memory-based decisions.

MAMA Net: Multi-Scale Attention Memory Autoencoder Network for Anomaly Detection.

Anomaly detection refers to the identification of cases that do not conform to the expected pattern, which takes a key role in diverse research areas and application domains. Most of existing methods can be summarized as anomaly object detection-based and reconstruction error-based techniques. However, due to the bottleneck of defining encompasses of real-world high-diversity outliers and inaccessible inference process, individually, most of them have not derived groundbreaking progress. To deal with those imperfectness, and motivated by memory-based decision-making and visual attention mechanism as a filter to select environmental information in human vision perceptual system, in this paper, we propose a Multi-scale Attention Memory with hash addressing Autoencoder network (MAMA Net) for anomaly detection. First, to overcome a battery of problems result from the restricted stationary receptive field of convolution operator, we coin the multi-scale global spatial attention block which can be straightforwardly plugged into any networks as sampling, upsampling and downsampling function. On account of its efficient features representation ability, networks can achieve competitive results with only several level blocks. Second, it's observed that traditional autoencoder can only learn an ambiguous model that also reconstructs anomalies "well" due to lack of constraints in training and inference process. To mitigate this challenge, we design a hash addressing memory module that proves abnormalities to produce higher reconstruction error for classification. In addition, we couple the mean square error (MSE) with Wasserstein loss to improve the encoding data distribution. Experiments on various datasets, including two different COVID-19 datasets and one brain MRI (RIDER) dataset prove the robustness and excellent generalization of the proposed MAMA Net.

Salience effects in memory-based decisions: an improved replication

ABSTRACT An experimental report by Platzer and Bröder ([2012]. Most people do not ignore salient invalid cues in memory-based decisions. Psychonomic Bulletin & Review, 19(4), 654–661) claimed that in memory-based decisions, salient attributes are often not ignored even if they are less valid than other cues. When the rank order of cue validities was congruent with their salience hierarchy, people predominantly used a noncompensatory take-the-best strategy (TTB) based on the most valid cue whereas they used more compensatory strategies when hierarchies were incongruent (i.e. the least valid cue was most salient). Given the recent replication crisis in psychology and methodological shortcomings of the original study, a better-controlled replication with new stimuli and a larger sample was conducted. A pilot study established a visual salience hierarchy of the cues used. The main experiment clearly replicated the salience effect at the strategy selection level and the longer response times for compensatory strategies compared to TTB. A response time interaction of strategy and condition did not replicate.

The Reflection Effect in Memory-Based Decisions

Previous research has indicated a bias in memory-based decision-making, with people preferring options that they remember better. However, the cognitive mechanisms underlying this memory bias remain elusive. Here, we propose that choosing poorly remembered options is conceptually similar to choosing options with uncertain outcomes. We predicted that the memory bias would be reduced when options had negative subjective value, analogous to the reflection effect, according to which uncertainty aversion is stronger in gains than in losses. In two preregistered experiments (N = 36 each), participants made memory-based decisions between appetitive and aversive stimuli. People preferred better-remembered options in the gain domain, but this behavioral pattern reversed in the loss domain. This effect was not related to participants’ ambiguity or risk attitudes, as measured in a separate task. Our results increase the understanding of memory-based decision-making and connect this emerging field to well-established research on decisions under uncertainty.

Response time models separate single- and dual-process accounts of memory-based decisions

Human decisions often deviate from economic rationality and are influenced by cognitive biases. One such bias is the memory bias according to which people prefer choice options they have a better memory of—even when the options’ utilities are comparatively low. Although this phenomenon is well supported empirically, its cognitive foundation remains elusive. Here we test two conceivable computational accounts of the memory bias against each other. On the one hand, a single-process account explains the memory bias by assuming a single biased evidence-accumulation process in favor of remembered options. On the contrary, a dual-process account posits that some decisions are driven by a purely memory-driven process and others by a utility-maximizing one. We show that both accounts are indistinguishable based on choices alone as they make similar predictions with respect to the memory bias. However, they make qualitatively different predictions about response times. We tested the qualitative and quantitative predictions of both accounts on behavioral data from a memory-based decision-making task. Our results show that a single-process account provides a better account of the data, both qualitatively and quantitatively. In addition to deepening our understanding of memory-based decision-making, our study provides an example of how to rigorously compare single- versus dual-process models using empirical data and hierarchical Bayesian parameter estimation methods.

Properties and temporal dynamics of choice- and action-predictive signals during item recognition decisions

Decision-making is in the service of action regardless of whether the decision concerns perceptual information, goods or memories. Compared to recent advances in the neurobiology of perceptual or value-based decisions, however, the neural bases supporting the sampling of evidence in long-term memory, and the transformation of memory-based decisions into appropriate actions, are still poorly understood. In the present fMRI study, we used multivariate pattern analysis to investigate the temporal dynamics of choice- and action-predictive signals during an item recognition task that manipulated the association between memory choices (old/new) and motor responses (eye/hand) across subjects. Choice-predictive activity was mainly observed in striatal, lateral prefrontal and lateral parietal regions, was sensitive to the amount of decision evidence and showed a rapid increase after stimulus onset, followed by a fast decay. Action-predictive signals were found in primary sensory motor, premotor and occipito–parietal regions, were generally observed at the end of the decision phase and were not modulated by decision evidence. These findings suggest that a memory decision variable, potentially represented in a fronto–striato–parietal network, is not directly transformed into an action plan as often observed in perceptual decisions. Regions exhibiting choice predictive activity, and especially the striatum, however, also showed a second peak of decision-related activity that, unlike pure choice- or action-predictive signals, depended on the particular choice–response association. This second peak of activity in the striatum might represent the neural signature of the transformation of a memory decision into an appropriate motor response based on the specific choice–response association.

Pupil-linked phasic arousal predicts a reduction of choice bias across species and decision domains.

Decisions are often made by accumulating ambiguous evidence over time. The brain's arousal systems are activated during such decisions. In previous work in humans, we found that evoked responses of arousal systems during decisions are reported by rapid dilations of the pupil and track a suppression of biases in the accumulation of decision-relevant evidence (de Gee et al., 2017). Here, we show that this arousal-related suppression in decision bias acts on both conservative and liberal biases, and generalizes from humans to mice, and from perceptual to memory-based decisions. In challenging sound-detection tasks, the impact of spontaneous or experimentally induced choice biases was reduced under high phasic arousal. Similar bias suppression occurred when evidence was drawn from memory. All of these behavioral effects were explained by reduced evidence accumulation biases. Our results point to a general principle of interplay between phasic arousal and decision-making.

Choice-predictive activity in parietal cortex during source memory decisions

Neurobiological research has classically focused on perceptual decision-making, although many real-life decisions are based on information that is not currently available but stored in long-term memory. Previous studies have suggested that the lateral parietal cortex encodes decision-related signals during item recognition judgments. In the present fMRI study, we employed a parametric manipulation of evidence for source memory judgments and tested several hypotheses concerning memory decision signals in parietal cortex. As expected, the mean BOLD signal in several parietal regions was modulated by decision evidence. An analysis of the locally distributed pattern of activity, moreover, identified a parietal cluster showing significant choice-predictive activity even at the lowest level of decision evidence, with decoding accuracy that increased as a function of evidence. Decoding patterns were consistent across subjects as shown by a leave-one-subject-out classification analysis. Finally, we found that the pattern of choice-predictive activity in parietal lobe was temporally correlated with that observed in medial temporal regions traditionally associated with long-term memory functions. The present findings are consistent with a general role of lateral parietal regions located around the intraparietal sulcus in representing a decision variable for memory-based decisions.

Retrieval from memory and cue complexity both trigger exemplar-based processes in judgment

ABSTRACTJudgments and decisions can be achieved by a rule-based integration of cues or by retrieving similar exemplars from memory. In judgments based on currently available object descriptions (on-line judgments), people tend to rely on rules unless the cue-criterion relations are difficult to extract. In memory-based decisions, more evidence for exemplarbased reasoning has been found, but hitherto restricted to conditions with unknown cue polarity. These conclusions are threatened by methodological confounds concerning materials, training regime, and type of judgment task. In a factorial experiment, memory-based vs. on-line judgments as well as obvious vs. unknown cue polarity conditions were pitted against each other. Participants judged disease severity based on symptoms. Using the measurement model RulEx-J, we confirmed that both memory retrieval and unknown cue polarity have additive effects on the use of exemplar-based processes. This corroborates the idea that exemplar-based strategies are used as a backup if cue abstraction is unfeasible.

Memory, Numbers, and Action Decision in Human Posterior Parietal Cortex

Human lateral PPC demonstrates rich, functional heterogeneity across its subregions, including during mnemonic and numerical decision tasks. In this issue of Neuron, Rutishauser etal. (2018) report striking local heterogeneity within a small patch of anterior IPS at the neuronal level during memory-based decisions.

Linking process and measurement models of recognition-based decisions.

When making inferences about pairs of objects, one of which is recognized and the other is not, the recognition heuristic states that participants choose the recognized object in a noncompensatory way without considering any further knowledge. In contrast, information-integration theories such as parallel constraint satisfaction (PCS) assume that recognition is merely one of many cues that is integrated with further knowledge in a compensatory way. To test both process models against each other without manipulating recognition or further knowledge, we include response times into the r-model, a popular multinomial processing tree model for memory-based decisions. Essentially, this response-time-extended r-model allows to test a crucial prediction of PCS, namely, that the integration of recognition-congruent knowledge leads to faster decisions compared to the consideration of recognition only-even though more information is processed. In contrast, decisions due to recognition-heuristic use are predicted to be faster than decisions affected by any further knowledge. Using the classical German-cities example, simulations show that the novel measurement model discriminates between both process models based on choices, decision times, and recognition judgments only. In a reanalysis of 29 data sets including more than 400,000 individual trials, noncompensatory choices of the recognized option were estimated to be slower than choices due to recognition-congruent knowledge. This corroborates the parallel information-integration account of memory-based decisions, according to which decisions become faster when the coherence of the available information increases. (PsycINFO Database Record

Neural Signatures of Controlled and Automatic Retrieval Processes in Memory-based Decision-making.

Decision-making often requires retrieval from memory. Drawing on the neural ACT-R theory [Anderson, J. R., Fincham, J. M., Qin, Y., & Stocco, A. A central circuit of the mind. Trends in Cognitive Sciences, 12, 136-143, 2008] and other neural models of memory, we delineated the neural signatures of two fundamental retrieval aspects during decision-making: automatic and controlled activation of memory representations. To disentangle these processes, we combined a paradigm developed to examine neural correlates of selective and sequential memory retrieval in decision-making with a manipulation of associative fan (i.e., the decision options were associated with one, two, or three attributes). The results show that both the automatic activation of all attributes associated with a decision option and the controlled sequential retrieval of specific attributes can be traced in material-specific brain areas. Moreover, the two facets of memory retrieval were associated with distinct activation patterns within the frontoparietal network: The dorsolateral prefrontal cortex was found to reflect increasing retrieval effort during both automatic and controlled activation of attributes. In contrast, the superior parietal cortex only responded to controlled retrieval, arguably reflecting the sequential updating of attribute information in working memory. This dissociation in activation pattern is consistent with ACT-R and constitutes an important step toward a neural model of the retrieval dynamics involved in memory-based decision-making.

Decisions about the past are guided by reinstatement of specific memories in the hippocampus and perirhinal cortex

When faced with a new challenge, we often reflect on related past experiences to guide our behavior. The ability to retrieve memories that overlap with current experience, a process known as pattern completion, is theorized as a critical function of the hippocampus. Although this view has influenced research for decades, there is little empirical support for hippocampal pattern completion to individual memory elements and its influence on behavior. We used pattern analysis of brain activity measured with functional magnetic resonance imaging to demonstrate that specific elements of past experiences are reinstated in the hippocampus, as well as perirhinal cortex (PRC), when making decisions about those experiences. Linking neural measures of specific memory reinstatement in the hippocampus and PRC to behavior with computational modeling revealed that reinstatement predicts the speed of memory-based decisions. Moreover, hippocampal activation during retrieval was selectively coupled to regions of occipito-temporal cortex that showed content-specific item reinstatement. These results provide evidence for hippocampal pattern completion and its role in the mechanisms of decision making.

Take-the-best and the influence of decision-inconsistent attributes on decision confidence and choices in memory-based decisions

ABSTRACTTake-the-best (TTB) is a decision strategy according to which attributes about choice options are sequentially processed in descending order of validity, and attribute processing is stopped once an attribute discriminates between options. Consequently, TTB-decisions rely on only one, the best discriminating, attribute, and lower-valid attributes need not be processed because they are TTB-irrelevant. Recent research suggests, however, that when attribute information is visually present during decision-making, TTB-irrelevant attributes are processed and integrated into decisions nonetheless. To examine whether TTB-irrelevant attributes are retrieved and integrated when decisions are made memory-based, we tested whether the consistency of a TTB-irrelevant attribute affects TTB-users’ decision behaviour in a memory-based decision task. Participants first learned attribute configurations of several options. Afterwards, they made several decisions between two of the options, and we manipulated conflict between the second-best attribute and the TTB-decision. We assessed participants’ decision confidence and the proportion of TTB-inconsistent choices. According to TTB, TTB-irrelevant attributes should not affect confidence and choices, because these attributes should not be retrieved. Results showed, however, that TTB-users were less confident and made more TTB-inconsistent choices when TTB-irrelevant information was in conflict with the TTB-decision than when it was not, suggesting that TTB-users retrieved and integrated TTB-irrelevant information.