Biology subjects

Grohn, J.

Publications and source records attributed to Grohn, J..

5 recordsLinked to original sources

bioRxiv2026

Context-dependent effects of ketamine on emotional evidence integration

Understanding others' emotions requires continuously integrating noisy affective information as emotional states change over time. Research on emotion processing, however, has thus far focused on static environments or discrete events. Here, we developed a continuous emotional evidence integration task in which participants had to track the average emotional state in a rapidly changing sequence of faces under different levels of environmental volatility. This allowed us to examine emotion integration in changing environments. In online (n=92) and in-person (n=29) cohorts, participants integrated emotional information over longer timescales in stable environments while relying more strongly on recent evidence under volatility, consistent with principles established in perceptual decision-making. We further examined whether this process was modulated by ketamine, an NMDA receptor antagonist associated with evidence integration in the perceptual domain. We conducted a double-blind, placebo-controlled, within-subject study and probed emotion integration during the post-acute period, approximately 1.5 hours after the infusion. Ketamine modulated the temporal weighting of emotional evidence, shifting the balance between recent and past information depending on environmental volatility. Together, these findings show that emotional evidence integration dynamically adapts to the statistical structure of the environment and that ketamine can further modulate this process, which may have relevance for affective disorders.

neuroscience↗

bioRxiv2025

Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering

It has been suggested that humans and other animals are driven by a fundamental desire to acquire information about opportunities available in their environments. Not only might such a desire explain pathological behaviors, but it may be needed to account for how everyday decisions are resolved. Here, we combine artificial neural networks (ANNs) with symbolic regression to extract an expressive yet interpretable model that specifies how human participants evaluate decision-relevant information during choice. This model accounts for behavior in our own data and in previous work, outperforming existing accounts of information sampling such as the Upper Confidence Bound heuristic. This modelling approach has broad potential for uncovering novel patterns in behavior and cognitive processes, while also specifying them in human-interpretable formats. We then used the value of information derived by our model, together with ultra-high field neuroimaging, to examine activity across a suite of subcortical neuromodulatory nuclei and two cortical regions that influence these nuclei. This established roles for midbrain dopaminergic nuclei, anterior cingulate cortex, and anterior insula in mediating the influence of value of information on behavior.

neuroscience↗

bioRxiv2024

General mechanisms of task engagement in the primate frontal cortex

Staying engaged with a task is necessary to maintain goal-directed behaviors. Although engagement varies with the specific task at hand it also exhibits continuous, intrinsic fluctuations widely. This intrinsic component of engagement is difficult to isolate behaviorally or neurally in controlled experiments with humans. By contrast, animals spontaneously move between periods of complete task engagement and disengagement, even in experimental settings. We, therefore, looked at behavior in macaques in a series of four tasks while recording fMRI signals. We identified consistent autocorrelation in task disengagement. This made it possible to build models capturing task-independent engagement and to link it to neural activity. Across all tasks, we identified common patterns of neural activity linked to impending task disengagement in mid-cingulate gyrus. By contrast, activity centered in perigenual anterior cingulate cortex (pgACC) was associated with maintenance of task performance. Importantly, we were able to carefully control for task-specific factors such as the reward history, choice value, and other motivational effects, such as response vigor, as indexed by response time, when identifying neural activity associated with task engagement. Moreover, we showed pgACC activity had a causal link to task engagement; in one of our tasks, transcranial ultrasound stimulation of pgACC, but not of control regions, changed task engagement/disengagement patterns.

neuroscience↗

bioRxiv2022

Strategic exploration in the macaque prefrontal cortex

Humans have been shown to strategically explore. They can identify situations in which gathering information about distant and uncertain options is beneficial for the future. Because primates rely on scare resources when they forage, they are also thought to strategically explore, but whether they use the same strategies as humans and the neural bases of strategic exploration in monkeys are largely unknown. We designed a sequential choice task to investigate whether monkeys mobilize strategic exploration based on whether that information can improve subsequent choice, but also to ask the novel question about whether monkeys adjust their exploratory choices based on the contingency between choice and information, by sometimes providing the counterfactual feedback, about the option not chosen. We show that monkeys decreased their reliance on expected value when exploration could be beneficial, but this was not mediated by changes in the effect of uncertainty on choices. We found strategic exploratory signals in anterior and mid-cingulate cortex (ACC/MCC) and dorsolateral prefrontal cortex (dlPFC). This network was most active when a low value option was chosen which suggests a role in counteracting expected value signals, when exploration away from value should to be considered. Such strategic exploration was abolished when the counterfactual feedback was available. Learning from counterfactual outcome was associated with the recruitment of a different circuit centered on the medial orbitofrontal cortex (OFC), where we showed that monkeys represent chosen and unchosen reward prediction errors. Overall, our study shows how ACC/MCC-dlPFC and OFC circuits together could support exploitation of available information to the fullest and drive behavior towards finding more information through exploration when it is beneficial.

neuroscience↗

bioRxiv2020

Reward prediction errors induce risk-seeking

Reward prediction errors (RPEs) and risk preferences have two things in common: both can shape decision making behavior, and both are commonly associated with dopamine. RPEs drive value learning and are thought to be represented in the phasic release of striatal dopamine. Risk preferences bias choices towards or away from uncertainty; they can be manipulated with drugs that target the dopaminergic system. The common neural substrate suggests that RPEs and risk preferences might be linked on the level of behavior as well, but this has never been tested. Here, we aim to close this gap. First, we apply a recent theory of learning in the basal ganglia to predict how exactly RPEs might influence risk preferences. We then test our behavioral predictions using a novel bandit task in which value and risk vary independently across options. Critically, conditions are included where options vary in risk but are matched for value. We find that subjects become more risk seeking if choices are preceded by positive RPEs, and more risk averse if choices are preceded by negative RPEs. These findings cannot be explained by other known effects, such as nonlinear utility curves or dynamic learning rates. Finally, we show that RPE-induced risk-seeking is indexed by pupil dilation: participants with stronger pupillary correlates of RPE also show more pronounced behavioral effects. Authors summaryMany of our decisions are based on expectations. Sometimes, however, surprises happen: outcomes are not as expected. Such discrepancies between expectations and actual outcomes are called prediction errors. Our brain recognises and uses such prediction errors to modify our expectations and make them more realistic--a process known as reinforcement learning. In particular, neurons that release the neurotransmitter dopamine show activity patterns that strongly resemble prediction errors. Interestingly, the same neurotransmitter is also known to regulate risk preferences: dopamine levels control our willingness to take risks. We theorised that, since learning signals cause dopamine release, they might change risk preferences as well. In this study, we test this hypothesis. We find that participants are more likely to make a risky choice just after they experienced an outcome that was better than expected, which is precisely what out theory predicts. This suggests that dopamine signalling can be ambiguous--a learning signal can be mistaken for an impulse to take a risk.

neuroscience↗