Evidence Accumulation and Choice Maintenance Are Dissociated in Human Perceptual Decision Making

Brain structure correlates of social information use: an exploratory machine learning approach

Introduction

Individual differences in social learning impact many important decisions, from voting behavior to polarization. Prior research has found that there are consistent and stable individual differences in social information use. However, the underlying mechanisms of these individual differences are still poorly understood.

Methods

We used two complementary exploratory machine learning approaches to identify brain volumes related to individual differences in social information use.

Results and discussion

Using lasso regression and random forest regression we were able to capture linear and non-linear brain-behavior relationships. Consistent with previous studies, our results suggest there is a robust positive relationship between the volume of the left pars triangularis and social information use. Moreover, our results largely overlap with common social brain network regions, such as the medial prefrontal cortex, superior temporal sulcus, temporal parietal junction, and anterior cingulate cortex. Besides, our analyses also revealed several novel regions related to individual differences in social information use, such as the postcentral gyrus, the left caudal middle frontal gyrus, the left pallidum, and the entorhinal cortex. Together, these results provide novel insights into the neural mechanisms that underly individual differences in social learning and provide important new leads for future research.

Mild exogenous inflammation blunts neural signatures of bounded evidence accumulation and reward prediction error processing in healthy male participants

BACKGROUND

Altered neural haemodynamic activity during decision making and learning has been linked to the effects of inflammation on mood and motivated behaviours. So far, it has been reported that blunted mesolimbic dopamine reward signals are associated with inflammation-induced anhedonia and apathy. Nonetheless, it is still unclear whether inflammation impacts neural activity underpinning decision dynamics. The process of decision making involves integration of noisy evidence from the environment until a critical threshold of evidence is reached. There is growing empirical evidence that such process, which is usually referred to as bounded accumulation of decision evidence, is affected in the context of mental illness.

METHODS

In a randomised, placebo-controlled, crossover study, 19 healthy male participants were allocated to placebo and typhoid vaccination. Three to four hours post-injection, participants performed a probabilistic reversal-learning task during functional magnetic resonance imaging. To capture the hidden neurocognitive operations underpinning decision-making, we devised a hybrid sequential sampling and reinforcement learning computational model. We conducted whole brain analyses informed by the modelling results to investigate the effects of inflammation on the efficiency of decision dynamics and reward learning.

RESULTS

We found that during the decision phase of the task, typhoid vaccination attenuated neural signatures of bounded evidence accumulation in the dorsomedial prefrontal cortex, only for decisions requiring short integration time. Consistent with prior work, we showed that, in the outcome phase, mild acute inflammation blunted the reward prediction error in the bilateral ventral striatum and amygdala.

CONCLUSIONS

Our study extends current insights into the effects of inflammation on the neural mechanisms of decision making and shows that exogenous inflammation alters neural activity indexing efficiency of evidence integration, as a function of choice discriminability. Moreover, we replicate previous findings that inflammation blunts striatal reward prediction error signals.

Evidence accumulation, not 'self-control', explains dorsolateral prefrontal activation during normative choice

What role do regions like the dorsolateral prefrontal cortex (dlPFC) play in normative behavior (e.g., generosity, healthy eating)? Some models suggest that dlPFC activation during normative choice reflects controlled inhibition or modulation of default hedonistic preferences. Here, we develop an alternative account, showing that evidence accumulation models predict trial-by-trial variation in dlPFC response across three fMRI paradigms and two self-control contexts (altruistic sacrifice and healthy eating). Using these models to simulate a variety of self-control dilemmas generated a novel prediction: although dlPFC activity might typically increase for norm-consistent choices, deliberate self-regulation focused on normative goals should decrease or even reverse this pattern (i.e., greater dlPFC response for hedonistic, self-interested choices). We confirmed these predictions in both altruistic and dietary choice contexts. Our results suggest that dlPFC response during normative choice may depend more on value-based evidence accumulation than inhibition of our baser instincts.

Financial incentives facilitate stronger neural computation of prosocial decisions in lower empathic adult females

Financial incentives are commonly used to motivate behaviors. However, there is also evidence that incentives can impede the behavior they are supposed to foster, for example, documented by a decrease in blood donations if a financial incentive is offered. Based on these findings, previous studies assumed that prosocial motivation is shaped by incentives. However, so far, there is no direct evidence showing an interaction between financial incentives and a specific prosocial motive. Combining drift-diffusion modeling and fMRI, we investigated the effect of financial incentives on empathy, i.e., one of the key motives driving prosocial decisions. In the empathy-alone condition, participants made prosocial decisions based on empathy. In the empathy-bonus condition, they were offered a financial bonus for prosocial decisions, in addition to empathy induction. On average, the bonus enhanced the information accumulation in empathy-based decisions. On the neural level, this enhancement was related to the anterior insula, the same region that also correlated with empathy ratings. Moreover, the effect of the financial incentive on anterior insula activation was stronger the lower a person scored on empathy. These findings show that financial incentives enhance prosocial motivation in the absence of empathy.

Signal neutrality, scalar property, and collapsing boundaries as consequences of a learned multi-timescale strategy

We postulate that three fundamental elements underlie a decision making process: perception of time passing, information processing in multiple timescales and reward maximisation. We build a simple reinforcement learning agent upon these principles that we train on a random dot-like task. Our results, similar to the experimental data, demonstrate three emerging signatures. (1) signal neutrality: insensitivity to the signal coherence in the interval preceding the decision. (2) Scalar property: the mean of the response times varies widely for different signal coherences, yet the shape of the distributions stays almost unchanged. (3) Collapsing boundaries: the “effective” decision-making boundary changes over time in a manner reminiscent of the theoretical optimal. Removing the perception of time or the multiple timescales from the model does not preserve the distinguishing signatures. Our results suggest an alternative explanation for signal neutrality. We propose that it is not part of motor planning. It is part of the decision-making process and emerges from information processing on multiple timescales.

The drift diffusion model as the choice rule in reinforcement learning

Current reinforcement-learning models often assume simplified decision processes that do not fully reflect the dynamic complexities of choice processes. Conversely, sequential-sampling models of decision making account for both choice accuracy and response time, but assume that decisions are based on static decision values. To combine these two computational models of decision making and learning, we implemented reinforcement-learning models in which the drift diffusion model describes the choice process, thereby capturing both within- and across-trial dynamics. To exemplify the utility of this approach, we quantitatively fit data from a common reinforcement-learning paradigm using hierarchical Bayesian parameter estimation, and compared model variants to determine whether they could capture the effects of stimulant medication in adult patients with attention-deficit hyperactivity disorder (ADHD). The model with the best relative fit provided a good description of the learning process, choices, and response times. A parameter recovery experiment showed that the hierarchical Bayesian modeling approach enabled accurate estimation of the model parameters. The model approach described here, using simultaneous estimation of reinforcement-learning and drift diffusion model parameters, shows promise for revealing new insights into the cognitive and neural mechanisms of learning and decision making, as well as the alteration of such processes in clinical groups.