The Computation and Comparison of Value in Goal-directed Choice

doi:10.1016/B978-0-12-374176-9.00028-2

Neuroeconomics

Decision making and the brain

2009, Pages 425-440

https://doi.org/10.1016/B978-0-12-374176-9.00028-2 Get rights and content

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Value-based decision-making is a complex process that requires, at the very least, the deployment of five basic types of computations. First, a representation of the decision problem needs to be constructed. This entails identifying potential courses of action (e.g., to pursue a prey item or not), as well internal states (e.g., the hunger level) and external states (e.g., the threat level). Second, a value needs to be assigned to the different actions under consideration. In order to make sound decisions, these values need to be aligned with the benefits associated with each action. Third, the valuations are compared in order to make a choice. Fourth, after implementing the decision, the brain needs to measure the desirability of the outcomes that follow. Finally, this feedback is used to update the other processes in order to improve the quality of future decisions. These are not rigid categories, but they provide a useful and reasonable decomposition of decision-making into its composite parts.

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Cited by (19)

Value computation in humans
2022, Evolution and Human Behavior
Things afford positive, neutral, or negative long-run effects on the replicative probability of the focal individual's genes. At the most general level, values are internal estimates of those effects. Value information steers physiology and behavior in the right direction: approach apple, avoid lion. Thus, value computation is of paramount biological importance. Task analysis suggests there are many prerequisites for valuing things aptly. Here, I focus on two: the need to compute value accurately, and the need to properly feed and integrate value information into the various systems that use value information (e.g., emotion systems). For example, the subjective food value imputed to an apple needs to reflect the nutrient content of the apple (accuracy); the intensity of gratitude aroused if someone gave you an apple needs to reflect the food value imputed to the apple (integration). Here, I evaluate these hypotheses with two preregistered studies. Consistent with the integration hypothesis, there are close correspondences between (i) the food values that participants impute to each of 40 food items (Study 1; goods) and (ii) the social values and the social emotions (including: gratitude, anger, shame, and pride) that result when those food items occur as constituents of broader social events. Similar correspondences are observed when participants evaluate each of 28 diseases and injuries (Study 2; bads). Consistent with the accuracy hypothesis, exploratory analyses indicate that the food values, the social values, and the social emotions elicited by the food items all track the nutrient content of those food items. Valuation is inherently a computational process. For this reason, a computational–functionalist perspective is distinctively suited to spur progress in our understanding of human values.
Filling the gaps: Cognitive control as a critical lens for understanding mechanisms of value-based decision-making
2022, Neuroscience and Biobehavioral Reviews
Citation Excerpt :
In a typical cognitive control experiment (e.g., a Stroop, Eriksen flanker, or Simon task; Friedman and Robbins, 2021), the best course of action is indicated unambiguously (e.g., the participant is instructed to name the stimulus color, and that color is easy to identify), but choosing that response requires engaging control processes to overcome a bias towards automatically responding in a different way (e.g., based on another salient feature of the stimulus). While these two sets of tasks involve similar sets of computations (Rangel, 2009; Ritz et al., 2021), they are used to address questions that are in large part non-overlapping (Fig. 1). Decision-making research typically gives greater focus to the process of integrating information about the stimuli to select a response (Fig. 1A), taking account of previous learning, long-run preferences (e.g., risk aversion), motivational state (e.g., current hunger level), and more (Campbell-Meiklejohn et al., 2016; Lempert and Phelps, 2016; Louie et al., 2011).
While often seeming to investigate rather different problems, research into value-based decision making and cognitive control have historically offered parallel insights into how people select thoughts and actions. While the former studies how people weigh costs and benefits to make a decision, the latter studies how they adjust information processing to achieve their goals. Recent work has highlighted ways in which decision-making research can inform our understanding of cognitive control. Here, we provide the complementary perspective: how cognitive control research has informed understanding of decision-making. We highlight three particular areas of research where this critical interchange has occurred: (1) how different types of goals shape the evaluation of choice options, (2) how people use control to adjust the ways they make their decisions, and (3) how people monitor decisions to inform adjustments to control at multiple levels and timescales. We show how adopting this alternate viewpoint offers new insight into the determinants of both decisions and control; provides alternative interpretations for common neuroeconomic findings; and generates fruitful directions for future research.
Change in excitability of a putative decision-making neuron in Aplysia serves as a mechanism in the decision not to feed following food satiation
2015, Behavioural Brain Research
Although decision making is a ubiquitous function, the understanding of its underlying mechanisms remains limited, particularly at the single-cell level. In this study, we used the decision not to feed that follows satiation in the marine mollusk Aplysia to examine the role of putative decision-making neuron B51 in this process. B51 is a neuron in the feeding neural circuit that exhibits decision-making characteristics in vitro, which bias the circuit toward producing the motor programs responsible for biting behavior. Once satiated, Aplysia decided not to bite for a prolonged period of time (≥24 h) when presented with a food stimulus that normally elicits feeding in non-satiated animals. Twenty-four hours after satiation, suppressed feeding was accompanied by a significant decrease of B51 excitability compared to the control group of unfed animals. No differences were measured in B51 resting membrane properties or synaptic input to B51 between the satiated and control groups. When B51 properties were measured at a time point in which feeding had recovered from the suppressive effects of satiation (i.e., 96 h after satiation), no difference in B51 excitability was observed between satiated and control groups. These findings indicate that B51 excitability changes in a manner that is coherent with the modifications in biting resulting from food satiation, thus implicating this neuron as a site of plasticity underlying the decision not to bite following food satiation in Aplysia.
Attention and choice: A review on eye movements in decision making
2013, Acta Psychologica
Citation Excerpt :
Although several theories have been described under the term ‘evidence accumulation model’ (see for instance Busemeyer & Townsend, 1993; Ratcliff & Smith, 2004), this paper considers the attentional drift diffusion model (aDDM), as outlined by Krajbich et al. (2010). Earlier versions of the drift diffusion model (Ratcliff & Smith, 2004) were concerned with perceptual discrimination; the aDDM extended this modus operandi to decision making (see Rangel, 2009 for an overview of both models). The aDDM assumes that decision makers accumulate evidence in favor of an alternative when fixating it, and that the speed of accumulation is a function of the value of the alternative relative to other alternatives in the choice set, which is called the bias rate.
This paper reviews studies on eye movements in decision making, and compares their observations to theoretical predictions concerning the role of attention in decision making. Four decision theories are examined: rational models, bounded rationality, evidence accumulation, and parallel constraint satisfaction models. Although most theories were confirmed with regard to certain predictions, none of the theories adequately accounted for the role of attention during decision making. Several observations emerged concerning the drivers and down-stream effects of attention on choice, suggesting that attention processes plays an active role in constructing decisions. So far, decision theories have largely ignored the constructive role of attention by assuming that it is entirely determined by heuristics, or that it consists of stochastic information sampling. The empirical observations reveal that these assumptions are implausible, and that more accurate assumptions could have been made based on prior attention and eye movement research. Future decision making research would benefit from greater integration with attention research.
Is neuroaccounting waiting in the wings? An essay
2012, Accounting, Organizations and Society
Citation Excerpt :
Also, evidence on how the brain actually perceives and evaluates rewards and losses, and how it directs the human animal to achieve the intended rewards and avoid losses, e.g. the dopamine studies of Schultz (2009H), is expected to help in fine-tuning utility theory including its major variant, Prospect Theory (Fox & Poldrack, 2009H). Similarly, neural-level evidence on how values translate to choices (Bossaerts et al., 2009H; Glimcher, 2009H; Rangel, 2009H) and how primates, both those close to humans, e.g., chimpanzees in Silk (2009H), and further from humans, e.g., capuchin monkeys in Brosnan (2009H) perceive inequities, is likely to help understand and model choices and decisions at the individual level. Lastly, the ability of neuroeconomics methods to measure a person’s well-being in a variety of ways has the potential to shift the focus of behavioral researchers from choice to welfare (Jamison, 2008).
This paper discusses a recently published handbook on neuroeconomics (Glimcher et al., 2009Ha, Glimcher et al., 2009Hb) and extends the discussion to reasons why this newly emerging discipline should be of interest to behavioral accounting researchers. We evaluate the achieved and potential contribution of neuroeconomics to the study of human economic behavior, and examine what behavioral accounting researchers can learn from neuroeconomics and whether we should expect to see a similar sub-field emerge within behavioral accounting in the near future. We conclude that a separate sub-field within behavioral accounting is not likely in the near future due mostly to practical reasons. However, the behavioral accounting researcher would do well to follow research in this discipline closely, and behavioral accountants in the near future are likely to collaborate with neuroscientists and neuroeconomists on questions of mutual interest.
Value, pleasure and choice in the ventral prefrontal cortex
2011, Trends in Cognitive Sciences
Citation Excerpt :
For example, Behrens and colleagues found that the VMPFC encoded the expected value of the chosen option based on the subjects’ own experiences as well as on social advice [64]. On the basis of these findings, it has been suggested that the VMPFC represents a common valuation signal that underlies different types of decision as well as decisions about different types of goods [31,41,59,68]. A related account [69] suggests that, whereas the OFC is involved in encoding the value of specific rewards, the VMPFC plays a specific role in value-guided decision-making about which of several options to pursue by encoding the expected value of the chosen option [64,70,71].
Rapid advances have recently been made in understanding how value-based decision-making processes are implemented in the brain. We integrate neuroeconomic and computational approaches with evidence on the neural correlates of value and experienced pleasure to describe how systems for valuation and decision-making are organized in the prefrontal cortex of humans and other primates. We show that the orbitofrontal and ventromedial prefrontal (VMPFC) cortices compute expected value, reward outcome and experienced pleasure for different stimuli on a common value scale. Attractor networks in VMPFC area 10 then implement categorical decision processes that transform value signals into a choice between the values, thereby guiding action. This synthesis of findings across fields provides a unifying perspective for the study of decision-making processes in the brain.

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Chapter 28 - The Computation and Comparison of Value in Goal-directed Choice

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