The neural computation of inconsistent choice behavior

Decomposing decision-making in preschoolers: Making decisions under ambiguity versus risk

Decision-making in the real world involves multiple abilities. The main goal of the current study was to examine the abilities underlying the Preschool Gambling task (PGT), a preschool variant of the Iowa Gambling task (IGT), in the context of an integrative decision-making framework. Preschoolers (n = 144) were given the PGT along with four novel decision-making tasks assessing either decision-making under ambiguity or decision-making under risk. Results indicated that the ability to learn from feedback, to maintain a stable preference, and to integrate losses and gains contributed to the variance in decision-making on the PGT. Furthermore, children's awareness level on the PGT contributed additional variance, suggesting both implicit and explicit processes are involved. The results partially support the integrative decision-making framework and suggest that multiple abilities contribute to individual differences in decision-making on the PGT.

Pre-acquired Functional Connectivity Predicts Choice Inconsistency

Economic choice theories usually assume that humans maximize utility in their choices. However, studies have shown that humans make inconsistent choices, leading to suboptimal behavior, even without context-dependent manipulations. Previous studies showed that activation in value and motor networks are associated with inconsistent choices at the moment of choice. Here, we investigated if the neural predispositions, measured before a choice task, can predict choice inconsistency in a later risky choice task. Using functional connectivity (FC) measures from resting-state functional magnetic resonance imaging (rsfMRI), derived before any choice was made, we aimed to predict subjects' inconsistency levels in a later-performed choice task. We hypothesized that rsfMRI FC measures extracted from value and motor brain areas would predict inconsistency. Forty subjects (21 females) completed a rsfMRI scan before performing a risky choice task. We compared models that were trained on FC that included only hypothesized value and motor regions with models trained on whole-brain FC. We found that both model types significantly predicted inconsistency levels. Moreover, even the whole-brain models relied mostly on FC between value and motor areas. For external validation, we used a neural network pretrained on FC matrices of 37,000 subjects and fine-tuned it on our data and again showed significant predictions. Together, this shows that the tendency for choice inconsistency is predicted by predispositions of the nervous system and that synchrony between the motor and value networks plays a crucial role in this tendency.

Divisive normalization is an efficient code for multivariate Pareto-distributed environments

Divisive normalization is a canonical computation in the brain, observed across neural systems, that is often considered to be an implementation of the efficient coding principle. We provide a theoretical result that makes the conditions under which divisive normalization is an efficient code analytically precise: We show that, in a low-noise regime, encoding an n-dimensional stimulus via divisive normalization is efficient if and only if its prevalence in the environment is described by a multivariate Pareto distribution. We generalize this multivariate analog of histogram equalization to allow for arbitrary metabolic costs of the representation, and show how different assumptions on costs are associated with different shapes of the distributions that divisive normalization efficiently encodes. Our result suggests that divisive normalization may have evolved to efficiently represent stimuli with Pareto distributions. We demonstrate that this efficiently encoded distribution is consistent with stylized features of naturalistic stimulus distributions such as their characteristic conditional variance dependence, and we provide empirical evidence suggesting that it may capture the statistics of filter responses to naturalistic images. Our theoretical finding also yields empirically testable predictions across sensory domains on how the divisive normalization parameters should be tuned to features of the input distribution.

Individuals with ventromedial frontal damage display unstable but transitive preferences during decision making

The ventromedial frontal lobe (VMF) is important for decision-making, but the precise causal role of the VMF in the decision process has not been fully established. Previous studies have suggested that individuals with VMF damage violate transitivity, a hallmark axiom of rational decisions. However, these prior studies cannot properly distinguish whether individuals with VMF damage are truly prone to choosing irrationally from whether their preferences are simply more variable. We had individuals with focal VMF damage, individuals with other frontal damage, and healthy controls make repeated choices across three categories-artworks, chocolate bar brands, and gambles. Using proper tests of transitivity, we find that, in our study, individuals with VMF damage make rational decisions consistent with transitive preferences, even though they exhibit greater variability in their preferences. That is, the VMF is necessary for having strong and reliable preferences, but not for being a rational decision maker. VMF damage affects the variability with which value is assessed, but not the consistency with which value is sought.

On the reliability of individual economic rationality measurements

A contemporary research agenda in behavioral economics and neuroeconomics aims to identify individual differences and (neuro)psychological correlates of rationality. This research has been widely received in important interdisciplinary and field outlets. However, the psychometric reliability of such measurements of rationality has been presumed without enough methodological scrutiny. Drawing from multiple original and published datasets (in total over 1,600 participants), we unequivocally show that contemporary measurements of rationality have moderate to poor reliability according to common standards. Further analyses of the variance components, as well as a allowing participants to revise previous choices, suggest that this is driven by low between-subject variance rather than high measurement error. As has been argued previously for other behavioral measurements, this poses a challenge to the predominant correlational research designs and the search for sociodemographic or neural predictors. While our results draw a sobering picture of the prospects of contemporary measurements of rationality, they are not necessarily surprising from a theoretical perspective, which we outline in our discussion.

Decision neuroscience and neuroeconomics: Recent progress and ongoing challenges

In the past decade, decision neuroscience and neuroeconomics have developed many new insights in the study of decision making. This review provides an overarching update on how the field has advanced in this time period. Although our initial review a decade ago outlined several theoretical, conceptual, methodological, empirical, and practical challenges, there has only been limited progress in resolving these challenges. We summarize significant trends in decision neuroscience through the lens of the challenges outlined for the field and review examples where the field has had significant, direct, and applicable impacts across economics and psychology. First, we review progress on topics including reward learning, explore-exploit decisions, risk and ambiguity, intertemporal choice, and valuation. Next, we assess the impacts of emotion, social rewards, and social context on decision making. Then, we follow up with how individual differences impact choices and new exciting developments in the prediction and neuroforecasting of future decisions. Finally, we consider how trends in decision-neuroscience research reflect progress toward resolving past challenges, discuss new and exciting applications of recent research, and identify new challenges for the field. This article is categorized under: Psychology > Reasoning and Decision Making Psychology > Emotion and Motivation.

The effects of acute and chronic stress on choice consistency

Important decisions are often made under some degree of stress. It is now well-established that acute stress affects preferences and priorities in our decisions. However, it is hard to make a general case on the net impact of stress on decision-making quality in a normative sense as evidence for or against a direct effect of stress on decision-making quality is sparse. Here, we used the revealed preference framework of choice consistency to investigate decision-making quality without the assumption of an objectively correct choice. Specifically, we tested whether acute stress influences choice consistency in a time dependent fashion. A sample of 144 participants solved a food choice task before, immediately after and in the aftermath of the Trier Social Stress Test (TSST) or a matched control procedure. We confirmed the effectiveness of our stress manipulation via an array of subjective and physiological stress measures. Using Bayesian statistics, we found strong evidence against an effect of acute stress on choice consistency. However, we found exploratory evidence for a negative association of self-reported chronic stress and choice consistency. We discuss our results in the context of previous findings of stress effects on choice consistency and preference changes.

Does Ventromedial Prefrontal Cortex Damage Really Increase Impulsiveness? Delay and Probability Discounting in Patients with Focal Lesions

If the tendency to discount rewards reflects individuals' general level of impulsiveness, then the discounting of delayed and probabilistic rewards should be negatively correlated: The less a person is able to wait for delayed rewards, the more they should take chances on receiving probabilistic rewards. It has been suggested that damage to the ventromedial prefrontal cortex (vMPFC) increases individuals' impulsiveness, but both intertemporal choice and risky choice have only recently been assayed in the same patients with vMPFC damage. Here, we assess both delay and probability discounting in individuals with vMPFC damage (n = 8) or with medial temporal lobe (MTL) damage (n = 10), and in age- and education-matched controls (n = 30). On average, MTL-lesioned individuals discounted delayed rewards at normal rates but discounted probabilistic rewards more shallowly than controls. In contrast, vMPFC-lesioned individuals discounted delayed rewards more steeply but probabilistic rewards more shallowly than controls. These results suggest that vMPFC lesions affect the weighting of reward amount relative to delay and certainty in opposite ways. Moreover, whereas MTL-lesioned individuals and controls showed typical, nonsignificant correlations between the discounting of delayed and probabilistic rewards, vMPFC-lesioned individuals showed a significant negative correlation, as would be expected if vMPFC damage increases impulsiveness more in some patients than in others. Although these results are consistent with the hypothesis that vMPFC plays a role in impulsiveness, it is unclear how they could be explained by a single mechanism governing valuation of both delayed and probabilistic rewards.

Fear-induced increases in loss aversion are linked to increased neural negative-value coding

Human decisions are often influenced by emotions. An economically relevant example is the role of fear in generating loss aversion. Previous research implicates the amygdala as a key brain structure in the experience of fear and loss aversion. The neural mechanism behind emotional influences on loss aversion is, however, unclear. To address this, we measured brain activation with functional magnetic resonance imaging (fMRI) while participants made decisions about monetary gambles after viewing fearful or neutral faces. We observed that loss aversion following the presentation of neutral faces was mainly predicted by greater deactivations for prospective losses (relative to activations for prospective gains) in several brain regions, including the amygdala. By contrast, increases in loss aversion following the presentation of fearful faces were mainly predicted by greater activations for prospective losses. These findings suggest a fear-induced shift from positive to negative value coding that reflects a context-dependent involvement of distinct valuation processes.

A Preferential Role for Ventromedial Prefrontal Cortex in Assessing "the Value of the Whole" in Multiattribute Object Evaluation

Everyday decision-making commonly involves assigning values to complex objects with multiple value-relevant attributes. Drawing on object recognition theories, we hypothesized two routes to multiattribute evaluation: assessing the value of the whole object based on holistic attribute configuration or summing individual attribute values. In two samples of healthy human male and female participants undergoing eye tracking and functional magnetic resonance imaging (fMRI) while evaluating novel pseudo objects, we found evidence for both forms of evaluation. Fixations to and transitions between attributes differed systematically when the value of pseudo objects was associated with individual attributes or attribute configurations. Ventromedial prefrontal cortex (vmPFC) and perirhinal cortex were engaged when configural processing was required. These results converge with our recent findings that individuals with vmPFC lesions were impaired in decisions requiring configural evaluation but not when evaluating the sum of the parts. This suggests that multiattribute decision-making engages distinct evaluation mechanisms relying on partially dissociable neural substrates, depending on the relationship between attributes and value.SIGNIFICANCE STATEMENT Decision neuroscience has only recently begun to address how multiple choice-relevant attributes are brought together during evaluation and choice among complex options. Object recognition research makes a crucial distinction between individual attribute and holistic/configural object processing, but how the brain evaluates attributes and whole objects remains unclear. Using fMRI and eye tracking, we found that the vmPFC and the perirhinal cortex contribute to value estimation specifically when value was related to whole objects, that is, predicted by the unique configuration of attributes and not when value was predicted by the sum of individual attribute values. This perspective on the interactions between subjective value and object processing mechanisms provides a novel bridge between the study of object recognition and reward-guided decision-making.

Responses to Heartbeats in Ventromedial Prefrontal Cortex Contribute to Subjective Preference-Based Decisions

Forrest Gump or The Matrix? Preference-based decisions are subjective and entail self-reflection. However, these self-related features are unaccounted for by known neural mechanisms of valuation and choice. Self-related processes have been linked to a basic interoceptive biological mechanism, the neural monitoring of heartbeats, in particular in ventromedial prefrontal cortex (vmPFC), a region also involved in value encoding. We thus hypothesized a functional coupling between the neural monitoring of heartbeats and the precision of value encoding in vmPFC. Human participants of both sexes were presented with pairs of movie titles. They indicated either which movie they preferred or performed a control objective visual discrimination that did not require self-reflection. Using magnetoencephalography, we measured heartbeat-evoked responses (HERs) before option presentation and confirmed that HERs in vmPFC were larger when preparing for the subjective, self-related task. We retrieved the expected cortical value network during choice with time-resolved statistical modeling. Crucially, we show that larger HERs before option presentation are followed by stronger value encoding during choice in vmPFC. This effect is independent of overall vmPFC baseline activity. The neural interaction between HERs and value encoding predicted preference-based choice consistency over time, accounting for both interindividual differences and trial-to-trial fluctuations within individuals. Neither cardiac activity nor arousal fluctuations could account for any of the effects. HERs did not interact with the encoding of perceptual evidence in the discrimination task. Our results show that the self-reflection underlying preference-based decisions involves HERs, and that HER integration to subjective value encoding in vmPFC contributes to preference stability.SIGNIFICANCE STATEMENT Deciding whether you prefer Forrest Gump or The Matrix is based on subjective values, which only you, the decision-maker, can estimate and compare, by asking yourself. Yet, how self-reflection is biologically implemented and its contribution to subjective valuation are not known. We show that in ventromedial prefrontal cortex, the neural response to heartbeats, an interoceptive self-related process, influences the cortical representation of subjective value. The neural interaction between the cortical monitoring of heartbeats and value encoding predicts choice consistency (i.e., whether you consistently prefer Forrest Gump over Matrix over time. Our results pave the way for the quantification of self-related processes in decision-making and may shed new light on the relationship between maladaptive decisions and impaired interoception.

Better the devil you know than the devil you don't: Neural processing of risk and ambiguity

Risk and ambiguity are inherent in virtually all human decision-making. Risk refers to a situation in which we know the precise probability of potential outcomes of each option, whereas ambiguity refers to a situation in which outcome probabilities are not known. A large body of research has shown that individuals prefer known risks to ambiguity, a phenomenon known as ambiguity aversion. One heated debate concerns whether risky and ambiguous decisions rely on the same or distinct neural circuits. In the current meta-analyses, we integrated the results of neuroimaging research on decision-making under risk (n = 69) and ambiguity (n = 31). Our results showed that both processing of risk and ambiguity showed convergence in anterior insula, indicating a key role of anterior insula in encoding uncertainty. Risk additionally engaged dorsomedial prefrontal cortex (dmPFC) and ventral striatum, whereas ambiguity specifically recruited the dorsolateral prefrontal cortex (dlPFC), inferior parietal lobe (IPL) and right anterior insula. Our findings demonstrate overlapping and distinct neural substrates underlying different types of uncertainty, guiding future neuroimaging research on risk-taking and ambiguity aversion.

Single-Dimensional Human Brain Signals for Two-Dimensional Economic Choice Options

Rewarding choice options typically contain multiple components, but neural signals in single brain voxels are scalar and primarily vary up or down. In a previous study, we had designed reward bundles that contained the same two milkshakes with independently set amounts; we had used psychophysics and rigorous economic concepts to estimate two-dimensional choice indifference curves (ICs) that represented revealed stochastic preferences for these bundles in a systematic, integrated manner. All bundles on the same ICs were equally revealed preferred (and thus had same utility, as inferred from choice indifference); bundles on higher ICs (higher utility) were preferred to bundles on lower ICs (lower utility). In the current study, we used the established behavior for testing with functional magnetic resonance imaging (fMRI). We now demonstrate neural responses in reward-related brain structures of human female and male participants, including striatum, midbrain, and medial orbitofrontal cortex (mid-OFC) that followed the characteristic pattern of ICs: similar responses along ICs (same utility despite different bundle composition), but monotonic change across ICs (different utility). Thus, these brain structures integrated multiple reward components into a scalar signal, well beyond the known subjective value coding of single-component rewards.SIGNIFICANCE STATEMENT Rewards have several components, like the taste and size of an apple, but it is unclear how each component contributes to the overall value of the reward. While choice indifference curves (ICs) of economic theory provide behavioral approaches to this question, it is unclear whether brain responses capture the preference and utility integrated from multiple components. We report activations in striatum, midbrain, and orbitofrontal cortex (OFC) that follow choice ICs representing behavioral preferences over and above variations of individual reward components. In addition, the concept-driven approach encourages future studies on natural, multicomponent rewards that are prone to irrational choice of normal and brain-damaged individuals.

Stochastic representation decision theory: How probabilities and values are entangled dual characteristics in cognitive processes

Humans are notoriously bad at understanding probabilities, exhibiting a host of biases and distortions that are context dependent. This has serious consequences on how we assess risks and make decisions. Several theories have been developed to replace the normative rational expectation theory at the foundation of economics. These approaches essentially assume that (subjective) probabilities weight multiplicatively the utilities of the alternatives offered to the decision maker, although evidence suggest that probability weights and utilities are often not separable in the mind of the decision maker. In this context, we introduce a simple and efficient framework on how to describe the inherently probabilistic human decision-making process, based on a representation of the deliberation activity leading to a choice through stochastic processes, the simplest of which is a random walk. Our model leads naturally to the hypothesis that probabilities and utilities are entangled dual characteristics of the real human decision making process. It predicts the famous fourfold pattern of risk preferences. Through the analysis of choice probabilities, it is possible to identify two previously postulated features of prospect theory: the inverse S-shaped subjective probability as a function of the objective probability and risk-seeking behavior in the loss domain. It also predicts observed violations of stochastic dominance, while it does not when the dominance is "evident". Extending the model to account for human finite deliberation time and the effect of time pressure on choice, it provides other sound predictions: inverse relation between choice probability and response time, preference reversal with time pressure, and an inverse double-S-shaped probability weighting function. Our theory, which offers many more predictions for future tests, has strong implications for psychology, economics and artificial intelligence.

Focal stimulation of the temporoparietal junction improves rationality in prosocial decision-making

We tested the hypothesis that modulation of neurocomputational inputs to value-based decision-making affects the rationality of economic choices. The brain's right temporoparietal junction (rTPJ) has been functionally associated with both social behavior and with domain-general information processing and attention. To identify the causal function of rTPJ in prosocial decisions, we administered focal high definition transcranial direct current stimulation (HD-tDCS) while participants allocated money between themselves and a charity in a modified dictator game. Anodal stimulation led to improved rationality as well as increased charitable giving and egalitarianism, resulting in more consistent and efficient choices and increased sensitivity to the price of giving. These results are consistent with the theory that anodal stimulation of the rTPJ increases the precision of value computations in social decision-making. Our results demonstrate that theories of rTPJ function should account for the multifaceted role of the rTPJ in the representation of social inputs into value-based decisions.

Experimentally revealed stochastic preferences for multicomponent choice options

Realistic, everyday rewards contain multiple components. An apple has taste and size. However, we choose in single dimensions, simply preferring some apples to others. How can such single-dimensional preference relationships refer to multicomponent choice options? Here, we measured how stochastic choices revealed preferences for 2-component milkshakes. The preferences were intuitively graphed as indifference curves that represented the orderly integration of the 2 components as trade-off: parts of 1 component were given up for obtaining 1 additional unit of the other component without a change in preference. The well-ordered, nonoverlapping curves satisfied leave-one-out tests, followed predictions by machine learning decoders and correlated with single-dimensional Becker-DeGroot-Marschak (BDM) auction-like bids for the 2-component rewards. This accuracy suggests a decision process that integrates multiple reward components into single-dimensional estimates in a systematic fashion. In interspecies comparisons, human performance matched that of highly experienced laboratory monkeys, as measured by accuracy of the critical trade-off between bundle components. These data describe the nature of choices of multicomponent choice options and attest to the validity of the rigorous economic concepts and their convenient graphic schemes for explaining choices of human and nonhuman primates. The results encourage formal behavioral and neural investigations of normal, irrational, and pathological economic choices. (PsycInfo Database Record (c) 2020 APA, all rights reserved).