Biology subjects

Stuphorn, V.

Publications and source records attributed to Stuphorn, V..

7 recordsLinked to original sources

bioRxiv2026

Neural Dynamics of Self-Control: Pre-Choice EEG Signals Predict Yielding to Temptation

Self-control is the capacity to resist self-defeating behavior in favor of long-term goals. Such behavioral control is fundamental to adaptive human behavior, yet its neural mechanism remains unclear. A key limitation of prior research is that tasks often conflate self-control with cost-benefit evaluation, making it difficult to isolate self-control encoding. We introduced a novel dynamometer-based task where participants chose between high-effort, larger-later (L) and low-effort, smaller-sooner (S) rewards. In some trials, referred to as temptation trials, participants could maintain their initial commitment (LL/SS) or switch (LS/SL). Twenty-six participants performed the task during a 32-channel EEG recording. Behavioral results showed a significant shift from L to S options in temptation trials, suggesting frequent yielding to temptation. EEG activity covaried with switching behavior. The late positive potential (LPP) differentiated stay (LL) from switch (LS) trials before initial choice, localizing to the posterior cingulate visual area and left somatosensory regions. Enhanced LPP in LS trials is thought to reflect heightened motivational conflict and attentional engagement with alternatives. Time-resolved multivariate decoding reliably discriminated LL from LS outcomes, peaking 304-348ms post-option presentation. This was driven by distributed frontal and centroparietal networks, demonstrating that a neural bias toward yielding is established well before the initial choice at the network level. The EEG activity preceding switching predicted the final choice, reflecting reward anticipation rather than self-control signals. Together, these findings suggest that proactive neural states during deliberation, established before the choice, shape self-control success and failure.

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bioRxiv2025

The representation of mood in primate anterior insular cortex

Understanding how the brain reflects and shapes mood requires resolving the disconnect between behavioral measures of mood that can only be made in humans (typically based on subjective reports of happiness) and detailed measures of brain activity only available in animals. To achieve this, we developed a mood model to predict behavioral fluctuations in human subjective happiness as individuals experienced wins and losses during a gambling task. Next, we investigated how this operationalization of mood was reflected behaviorally and in the brains of two monkeys engaged in the same gambling task. We found a remarkable alignment between human mood model signatures, the impact of estimated mood on monkey choice, and the persistent responses of units in monkey anterior insular cortex -- including a matched timescale of integration across events. In comparison, the same signatures were only weakly reflected in lateral prefrontal cortex, suggesting that insular mood representations do not trivially follow from a signal broadcast to all higher brain areas. These results are consistent with a model in which the brain transforms experiences into mood by integrating events through a recurrently connected network of excitatory and inhibitory pools of neurons. These are among the first detailed insights into the nature of putative mood representations in the primate brain.

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bioRxiv2024

Neural basis of self-control

Self-control is the capacity to inhibit self-defeating behavior in the face of temptation. The neural basis of self-control remains elusive, due to the difficulty of disentangling self-control from value-based economic choice. To overcome this problem, we designed a novel task for monkeys that allows to identify states of high and low self-control. We found that neurons in the Supplementary Eye Field encode self-control and predict whether and when monkeys give in to temptation. This neuronal activity was present early in the trial, even preceding presentation of choice options. Our findings suggest that Supplementary Eye Field is part of a neuronal circuit exerting proactive self-control, which is crucial for selecting and maintaining the pursuit of costly goals that are beneficial in the long run. One-Sentence SummarySupplementary eye field encodes proactive self-control signals independent from cost-benefit estimation.

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bioRxiv2023

The Role of Attention in Multi Attribute Decision Making

Real-life decisions typically involve multiple options, each with multiple attributes affecting value. In such complex cases, sequential shifts of attention to specific options and attributes are thought to guide the decision process. We designed a task that allowed us to monitor attention in monkeys engaged in such multi-attribute decisions. We recorded pre-supplementary motor area neurons encoding action value signals reflecting the decision process. Attention guides this process through two mechanisms. First, attention enhances the activity of neurons representing the currently sampled option, independent of the attended option value. Second, attention up-regulates the gain of information integration towards the evolving value estimate for the attended option. In contrast, we found no evidence for a third suggested mechanism, in which only the attended option is represented. Instead, attention influences the ongoing information accumulation and competition between the options by modulating the strength of the value information that drives this circuit.

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bioRxiv2020

Overt visual attention and value computation in complex risky choice

Traditional models of decision making under uncertainty explain human behavior in simple situations with a minimal set of alternatives and attributes. Some of them, such as prospect theory, have been proven successful and robust in such simple situations. Yet, less is known about the preference formation during decision making in more complex cases. Furthermore, it is generally accepted that attention plays a role in the decision process but most theories make simplifying assumptions about where attention is deployed. In this study, we replace these assumptions by measuring where humans deploy overt attention, i.e. where they fixate. To assess the influence of task complexity, participants perform two tasks. The simpler of the two requires participants to choose between two alternatives with two attributes each (four items to consider). The more complex one requires a choice between four alternatives with four attributes each (16 items to consider). We then compare a large set of model classes, of different levels of complexity, by considering the dynamic interactions between uncertainty, attention and pairwise comparisons between attribute values. The task of all models is to predict what choices humans make, using the sequence of observed eye movements for each participant as input to the model. We find that two models outperform all others. The first is the two-layer leaky accumulator which predicts human choices on the simpler task better than any other model. We call the second model, which is introduced in this study, TNPRO. It is modified from a previous model from management science and designed to deal with highly complex decision problems. Our results show that this model performs well in the simpler of our two tasks (second best, after the accumulator model) and best for the complex task. Our results suggest that, when faced with complex choice problems, people prefer to accumulate preference based on attention-guided pairwise comparisons.

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bioRxiv2020

Primate anterior insular cortex represents economic decision variables postulated by Prospect theory

In humans, risk attitude is highly context-dependent, varying with wealth levels or for different potential outcomes, such as gains or losses. These behavioral effects are well described by Prospect Theory, with the key assumption that humans represent the value of each available option asymmetrically as gain or loss relative to a reference point. However, it remains unknown how these computations are implemented at the neuronal level. Using a new token gambling task, we found that macaques, like humans, change their risk attitude across wealth levels and gain/loss contexts. Neurons in their anterior insular cortex (AIC) encode the reference point (i.e. the current wealth level of the monkey) and the asymmetric value function (i.e. option value signals are more sensitive to change in the loss than in the gain context) as postulated by Prospect Theory. In addition, changes in the activity of a subgroup of AIC neurons are correlated with the inter-trial fluctuations in choice and risk attitude. Taken together, we find that the role of primate AIC in risky decision-making is to monitor contextual information used to guide the animals willingness to accept risk.

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bioRxiv2018

Inactivation of medial frontal cortex changes risk preference

Humans and other animals need to make decisions under varying degrees of uncertainty. These decisions are strongly influenced by an individuals risk preference, however the neuronal circuitry by which risk preference shapes choice is still unclear [1]. Supplementary eye field (SEF), an oculomotor area within primate medial frontal cortex, is thought to be an essential part of the neuronal circuit underlying oculomotor decision-making, including decisions under risk [2-5]. Consistent with this view, risk-related action value and monitoring signals have been observed in SEF [6-8]. However, such activity has also been observed in other frontal areas, including orbitofrontal [9-11], cingulate [12-14], and dorsal lateral frontal cortex [15]. It is thus unknown whether the activity in SEF causally contributes to risky decisions, or if it is merely a reflection of neural processes in other cortical regions. Here, we tested a causal role of SEF in risky oculomotor choices. We found that SEF inactivation strongly reduced the frequency of risky choices. This reduction was largely due to a reduced attraction to reward uncertainty and high reward gain, but not due to changes in the subjective estimation of reward probability or average expected reward. Moreover, SEF inactivation also led to increased sensitivity to differences between expected and actual reward during free choice. Nevertheless, it did not affect adjustments of decisions based on reward history.

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