Ian Krajbich

Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions.

How do we make decisions when confronted with several alternatives (e.g., on a supermarket shelf)? Previous work has shown that accumulator models, such as the drift-diffusion model, can provide accurate descriptions of the psychometric data for binary value-based choices, and that the choice process is guided by visual attention. However, the computational processes used to make choices in more complicated situations involving three or more options are unknown. We propose a model of trinary value-based choice that generalizes what is known about binary choice, and test it using an eye-tracking experiment. We find that the model provides a quantitatively accurate description of the relationship between choice, reaction time, and visual fixation data using the same parameters that were estimated in previous work on binary choice. Our findings suggest that the brain uses similar computational processes to make binary and trinary choices.

The Wick in the Candle of Learning Epistemic Curiosity Activates Reward Circuitry and Enhances Memory

Curiosity has been described as a desire for learning and knowledge, but its underlying mechanisms are not well understood. We scanned subjects with functional magnetic resonance imaging while they read trivia questions. The level of curiosity when reading questions was correlated with activity in caudate regions previously suggested to be involved in anticipated reward. This finding led to a behavioral study, which showed that subjects spent more scarce resources (either limited tokens or waiting time) to find out answers when they were more curious. The functional imaging also showed that curiosity increased activity in memory areas when subjects guessed incorrectly, which suggests that curiosity may enhance memory for surprising new information. This prediction about memory enhancement was confirmed in a behavioral study: Higher curiosity in an initial session was correlated with better recall of surprising answers 1 to 2 weeks later.

Economic games quantify diminished sense of guilt in patients with damage to the prefrontal cortex

Damage to the ventromedial prefrontal cortex (VMPFC) impairs concern for other people, as reflected in the dysfunctional real-life social behavior of patients with such damage, as well as their abnormal performances on tasks ranging from moral judgment to economic games. Despite these convergent data, we lack a formal model of how, and to what degree, VMPFC lesions affect an individuals social decision-making. Here we provide a quantification of these effects using a formal economic model of choice that incorporates terms for the disutility of unequal payoffs, with parameters that index behaviors normally evoked by guilt and envy. Six patients with focal VMPFC lesions participated in a battery of economic games that measured concern about payoffs to themselves and to others: dictator, ultimatum, and trust games. We analyzed each task individually, but also derived estimates of the guilt and envy parameters from aggregate behavior across all of the tasks. Compared with control subjects, the patients donated significantly less and were less trustworthy, and overall our model found a significant insensitivity to guilt. Despite these abnormalities, the patients had normal expectations about what other people would do, and they also did not simply generate behavior that was more noisy. Instead, the findings argue for a specific insensitivity to guilt, an abnormality that we suggest characterizes a key contribution made by the VMPFC to social behavior.

The attentional drift-diffusion model extends to simple purchasing decisions

How do we make simple purchasing decisions (e.g., whether or not to buy a product at a given price)? Previous work has shown that the attentional drift-diffusion model (aDDM) can provide accurate quantitative descriptions of the psychometric data for binary and trinary value-based choices, and of how the choice process is guided by visual attention. Here we extend the aDDM to the case of purchasing decisions, and test it using an eye-tracking experiment. We find that the model also provides a reasonably accurate quantitative description of the relationship between choice, reaction time, and visual fixations using parameters that are very similar to those that best fit the previous data. The only critical difference is that the choice biases induced by the fixations are about half as big in purchasing decisions as in binary choices. This suggests that a similar computational process is used to make binary choices, trinary choices, and simple purchasing decisions.

Neural Oscillations and Synchronization Differentially Support Evidence Accumulation in Perceptual and Value-Based Decision Making

Organisms make two types of decisions on a regular basis. Perceptual decisions are determined by objective states of the world (e.g., melons are bigger than apples), whereas value-based decisions are determined by subjective preferences (e.g., I prefer apples to melons). Theoretical accounts suggest that both types of choice involve neural computations accumulating evidence for the choice alternatives; however, little is known about the overlap or differences in the processes underlying perceptual versus value-based decisions. We analyzed EEG recordings during a paradigm where perceptual- and value-based choices were based on identical stimuli. For both types of choice, evidence accumulation was evident in parietal gamma-frequency oscillations, whereas a similar frontal signal was unique for value-based decisions. Fronto-parietal synchronization of these signals predicted value-based choice accuracy. These findings uncover how decisions emerge from topographic- and frequency-specific oscillations that accumulate distinct aspects of evidence, with large-scale synchronization as a mechanism integrating these spatially distributed signals.

Using Neural Measures of Economic Value to Solve the Public Goods Free-Rider Problem

Observing Unrevealed Preferences Ideally, it would be possible to design a system of incentives for the production and allocation of public goods with the following properties: (i) it would be budget-balanced; (ii) people would participate willingly because they would not be made worse off by doing so; (iii) it would be easy for each participant to find his or her optimal strategy; and (iv) the equilibrium solution would yield the optimal production of the public good. Sadly, this set of conditions cannot be satisfied simultaneously because it requires that self-interested individuals reveal voluntarily and truthfully how much they value the public good. Krajbich et al. (p. 596, published online 10 September) ask whether a neuroimaging measurement can be used to circumvent this reliance on observed behavior by decoding individual valuations. A decoding accuracy of 55% would be sufficient, and, in a laboratory experiment, an optimal provision of public goods was indeed achieved. Neuroimaging measures of individuals’ valuation of public goods suggests a path to a coherent public goods economy. Every social group needs to decide when to provide public goods and how to allocate the costs among its members. Ideally, this decision would maximize the group’s net benefits while also ensuring that every individual’s benefit is greater than the cost he or she has to pay. Unfortunately, the economic theory of mechanism design has shown that this ideal solution is not feasible when the group leadership does not know the values of the individual group members for the public good. We show that this impossibility result can be overcome in laboratory settings by combining technologies for obtaining neural measures of value (functional magnetic resonance imaging–based pattern classification) with carefully designed institutions that allocate costs based on both reported and neurally measured values.

A Common Mechanism Underlying Food Choice and Social Decisions

People make numerous decisions every day including perceptual decisions such as walking through a crowd, decisions over primary rewards such as what to eat, and social decisions that require balancing own and others’ benefits. The unifying principles behind choices in various domains are, however, still not well understood. Mathematical models that describe choice behavior in specific contexts have provided important insights into the computations that may underlie decision making in the brain. However, a critical and largely unanswered question is whether these models generalize from one choice context to another. Here we show that a model adapted from the perceptual decision-making domain and estimated on choices over food rewards accurately predicts choices and reaction times in four independent sets of subjects making social decisions. The robustness of the model across domains provides behavioral evidence for a common decision-making process in perceptual, primary reward, and social decision making.

The Role of Risk Aversion in Non-Conscious Decision Making

To what extent can people choose advantageously without knowing why they are making those choices? This hotly debated question has capitalized on the Iowa Gambling Task (IGT), in which people often learn to choose advantageously without appearing to know why. However, because the IGT is unconstrained in many respects, this finding remains debated and other interpretations are possible (e.g., risk aversion, ambiguity aversion, limits of working memory, or insensitivity to reward/punishment can explain the finding of the IGT). Here we devised an improved variant of the IGT in which the deck-payoff contingency switches after subjects repeatedly choose from a good deck, offering the statistical power of repeated within-subject measures based on learning the reward contingencies associated with each deck. We found that participants exhibited low confidence in their choices, as probed with post-decision wagering, despite high accuracy in selecting advantageous decks in the task, which is putative evidence for non-conscious decision making. However, such a behavioral dissociation could also be explained by risk aversion, a tendency to avoid risky decisions under uncertainty. By explicitly measuring risk aversion for each individual, we predicted subjects’ post-decision wagering using Bayesian modeling. We found that risk aversion indeed does play a role, but that it did not explain the entire effect. Moreover, independently measured risk aversion was uncorrelated with risk aversion exhibited during our version of the IGT, raising the possibility that the latter risk aversion may be non-conscious. Our findings support the idea that people can make optimal choices without being fully aware of the basis of their decision. We suggest that non-conscious decision making may be mediated by emotional feelings of risk that are based on mechanisms distinct from those that support cognitive assessment of risk.

Chapter 11 – Social Preferences and the Brain

What motivates people to care about others is a fundamental question in the social and cognitive sciences. Here we discuss economic models of social preferences and how they help us to understand the psychological costs and benefits in social decisions. We then analyze recent neuroeconomic findings on social preferences with the goal of creating a coherent picture of the neural circuitry involved in social decisions. We argue that the insula and anterior cingulate cortex first determine what is socially appropriate and whether any norms have or will be violated, the amygdala generates emotional responses to these outcomes, the temporoparietal junction promotes perspective-taking, and finally the dorsolateral prefrontal cortex incorporates this information to modulate the overall utilities, and thus decisions, in the striatum and ventromedial prefrontal cortex. We conclude by discussing the implications of this research for understanding deficits in social behavior and how to potentially improve our own social behavior.

Gaze data reveal distinct choice processes underlying model-based and model-free reinforcement learning

Organisms appear to learn and make decisions using different strategies known as model-free and model-based learning; the former is mere reinforcement of previously rewarded actions and the latter is a forward-looking strategy that involves evaluation of action-state transition probabilities. Prior work has used neural data to argue that both model-based and model-free learners implement a value comparison process at trial onset, but model-based learners assign more weight to forward-looking computations. Here using eye-tracking, we report evidence for a different interpretation of prior results: model-based subjects make their choices prior to trial onset. In contrast, model-free subjects tend to ignore model-based aspects of the task and instead seem to treat the decision problem as a simple comparison process between two differentially valued items, consistent with previous work on sequential-sampling models of decision making. These findings illustrate a problem with assuming that experimental subjects make their decisions at the same prescribed time.