Human Choice Behavior Research Articles

Neuroeconomics: Sharpened Tools of Value for Clinical Cognitive and Affective Neuroscience

This special issue highlights the application of neuroeconomics to understand the cognitive and neural mechanisms underlying impaired behavior and symptoms in mental disorders. Neuroeconomics is a convergence of economics, decision science, psychology, and neuroscience ( 1 Glimcher P.W. Decisions Uncertainty and the Brain: The Science of Neuroeconomics. MIT Press, Cambridge, MA2003 Google Scholar ) that seeks to build formal quantitative models of the mental representations and neural processes that underlie human motivation and choice behavior. Neuroeconomics has already had a significant impact on economics, challenging traditional economic modeling that has been agnostic to the psychological, let alone neural, processes underlying behavior. Loewenstein et al. ( 2 Loewenstein G. Rick S. Cohen J.D. Neuroeconomics. Annu Rev Psychol. 2008; 59: 647-672 Crossref PubMed Scopus (254) Google Scholar ) have highlighted the fact that in contrast to traditional economic theory, the brain does not compute a unitary process related to maximizing utility. Rather, choice behavior relies on the integration in the brain of a diverse array of specialized processes that vary according to changing context.

Bayesian networks and agent-based modeling approach for urban land-use and population density change: a BNAS model

Land-use change models grounded in complexity theory such as agent-based models (ABMs) are increasingly being used to examine evolving urban systems. The objective of this study is to develop a spatial model that simulates land-use change under the influence of human land-use choice behavior. This is achieved by integrating the key physical and social drivers of land-use change using Bayesian networks (BNs) coupled with agent-based modeling. The BNAS model, integrated Bayesian network–based agent system, presented in this study uses geographic information systems, ABMs, BNs, and influence diagram principles to model population change on an irregular spatial structure. The model is parameterized with historical data and then used to simulate 20 years of future population and land-use change for the City of Surrey, British Columbia, Canada. The simulation results identify feasible new urban areas for development around the main transportation corridors. The obtained new development areas and the projected population trajectories with the“what-if” scenario capabilities can provide insights into urban planners for better and more informed land-use policy or decision-making processes.

Salience driven value integration explains decision biases and preference reversal

Human choice behavior exhibits many paradoxical and challenging patterns. Traditional explanations focus on how values are represented, but little is known about how values are integrated. Here we outline a psychophysical task for value integration that can be used as a window on high-level, multiattribute decisions. Participants choose between alternative rapidly presented streams of numerical values. By controlling the temporal distribution of the values, we demonstrate that this process underlies many puzzling choice paradoxes, such as temporal, risk, and framing biases, as well as preference reversals. These phenomena can be explained by a simple mechanism based on the integration of values, weighted by their salience. The salience of a sampled value depends on its temporal order and momentary rank in the decision context, whereas the direction of the weighting is determined by the task framing. We show that many known choice anomalies may arise from the microstructure of the value integration process.

Neural Correlates of Dynamically Evolving Interpersonal Ties Predict Prosocial Behavior

There is a growing interest for the determinants of human choice behavior in social settings. Upon initial contact, investment choices in social settings can be inherently risky, as the degree to which the other person will reciprocate is unknown. Nevertheless, people have been shown to exhibit prosocial behavior even in one-shot laboratory settings where all interaction has been taken away. A logical step has been to link such behavior to trait empathy-related neurobiological networks. However, as a social interaction unfolds, the degree of uncertainty with respect to the expected payoff of choice behavior may change as a function of the interaction. Here we attempt to capture this factor. We show that the interpersonal tie one develops with another person during interaction – rather than trait empathy – motivates investment in a public good that is shared with an anonymous interaction partner. We examined how individual differences in trait empathy and interpersonal ties modulate neural responses to imposed monetary sharing. After, but not before interaction in a public good game, sharing prompted activation of neural systems associated with reward (striatum), empathy (anterior insular cortex and anterior cingulate cortex) as well as altruism, and social significance [posterior superior temporal sulcus (pSTS)]. Although these activations could be linked to both empathy and interpersonal ties, only tie-related pSTS activation predicted prosocial behavior during subsequent interaction, suggesting a neural substrate for keeping track of social relevance.

A computational approach to “free will” constrained by the games we play

Human choice is not free—we are bounded by a multitude of biological constraints. Yet, within the various landscapes we face, we do express choice, preference, and varying degrees of so-called willful behavior. Moreover, it appears that the capacity for choice in humans is variable. Empirical studies aimed at investigating the experience of “free will” will benefit from theoretical disciplines that constrain the language used to frame the relevant issues. The combination of game theory and computational reinforcement learning theory with empirical methods is already beginning to provide valuable insight into the biological variables underlying capacity for choice in humans and how things may go awry in individuals with brain disorders. These disciplines operate within abstract quantitative landscapes, but have successfully been applied to investigate strategic and adaptive human choice guided by formal notions of optimal behavior. Psychiatric illness is an extreme, but interesting arena for studying human capacity for choice. The experiences and behaviors of patients suggest these individuals fundamentally suffer from a diminished capacity of willful choice. Herein, I will briefly discuss recent applications of computationally guided approaches to human choice behavior and the underlying neurobiology. These approaches can be integrated into empirical investigation at multiple temporal scales of analysis including the growing body of experiments in human functional magnetic resonance imaging (fMRI), and newly emerging sub-second electrochemical and electrophysiological measurements in the human brain. These cross-disciplinary approaches hold promise for revealing the underlying neurobiological mechanisms for the variety of choice capacity in humans.

Contexts and Individual Differences as Influences on Consumers’ Delay Discounting

Delay discounting is often considered a universal feature of human choice behavior, but there is controversy over whether it is an individual difference that reflects an underlying psychological trait or a domain-specific behavior. Trait influence on discounting would manifest in (a) highly correlated discount rates for all decisions, regardless of context, and (b) the reflection of discounting behavior in psychometric measures of individual difference. We examined these propositions for consumers making hypothetical decisions with respect to financial returns, health outcomes, and vacation alternatives. Questionnaires were employed to assess discounting rates, and respondents’ (N = 74) cognitive styles were measured by the Kirton Adaption-Innovation Inventory (KAI). Results suggested that discounting is a domain-specific behavior rather than a single trait. Individual discounting rates differed markedly among product contexts; moreover, individual differences in cognitive style were not related to discounting behavior.

Reward Value-Based Gain Control: Divisive Normalization in Parietal Cortex

The representation of value is a critical component of decision making. Rational choice theory assumes that options are assigned absolute values, independent of the value or existence of other alternatives. However, context-dependent choice behavior in both animals and humans violates this assumption, suggesting that biological decision processes rely on comparative evaluation. Here we show that neurons in the monkey lateral intraparietal cortex encode a relative form of saccadic value, explicitly dependent on the values of the other available alternatives. Analogous to extra-classical receptive field effects in visual cortex, this relative representation incorporates target values outside the response field and is observed in both stimulus-driven activity and baseline firing rates. This context-dependent modulation is precisely described by divisive normalization, indicating that this standard form of sensory gain control may be a general mechanism of cortical computation. Such normalization in decision circuits effectively implements an adaptive gain control for value coding and provides a possible mechanistic basis for behavioral context-dependent violations of rationality.

Neurobiology of Value Integration: When Value Impacts Valuation

Everyday choice options have advantages (positive values) and disadvantages (negative values) that need to be integrated into an overall subjective value. For decades, economic models have assumed that when a person evaluates a choice option, different values contribute independently to the overall subjective value of the option. However, human choice behavior often violates this assumption, suggesting interactions between values. To investigate how qualitatively different advantages and disadvantages are integrated into an overall subjective value, we measured the brain activity of human subjects using fMRI while they were accepting or rejecting choice options that were combinations of monetary reward and physical pain. We compared different subjective value models on behavioral and neural data. These models all made similar predictions of choice behavior, suggesting that behavioral data alone are not sufficient to uncover the underlying integration mechanism. Strikingly, a direct model comparison on brain data decisively demonstrated that interactive value integration (where values interact and affect overall valuation) predicts neural activity in value-sensitive brain regions significantly better than the independent mechanism. Furthermore, effective connectivity analyses revealed that value-dependent changes in valuation are associated with modulations in subgenual anterior cingulate cortex-amygdala coupling. These results provide novel insights into the neurobiological underpinnings of human decision making involving the integration of different values.

Model-Based Influences on Humans' Choices and Striatal Prediction Errors

SummaryThe mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors and preferences is underexplored. We designed a multistep decision task in which model-based and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences we could then test the purity of the ventral striatal BOLD signal as a model-free report. Contrary to expectations, the signal reflected both model-free and model-based predictions in proportions matching those that best explained choice behavior. These results challenge the notion of a separate model-free learner and suggest a more integrated computational architecture for high-level human decision-making.

The role of context in risky choice.

Human choice behavior was assessed in a concurrent-chain schedule, where two equal initial links (IL) each led to a distinct terminal-link (TL). One TL was associated with a fixed ratio schedule of reinforcement, while the other was associated with a bi-valued mixed ratio schedule of reinforcement, whose arithmetic mean equaled the Fixed TL schedule. The fixed component (FR50; FR25; FR5) was arranged to be equal to the alternative mixed component in each condition (FR1/99; FR1/49; FR1/9), and choice behavior was measured by proportion of responses to each IL. In addition, the IL duration varied across conditions (VI 30s; VI 15s; FI 1s). Preference for the mixed option was observed with longer durations (e.g., when IL=VI 30s and TL=FR1/99). Participants were relatively indifferent in other conditions, though the results suggested a monotonic increase in preference as either durations or programmed efforts increased. It is concluded that both choice and the conditioned reinforcement value of the mixed option is contextually based, so that the value of a stimulus correlated with an immediate reward (i.e., FR 1) is enhanced the greater the temporal context in which the FR1 is embedded.

Applicability of an Extended Prospect Theory to Travel Behaviour Research: A Meta‐Analysis

In transport research, choice behaviour is commonly investigated by using discrete choice models that comply with the utility theory (UT) paradigm. The objective of this article is to assess the extent to which the assumptions of an extended version of prospect theory (PT) allow for a better description of travellers’ choice behaviour than UT. To that aim the travel behaviour literature was reviewed for information about observed choice behaviour suitable for such a comparison. The review yielded 106 studies that covered almost the whole range of domains and contexts of travel behaviour. Compared to the corresponding assumptions of UT, a meta‐analysis showed a better descriptive performance of reference‐dependent framing connected with loss‐aversive valuation, and of interpersonal heterogeneity in choice behaviour strategies. Diminishing sensitivity approached the observed behaviour better than the sign‐independent utility function of UT in several studies where a comparison was feasible. In choice under risk or uncertainty, the non‐linear weighting of probabilities in connection with loss aversion appeared to offer a better description than expected UT. It is concluded that the joint application of the assumptions of an extended PT might provide a better understanding of human choice behaviour over the whole range of travel‐related contexts.

Social Simulation Based on Human Behavioral Data Collected by Web-Based Experimental System

We propose some models to simulate human choice behavior to select facilities. In order to consider models for human behavior, we develop a web-based experimental system to collect the data of human choice behavior in facility selection. Using the developed system, human subjects act their facility selection according to the information such as the congestion level of the facility selected last time. We examine several models to simulate human choice behavior based on the data collected by the experimental system. From our examination, we find that developing models according to experimental results improves simulation results.

The disunity of neuroeconomics: a methodological appraisal

The recent advancements at the interface between economics and neuroscience have encouraged neuroeconomists to raise several criticisms concerning the economic theory of choice. At present, however, there is little agreement with regard to the theoretical presuppositions and the explanatory aims of neuroeconomics. In this paper, I assess the scope and the significance of neuroeconomists' divergences, casting doubt on their attempts to provide a unified theoretical framework for analysing human choice behaviour. Moreover, I highlight some respects in which methodologically informed considerations can promote the consolidation of the neuroeconomic enterprise.

Prefrontal cortex, cognitive control, and the registration of decision costs

Human choice behavior takes account of internal decision costs: people show a tendency to avoid making decisions in ways that are computationally demanding and subjectively effortful. Here, we investigate neural processes underlying the registration of decision costs. We report two functional MRI experiments that implicate lateral prefrontal cortex (LPFC) in this function. In Experiment 1, LPFC activity correlated positively with a self-report measure of costs as this measure varied over blocks of simple decisions. In Experiment 2, LPFC activity also correlated with individual differences in effort-based choice, taking on higher levels in subjects with a strong tendency to avoid cognitively demanding decisions. These relationships persisted even when effects of reaction time and error were partialled out, linking LPFC activity to subjectively experienced costs and not merely to response accuracy or time on task. In contrast to LPFC, dorsomedial frontal cortex--an area widely implicated in performance monitoring--showed no relationship to decision costs independent of overt performance. Previous work has implicated LPFC in executive control. Our results thus imply that costs may be registered based on the degree to which control mechanisms are recruited during decision-making.

Model-based approaches to neuroimaging: combining reinforcement learning theory with fMRI data.

The combination of functional magnetic resonance imaging (fMRI) with computational models for a given cognitive process provides a powerful framework for testing hypotheses about the neural computations underlying such processes in the brain. Here, we outline the steps involved in implementing this approach with reference to the application of reinforcement learning (RL) models that can account for human choice behavior during value-based decision making. The model generates internal variables which can be used to construct fMRI predictor variables and regressed against individual subjects' fMRI data. The resulting regression coefficients reflect the strength of the correlation with blood oxygenation level dependent (BOLD) activity and the relevant internal variables from the model. In the second part of this review, we describe human neuroimaging studies that have employed this analysis strategy to identify brain regions involved in the computations mediating reward-related decision making. Copyright © 2010 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website.

Prospect Theory and Choice Behaviour Strategies: Review and Synthesis of Concepts from Social and Transport sciences

Utility Theory is commonly considered as the most useful descriptive theory of human choice behaviour. Alternative concepts are only incidentally considered. This paper reviews alternative assumptions and empirical findings about human choice behaviour. To facilitate comparison and synthesis the review starts with the proposal of a generic framework of choice behaviour. The micro-economic assumptions of Utility Theory and Prospect Theory are then mapped onto this framework. These are compared with each other and other assumptions against the background of theoretical and empirical findings from behavioural economics, several other social disciplines and transport sciences. An extension of Prospect Theory with assumptions about the valuation of attributes and the employment of different decision rules yields a functional concept of choice behaviour that is able to describe most of the reviewed empirical findings to a larger extent than Utility Theory.

Human choice behaviour in a frequently changing environment

The present experiment provided a replication in humans of an experimental procedure that has been used frequently with nonhumans to investigate choice behaviour in a changing environment. Six volunteers played a computer game, which required tracking of a moving balloon on two simultaneously available response panels for monetary reinforcers. Each of the 15 sessions randomly arranged the following concurrent variable-interval reinforcement schedules, which were in effect until six reinforcers had been obtained: 27:1, 9:1, 3:1, 1:1, 1:3, 1:9, and 1:27. Although many aspects of human performance appeared to be qualitatively similar to that of nonhumans on this procedure, such as the rapid preference shifts towards the within-session reinforcer ratios and the presence of local effects of reinforcers, values of sensitivity to reinforcement were very variable in the present study, as commonly reported in human choice studies. Future variations and refinements of the experimental methods are needed to explore how this variability may be reduced.

Validation of decision-making models and analysis of decision variables in the rat basal ganglia.

Reinforcement learning theory plays a key role in understanding the behavioral and neural mechanisms of choice behavior in animals and humans. Especially, intermediate variables of learning models estimated from behavioral data, such as the expectation of reward for each candidate choice (action value), have been used in searches for the neural correlates of computational elements in learning and decision making. The aims of the present study are as follows: (1) to test which computational model best captures the choice learning process in animals and (2) to elucidate how action values are represented in different parts of the corticobasal ganglia circuit. We compared different behavioral learning algorithms to predict the choice sequences generated by rats during a free-choice task and analyzed associated neural activity in the nucleus accumbens (NAc) and ventral pallidum (VP). The major findings of this study were as follows: (1) modified versions of an action-value learning model captured a variety of choice strategies of rats, including win-stay-lose-switch and persevering behavior, and predicted rats' choice sequences better than the best multistep Markov model; and (2) information about action values and future actions was coded in both the NAc and VP, but was less dominant than information about trial types, selected actions, and reward outcome. The results of our model-based analysis suggest that the primary role of the NAc and VP is to monitor information important for updating choice behaviors. Information represented in the NAc and VP might contribute to a choice mechanism that is situated elsewhere.

Human performance on a two-alternative rapid-acquisition choice task

Davison and Baum [Davison, M., Baum, W. M., 2000. Choice in a variable environment: every reinforcer counts. Journal of the Experimental Analysis of Behavior 74, 1-24.] developed a concurrent-schedule procedure where, within each session, different reinforcer ratios were arranged across components separated by brief black-outs. Behaviour adapted quickly to the reinforcer ratios and reinforcers also had local effects on responding. This procedure has been used with pigeons and rats. In the present experiment, we adapted the Davison and Baum procedure to study the effects of reinforcement on human choice behaviour. Eighteen participants were presented with four different reinforcer ratios within a single 50-minute session. Mean sensitivity to the reinforcer ratios increased within components, and preference was greater for the just-reinforced response alternative immediately following reinforcer delivery, similar to the results from non-human experiments. Although there were limitations to the current procedure, the local time scale analyses are a novel way of examining human operant behaviour.

The Influence of the Weather on Recreational Behavior: A Micro Econometric Approach

In this article the relationship between the weather and water-based recreational behavior is investigated at the microlevel. The relation is estimated by applying a zero-inflated negative binomial count model on a large dataset consisting of individually reported day-tripping behavior during a 2-week period. The weather conditions, measured by four indicators, were merged to these data. The analysis shows that effects of the weather on human choice behavior can be estimated at the microlevel. Various aspects of the weather (temperature, rain, wind, and sunshine) exert their particular influence on different recreational activities. In particular, temperature and wind force affect all types of water-based day trips. Rain and hours of sunshine appear to matter less.