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

Similar failures of consideration arise in human and machine planning

Humans are remarkably efficient at decision making, even in "open-ended" problems where the set of possible actions is too large for exhaustive evaluation. Our success relies, in part, on processes for calling to mind the right candidate actions. When these processes fail, the result is a kind of puzzle in which the value of a solution would be obvious once it is considered, but never gets considered in the first place. Recently, machine learning (ML) architectures have attained or even exceeded human performance on open-ended decision making tasks such as playing chess and Go. We ask whether the broad architectural principles that underlie ML success in these domains generate similar consideration failures to those observed in humans. We demonstrate a case in which they do, illuminating how humans make open-ended decisions, how this relates to ML approaches to similar problems, and how both architectures lead to characteristic patterns of success and failure.

Incorporating Best-Worst Scaling (BWS) Questions into Focus Groups to Improve Understanding of Patient Preferences and Refine BWS Attributes

BACKGROUND AND OBJECTIVE

Best-worst scaling (BWS) is a stated preference elicitation method used for prioritizing attributes of healthcare interventions. Best-worst scaling attribute development is commonly based on literature review, qualitative work, and methodological/clinical expert input. There is limited research incorporating BWS in focus groups as part of the attribute development process. We sought to explore how incorporating BWS questions using the list of potential attributes in focus groups could be used to improve understanding of patient preferences and refine the list of potential BWS attributes as part of the attribute development process.

METHODS

We administered BWS questions on healthcare priorities for inflammatory bowel disease in five focus groups with Canadian patients with inflammatory bowel disease to (1) understand the "what," "how," and "why" of participant choices and (2) note how participants understand the attributes and the language they use to refine the list of potential BWS attributes. A list of 20 potential attributes was used to generate the BWS questions. We coded most/least important choices ("what") and used a thematic analysis to derive subthemes indicating "how" and "why" participants made their choices. We coded how participants understood the attributes/BWS questions and language used when discussing the attributes.

RESULTS

Across the 36 participants, the most frequently chosen most/least important attributes were summarized. Three subthemes explaining the "how" and "why" of participant choices were identified: perceived gain; influence of individual experiences; current health state and personal circumstances. Participants identified challenges understanding specific attributes and BWS questions, and provided suggestions for modifications to attribute language/descriptions. Administering BWS questions in focus groups provided: (1) insight into the assumptions participants made when completing the BWS questions; (2) clarity in language and attribute descriptions, and challenges participants had when completing the BWS questions that can be used to refine the list of potential attributes as part of the attribute development process; and (3) understanding of which attributes were most/least important and why to identify potential attributes to remove during the next steps of the attribute development process.

CONCLUSIONS

Best-worst scaling questions conducted within focus groups can stimulate discussions around relative importance and prioritization of attributes. Through open dialogue, this method can unveil unforeseen responses or identify areas that are unclear and enable a transparent approach to refine the list of potential attributes as part of the attribute development process.

Distinguishing deception from its confounds by improving the validity of fMRI-based neural prediction

Deception is a universal human behavior. Yet longstanding skepticism about the validity of measures used to characterize the biological mechanisms underlying deceptive behavior has relegated such studies to the scientific periphery. Here, we address these fundamental questions by applying machine learning methods and functional magnetic resonance imaging (fMRI) to signaling games capturing motivated deception in human participants. First, we develop an approach to test for the presence of confounding processes and validate past skepticism by showing that much of the predictive power of neural predictors trained on deception data comes from processes other than deception. Specifically, we demonstrate that discriminant validity is compromised by the predictor's ability to predict behavior in a control task that does not involve deception. Second, we show that the presence of confounding signals need not be fatal and that the validity of the neural predictor can be improved by removing confounding signals while retaining those associated with the task of interest. To this end, we develop a "dual-goal tuning" approach in which, beyond the typical goal of predicting the behavior of interest, the predictor also incorporates a second compulsory goal that enforces chance performance in the control task. Together, these findings provide a firmer scientific foundation for understanding the neural basis of a neglected class of behavior, and they suggest an approach for improving validity of neural predictors.

Storytelling changes the content and perceived value of event memories

Memories are not only stored for personal recall, but also to communicate knowledge to others in service of adaptive decision-making. Prior research shows that goals to share information can change which content is communicated in memory as well as the linguistic style embedded in this communication. Yet, little is known as to how communication-related alterations in memory narration drive differences of value processing in listeners. Here, we test how memory communication alters multi-featural recall for complex events and the downstream consequence on value estimations in naïve listeners. Participants recalled a memory of playing an exploratory videogame at a 24-h delay under instructions to either share (i.e., social condition) or recall (i.e., control condition) their memory. Sharing goals systematically altered the content and linguistic style of recall, such that narrators from the social condition were biased towards recall of non-episodic details and communicated their memories with more clout, less formality, and less authenticity. Across two independent samples of naïve listeners, these features differentially influenced value estimations of the video game. We found that greater clout was associated with greater enjoyment while listening to memories (hedonic value), and that greater inclusion of non-episodic details resulted in greater willingness to purchase the video game (motivational drive). These findings indicate that sharing an experience as a story can change the content and linguistic tone of memory recall, which in turn shape perceived value in naïve listeners.

Computational Social Psychology

Social psychologists attempt to explain how we interact by appealing to basic principles of how we think. To make good on this ambition, they are increasingly relying on an interconnected set of formal tools that model inference, attribution, value-guided decision making, and multi-agent interactions. By reviewing progress in each of these areas and highlighting the connections between them, we can better appreciate the structure of social thought and behavior, while also coming to understand when, why, and how formal tools can be useful for social psychologists.

Locating what comes to mind in empirically derived representational spaces

Real-world judgements and decisions often require choosing from an open-ended set of options which cannot be exhaustively considered before a choice is made. Recent work has found that the options people do consider tend to have particular features, such as high historical value. Here, we pursue the idea that option generation during decision making may reflect a more general mechanism for calling things to mind, by which relevant features in a context-appropriate representational space guide what comes to mind. In this paper, we evaluate this proposal primarily based on what comes to mind in different familiar categories. We first introduce an empirical approach for deriving the space of features that people use to represent items in a particular category and for locating the category members that come to mind within that space. We show that in both familiar and ad hoc categories, a category member's location along certain dimensions of the derived feature space predicts its likelihood of coming to mind. Next, we show that category members from these feature space locations come to mind by default in a way that is somewhat impervious to conscious control. We then demonstrate that the extent to which a given dimension is a predictor of what comes to mind within a category is related to how relevant that feature is for representing the category in question, using a novel measure of general feature relevance. Finally, we illustrate the usefulness of this framework in the context of a decision making task. We close with the proposal that people call category members to mind according to their location in representational space, specifically based on the predicted usefulness of considering category members with particular features.

A Theory of Mental Frameworks

Problem-solving skills are highly valued in modern society and are often touted as core elements of school mission statements, desirable traits for job applicants, and as some of the most complex thinking that the brain is capable of executing. While learning to problem-solve is a goal of education, and many strategies, methodologies, and activities exist to help teachers guide the development of these skills, there are few formal curriculum structures or broader frameworks that guide teachers toward the achievement of this educational objective. Problem-solving skills have been called "higher order cognitive functions" in cognitive neuroscience as they involve multiple complex networks in the brain, rely on constant rehearsal, and often take years to form. Children of all ages employ problem solving, from a newborn seeking out food to children learning in school settings, or adults tackling real-world conflicts. These skills are usually considered the end product of a good education when in fact, in order to be developed they comprise an ongoing process of learning. "Ways of thinking" have been studied by philosophers and neuroscientists alike, to pinpoint cognitive preferences for problem solving approaches that develop from exposure to distinct models, derived from and resulting in certain heuristics used by learners. This new theory paper suggests a novel understanding of the brain's approach to problem solving that structures existing problem-solving frameworks into an organized design. The authors surveyed problem-solving frameworks from business administration, design, engineering, philosophy, psychology, education, neuroscience and other learning sciences to assess their differences and similarities. This review lead to an appreciation that different problem-solving frameworks from different fields respond more or less accurately and efficiently depending on the kinds of problems being tackled, leading to our conclusion that a wider range of frameworks may help individuals approach more varied problems across fields, and that such frameworks can be organized in school curriculum. This paper proposes that explicit instruction of "mental frameworks" may help organize and formalize the instruction of thinking skills that underpin problem-solving-and by extension-that the more such models a person learns, the more tools they will have for future complex problem-solving. To begin, this paper explains the theoretical underpinnings of the mental frameworks concept, then explores some existing mental frameworks which are applicable to all age groups and subject areas. The paper concludes with a list of five limitations to this proposal and pairs them with counter-balancing benefits.

Episodic Memory Retrieval Affects the Onset and Dynamics of Evidence Accumulation during Value-based Decisions

In neuroeconomics, there is much interest in understanding simple value-based choices where agents choose between visually presented goods, comparable to a shopping scenario in a supermarket. However, many everyday decisions are made in the physical absence of the considered goods, requiring agents to recall information about the goods from memory. Here, we asked whether and how this reliance on an internal memory representation affects the temporal dynamics of decision making on a cognitive and neural level. Participants performed a remember-and-decide task in which they made simple purchasing decisions between money offers and snack items while undergoing EEG. Snack identity was presented either visually (value trials) or had to be recalled from memory (memory trials). Behavioral data indicated comparable choice consistency across both trial types, but considerably longer RTs in memory trials. Drift-diffusion modeling suggested that this RT difference was because of longer nondecision time of decision processes as well as altered evidence accumulation dynamics (lower accumulation rate and higher decision threshold). The nondecision time effect was supported by a delayed onset of the lateralized readiness potential. These results show that both decision and nondecision processes are prolonged when participants need to resort to internal memory representations during value-based decisions.

Item memorability has no influence on value-based decisions

While making decisions, we often rely on past experiences to guide our choices. However, not all experiences are remembered equally well, and some elements of an experience are more memorable than others. Thus, the intrinsic memorability of past experiences may bias our decisions. Here, we hypothesized that individuals would tend to choose more memorable options than less memorable ones. We investigated the effect of item memorability on choice in two experiments. First, using food images, we found that the same items were consistently remembered, and others consistently forgotten, across participants. However, contrary to our hypothesis, we found that participants did not prefer or choose the more memorable over the less memorable items when choice options were matched for the individuals' valuation of the items. Second, we replicated these findings in an alternate stimulus domain, using words that described the same food items. These findings suggest that stimulus memorability does not play a significant role in determining choice based on subjective value.

Neural Support for Contributions of Utility and Narrative Processing of Evidence in Juror Decision Making

Efforts to explain complex human decisions have focused on competing theories emphasizing utility and narrative mechanisms. These are difficult to distinguish using behavior alone. Both narrative and utility theories have been proposed to explain juror decisions, which are among the most consequential complex decisions made in a modern society. Here, we asked jury-eligible male and female subjects to rate the strength of a series of criminal cases while recording the resulting patterns of brain activation. We compared patterns of brain activation associated with evidence accumulation to patterns of brain activation derived from a large neuroimaging database to look for signatures of the cognitive processes associated with different models of juror decision-making. Evidence accumulation correlated with multiple narrative processes, including reading and recall. Of the cognitive processes traditionally viewed as components of utility, activation patterns associated with uncertainty, but not value, were more active with stronger evidence. Independent of utility and narrative, activations linked to reasoning and relational logic also correlated with increasing evidence. Hierarchical modeling of cognitive processes associated with evidence accumulation supported a more prominent role for narrative in weighing evidence in complex decisions. However, utility processes were also associated with evidence accumulation. These complementary findings support an emerging view that integrates utility and narrative processes in complex decisions.SIGNIFICANCE STATEMENT The last decade has seen a sharply increased interest in narrative as a central cognitive process in human decision-making and as an important factor in the evolution of human societies. However, the roles of narrative versus utility models of decision-making remain hotly debated. While available models frequently produce similar behavioral predictions, they rely on different cognitive processes and so their roles can be separated using the right neural tests. Here, we use brain imaging during mock juror decisions to show that cognitive processes associated with narrative, and to a lesser extent utility, were engaged while subjects evaluated evidence. These results are consistent with interactions between narrative and utility processes during complex decision-making.

The Role of the Ventromedial Prefrontal Cortex in Preferential Decisions for Own- and Other-Age Faces

Own-age bias is a well-known bias reflecting the effects of age, and its role has been demonstrated, particularly, in face recognition. However, it remains unclear whether an own-age bias exists in facial impression formation. In the present study, we used three datasets from two published and one unpublished functional magnetic resonance imaging (fMRI) study that employed the same pleasantness rating task with fMRI scanning and preferential choice task after the fMRI to investigate whether healthy young and older participants showed own-age effects in face preference. Specifically, we employed a drift-diffusion model to elaborate the existence of own-age bias in the processes of preferential choice. The behavioral results showed higher rating scores and higher drift rate for young faces than for older faces, regardless of the ages of participants. We identified a young-age effect, but not an own-age effect. Neuroimaging results from aggregation analysis of the three datasets suggest a possibility that the ventromedial prefrontal cortex (vmPFC) was associated with evidence accumulation of own-age faces; however, no clear evidence was provided. Importantly, we found no age-related decline in the responsiveness of the vmPFC to subjective pleasantness of faces, and both young and older participants showed a contribution of the vmPFC to the parametric representation of the subjective value of face and functional coupling between the vmPFC and ventral visual area, which reflects face preference. These results suggest that the preferential choice of face is less susceptible to the own-age bias across the lifespan of individuals.

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.