What is in the feedback? Effect of induced happiness vs. sadness on probabilistic learning with vs. without exploration

Achievement emotions in kindergarten: the association of solution accuracy with discrete joy, sadness, and surprise

Children experience a variety of emotions in achievement settings. Yet, mathematics-related emotions other than anxiety are understudied, especially for young children entering primary school. The current study reports the prevalence and intensity of six basic, discrete achievement emotions (joy/happiness, sadness, surprise, anger, fear, and disgust) expressed on the faces of 15 kindergarten-aged children as they solved increasingly complex arithmetic story problems in a 3-month teaching experiment. We also examine how the extent to which the expressed emotions influenced arithmetic accuracy at the end of an instructional session at the beginning, middle, and end of the teaching experiment. Through the application of FaceReader9, the three most intensely expressed emotions at the launch of the instructional sessions were happiness/joy, sadness, and surprise. Using functional regressions, these expressed achievement emotions predicted arithmetic accuracy at the end of the instructional session. However, when the effect of session over time was added to the model, the relationship between happiness/joy and accuracy, as well as sadness and accuracy, became non-significant. In contrast, the relationship between surprise and accuracy remained significant. We discuss potential explanations for these patterns of significance and non-significance. This study serves as a critical first step in clarifying how emotions contribute to problem-solving behavior as we grapple with how to respond to the sometimes intense, but always present emotions of young learners in ways that are affirming, as well as mathematically productive and generative.

Dynamic computational phenotyping of human cognition

Computational phenotyping has emerged as a powerful tool for characterizing individual variability across a variety of cognitive domains. An individual's computational phenotype is defined as a set of mechanistically interpretable parameters obtained from fitting computational models to behavioural data. However, the interpretation of these parameters hinges critically on their psychometric properties, which are rarely studied. To identify the sources governing the temporal variability of the computational phenotype, we carried out a 12-week longitudinal study using a battery of seven tasks that measure aspects of human learning, memory, perception and decision making. To examine the influence of state effects, each week, participants provided reports tracking their mood, habits and daily activities. We developed a dynamic computational phenotyping framework, which allowed us to tease apart the time-varying effects of practice and internal states such as affective valence and arousal. Our results show that many phenotype dimensions covary with practice and affective factors, indicating that what appears to be unreliability may reflect previously unmeasured structure. These results support a fundamentally dynamic understanding of cognitive variability within an individual.

Reduced positive affect alters reward learning via reduced information encoding in the Reward Positivity

Reward Positivity (RewP) is a feedback-locked event-related potential component that is specifically elicited by rewarding feedback and scales with positive reward prediction error, a hallmark of reinforcement learning models. The RewP is also diminished in depression, suggesting that it may be a novel marker of anhedonia. Here, we examined if a sad mood induction offered an opportunity to causally induce a mood-related alteration of the RewP and reward-related learning. In Experiment 1 (N = 50 total), participants were randomly assigned to previously established sad or neutral mood induction procedures before a probabilistic selection task. This manipulation failed to induce changes in affect, suggesting that standard methods are inadequate. In Experiment 2 (N = 50 total), participants were randomly assigned to newly developed happy versus sad mood manipulations, which successfully induced large changes in affect. While the RewP was unaffected by mood induction, positive mood moderated the relationship between prediction error encoding in the RewP and reward learning, such that low positive mood and low prediction error encoding resulted in poorer reward learning. These findings provide a mechanistic example of how reduced positive affect moderates reward learning via poorer information encoding in the RewP.

A bias toward the unknown: individual and environmental factors influencing exploratory behavior

With limited resources, exploring new opportunities is crucial for survival. Exploring novel options, however, comes at the cost of uncertainty. Therefore, there is a trade‐off between exploiting options with a known beneficial outcome and exploring novel options with a potentially higher gain. Computational models have suggested that novelty may promote exploratory behavior by inducing a so‐called novelty bonus through reward‐related processes. So far, few studies have provided behavioral evidence for such a novelty bonus. In this study, we aimed to investigate whether spatial novelty can stimulate exploratory behavior (Experiment 1), and whether age, novelty‐seeking, and reduced action radius or social interactions due to COVID‐19 restrictions influenced the exploration–exploitation trade‐off (Experiment 2). In both experiments, we employed a novel paradigm in which participants made binary decisions between food items, while on rare trials, a surprise option was presented. Results from Experiment 1 are in line with a novelty bonus, with spatial novelty promoting exploratory behavior. In Experiment 2, we found that exploratory behavior declined with age, high novelty seekers made more exploratory choices than low novelty seekers, and participants with a smaller action radius made fewer exploratory choices. These findings are consistent with previous findings in animals and predictions from computational models.

Individual differences of conflict monitoring and feedback processing during reinforcement learning in a mock forensic context

This study investigated individual differences of conflict monitoring (N2 component), feedback processing (feedback negativity component), and reinforcement learning in a discrimination learning task using a mock (fictitious) forensic scenario to set participants in a semantic task context. We investigated individual differences of anxiety-related, impulsivity-related traits and reasoning ability during trial-and-error learning of mock suspect and nonsuspect faces. Thereby, we asked how the differential investment of cognitive-motivational processes facilitates learning in a mock forensic context. As learning can be studied by means of time-on-task effects (i.e., variations of cognitive processes across task blocks), we investigated the differential investment of cognitive-motivational processes block-wise in N = 100 participants. By performing structural equation modeling, we demonstrate that conflict monitoring decreased across task blocks, whereas the percentage of correct responses increased across task blocks. Individuals with higher reasoning scores and higher impulsivity-related traits relied rather on feedback processing (i.e., external indicators) during reinforcement learning. Individuals with higher anxiety-related traits intensified their conflict monitoring throughout the task to learn successfully. Observation by relevant others intensified conflict monitoring more than nonobservation. Our data highlight that individual differences and social context modulate the intensity of information processing in a discrimination learning task using a mock forensic task scenario. We discuss our data with regard to recent cognitive-motivational approaches and in terms of reinforcement learning.

Mood congruent tuning of reward expectation in positive mood: evidence from FRN and theta modulations

Positive mood broadens attention and builds additional mental resources. However, its effect on performance monitoring and reward prediction errors remain unclear. To examine this issue, we used a standard mood induction procedure (based on guided imagery) and asked 45 participants to complete a gambling task suited to study reward prediction errors by means of the feedback-related negativity (FRN) and mid-frontal theta band power. Results showed a larger FRN for negative feedback as well as a lack of reward expectation modulation for positive feedback at the theta level with positive mood, relative to a neutral mood condition. A control analysis showed that this latter result could not be explained by the mere superposition of the event-related brain potential component on the theta oscillations. Moreover, these neurophysiological effects were evidenced in the absence of impairments at the behavioral level or increase in autonomic arousal with positive mood, suggesting that this mood state reliably altered brain mechanisms of reward prediction errors during performance monitoring. We interpret these new results as reflecting a genuine mood congruency effect, whereby reward is anticipated as the default outcome with positive mood and therefore processed as unsurprising (even when it is unlikely), while negative feedback is perceived as unexpected.

Does emotion change auditory prediction and deviance detection?

In the last decades, a growing number of studies provided compelling evidence supporting the interplay of cognitive and affective processes. However, it remains to be clarified whether and how an emotional context affects the prediction and detection of change in unattended sensory events. In an event-related potential (ERP) study, we probed the modulatory role of pleasant, unpleasant and neutral visual contexts on the brain response to automatic detection of change in spectral (intensity) vs. temporal (duration) sound features. Twenty participants performed a passive auditory oddball task. Additionally, we tested the relationship between ERPs and self-reported mood. Participants reported more negative mood after the negative block. The P2 amplitude elicited by standards was increased in a positive context. Mismatch Negativity (MMN) amplitude was decreased in the negative relative to the neutral and positive contexts, and was associated with self-reported mood. These findings suggest that the detection of regularities in the auditory stream was facilitated in a positive context, whereas a negative visual context interfered with prediction error elicitation, through associated mood changes. Both ERP and behavioral effects highlight the intricate links between emotion, perception and cognitive processes.