Frontal theta oscillatory activity is a common mechanism for the computation of unexpected outcomes and learning rate

In decision-making processes, the relevance of the information yielded by outcomes varies across time and situations. It increases when previous predictions are not accurate and in contexts with high environmental uncertainty. Previous fMRI studies have shown an important role of medial pFC in coding both reward prediction errors and the impact of this information to guide future decisions. However, it is unclear whether these two processes are dissociated in time or occur simultaneously, suggesting that a common mechanism is engaged. In the present work, we studied the modulation of two electrophysiological responses associated to outcome processing the feedback-related negativity ERP and frontocentral theta oscillatory activity with the reward prediction error and the learning rate. Twenty-six participants performed two learning tasks differing in the degree of predictability of the outcomes: a reversal learning task and a probabilistic learning task with multiple blocks of novel cueoutcome associations. We implemented a reinforcement learning model to obtain the single-trial reward prediction error and the learning rate for each participant and task. Our results indicated that midfrontal theta activity and feedback-related negativity increased linearly with the unsigned prediction error. In addition, variations of frontal theta oscillatory activity predicted the learning rate across tasks and participants. These results support the existence of a common brain mechanism for the computation of unsigned prediction error and learning rate

Intra- and inter-brain synchrony oscillations underlying social adjustment

Humans naturally synchronize their behavior with other people. However, although it happens almost automatically, adjusting behavior and conformity to others is a complex phenomenon whose neural mechanisms are still yet to be understood entirely. The present experiment aimed to study the oscillatory synchronization mechanisms underlying automatic dyadic convergence in an EEG hyperscanning experiment. Thirty-six people performed a cooperative decision-making task where dyads had to guess the correct position of a point on a line. A reinforcement learning algorithm was used to model different aspects of the participants’ behavior and their expectations of their peers. Intra- and inter-connectivity among electrode sites were assessed using inter-site phase clustering in three main frequency bands (theta, alpha, beta) using a two-level Bayesian mixed-effects modeling approach. The results showed two oscillatory synchronization dynamics related to attention and executive functions in alpha and reinforcement learning in theta. In addition, inter-brain synchrony was mainly driven by beta oscillations. This study contributes preliminary evidence on the phase-coherence mechanism underlying inter-personal behavioral adjustment

Modulatory effects of positive mood and approach motivation on reward processing : two sides of the same coin?

In a previous study (Paul & Pourtois, 2017), we found that positive mood substantially influenced the neural processing of reward, mostly by altering expectations and creating an optimistic bias. Under positive mood, the Reward Positivity (RewP) component and fronto-medial theta activity (FM theta) in response to monetary feedback were both changed compared with neutral mood. Nevertheless, whether positive valence per se or motivational intensity drove these neurophysiological effects remained unclear. To address this question, we combined a mindset manipulation with an imagery procedure to create and maintain three different affective states using a between-subjects design: a neutral mood, and positive mood with either high or low motivational intensity. After mood induction, 161 participants performed a simple gambling task while 64-channel EEG was recorded. FM theta activity results showed that irrespective of motivational intensity, positive compared with neutral mood altered reward expectancy. By comparison, RewP was not affected by positive mood nor motivational intensity. These results suggest that positive mood, rather than motivational intensity, is likely driving the change in reward expectation during gambling, which could reflect the presence of an optimistic bias. Moreover, at the methodological level, they confirm that the RewP ERP component and FM theta activity can capture dissociable effects during reward processing

Principal components analysis of reward prediction errors in a reinforcement learning task

Models of reinforcement learning represent reward and punishment in terms of reward prediction errors (RPEs), quantitative signed terms describing the degree to which outcomes are better than expected (positive RPEs) or worse (negative RPEs). An electrophysiological component known as feedback related negativity (FRN) occurs at frontocentral sites 240-340 ms after feedback on whether a reward or punishment is obtained, and has been claimed to neurally encode an RPE. An outstanding question however, is whether the FRN is sensitive to the size of both positive RPEs and negative RPEs. Previous attempts to answer this question have examined the simple effects of RPE size for positive RPEs and negative RPEs separately. However, this methodology can be compromised by overlap from components coding for unsigned prediction error size, or "salience", which are sensitive to the absolute size of a prediction error but not its valence. In our study, positive and negative RPEs were parametrically modulated using both reward likelihood and magnitude, with principal components analysis used to separate out overlying components. This revealed a single RPE encoding component responsive to the size of positive RPEs, peaking at similar to 330ms, and occupying the delta frequency band. Other components responsive to unsigned prediction error size were shown, but no component sensitive to negative RPE size was found. (C) 2015 Elsevier Inc. All rights reserved

Subgoal- and goal-related reward prediction errors in medial prefrontal cortex

A longstanding view of the organization of human and animal behavior holds that behavior is hierarchically organizedin other words, directed toward achieving superordinate goals through the achievement of subordinate goals or subgoals. However, most research in neuroscience has focused on tasks without hierarchical structure. In past work, we have shown that negative reward prediction error (RPE) signals in medial prefrontal cortex (mPFC) can be linked not only to superordinate goals but also to subgoals. This suggests that mPFC tracks impediments in the progression toward subgoals. Using fMRI of human participants engaged in a hierarchical navigation task, here we found that mPFC also processes positive prediction errors at the level of subgoals, indicating that this brain region is sensitive to advances in subgoal completion. However, when subgoal RPEs were elicited alongside with goal-related RPEs, mPFC responses reflected only the goal-related RPEs. These findings suggest that information from different levels of hierarchy is processed selectively, depending on the task context

Neurophysiological correlates of interpersonal discrepancy and social adjustment in an interactive decision-making task in dyads

Introduction: The pursuit of convergence and the social behavioral adjustment of conformity are fundamental cooperative behaviors that help people adjust their mental frameworks to reach a common goal. However, while social psychology has extensively studied conformity by its influence context, there is still plenty to investigate about the neural cognitive mechanisms involved in this behavior. Methods: We proposed a paradigm with two phases, a pre-activation phase to enhance cooperative tendencies and, later, a social decision-making phase in which dyads had to make a perceptual estimation in three consecutive trials and could converge in their decisions without an explicit request or reward to do so. In Study 1, 80 participants were divided in two conditions. In one condition participants did the pre-activation phase alone, while in the other condition the two participants did it with their partners and could interact freely. In Study 2, we registered the electroencephalographical (EEG) activity of 36 participants in the social decision-making phase. Results: Study 1 showed behavioral evidence of higher spontaneous convergence in participants who interacted in the pre-activation phase. Event related Potentials (ERP) recorded in Study 2 revealed signal differences in response divergence in different time intervals. Time-frequency analysis showed theta, alpha, and beta evidence related to cognitive control, attention, and reward processing associated with social convergence. Discussion: Current results support the spontaneous convergence of behavior in dyads, with increased behavioral adjustment in those participants who have previously cooperated. In addition, neurophysiological components were associated with discrepancy levels between participants, and supported the validity of the experimental paradigm to study spontaneous social behavioral adaptation in experimental settings

State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward-based learning

Anxiety influences how the brain estimates and responds to uncertainty. The consequences of these processes on behaviour have been described in theoretical and empirical studies, yet the associated neural correlates remain unclear. Rhythm-based accounts of Bayesian predictive coding propose that predictions in generative models of perception are represented in alpha (8–12 Hz) and beta oscillations (13–30 Hz). Updates to predictions are driven by prediction errors weighted by precision (inverse variance), and are encoded in gamma oscillations (>30 Hz) and associated with suppression of beta activity. We tested whether state anxiety alters the neural oscillatory activity associated with predictions and precision-weighted prediction errors (pwPE) during learning. Healthy human participants performed a probabilistic reward-based learning task in a volatile environment. In our previous work, we described learning behaviour in this task using a hierarchical Bayesian model, revealing more precise (biased) beliefs about the tendency of the reward contingency in state anxiety, consistent with reduced learning in this group. The model provided trajectories of predictions and pwPEs for the current study, allowing us to assess their parametric effects on the time-frequency representations of EEG data. Using convolution modelling for oscillatory responses, we found that, relative to a control group, state anxiety increased beta activity in frontal and sensorimotor regions during processing of pwPE, and in fronto-parietal regions during encoding of predictions. No effects of state anxiety on gamma modulation were found. Our findings expand prior evidence on the oscillatory representations of predictions and pwPEs into the reward-based learning domain. The results suggest that state anxiety modulates beta-band oscillatory correlates of pwPE and predictions in generative models, providing insights into the neural processes associated with biased belief updating and poorer learning

Neurophysiological correlates of interpersonal discrepancy and social adjustment in an interactive decision-making task in dyads

IntroductionThe pursuit of convergence and the social behavioral adjustment of conformity are fundamental cooperative behaviors that help people adjust their mental frameworks to reach a common goal. However, while social psychology has extensively studied conformity by its influence context, there is still plenty to investigate about the neural cognitive mechanisms involved in this behavior.MethodsWe proposed a paradigm with two phases, a pre-activation phase to enhance cooperative tendencies and, later, a social decision-making phase in which dyads had to make a perceptual estimation in three consecutive trials and could converge in their decisions without an explicit request or reward to do so. In Study 1, 80 participants were divided in two conditions. In one condition participants did the pre-activation phase alone, while in the other condition the two participants did it with their partners and could interact freely. In Study 2, we registered the electroencephalographical (EEG) activity of 36 participants in the social decision-making phase.ResultsStudy 1 showed behavioral evidence of higher spontaneous convergence in participants who interacted in the pre-activation phase. Event related Potentials (ERP) recorded in Study 2 revealed signal differences in response divergence in different time intervals. Time-frequency analysis showed theta, alpha, and beta evidence related to cognitive control, attention, and reward processing associated with social convergence.DiscussionCurrent results support the spontaneous convergence of behavior in dyads, with increased behavioral adjustment in those participants who have previously cooperated. In addition, neurophysiological components were associated with discrepancy levels between participants, and supported the validity of the experimental paradigm to study spontaneous social behavioral adaptation in experimental settings