The dorsomedial prefrontal cortex represents subjective value across effort-based and risky decision-making.

Decisions that require taking effort costs into account are ubiquitous in real life. The neural common currency theory hypothesizes that a particular neural network integrates different costs (e.g., risk) and rewards into a common scale to facilitate value comparison. Although there has been a surge of interest in the computational and neural basis of effort-related value integration, it is still under debate if effort-based decision-making relies on a domain-general valuation network as implicated in the neural common currency theory. Therefore, we comprehensively compared effort-based and risky decision-making using a combination of computational modeling, univariate and multivariate fMRI analyses, and data from two independent studies. We found that effort-based decision-making can be best described by a power discounting model that accounts for both the discounting rate and effort sensitivity. At the neural level, multivariate decoding analyses indicated that the neural patterns of the dorsomedial prefrontal cortex (dmPFC) represented subjective value across different decision-making tasks including either effort or risk costs, although univariate signals were more diverse. These findings suggest that multivariate dmPFC patterns play a critical role in computing subjective value in a task-independent manner and thus extend the scope of the neural common currency theory.

What do we GANE with age?

Mather and colleagues provide an impressive cross-level account of how arousal levels modulate behavior, and they support it with data ranging from receptor pharmacology to measures of cognitive function. Here we consider two related questions: (1) Why should the brain engage in different arousal levels? and (2) What are the predicted consequences of age-related changes in norepinephrine signaling for cognitive function?

Signed and unsigned effects of prediction error on memory: Is it a matter of choice?

Adaptive decision-making is governed by at least two types of memory processes. On the one hand, learned predictions through integrating multiple experiences, and on the other hand, one-shot episodic memories. These two processes interact, and predictions - particularly prediction errors - influence how episodic memories are encoded. However, studies using computational models disagree on the exact shape of this relationship, with some findings showing an effect of signed prediction errors and others showing an effect of unsigned prediction errors on episodic memory. We argue that the choice-confirmation bias, which reflects stronger learning from choice-confirming compared to disconfirming outcomes, could explain these seemingly diverging results. Our perspective implies that the influence of prediction errors on episodic encoding critically depends on whether people can freely choose between options (i.e., instrumental learning tasks) or not (Pavlovian learning tasks). The choice-confirmation bias on memory encoding might have evolved to prioritize memory representations that optimize reward-guided decision-making. We conclude by discussing open issues and implications for future studies.

The effect of prediction error on episodic memory encoding is modulated by the outcome of the predictions.

Expectations can lead to prediction errors of varying degrees depending on the extent to which the information encountered in the environment conforms with prior knowledge. While there is strong evidence on the computationally specific effects of such prediction errors on learning, relatively less evidence is available regarding their effects on episodic memory. Here, we had participants work on a task in which they learned context/object-category associations of different strengths based on the outcomes of their predictions. We then used a reinforcement learning model to derive subject-specific trial-to-trial estimates of prediction error at encoding and link it to subsequent recognition memory. Results showed that model-derived prediction errors at encoding influenced subsequent memory as a function of the outcome of participants' predictions (correct vs. incorrect). When participants correctly predicted the object category, stronger prediction errors (as a consequence of weak expectations) led to enhanced memory. In contrast, when participants incorrectly predicted the object category, stronger prediction errors (as a consequence of strong expectations) led to impaired memory. These results highlight the important moderating role of choice outcome that may be related to interactions between the hippocampal and striatal dopaminergic systems.

Response-based outcome predictions and confidence regulate feedback processing and learning.

Influential theories emphasize the importance of predictions in learning: we learn from feedback to the extent that it is surprising, and thus conveys new information. Here, we explore the hypothesis that surprise depends not only on comparing current events to past experience, but also on online evaluation of performance via internal monitoring. Specifically, we propose that people leverage insights from response-based performance monitoring - outcome predictions and confidence - to control learning from feedback. In line with predictions from a Bayesian inference model, we find that people who are better at calibrating their confidence to the precision of their outcome predictions learn more quickly. Further in line with our proposal, EEG signatures of feedback processing are sensitive to the accuracy of, and confidence in, post-response outcome predictions. Taken together, our results suggest that online predictions and confidence serve to calibrate neural error signals to improve the efficiency of learning.

Statistical context dictates the relationship between feedback-related EEG signals and learning.

Learning should be adjusted according to the surprise associated with observed outcomes but calibrated according to statistical context. For example, when occasional changepoints are expected, surprising outcomes should be weighted heavily to speed learning. In contrast, when uninformative outliers are expected to occur occasionally, surprising outcomes should be less influential. Here we dissociate surprising outcomes from the degree to which they demand learning using a predictive inference task and computational modeling. We show that the P300, a stimulus-locked electrophysiological response previously associated with adjustments in learning behavior, does so conditionally on the source of surprise. Larger P300 signals predicted greater learning in a changing context, but less learning in a context where surprise was indicative of a one-off outlier (oddball). Our results suggest that the P300 provides a surprise signal that is interpreted by downstream learning processes differentially according to statistical context in order to appropriately calibrate learning across complex environments.

Computational neuroscience across the lifespan: Promises and pitfalls.

In recent years, the application of computational modeling in studies on age-related changes in decision making and learning has gained in popularity. One advantage of computational models is that they provide access to latent variables that cannot be directly observed from behavior. In combination with experimental manipulations, these latent variables can help to test hypotheses about age-related changes in behavioral and neurobiological measures at a level of specificity that is not achievable with descriptive analysis approaches alone. This level of specificity can in turn be beneficial to establish the identity of the corresponding behavioral and neurobiological mechanisms. In this paper, we will illustrate applications of computational methods using examples of lifespan research on risk taking, strategy selection and reinforcement learning. We will elaborate on problems that can occur when computational neuroscience methods are applied to data of different age groups. Finally, we will discuss potential targets for future applications and outline general shortcomings of computational neuroscience methods for research on human lifespan development.

Age differences in learning emerge from an insufficient representation of uncertainty in older adults.

Healthy aging can lead to impairments in learning that affect many laboratory and real-life tasks. These tasks often involve the acquisition of dynamic contingencies, which requires adjusting the rate of learning to environmental statistics. For example, learning rate should increase when expectations are uncertain (uncertainty), outcomes are surprising (surprise) or contingencies are more likely to change (hazard rate). In this study, we combine computational modelling with an age-comparative behavioural study to test whether age-related learning deficits emerge from a failure to optimize learning according to the three factors mentioned above. Our results suggest that learning deficits observed in healthy older adults are driven by a diminished capacity to represent and use uncertainty to guide learning. These findings provide insight into age-related cognitive changes and demonstrate how learning deficits can emerge from a failure to accurately assess how much should be learned.