Decisions, Decisions, Decisions Choosing a Biological Science of Choice

AbstractBehavioral ecologists argue that evolution drives animal behavior to efficiently solve the problems animals face in their environmental niches. The ultimate evolutionary causes of decision making, they contend, can be found in economic analyses of organisms and their environments. Neurobiologists interested in how animals make decisons have, in contrast, focused their efforts on understanding the neurobiological hardware that serves as a more proximal cause of that same behavior. Describing the flow of information within the nervous system without regard to these larger goals has been their focus. Recent work in a number of laboratories has begun to suggest that these two approaches are beginning to fuse. It may soon be possible to view the nervous system as a representational process that solves the mathematically defined economic problems animals face by making efficient decisions. These developments in the neurobiological theory of choice, and the new schema they imply, form the subject of this article

The Neural Correlated of Subjective Value During Intertemporal Choice

Neuroimaging studies of decision-making have generally related neural activity to objective measures (such as reward magnitude, probability or delay), despite choice preferences being subjective. However, economic theories posit that decision-makers behave as though different options have different subjective values. Here we use functional magnetic resonance imaging to show that neural activity in several brain regions—particularly the ventral striatum, medial prefrontal cortex and posterior cingulate cortex—tracks the revealed subjective value of delayed monetary rewards. This similarity provides unambiguous evidence that the subjective value of potential rewards is explicitly represented in the human brain

The Neurobiology of Decision: Consensus and Controversy

We review and synthesize recent neurophysiological studies of decision making in humans and nonhuman primates. From these studies, the basic outline of the neurobiological mechanism for primate choice is beginning to emerge. The identified mechanism is now known to include a multicomponent valuation stage, implemented in ventromedial prefrontal cortex and associated parts of striatum, and a choice stage, implemented in lateral prefrontal and parietal areas. Neurobiological studies of decision making are beginning to enhance our understanding of economic and social behavior as well as our understanding of significant health disorders where people\u27s behavior plays a key role

Neuroeconomic Studies of Impulsivity: Now or Just as Soon as Possible?

Existing behavioral studies of intertemporal choice suggest that both human and animal choosers are impulsive. One possible explanation for this is that they discount future gains in a hyperbolic or quasi-hyperbolic fashion (David I. Laibson 1997; Shane Frederick, George Loewenstein, and Ted O’Donoghue 2002). This observation stands in contrast to standard normative theory, which predicts exponential discounting for any single maximizing agent (Robert H. Strotz 1956). This disparity between empirical and normative approaches is typically explained by proposing that human choosers suffer from inner conflict, balancing an impulse for an immediate gratification against other forces calling for delayed gratification (Richard H. Thaler and H. M. Shefrin 1981; Laibson 1997; Drew Fudenberg and David K. Levine 2006; Jess Benhabib and Alberto Bisin 2005; B. Douglas Bernheim and Antonio Rangel 2004; Faruk Gul and Wolfgang Pesendorfer 2001). We hoped to better understand both the behavioral and algorithmic roots of this phenomenon by conducting a series of behavioral and neurobiological experiments on intertemporal choice. The results of our behavioral experiments deviate significantly from the predictions of both normative and inner conflict models. The results of our neurobiological experiments provide new algorithmic insights into the mechanisms of intertemporal choice

Expected subjective value theory (ESVT): A representation of decision under risk and certainty.

We present a descriptive model of choice derived from neuroscientific models of efficient value representation in the brain. Our basic model, a special case of Expected Utility Theory, can capture a number of behaviors predicted by Prospect Theory. It achieves this with only two parameters: a time-indexed “payoff expectation”(reference point) and a free parameter we call “predisposition”. A simple extension of the model outside the domain of Expected Utility also captures the Allais Paradox. Our models shed new light on the computational origins and evolution of risk attitudes and aversion to outcomes below reward expectation (reference point). It delivers novel explanations of the endowment effect, the observed heterogeneity in probability weighting functions, and the Allais Paradox, all with fewer parameters and higher descriptive accuracy than Prospect Theory

Free choice shapes normalized value signals in medial orbitofrontal cortex

Normalization is a common cortical computation widely observed in sensory perception, but its importance in perception of reward value and decision making remains largely unknown. We examined (1) whether normalized value signals occur in the orbitofrontal cortex (OFC) and (2) whether changes in behavioral task context influence the normalized representation of value. We record medial OFC (mOFC) single neuron activity in awake-behaving monkeys during a reward-guided lottery task. mOFC neurons signal the relative values of options via a divisive normalization function when animals freely choose between alternatives. The normalization model, however, performed poorly in a variant of the task where only one of the two possible choice options yields a reward and the other was certain not to yield a reward (so called: “forced choice”). The existence of such context-specific value normalization may suggest that the mOFC contributes valuation signals critical for economic decision making when meaningful alternative options are available

Neuroanatomy Predicts Individual Risk Attitudes

Over the course of the last decade a multitude of studies have investigated the relationship between neural activations and individual human decision-making. Here we asked whether the anatomical features of individual human brains could be used to predict the fundamental preferences of human choosers. To that end, we quantified the risk attitudes of human decision-makers using standard economic tools and quantified the gray matter cortical volume in all brain areas using standard neurobiological tools. Our whole-brain analysis revealed that the gray matter volume of a region in the right posterior parietal cortex was significantly predictive of individual risk attitudes. Participants with higher gray matter volume in this region exhibited less risk aversion. To test the robustness of this finding we examined a second group of participants and used econometric tools to test the ex ante hypothesis that gray matter volume in this area predicts individual risk attitudes. Our finding was confirmed in this second group. Our results, while being silent about causal relationships, identify what might be considered the first stable biomarker for financial risk-attitude. If these results, gathered in a population of midlife northeast American adults, hold in the general population, they will provide constraints on the possible neural mechanisms underlying risk attitudes. The results will also provide a simple measurement of risk attitudes that could be easily extracted from abundance of existing medical brain scans, and could potentially provide a characteristic distribution of these attitudes for policy makers

Computational Markers of Risky Decision-making for Identification of Temporal Windows of Vulnerability to Opioid Use in a Real-world Clinical Setting

Importance: Opioid addiction is a major public health problem. Despite availability of evidence-based treatments, relapse and dropout are common outcomes. Efforts aimed at identifying reuse risk and gaining more precise understanding of the mechanisms conferring reuse vulnerability are needed. Objective: To use tools from computational psychiatry and decision neuroscience to identify changes in decision-making processes preceding opioid reuse. Design, Setting, and Participants: A cohort of individuals with opioid use disorder were studied longitudinally at a community-based treatment setting for up to 7 months (1-15 sessions per person). At each session, patients completed a risky decision-making task amenable to computational modeling and standard clinical assessments. Time-lagged mixed-effects logistic regression analyses were used to assess the likelihood of opioid use between sessions (t to t + 1; within the subsequent 1-4 weeks) from data acquired at the current session (t). A cohort of control participants completed similar procedures (1-5 sessions per person), serving both as a baseline comparison group and an independent sample in which to assess measurement test-retest reliability. Data were analyzed between January 1, 2018, and September 5, 2019. Main Outcomes and Measures: Two individual model-based behavioral markers were derived from the task completed at each session, capturing a participant's current tolerance of known risks and ambiguity (partially unknown risks). Current anxiety, craving, withdrawal, and nonadherence were assessed via interview and clinic records. Opioid use was ascertained from random urine toxicology tests and self-reports. Results: Seventy patients (mean [SE] age, 44.7 [1.3] years; 12 women and 58 men [82.9% male]) and 55 control participants (mean [SE] age, 42.4 [1.5] years; 13 women and 42 men [76.4% male]) were included. Of the 552 sessions completed with patients (mean [SE], 7.89 [0.59] sessions per person), 252 (45.7%) directly preceded opioid use events (mean [SE], 3.60 [0.44] sessions per person). From the task parameters, only ambiguity tolerance was significantly associated with increased odds of prospective opioid use (adjusted odds ratio, 1.37 [95% CI, 1.07-1.76]), indicating patients were more tolerant specifically of ambiguous risks prior to these use events. The association of ambiguity tolerance with prospective use was independent of established clinical factors (adjusted odds ratio, 1.29 [95% CI, 1.01-1.65]; P =.04), such that a model combining these factors explained more variance in reuse risk. No significant differences in ambiguity tolerance were observed between patients and control participants, who completed 197 sessions (mean [SE], 3.58 [0.21] sessions per person); however, patients were more tolerant of known risks (B = 0.56 [95% CI, 0.05-1.07]). Conclusions and Relevance: Computational approaches can provide mechanistic insights about the cognitive factors underlying opioid reuse vulnerability and may hold promise for clinical use. © 2019 American Medical Association. All rights reserved

Singapore boards and directors – Of those who govern and direct

<div><p>Environmental public goods—including national parks, clean air/water, and ecosystem services—provide substantial benefits on a global scale. These goods have unique characteristics in that they are typically “nonmarket” goods, with values from both use and passive use that accrue to a large number of individuals both in current and future generations. In this study, we test the hypothesis that neural signals in areas correlated with subjective valuations for essentially all other previously studied categories of goods (ventromedial prefrontal cortex and ventral striatum) also correlate with environmental valuations. We use contingent valuation (CV) as our behavioral tool for measuring valuations of environmental public goods. CV is a standard stated preference approach that presents survey respondents with information on an issue and asks questions that help policymakers determine how much citizens are willing to pay for a public good or policy. We scanned human subjects while they viewed environmental proposals, along with three other classes of goods. The presentation of all four classes of goods yielded robust and similar patterns of temporally synchronized brain activation within <i>attentional </i>networks. The activations associated with the traditional classes of goods replicate previous correlations between neural activity in valuation areas and behavioral preferences. In contrast, CV-elicited values for environmental proposals did not correlate with brain activity at either the individual or population level. For a sub-population of participants, CV-elicited values were correlated with activity within the dorsomedial prefrontal cortex, a region associated with cognitive control and shifting decision strategies. The results show that neural activity associated with the subjective valuation of environmental proposals differs profoundly from the neural activity associated with previously examined goods and preference measures.</p></div

Translating upwards: linking the neural and social sciences via neuroeconomics

The social and neural sciences share a common interest in understanding the mechanisms that underlie human behaviour. However, interactions between neuroscience and social science disciplines remain strikingly narrow and tenuous. We illustrate the scope and challenges for such interactions using the paradigmatic example of neuroeconomics. Using quantitative analyses of both its scientific literature and the social networks in its intellectual community, we show that neuroeconomics now reflects a true disciplinary integration, such that research topics and scientific communities with interdisciplinary span exert greater influence on the field. However, our analyses also reveal key structural and intellectual challenges in balancing the goals of neuroscience with those of the social sciences. To address these challenges, we offer a set of prescriptive recommendations for directing future research in neuroeconomics