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Subjects are often willing to pay a cost for information. In a procedure that promotes paradoxical choices, animals choose between a richer option followed by a cue that is rewarded 50% of the time (No Info) vs. a leaner option followed by one of two cues that signal certain outcomes: one always rewarded (100%) and the other never rewarded, 0% (Info). Since decisions involve comparing the subjective value of options after integrating all their features, preference for information may rely on cortico-amygdalar circuitry. To test this, male and female rats were prepared with bilateral inhibitory Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) in the anterior cingulate cortex, orbitofrontal cortex, basolateral amygdala, or null virus (control). We inhibited these regions after stable preference was acquired. We found that inhibition of the anterior cingulate cortex destabilized choice preference in female rats without affecting latency to choose or response rate to cues. A logistic regression fit revealed that previous choice predicted current choice in all conditions, however previously rewarded Info trials strongly predicted preference in all conditions except in female rats following anterior cingulate cortex inhibition. The results reveal a causal, sex-dependent role for the anterior cingulate cortex in decisions involving information.

Crisismanship performance is an acid test of governance. Legitimate policies can significantly contribute to the crisismanship performance because they can ensure the willing obedience of public stakeholders and citizens. Therefore, a key research question is how a government can frame crises and pertinent policies to ensure its legitimacy and crisismanship performance? To legitimate policies, this study proposes a contingent model which is based on matching the legitimation strategies (i.e., input, throughput, and output) with different types of value decisions involved in a crisis (i.e., taboo, routine, and tragic trade-offs). This study provides three main propositions. First, in the most salient crises, characterized by taboo trade-offs, the vital role of the input legitimacy (i.e., the responsiveness of a government) can rule out the throughput (i.e., the quality of policy-making process) and the output legitimacy (i.e., the quality of policy outcome). Second, regarding the moderately salient crises, characterized by tragic trade-offs, the throughput legitimacy plays a significant role in determining the crisismanship performance. Finally, in the least salient crises, characterized by routine trade-offs, the output legitimacy can make up for the lack of input and throughput legitimacy. [ABSTRACT FROM AUTHOR]

Habits pose a fundamental puzzle for those aiming to understand human behavior. They pervade our everyday lives and dominate some forms of psychopathology but are extremely hard to elicit in the lab. In this Registered Report, we developed novel experimental paradigms grounded in computational models, which suggest that habit strength should be proportional to the frequency of behavior and, in contrast to previous research, independent of value. Specifically, we manipulated how often participants performed responses in two tasks varying action repetition without, or separately from, variations in value. Moreover, we asked how this frequency-based habitization related to value-based operationalizations of habit and self-reported propensities for habitual behavior in real life. We find that choice frequency during training increases habit strength at test and that this form of habit shows little relation to value-based operationalizations of habit. Our findings empirically ground a novel perspective on the constituents of habits and suggest that habits may arise in the absence of external reinforcement. We further find no evidence for an overlap between different experimental approaches to measuring habits and no associations with self-reported real-life habits. Thus, our findings call for a rigorous reassessment of our understanding and measurement of human habitual behavior in the lab. [ABSTRACT FROM AUTHOR]

Task-related information is widely distributed across the brain with different coding properties, such as persistency. We found in mice that coding persistency of action history and value was variable across areas, learning phases, and task context, with the highest persistency in the retrosplenial cortex of expert mice performing value-based decisions where history needs to be maintained across trials. Persistent coding also emerged in artificial networks trained to perform mouse-like reinforcement learning. Persistency allows temporally untangled value representations in neuronal manifolds where population activity exhibits cyclic trajectories that transition along the value axis after action outcomes, collectively forming cylindrical dynamics. Simulations indicated that untangled persistency facilitates robust value retrieval by downstream networks. Even leakage of persistently maintained value through non-specific connectivity could contribute to the brain-wide distributed value coding with different levels of persistency. These results reveal that context-dependent, untangled persistency facilitates reliable signal coding and its distribution across the brain.

The current study aimed to explore differential value-based decision-making patterns across three groups—individuals diagnosed with mild-to-moderate depression, a healthy matched control group, and a negative mood induction group. In the current study, drug- and therapy-naïve individuals diagnosed with first episode of mild-to-moderate depression (n = 40), healthy individuals matched on age, gender, and education (n = 40), and healthy individuals with no current, past, or family history of any psychiatric conditions in a negative mood-induced state (n = 40) were administered the IOWA Gambling Task (IGT) and the Balloon Analog Risk Task (BART). Results indicated that individuals with depression showed heightened punishment sensitivity on both the IGT and the BART (p < 0.05 on the BART and p < 0.05 on the IGT), and performed poorly on the IGT indicating poor and slow learning (p < 0.01). A similar, less severe, pattern was observed in the negative mood induction group. Individuals with mild-to-moderate depression performed poorly on tasks of value-based decision making. The significance of process factors in decision making, such as reward and punishment sensitivity, valuation of outcomes and learning, was highlighted in this study. The study also demonstrated how a negative affective state, without the other clusters of depressive symptomatology, can also lead to a less severe, but impaired decision making. [ABSTRACT FROM AUTHOR]

Despite being unpredictable and uncertain, reward environments often exhibit certain regularities, and animals navigating these environments try to detect and utilize such regularities to adapt their behavior. However, successful learning requires that animals also adjust to uncertainty associated with those regularities. Here, we analyzed choice data from two comparable dynamic foraging tasks in mice and monkeys to investigate mechanisms underlying adjustments to different types of uncertainty. In these tasks, animals selected between two choice options that delivered reward probabilistically, while baseline reward probabilities changed after a variable number (block) of trials without any cues to the animals. To measure adjustments in behavior, we applied multiple metrics based on information theory that quantify consistency in behavior, and fit choice data using reinforcement learning models. We found that in both species, learning and choice were affected by uncertainty about reward outcomes (in terms of determining the better option) and by expectation about when the environment may change. However, these effects were mediated through different mechanisms. First, more uncertainty about the better option resulted in slower learning and forgetting in mice, whereas it had no significant effect in monkeys. Second, expectation of block switches accompanied slower learning, faster forgetting, and increased stochasticity in choice in mice, whereas it only reduced learning rates in monkeys. Overall, while demonstrating the usefulness of metrics based on information theory in examining adaptive behavior, our study provides evidence for multiple types of adjustments in learning and choice behavior according to uncertainty in the reward environment. [ABSTRACT FROM AUTHOR]

We make choices based on the values of expected outcomes, informed by previous experience in similar settings. When the outcomes of our decisions consistently violate expectations, new learning is needed to maximize rewards. Yet not every surprising event indicates a meaningful change in the environment. Even when conditions are stable overall, outcomes of a single experience can still be unpredictable due to small fluctuations (i.e., expected uncertainty) in reward or costs. In the present work, we investigate causal contributions of the basolateral amygdala (BLA) and orbitofrontal cortex (OFC) in rats to learning under expected outcome uncertainty in a novel delay-based task that incorporates both predictable fluctuations and directional shifts in outcome values. We demonstrate that OFC is required to accurately represent the distribution of wait times to stabilize choice preferences despite trial-by-trial fluctuations in outcomes, whereas BLA is necessary for the facilitation of learning in response to surprising events.

Attitudes towards risk are highly consequential in clinical disorders thought to be prone to "risky behavior", such as substance dependence, as well as those commonly associated with excessive risk aversion, such as obsessive-compulsive disorder (OCD) and hoarding disorder (HD). Moreover, it has recently been suggested that attitudes towards risk may serve as a behavioral biomarker for OCD. We investigated the risk preferences of participants with OCD and HD using a novel adaptive task and a quantitative model from behavioral economics that decomposes risk preferences into outcome sensitivity and probability sensitivity. Contrary to expectation, compared to healthy controls, participants with OCD and HD exhibited less outcome sensitivity, implying less risk aversion in the standard economic framework. In addition, risk attitudes were strongly correlated with depression, hoarding, and compulsion scores, while compulsion (hoarding) scores were associated with more (less) "rational" risk preferences. These results demonstrate how fundamental attitudes towards risk relate to specific psychopathology and thereby contribute to our understanding of the cognitive manifestations of mental disorders. In addition, our findings indicate that the conclusion made in recent work that decision making under risk is unaltered in OCD is premature.

In decision neuroscience, the motor system has primarily been considered to be involved in executing choice actions. However, a competing perspective suggests its engagement in the evaluation of options, traditionally considered to be performed by the brain's valuation system. Here, we investigate the role of the motor system in value-based decision making by determining the neural circuitries associated with the sensorimotor beta oscillations previously identified to encode decision options. In a simultaneous EEG-fMRI study, participants evaluated reward and risk associated with a forthcoming action. A significant sensorimotor beta desynchronization was identified prior to and independent of response. The level of beta desynchronization showed evidence of encoding the reward levels. This beta desynchronization covaried, on a trial-by-trial level, with BOLD activity in the cortico-basal ganglia-thalamic circuitry. In contrast, there was only a weak covariation within the valuation network, despite significant modulation of its BOLD activity by reward levels. These results suggest that the way in which decision variables are processed differs in the valuation network and in the cortico-basal ganglia-thalamic circuitry. We propose that sensorimotor beta oscillations indicate incentive motivational drive towards a choice action computed from the decision variables even prior to making a response, and it arises from the cortico-basal ganglia-thalamic circuitry.

Liuting Diao,1,2 Wenping Li,3 Wuke Zhang,1,2 Qingguo Ma,1,2,4 Jia Jin5 1Business School, Ningbo University, Ningbo, People’s Republic of China; 2Academy of Neuroeconomics and Neuromanagement, Ningbo University, Ningbo, People’s Republic of China; 3Prudence College, Zhejiang Business Technology Institute, Ningbo, People’s Republic of China; 4School of Management, Zhejiang University, Hangzhou, People’s Republic of China; 5School of Business and Management, Shanghai International Studies University, Shanghai, People’s Republic of ChinaCorrespondence: Qingguo Ma; Jia Jin Email maqingguo3669@zju.edu.cn; jinjia@nbu.edu.cnBackground: Previous studies have shown that people always pay more attention to highly preferred items of choice, which is well defined by behavioral measurements and eye-tracking. However, less is known about the neural dynamics underlying the role that visual attention plays in value-based decisions, especially in those characterized by the “relative value” (ie, value difference) between two items displayed simultaneously in a binary choice.Purpose: This study examined the neural temporal and neural oscillatory features underlying selective attention to subjective preferences in value-based decision making.Methods: In this study, we recorded electroencephalography (EEG) measurements while participants performed a binary choice task in which they were instructed to respond to their preferred snack in high value difference (HVD) or low value difference (LVD) conditions.Results: Behaviorally, participants showed faster responses and lower error rates in the HVD condition than in the LVD condition. In parallel, participants exerted a reduced prefrontal N2 component and attenuated frontal theta-band synchronization in the HVD condition as opposed to the LVD condition. Crucially, participants showed greater N2pc component and theta-band synchronization over the human posterior cortex in the HVD condition than in the LVD condition. Moreover, there was a direct correlation between frontal and posterior theta-band synchronization.Conclusion: The results show that theta-band oscillatory dynamics may represent attentional bias to subjective preferences, and this effect can be modulated by the level attentional bias to subjective preferences, and this effect can be modulated by the level of value difference. Our research provides insights into a new avenue via which the processing of selective attention and value representation in the value-based decisions can be implicated in an integrative neural oscillatory mechanism.Keywords: theta-band synchronization, attentional bias, subjective preferences, value-based decision making, consumer neuroscience

Background: The importance of menstrual cycle physiology in appetite and obesity is poorly understood. We investigated the effects of body mass index (BMI), menstrual cycle phase and sweet and salty taste on monetary valuation of snack foods. Methods: We recruited 72 women and after the application of in- and exclusion criteria 31 participants with healthy weight and 25 with obesity remained. The participants completed a willingness to pay (WTP) task to measure subjective value of 30 snack food items in the pre-ovulatory and mid-luteal cycle phases. Results: Generalized linear mixed model (GLMM) analysis revealed that BMI, cycle phase and snack taste interacted to influence WTP (−0.15 [−0.22, −0.03], p = 0.002). Hence, WTP was inversely related to BMI, but the strength of the relation depended on cycle phase and taste. The WTP of participants with healthy weight for salty taste changed across cycle phase but the WTP for sweet taste was not affected by cycle phase. Moreover, the cycle effect for the salty snacks ceased in participants with obesity. Conclusion: The inverse effect of BMI on WTP valuation of snack foods contrasts with the positive effect of BMI on pleasantness ratings for milkshakes by the same women that we previously reported. This indicates that the two measures reflect different aspects of food-related valuative processing in obesity. Furthermore, the WTP data suggest that the selection of salty snacks may differ from that of sweet snacks in the pre-ovulatory phase of the menstrual cycle for individuals of healthy weight. The cycle phase does not seem to affect food valuation of participants with obesity. These findings are relevant to understanding and treating obesity in women. [ABSTRACT FROM AUTHOR]

Objectives: Disinvestment from low value health technologies is growing globally. Diverse evidence gathering and assessment methods were used to implement disinvestment initiatives, however, less than half of the empirical studies report reduced use of the low-value services. This scoping review aimed to synthesize the information from available reviews on the concepts and purposes of disinvestment in healthcare, the approaches and methods used, the role of stakeholders and facilitators and barriers in its implementation.Methods: This scoping review was guided by the Joanna Briggs Institute Manual for Evidence Synthesis and PRISMA statement for scoping review. Published reviews on disinvestment were identified from scientific databases including health technology assessment (HTA) Web sites using the terms "disinvestment," "health technology reassessment," and "healthcare." The data obtained was synthesized narratively to identify similarities and differences across the approaches based on the prespecified categories.Results: Seventeen reviews were included with thirty-four initiatives identified across sixteen countries at various levels of implementation and responsible agencies for the activities. Two most used methods to facilitate disinvestment decisions are Programme Budgeting and Marginal Analysis (PBMA) and HTA. Stakeholder involvement is the most important aspect to be addressed, as it acts as both facilitator and barrier in disinvestment initiatives implementation.Conclusions: Disinvestment programs have been implemented at multilevel, involving multistakeholders and using multiple methods such as PBMA and HTA. However, there is a lack of clarity on the additional dimensions of technical analysis related to these tools. Further research could focus on technology optimization in healthcare as part of overall health technology management. [ABSTRACT FROM AUTHOR]

Objectives: Disinvestment from low value health technologies is growing globally. Diverse evidence gathering and assessment methods were used to implement disinvestment initiatives, however, less than half of the empirical studies report reduced use of the low-value services. This scoping review aimed to synthesize the information from available reviews on the concepts and purposes of disinvestment in healthcare, the approaches and methods used, the role of stakeholders and facilitators and barriers in its implementation. Methods: This scoping review was guided by the Joanna Briggs Institute Manual for Evidence Synthesis and PRISMA statement for scoping review. Published reviews on disinvestment were identified from scientific databases including health technology assessment (HTA) Web sites using the terms "disinvestment," "health technology reassessment," and "healthcare." The data obtained was synthesized narratively to identify similarities and differences across the approaches based on the prespecified categories. Results: Seventeen reviews were included with thirty-four initiatives identified across sixteen countries at various levels of implementation and responsible agencies for the activities. Two most used methods to facilitate disinvestment decisions are Programme Budgeting and Marginal Analysis (PBMA) and HTA. Stakeholder involvement is the most important aspect to be addressed, as it acts as both facilitator and barrier in disinvestment initiatives implementation. Conclusions: Disinvestment programs have been implemented at multilevel, involving multistakeholders and using multiple methods such as PBMA and HTA. However, there is a lack of clarity on the additional dimensions of technical analysis related to these tools. Further research could focus on technology optimization in healthcare as part of overall health technology management. [ABSTRACT FROM AUTHOR]

“Values-based decision-making” frameworks and models are widely described in the literature in various disciplines, including healthcare settings. However, there is a paucity of literature on the application of systematic methods or models in the biopharmaceutical research and development (R&D) field of drugs, vaccines, and immunotherapeutics. In this report, we describe our model that uses company values along with framing questions in a five-step process to guide ethical decisions in the vaccines R&D context. The model uniquely supports practical prospective decision-making: employees are engaged as moral agents applying values and principles to guide their decision in a specific situation. We illustrate, by way of case studies, how the model is being used in practice. The consistent application of company values during decision-making calls upon employees to use their judgment, therefore reducing the need for the organization to systematically generate written instructions. Finally, we report on preliminary results of model adoption by teams within our organization, discuss its limitations and likely future contribution. We applied our model within a vaccines R&D context and believe its use can be extended to other areas where business-related decisions impact patients.

BackgroundThe importance of menstrual cycle physiology in appetite and obesity is poorly understood. We investigated the effects of body mass index (BMI), menstrual cycle phase and sweet and salty taste on monetary valuation of snack foods.MethodsWe recruited 72 women and after the application of in- and exclusion criteria 31 participants with healthy weight and 25 with obesity remained. The participants completed a willingness to pay (WTP) task to measure subjective value of 30 snack food items in the pre-ovulatory and mid-luteal cycle phases.ResultsGeneralized linear mixed model (GLMM) analysis revealed that BMI, cycle phase and snack taste interacted to influence WTP (−0.15 [−0.22, −0.03], p = 0.002). Hence, WTP was inversely related to BMI, but the strength of the relation depended on cycle phase and taste. The WTP of participants with healthy weight for salty taste changed across cycle phase but the WTP for sweet taste was not affected by cycle phase. Moreover, the cycle effect for the salty snacks ceased in participants with obesity.ConclusionThe inverse effect of BMI on WTP valuation of snack foods contrasts with the positive effect of BMI on pleasantness ratings for milkshakes by the same women that we previously reported. This indicates that the two measures reflect different aspects of food-related valuative processing in obesity. Furthermore, the WTP data suggest that the selection of salty snacks may differ from that of sweet snacks in the pre-ovulatory phase of the menstrual cycle for individuals of healthy weight. The cycle phase does not seem to affect food valuation of participants with obesity. These findings are relevant to understanding and treating obesity in women.

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.

Despite its relevance for health and education, the neurocognitive mechanism of real-life self-control is largely unknown. While recent research revealed a prominent role of the ventromedial prefrontal cortex in the computation of an integrative value signal, the contribution and relevance of other brain regions for real-life self-control remains unclear. To investigate neural correlates of decisions in line with long-term consequences and to assess the potential of brain decoding methods for the individual prediction of real-life self-control, we combined functional magnetic resonance imaging during preference decision making with ecological momentary assessment of daily self-control in a large community sample (N = 266). Decisions in line with long-term consequences were associated with increased activity in bilateral angular gyrus and precuneus, regions involved in different forms of perspective taking, such as imagining one's own future and the perspective of others. Applying multivariate pattern analysis to the same clusters revealed that individual patterns of activity predicted the probability of real-life self-control. Brain activations are discussed in relation to episodic future thinking and mentalizing as potential mechanisms mediating real-life self-control. [ABSTRACT FROM AUTHOR]

Value-based decision making in complex environments, such as those with uncertain and volatile mapping of reward probabilities onto options, may engender computational strategies that are not necessarily optimal in terms of normative frameworks but may ensure effective learning and behavioral flexibility in conditions of limited neural computational resources. In this article, we review a suboptimal strategy – additively combining reward magnitude and reward probability attributes of options for value-based decision making. In addition, we present computational intricacies of a recently developed model (named MIX model) representing an algorithmic implementation of the additive strategy in sequential decision-making with two options. We also discuss its opportunities; and conceptual, inferential, and generalization issues. Furthermore, we suggest future studies that will reveal the potential and serve the further development of the MIX model as a general model of value-based choice making. [ABSTRACT FROM AUTHOR]

Procrastination is the voluntary but irrational postponing of a task despite being aware that the delay can lead to worse consequences. It has been extensively studied in psychological field, from contributing factors, to theoretical models. From value-based decision making and reinforcement learning (RL) perspective, procrastination has been suggested to be caused by non-optimal choice resulting from cognitive limitations. Exactly what sort of cognitive limitations are involved, however, remains elusive. In the current study, we examined if a particular type of cognitive limitation, namely, inaccurate valuation resulting from inadequate state representation, would cause procrastination. Recent work has suggested that humans may adopt a particular type of state representation called the successor representation (SR) and that humans can learn to represent states by relatively low-dimensional features. Combining these suggestions, we assumed a dimension-reduced version of SR. We modeled a series of behaviors of a "student" doing assignments during the school term, when putting off doing the assignments (i.e., procrastination) is not allowed, and during the vacation, when whether to procrastinate or not can be freely chosen. We assumed that the "student" had acquired a rigid reduced SR of each state, corresponding to each step in completing an assignment, under the policy without procrastination. The "student" learned the approximated value of each state which was computed as a linear function of features of the states in the rigid reduced SR, through temporal-difference (TD) learning. During the vacation, the "student" made decisions at each time-step whether to procrastinate based on these approximated values. Simulation results showed that the reduced SR-based RL model generated procrastination behavior, which worsened across episodes. According to the values approximated by the "student," to procrastinate was the better choice, whereas not to procrastinate was mostly better according to the true values. Thus, the current model generated procrastination behavior caused by inaccurate value approximation, which resulted from the adoption of the reduced SR as state representation. These findings indicate that the reduced SR, or more generally, the dimension reduction in state representation, can be a potential form of cognitive limitation that leads to procrastination. [ABSTRACT FROM AUTHOR]

Value-based decision making in complex environments, such as those with uncertain and volatile mapping of reward probabilities onto options, may engender computational strategies that are not necessarily optimal in terms of normative frameworks but may ensure effective learning and behavioral flexibility in conditions of limited neural computational resources. In this article, we review a suboptimal strategy – additively combining reward magnitude and reward probability attributes of options for value-based decision making. In addition, we present computational intricacies of a recently developed model (named MIX model) representing an algorithmic implementation of the additive strategy in sequential decision-making with two options. We also discuss its opportunities; and conceptual, inferential, and generalization issues. Furthermore, we suggest future studies that will reveal the potential and serve the further development of the MIX model as a general model of value-based choice making.

Procrastination is the voluntary but irrational postponing of a task despite being aware that the delay can lead to worse consequences. It has been extensively studied in psychological field, from contributing factors, to theoretical models. From value-based decision making and reinforcement learning (RL) perspective, procrastination has been suggested to be caused by non-optimal choice resulting from cognitive limitations. Exactly what sort of cognitive limitations are involved, however, remains elusive. In the current study, we examined if a particular type of cognitive limitation, namely, inaccurate valuation resulting from inadequate state representation, would cause procrastination. Recent work has suggested that humans may adopt a particular type of state representation called the successor representation (SR) and that humans can learn to represent states by relatively low-dimensional features. Combining these suggestions, we assumed a dimension-reduced version of SR. We modeled a series of behaviors of a “student” doing assignments during the school term, when putting off doing the assignments (i.e., procrastination) is not allowed, and during the vacation, when whether to procrastinate or not can be freely chosen. We assumed that the “student” had acquired a rigid reduced SR of each state, corresponding to each step in completing an assignment, under the policy without procrastination. The “student” learned the approximated value of each state which was computed as a linear function of features of the states in the rigid reduced SR, through temporal-difference (TD) learning. During the vacation, the “student” made decisions at each time-step whether to procrastinate based on these approximated values. Simulation results showed that the reduced SR-based RL model generated procrastination behavior, which worsened across episodes. According to the values approximated by the “student,” to procrastinate was the better choice, whereas not to procrastinate was mostly better according to the true values. Thus, the current model generated procrastination behavior caused by inaccurate value approximation, which resulted from the adoption of the reduced SR as state representation. These findings indicate that the reduced SR, or more generally, the dimension reduction in state representation, can be a potential form of cognitive limitation that leads to procrastination.

Modern manufacturing and assembly environments are characterized by a high variability in the built process which challenges human–robot cooperation. To reduce the cognitive workload of the operator, the robot should not only be able to learn from experience but also to plan and decide autonomously. Here, we present an approach based on Dynamic Neural Fields that apply brain-like computations to endow a robot with these cognitive functions. A neural integrator is used to model the gradual accumulation of sensory and other evidence as time-varying persistent activity of neural populations. The decision to act is modeled by a competitive dynamics between neural populations linked to different motor behaviors. They receive the persistent activation pattern of the integrators as input. In the first experiment, a robot learns rapidly by observation the sequential order of object transfers between an assistant and an operator to subsequently substitute the assistant in the joint task. The results show that the robot is able to proactively plan the series of handovers in the correct order. In the second experiment, a mobile robot searches at two different workbenches for a specific object to deliver it to an operator. The object may appear at the two locations in a certain time period with independent probabilities unknown to the robot. The trial-by-trial decision under uncertainty is biased by the accumulated evidence of past successes and choices. The choice behavior over a longer period reveals that the robot achieves a high search efficiency in stationary as well as dynamic environments. [ABSTRACT FROM AUTHOR]

Anxiety is characterized by low confidence in daily decisions, coupled with high levels of phenomenological stress. Ventromedial prefrontal cortex (vmPFC) plays an integral role in maladaptive anxious behaviors via decreased sensitivity to threatening vs. non-threatening stimuli (fear generalization). vmPFC is also a key node in approach-avoidance decision making requiring two-dimensional integration of rewards and costs. More recently, vmPFC has been implicated as a key cortical input to the sympathetic branch of the autonomic nervous system. However, little is known about the role of this brain region in mediating rapid stress responses elicited by changes in confidence during decision making. We used an approach-avoidance task to examine the relationship between sympathetically mediated cardiac stress responses, vmPFC activity and choice behavior over long and short time-scales. To do this, we collected concurrent fMRI, EKG and impedance cardiography recordings of sympathetic drive while participants made approach-avoidance decisions about monetary rewards paired with painful electric shock stimuli. We observe first that increased sympathetic drive (shorter pre-ejection period) in states lasting minutes are associated with choices involving reduced decision ambivalence. Thus, on this slow time scale, sympathetic drive serves as a proxy for “mobilization” whereby participants are more likely to show consistent value-action mapping. In parallel, imaging analyses reveal that on shorter time scales (estimated with a trial-to-trial GLM), increased vmPFC activity, particularly during low-ambivalence decisions, is associated with decreased sympathetic state. Our findings support a role of sympathetic drive in resolving decision ambivalence across long time horizons and suggest a potential role of vmPFC in modulating this response on a moment-to-moment basis.

Learning and decision-making are interactive processes, yet cognitive modeling of error-driven learning and decision-making have largely evolved separately. Recently, evidence accumulation models (EAMs) of decision-making and reinforcement learning (RL) models of error-driven learning have been combined into joint RL-EAMs that can in principle address these interactions. However, we show that the most commonly used combination, based on the diffusion decision model (DDM) for binary choice, consistently fails to capture crucial aspects of response times observed during reinforcement learning. We propose a new RL-EAM based on an advantage racing diffusion (ARD) framework for choices among two or more options that not only addresses this problem but captures stimulus difficulty, speed-accuracy trade-off, and stimulus-response-mapping reversal effects. The RL-ARD avoids fundamental limitations imposed by the DDM on addressing effects of absolute values of choices, as well as extensions beyond binary choice, and provides a computationally tractable basis for wider applications.

Anxiety is characterized by low confidence in daily decisions, coupled with high levels of phenomenological stress. Ventromedial prefrontal cortex (vmPFC) plays an integral role in maladaptive anxious behaviors via decreased sensitivity to threatening vs. non-threatening stimuli (fear generalization). vmPFC is also a key node in approach-avoidance decision making requiring two-dimensional integration of rewards and costs. More recently, vmPFC has been implicated as a key cortical input to the sympathetic branch of the autonomic nervous system. However, little is known about the role of this brain region in mediating rapid stress responses elicited by changes in confidence during decision making. We used an approach-avoidance task to examine the relationship between sympathetically mediated cardiac stress responses, vmPFC activity and choice behavior over long and short time-scales. To do this, we collected concurrent fMRI, EKG and impedance cardiography recordings of sympathetic drive while participants made approach-avoidance decisions about monetary rewards paired with painful electric shock stimuli. We observe first that increased sympathetic drive (shorter pre-ejection period) in states lasting minutes are associated with choices involving reduced decision ambivalence. Thus, on this slow time scale, sympathetic drive serves as a proxy for "mobilization" whereby participants are more likely to show consistent value-action mapping. In parallel, imaging analyses reveal that on shorter time scales (estimated with a trial-to-trial GLM), increased vmPFC activity, particularly during low-ambivalence decisions, is associated with decreased sympathetic state. Our findings support a role of sympathetic drive in resolving decision ambivalence across long time horizons and suggest a potential role of vmPFC in modulating this response on a moment-to-moment basis. [ABSTRACT FROM AUTHOR]

Values are attributed to goods during free viewing of objects which entails multi- and trans-saccadic cognitive processes. Using electroencephalographic eye-fixation related potentials, the present study investigated how neural signals related to value-guided choice evolved over time when viewing household and office products during an auction task. Participants completed a Becker-DeGroot-Marschak auction task whereby half of the stimuli were presented in either a free or forced bid protocol to obtain willingness-to-pay. Stimuli were assigned to three value categories of low, medium and high value based on subjective willingness-to-pay. Eye fixations were organised into five 800 ms time-bins spanning the objects total viewing time. Independent component analysis was applied to eye-fixation related potentials. One independent component (IC) was found to represent fixations for high value products with increased activation over the left parietal region of the scalp. An IC with a spatial maximum over a fronto-central region of the scalp coded the intermediate values. Finally, one IC displaying activity that extends over the right frontal scalp region responded to intermediate- and low-value items. Each of these components responded early on during viewing an object and remained active over the entire viewing period, both during free and forced bid trials. Results suggest that the subjective value of goods are encoded using sets of brain activation patterns which are tuned to respond uniquely to either low, medium, or high values. Data indicates that the right frontal region of the brain responds to low and the left frontal region to high values. Values of goods are determined at an early point in the decision making process and carried for the duration of the decision period via trans-saccadic processes.

Prominent theories of value-based decision making have assumed that choices are made via the maximization of some objective function (e.g., expected value) and that the process of decision making is serial and unfolds across modular subprocesses (e.g., perception, valuation, and action selection). However, the influence of a large number of contextual variables that are not related to expected value in any direct way and the ubiquitous reciprocity among variables thought to belong to different subprocesses suggest that these assumptions may not always hold. Here, we propose an interactive activation framework for value-based decision making that does not assume that objective function maximization is the only consideration affecting choice or that processing is modular or serial. Our framework holds that processing takes place via the interactive propagation of activation in a set of simple, interconnected processing elements. We use our framework to simulate a broad range of well-known empirical phenomena—primarily focusing on decision contexts that feature nonoptimal decision making and/or interactive (i.e., not serial or modular) processing. Our approach is constrained at Marr's (1982) algorithmic and implementational levels rather than focusing strictly on considerations of optimality at the computational theory level. It invites consideration of the possibility that choice is emergent and that its computation is distributed. [ABSTRACT FROM AUTHOR]

Obesity is associated with a diverse array of cognitive and affective deficits, among which impairments in food valuation and choices have received increasing attention. The neural underpinnings of such impairments, however, remain poorly understood, partly because a complete understanding of these processes under normal conditions has yet to be achieved. A rapidly growing literature on the interaction between memory and decision-making has begun to highlight the integral role of memory in decision making especially in the real world, as well as the role of the hippocampus in supporting flexible decision making. Perhaps not coincidentally, altered memory performances in obesity have been well documented, and the underlying neurobiological bases of these memory alterations have also started to be better described, involving pathologies at the biochemical, cellular, and circuit levels. Despite such correspondence, the link between memory impairments and food valuation/choice deficits in obesity has received little attention. In this article, we first summarize the growing empirical support for the relevance of memory for decision making, focusing on flexible value-based decisions. We then describe converging evidence on different forms of memory impairments accompanying obesity. Building on these findings, we formulate a general neuropsychological framework and discuss how dysfunctions in the formation and retrieval of memory may interfere with adaptive decision making for food. Finally, we stress the important practical implications of this framework, arguing that memory deficits are likely a significant contributor to suboptimal food purchase and eating behavior exhibited by obese individuals.

• Some social deficits in ASD are hypothesized to be due to neural under-valuation of social vs. non-social interactions. • The vmPFC is known to encode value across a variety of dimensions including actions. • ASD children show diminished single-stimulus responses to pictures of people, but not favorite objects. • Differences between ASD and TD children can be captured with single-stimulus fMRI using less than 30 s of BOLD data. From an early age, individuals with autism spectrum disorder (ASD) spend less time engaged in social interaction compared to typically developing peers (TD). One reason behind this behavior may be that the brains of children diagnosed with ASD do not attribute enough value to potential social exchanges as compared to the brains of typically developing children; thus, potential social exchanges are avoided because other environmental stimuli are more highly valued by default. Neurobiological investigations into the mechanisms underlying value-based decision-making has shown that the ventral medial prefrontal cortex (vmPFC) is critical for encoding the expected outcome value of different actions corresponding to distinct environmental cues. Here, we tested the hypothesis that the responsiveness of the vmPFC in children diagnosed with ASD (compared to TD controls) is diminished for visual cues that represent highly valued social interaction. Using a passive picture viewing task and functional magnetic resonance imaging (fMRI) we measured the response of an a priori defined region of interest in the vmPFC in children diagnosed with ASD and an age-matched TD cohort. We show that the average response of the vmPFC is significantly diminished in the ASD group. Further, we demonstrate that a single-stimulus and less than 30 s of fMRI data are sufficient to differentiate the ASD and TD cohorts. These findings are consistent with the hypothesis that the brains of children with ASD do not encode the value of social exchange in the same manner as TD children. The latter finding suggests the possibility of utilizing single-stimulus fMRI as a potential biologically based diagnostic tool to augment traditional clinical approaches. [ABSTRACT FROM AUTHOR]

Every day, people struggle to make healthy eating decisions. Nutrition labels have been used to help people properly balance the tradeoff between healthiness and taste, but research suggests that these labels vary in their effectiveness. Here, we investigated the cognitive mechanism underlying value-based decisions with nutrition labels as modulators of value.

Humans and other animals often violate economic principles when choosing between multiple alternatives, but the underlying neurocognitive mechanisms remain elusive. A robust finding is that adding a third option can alter the relative preference for the original alternatives, but studies disagree on whether the third option’s value decreases or increases accuracy. To shed light on this controversy, we used and extended the paradigm of one study reporting a positive effect. However, our four experiments with 147 human participants and a reanalysis of the original data revealed that the positive effect is neither replicable nor reproducible. In contrast, our behavioral and eye-tracking results are best explained by assuming that the third option’s value captures attention and thereby impedes accuracy. We propose a computational model that accounts for the complex interplay of value, attention, and choice. Our theory explains how choice sets and environments influence the neurocognitive processes of multi-alternative decision making.

Over the past decade many studies indicate that we utilize our own motor system to understand the actions of other people. This mirror neuron system (MNS) has been proposed to be involved in social cognition and motor learning. However, conflicting findings regarding the underlying mechanisms that drive these shared circuits make it difficult to decipher a common model of their function. Here we propose adapting a “value-driven” model to explain discrepancies in the human mirror system literature and to incorporate this model with existing models. We will use this model to explain discrepant activation patterns in multiple shared circuits in the human data, such that a unified model may explain reported activation patterns from previous studies as a function of value.

Neuroeconomics is the study of the neurobiological bases of subjective preferences and choices. We present a novel framework that synthesizes findings from the literatures on neuroeconomics and creativity to provide a neurobiological description of creative cognition. We propose that value-based decision-making processes and activity in the locus ceruleus-norepinephrine (LC-NE) neuromodulatory system underlie creative cognition, as well as the large-scale brain network dynamics shown to be associated with creativity. This reconceptualization leads to several falsifiable hypotheses that can further understanding of creativity, decision making, and brain network dynamics. [ABSTRACT FROM AUTHOR]

Abstract: Following a key developmental task of childhood—building a foundation of self‐knowledge in the form of domain‐specific self‐concepts—adolescents begin to explore their emerging identities in ways that foster autonomy and connectedness. Neuroimaging studies of self‐related processes demonstrate enhanced engagement of the ventromedial prefrontal cortex (PFC) in adolescence, which may facilitate and reflect the development of identity by integrating the value of potential actions and choices. Drawing from neuroeconomic and social‐cognitive accounts, we propose that motivated behavior during adolescence can be modeled by a general value‐based decision‐making process centered around value accumulation in the ventromedial PFC. This approach advances models of adolescent neurodevelopment that focus on reward sensitivity and cognitive control by considering more diverse value inputs, including contributions of developing self‐ and identity‐related processes. It also considers adolescent decision making and behavior from adolescents' point of view rather than adults' perspectives on what adolescents should value or how they should behave. [ABSTRACT FROM AUTHOR]

Value-based decision making (VBDM) is a principle that states that humans and other species adapt their behavior according to the dynamic subjective values of the chosen or unchosen options. The neural bases of this process have been extensively investigated using task-based fMRI and lesion studies. However, the growing field of resting-state functional connectivity (RSFC) may shed light on the organization and function of brain connections across different decision-making domains. With this aim, we used independent component analysis to study the brain network dynamics in a large cohort of young males (N = 145) and the relationship of these dynamics with VBDM. Participants completed a battery of behavioral tests that evaluated delay aversion, risk seeking for losses, risk aversion for gains, and loss aversion, followed by an RSFC scan session. We identified a set of large-scale brain networks and conducted our analysis only on the default mode network (DMN) and networks comprising cognitive control, appetitive-driven, and reward-processing regions. Higher risk seeking for losses was associated with increased connectivity between medial temporal regions, frontal regions, and the DMN. Higher risk seeking for losses was also associated with increased coupling between the left frontoparietal network and occipital cortices. These associations illustrate the participation of brain regions involved in prospective thinking, affective decision making, and visual processing in participants who are greater risk-seekers, and they demonstrate the sensitivity of RSFC to detect brain connectivity differences associated with distinct VBDM parameters. [ABSTRACT FROM AUTHOR]

Over the past decade many studies indicate that we utilize our own motor system to understand the actions of other people. This mirror neuron system (MNS) has been proposed to be involved in social cognition and motor learning. However, conflicting findings regarding the underlying mechanisms that drive these shared circuits make it difficult to decipher a common model of their function. Here we propose adapting a "value-driven" model to explain discrepancies in the human mirror system literature and to incorporate this model with existing models. We will use this model to explain discrepant activation patterns in multiple shared circuits in the human data, such that a unified model may explain reported activation patterns from previous studies as a function of value. [ABSTRACT FROM AUTHOR]

We make choices based on the values of expected outcomes, informed by previous experience in similar settings. When the outcomes of our decisions consistently violate expectations, new learning is needed to maximize rewards. Yet not every surprising event indicates a meaningful change in the environment. Even when conditions are stable overall, outcomes of a single experience can still be unpredictable due to small fluctuations (i.e., expected uncertainty) in reward or costs. In the present work, we investigate causal contributions of the basolateral amygdala (BLA) and orbitofrontal cortex (OFC) in rats to learning under expected outcome uncertainty in a novel delay-based task that incorporates both predictable fluctuations and directional shifts in outcome values. We demonstrate that OFC is required to accurately represent the distribution of wait times to stabilize choice preferences despite trial-by-trial fluctuations in outcomes, whereas BLA is necessary for the facilitation of learning in response to surprising events.

Attitudes towards risk are highly consequential in clinical disorders thought to be prone to “risky behavior”, such as substance dependence, as well as those commonly associated with excessive risk aversion, such as obsessive-compulsive disorder (OCD) and hoarding disorder (HD). Moreover, it has recently been suggested that attitudes towards risk may serve as a behavioral biomarker for OCD. We investigated the risk preferences of participants with OCD and HD using a novel adaptive task and a quantitative model from behavioral economics that decomposes risk preferences into outcome sensitivity and probability sensitivity. Contrary to expectation, compared to healthy controls, participants with OCD and HD exhibited less outcome sensitivity, implying less risk aversion in the standard economic framework. In addition, risk attitudes were strongly correlated with depression, hoarding, and compulsion scores, while compulsion (hoarding) scores were associated with more (less) “rational” risk preferences. These results demonstrate how fundamental attitudes towards risk relate to specific psychopathology and thereby contribute to our understanding of the cognitive manifestations of mental disorders. In addition, our findings indicate that the conclusion made in recent work that decision making under risk is unaltered in OCD is premature. [ABSTRACT FROM AUTHOR]

Biasing choices may prove a useful way to implement behavior change. Previous work has shown that a simple training task (the cue-approach task), which does not rely on external reinforcement, can robustly influence choice behavior by biasing choice toward items that were targeted during training. In the current study, we replicate previous behavioral findings and explore the neural mechanisms underlying the shift in preferences following cue-approach training. Given recent successes in the development and application of machine learning techniques to task-based fMRI data, which have advanced understanding of the neural substrates of cognition, we sought to leverage the power of these techniques to better understand neural changes during cue-approach training that subsequently led to a shift in choice behavior. Contrary to our expectations, we found that machine learning techniques applied to fMRI data during non-reinforced training were unsuccessful in elucidating the neural mechanism underlying the behavioral effect. However, univariate analyses during training revealed that the relationship between BOLD and choices for Go items increases as training progresses compared to choices of NoGo items primarily in lateral prefrontal cortical areas. This new imaging finding suggests that preferences are shifted via differential engagement of task control networks that interact with value networks during cue-approach training. [ABSTRACT FROM AUTHOR]

Classical economic theory contends that the utility of a choice option should be independent of other options. This view is challenged by the attraction effect, in which the relative preference between two options is altered by the addition of a third, asymmetrically dominated option. Here, we leveraged the attraction effect in the context of intertemporal choices to test whether both decisions and reward prediction errors (RPE) in the absence of choice violate the independence of irrelevant alternatives principle. We first demonstrate that intertemporal decision making is prone to the attraction effect in humans. In an independent group of participants, we then investigated how this affects the neural and behavioral valuation of outcomes using a novel intertemporal lottery task and fMRI. Participants' behavioral responses (i.e., satisfaction ratings) were modulated systematically by the attraction effect and this modulation was correlated across participants with the respective change of the RPE signal in the nucleus accumbens. Furthermore, we show that, because exponential and hyperbolic discounting models are unable to account for the attraction effect, recently proposed sequential sampling models might be more appropriate to describe intertemporal choices. Our findings demonstrate for the first time that the attraction effect modulates subjective valuation even in the absence of choice. The findings also challenge the prospect of using neuroscientific methods to measure utility in a context-free manner and have important implications for theories of reinforcement learning and delay discounting. [ABSTRACT FROM AUTHOR]

Episodic memory and value-based decision making are two central and intensively studied research domains in cognitive neuroscience, but we are just beginning to understand how they interact to enable memory-based decisions. The two brain regions that have been associated with episodic memory and value-based decision making are the hippocampus and the ventromedial prefrontal cortex, respectively. In this review article, we first give an overview of these brain-behavior associations and then focus on the mechanisms of potential interactions between the hippocampus and ventromedial prefrontal cortex that have been proposed and tested in recent neuroimaging studies. Based on those possible interactions, we discuss several directions for future research on the neural and cognitive foundations of memory-based decision making. [ABSTRACT FROM AUTHOR]

Cue-approach training has been shown to effectively shift choices for snack food items by associating a cued button-press motor response to particular food items. Furthermore, attention is biased toward previously cued items, even when the cued item is not chosen for real consumption during a choice phase. However, the exact mechanism by which preferences shift during cue-approach training is not entirely clear. In three experiments, we shed light on the possible underlying mechanisms at play during this novel paradigm: 1) Uncued, wholly predictable motor responses paired with particular food items were not sufficient to elicit a preference shift; 2) Cueing motor responses early – concurrently with food item onset – and thus eliminating the need for heightened top-down attention to the food stimulus in preparation for a motor response also eliminated the shift in food preferences. This finding reinforces our hypothesis that heightened attention at behaviorally relevant points in time is key to changing choice behavior in the cue-approach task; 3) Crucially, indicating choice using eye movements rather than manual button presses preserves the effect, thus demonstrating that the shift in preferences is not governed by a learned motor response but more likely via modulation of subjective value in higher associative regions, consistent with previous neuroimaging results. Cue-approach training drives attention at behaviorally relevant points in time to modulate the subjective value of individual items, providing a mechanism for behavior change that does not rely on external reinforcement and that holds great promise for developing real world behavioral interventions.