Biology subjects

Verguts, T.

Publications and source records attributed to Verguts, T..

At least 19 recordsLinked to original sources

bioRxiv2025

Rhythmicity Revisited: Evidence from Robust Behavioral Oscillations

Rhythmic fluctuations in neuronal excitability, called neural oscillations, pervade brain activity. If these oscillations have a functional role in behavior, they should also be expressed in a behavioral signature. However, identifying such a behavioral signature has remained controversial: evidence for behavioral oscillations is not yet firmly established, and aperiodic components can be misidentified as rhythmic. To test their existence while controlling for aperiodic dynamics, we applied a state-of-the-art autoregressive (AR) approach on two datasets: one published dataset, and a new dataset collected with a revised dense-sampling design. In both datasets, AR modeling recovered reliable behavioral oscillations in the theta band. Reanalyses of the published dataset also reproduced the reported theta shift with task demands, with more difficult tasks showing slower theta. In contrast, despite clear rhythmicity, the new dataset showed no consistent frequency shift across conditions. Overall, this study establishes rigorous and robust detection of behavioral oscillations. It also suggests that the frequency modulation of behavioral oscillations by task difficulty requires further study.

neuroscience↗

bioRxiv2025

Orthogonal representational geometry in dACC underpins human hierarchical reasoning

Flexible adaptation requires inferring the causes of feedback and adjusting accordingly. When tasks are hierarchically structured with high-level rules governing low-level perceptual judgements, good performance requires hierarchical reasoning. Yet the computational and neural mechanisms underlying this process remain unclear. Here, building on Bayesian modelling and recurrent neural network simulation of hierarchical reasoning, we verified that such algorithm and representations are implemented in human dorsal anterior cingulate cortex (dACC). dACC encodes accumulated errors and perceptual difficulty to estimate high-level rule switch confidence. These two variables were represented along two separable dimensions, and could be approximated by Gaussian basis functions. Orthogonality between these two dimensions yielded an optimal two-dimensional representation for hierarchical reasoning. A closer-to-orthogonal representational geometry also predicted better performance. Together, these findings provide an integrated computational and neural representational basis for hierarchical reasoning.

neuroscience↗

bioRxiv2025

Medial Frontal Theta Reduction Impairs Rule Switching via Prediction Error

Cognitive flexibility, the ability to switch behavior in response to changing rules in an uncertain environment, is crucial for adaptive decision making. Prior research has hypothesized a key role of prediction error and theta oscillations in medial frontal cortex in this process. However, the causal link between such processes remains to be established. To address this, we combined neural stimulation, EEG, behavioral measurement, and computational modelling. Specifically, we applied high-definition transcranial direct current stimulation (HD-tDCS) to modulate theta oscillations as measured via EEG followed by a probabilistic reversal learning task. We find that anodal stimulation reduces theta power and rule prediction error, and it increases the number of trials needed to reliably switch between rules. These findings support the role of rule prediction error signaling as a key mechanism linking neural oscillations to behavioral adaptation and highlight the importance of theta power and rule prediction error for cognitive flexibility. Significance statementCognitive flexibility--the ability to adjust behavior when rules change--is critical for adaptive behavior in uncertain environments. Although prediction error signaling and theta oscillations in medial frontal cortex have been proposed as key mechanisms, their causal relationship remains unclear. Here, we combine high-definition transcranial direct current stimulation (HD-tDCS), EEG, behavioral assessment, and computational modeling to establish a mechanistic link. We show that anodal stimulation reduces frontal theta power and rule-level prediction errors, leading to less efficient rule switching. These findings provide causal evidence that supports behavioral flexibility, advancing our understanding of the neural computations underlying adaptive decision making.

neuroscience↗

bioRxiv2025

Two time scales of adaptation in human learning rates

Different situations may require radically different information updating speeds (i.e., learning rates). Some demand fast learning rates, while others benefit from using slower ones. To adjust learning rates, decision makers could rely on either global, meta-learned differences between environments, or faster but transient adaptations to locally experienced prediction errors. Here, we introduce a new paradigm that allows researchers to measure and empirically disentangle both forms of adaptations. Participants performed short blocks of trials of a continuous estimation task - fishing for crabs - on six different islands that required different optimal (initial) learning rates. Across two experiments, participants showed fast adaptations in learning rate within a block. Critically, participants also learned global environment-specific learning rates over the time course of the experiment, as evidenced by computational modelling and by the learning rates calculated on the very first trial when revisiting an environment (i.e., unconfounded by transient adaptations). Using representational similarity analyses of fMRI data, we found that differences in voxel pattern responses in the central orbitofrontal cortex correlated with differences in these global environment-specific learning rates. Our findings show that humans adapt learning rates at both slow and fast time scales, and that the central orbitofrontal cortex may support meta-learning by representing environment-specific task-relevant features such as learning rates.

neuroscience↗

bioRxiv2025

Neural and computational evidence for a predictive learning account of the testing effect

Testing enhances memory more than studying. Although numerous studies have demonstrated the robustness of this classic effect, its neural and computational origin remains debated. Predictive learning is a potential mechanism behind this phenomenon: Because predictions and prediction errors (mismatch between predictions and feedback) are more likely to be generated in testing (relative to in studying), testing can benefit more from predictive learning. We shed light on the testing effect from a multi-level analysis perspective via a combination of cognitive neuroscience experiments (fMRI) and computational modeling. Behaviorally and computationally, only a model incorporating predictive learning can account for the full breadth of behavioral patterns and the robust testing effect. At the neural level, testing and prediction error both activate the canonical reward-related brain areas in the ventral striatum, insula, and midbrain. Crucially, back sorting analysis revealed that activation in the ventral striatum, insula, and midbrain can enhance declarative memory. These results provide strong and converging evidence for a predictive learning account of the testing effect. Significance StatementAn exam is not a neutral measurement of memory. The testing effect entails that a test (e.g., an exam), is more effective than study for learning and memory. The same effect can be harnessed also before events of significance take place, rendering it an important aspect of an active learning strategy. Nevertheless, its origin remains unknown. We propose a novel predictive learning account, which posits that testing (vs studying) facilitates predictions about study material and promotes learning from prediction errors. Computationally, the testing effect was explained through a predictive-learning-based neural network. Neurally, testing and prediction error activate common neural areas, which in turn enhance declarative memory. This account may extend beyond testing to support active learning.

neuroscience↗

bioRxiv2024

Decision Confidence and Outcome Variability Optimally Regulate Separate Aspects of Hyperparameter Setting

Reinforcement learning models describe how agents learn about the world (value learning), and how they interact with their environment based on the learned information (decision policy). As in any optimization problem, it is important to set the process hyperparameters, a process which also is thought to be learned (meta-learning). Here, we test a key prediction of meta-learning frameworks, namely that there exist one or more meta-signals that govern hyperparameter setting. Specifically, we test whether decision confidence, in a context of varying outcome variability, informs hyperparameter setting. Participants performed a 2-armed bandit task with confidence ratings. Model comparison shows that confidence and outcome variability are differentially involved in hyperparameter setting. A high level of confidence in the previous choice decreased hyperparameter setting of decision noise on the current trial: when a trial was made with low confidence, the choice on the next trial tended to be more explorative (i.e. high decision noise). Outcome variability influenced another hyperparameter, the learning rate for positive prediction errors (thus affecting value learning). Both strategies are rational approaches that maximize earnings at different temporal loci: the modulation by confidence causes more frequent exploration early after a change point, the modulation by outcome variability is advantageous late after a change point. Finally, we show that (reported) confidence in value-based choices reflects the action value of the chosen option (irrespective of the unchosen value). In sum, decision confidence and outcome variability reflect distinct signals that optimally guide the setting of hyperparameters in decision policy and value learning, respectively.

neuroscience↗

bioRxiv2024

Patch-leaving decisions and pupil-linked arousal systems

Deciding when to abandon a depleting resource in favour of potentially richer alternatives is fundamental to adaptive behaviour. Such patch-leaving decisions require balancing the expected advantage of leaving against both the cost of moving and the reward foregone in the current environment. Previous research suggests that activity of noradrenergic (NE) neurons in the locus coeruleus (LC) underpins patch-leaving. In the current study, we used pupil dilation as a time-resolved readout of subcortical neuromodulation during patch-leaving. We hypothesised that leave decisions will be preceded by a transient pupil dilation. Participants harvested from exponentially depleting patches (blueberry bushes) with three initial reward values, in two environments, which differed in the variability of initial reward values. Behavioural results show that, as predicted by the mathematically optimal solution, participants adjusted their decisions based on the instantaneous reward rate, but also displayed a bias to overharvest (stay longer in compared to the optimum), which was more pronounced in the high variability environment. Pupil size was overall larger in the high variability environment, associated with increased uncertainty. Importantly, we observed an increased transient pupil dilation in response to reward outcomes immediately preceding leave compared to stay decisions, as well as an increase in pupil dilation (and RT) across successive stay trials, leading up to leave decisions, presumably indicating an increased LC-NE activity associated with abandoning current options to explore alternatives.

neuroscience↗

bioRxiv2023

A low-dimensional approximation of optimal confidence

Human decision making is accompanied by a sense of confidence. According to Bayesian decision theory, confidence reflects the learned probability of making a correct response, given available data (e.g., accumulated stimulus evidence and response time). Although optimal, independently learning these probabilities for all possible combinations of data is computationally intractable. Here, we describe a novel model of confidence implementing a low-dimensional approximation of this optimal yet intractable solution. Using a low number of free parameters, this model allows efficient estimation of confidence, while at the same time accounting for idiosyncrasies, different kinds of biases and deviation from the optimal probability correct. Our model dissociates confidence biases resulting from individuals estimate of the reliability of evidence (captured by parameter ), from confidence biases resulting from general stimulus-independent under- and overconfidence (captured by parameter {beta}). We provide empirical evidence that this model accurately fits both choice data (accuracy, response time) and trial-by-trial confidence ratings simultaneously. Finally, we test and empirically validate two novel predictions of the model, namely that 1) changes in confidence can be independent of performance and 2) selectively manipulating each parameter of our model leads to distinct patterns of confidence judgments. As the first tractable and flexible account of the computation of confidence, our model provides concrete tools to construct computationally more plausible models, and offers a clear framework to interpret and further resolve different forms of confidence biases. Significance statementMathematical and computational work has shown that in order to optimize decision making, humans and other adaptive agents must compute confidence in their perception and actions. Currently, it remains unknown how this confidence is computed. We demonstrate how humans can approximate confidence in a tractable manner. Our computational model makes novel predictions about when confidence will be biased (e.g., over- or underconfidence due to selective environmental feedback). We empirically tested these predictions in a novel experimental paradigm, by providing continuous model-based feedback. We observed that different feedback manipulations elicited distinct patterns of confidence judgments, in ways predicted by the model. Overall, we offer a framework to both interpret optimal confidence and resolve confidence biases that characterize several psychiatric disorders.

animal behavior and cognition↗

bioRxiv2023

Humans adaptively select different computational strategies in different learning environments.

The Rescorla-Wagner rule remains the most popular tool to describe human behavior in reinforcement learning tasks. Nevertheless, it cannot fit human learning in complex environments. Previous work proposed several hierarchical extensions of this learning rule. However, it remains unclear when a flat (non-hierarchical) versus a hierarchical strategy is adaptive, or when it is implemented by humans. To address this question, current work applies a nested modelling approach to evaluate multiple models in multiple reinforcement learning environments both computationally (which approach performs best) and empirically (which approach fits human data best). We consider ten empirical datasets (N = 407) divided over three reinforcement learning environments. Our results demonstrate that different environments are best solved with different learning strategies; and that humans adaptively select the learning strategy that allows best performance. Specifically, while flat learning fitted best in less complex stable learning environments, humans employed more hierarchically complex models in more complex environments.

neuroscience↗

bioRxiv2022

Modulating hierarchical learning by high-definition transcranial alternating current stimulation at theta frequency

Considerable evidence highlights the dorsolateral prefrontal cortex (DLPFC) as a key region for hierarchical (i.e., multi-level) learning. In a previous electroencephalography (EEG) study we found that the low-level prediction errors (PEs) were encoded by frontal theta oscillations (4-7Hz), centered on right DLPFC (rDLPFC). However, the causal relationship between frontal theta oscillations and hierarchical learning remains poorly understood. To investigate this question, in the current study, participants received theta (6Hz) and sham high-definition transcranial alternating current stimulation (HD-tACS) over the rDLPFC, while performing the probabilistic reversal learning task. Behaviorally, theta tACS induced a significant reduction in accuracy for the stable environment, but not for the volatile environment, relative to the sham condition. Computationally, we implemented a combination of a hierarchical Bayesian learning and a decision model. Theta tACS induced a significant increase in low-level (i.e., probability-level) learning rate and uncertainty of low-level estimation relative to sham condition. Instead, the temperature parameter of the decision model, which represents (inverse) decision noise, was not significantly altered due to theta stimulation. These results indicate that theta frequency may modulate the (low-level) learning rate. Furthermore, environmental features (e.g., its stability) may determine whether learning is optimized as a result.

neuroscience↗

bioRxiv2022

Manipulating prior beliefs causally induces under- and overconfidence

Making a decision is invariably accompanied by a sense of confidence in that decision. Across subjects and tasks, there is widespread variability in the exact level of confidence, even for tasks that do not differ in objective difficulty. Such expressions of under- and overconfidence are of vital importance, as they relate to fundamental life outcomes. Yet, a computational account specifying the mechanisms underlying under- and overconfidence is currently missing. Here, we propose that prior beliefs in the ability to perform a task, based on prior experience with this or a similar task, explain why confidence can differ dramatically across subjects and tasks, despite similar performance. In two perceptual decision-making experiments, we provide evidence for this hypothesis by showing that manipulating prior beliefs about task performance in a training phase causally influences reported levels of confidence in a test phase, while leaving objective performance in the test phase unaffected. This is true both when prior beliefs are induced via manipulated comparative feedback and via manipulating task difficulty during the training phase. We account for these results within an accumulation-to-bound model by explicitly modeling prior beliefs based on earlier exposure to the task. Decision confidence is then quantified as the probability of being correct conditional on these prior beliefs, leading to under- or overconfidence depending on the task context. Our results provide a fundamental mechanistic insight into the computations underlying under- and overconfidence in perceptual decision-making.

neuroscience↗

bioRxiv2021

Time-based binding as a solution to and a limitation for flexible cognition

Why cant we keep as many items as we want in working memory? It has long been debated whether this resource limitation is a bug (a downside of our fallible biological system) or instead a feature (an optimal response to a computational problem). We propose that the resource limitation is a consequence of a useful feature. Specifically, we propose that flexible cognition requires time-based binding, and time-based binding necessarily limits the number of (bound) memoranda that can be stored simultaneously. Time-based binding is most naturally instantiated via neural oscillations, for which there exists ample experimental evidence. We report simulations that illustrate this theory and that relate it to empirical data. We also compare the theory to several other (feature and bug) resource theories.

neuroscience↗

bioRxiv2021

Using top-down gating to optimally balance shared versus separated task representations

Human adaptive behavior requires continually learning and performing a wide variety of tasks, often with very little practice. To accomplish this, it is crucial to separate neural representations of different tasks in order to avoid interference. At the same time, sharing neural representations supports generalization and allows faster learning. Therefore, a crucial challenge is to find an optimal balance between shared versus separated representations. Typically, models of human cognition employ top- down modulatory signals to separate task representations, but there exist surprisingly little systematic computational investigations of how such modulation is best implemented. We identify and systematically evaluate two crucial features of modulatory signals. First, top-down input can be processed in an additive or multiplicative manner. Second, the modulatory signals can be adaptive (learned) or non-adaptive (random). We cross these two features, resulting in four modulation networks which are tested on a variety of input datasets and tasks with different degrees of stimulus-action mapping overlap. The multiplicative adaptive modulation network outperforms all other networks in terms of accuracy. Moreover, this network develops hidden units that optimally share representations between tasks. Specifically, different than the binary approach of currently popular latent state models, it exploits partial overlap between tasks.

neuroscience↗

bioRxiv2021

Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits

Adaptive sequential behavior is a hallmark of human cognition. In particular, humans can learn to produce precise spatiotemporal sequences given a certain context. For instance, musicians can not only reproduce learned action sequences in a context-dependent manner, they can also quickly and flexibly reapply them in any desired tempo or rhythm without overwriting previous learning. Existing neural network models fail to account for these properties. We argue that this limitation emerges from the fact that sequence information (i.e., the position of the action) and timing (i.e., the moment of response execution) are typically stored in the same neural network weights. Here, we augment a biologically plausible recurrent neural network of cortical dynamics to include a basal ganglia-thalamic module which uses reinforcement learning to dynamically modulate action. This "associative cluster-dependent chain" (ACDC) model modularly stores sequence and timing information in distinct loci of the network. This feature increases computational power and allows ACDC to display a wide range of temporal properties (e.g., multiple sequences, temporal shifting, rescaling, and compositionality), while still accounting for several behavioral and neurophysiological empirical observations. Finally, we apply this ACDC network to show how it can learn the famous "Thunderstruck" song intro and then flexibly play it in a "bossa nova" rhythm without further training.

neuroscience↗

bioRxiv2021

Electrophysiological signatures of hierarchical learning

Human perception and learning is thought to rely on a hierarchical generative model that is continuously updated via precision-weighted prediction errors (pwPEs). However, the neural basis of such cognitive process and how it unfolds during decision making, remain poorly understood. To investigate this question, we combined a hierarchical Bayesian model (i.e., Hierarchical Gaussian Filter, HGF) with electrophysiological (EEG) recording, while participants performed a probabilistic reversal learning task in alternatingly stable and volatile environments. Behaviorally, the HGF fitted significantly better than two control, non-hierarchical, models. Neurally, low-level and high-level pwPEs were independently encoded by the P300 component. Low-level pwPEs were reflected in the theta (4-8 Hz) frequency band, but high-level pwPEs were not. Furthermore, the expressions of high-level pwPEs were stronger for participants with better HGF fit. These results indicate that the brain employs hierarchical learning, and encodes both low- and high-level learning signals separately and adaptively.

neuroscience↗

bioRxiv2020

Dynamic influences on static measures of metacognition

Humans differ in their capability to judge the accuracy of their own choices via confidence judgments. Signal detection theory has been used to quantify the extent to which confidence tracks accuracy via M-ratio, often referred to as metacognitive efficiency. This measure, however, is static in that it does not consider the dynamics of decision making. This could be problematic because humans may shift their level of response caution to alter the tradeoff between speed and accuracy. Such shifts could induce unaccounted-for sources of variation in the assessment of metacognition. Instead, evidence accumulation frameworks consider decision making, including the computation of confidence, as a dynamic process unfolding over time. We draw on evidence accumulation frameworks to examine the influence of response caution on metacognition. Simulation results demonstrate that response caution has an influence on M-ratio. We then tested and confirmed that this was also the case in human participants who were explicitly instructed to either focus on speed or accuracy. We next demonstrated that this association between M-ratio and response caution was also present in an experiment without any reference towards speed. The latter finding was replicated in an independent dataset. In contrast, when data were analyzed with a novel dynamic measure of metacognition, which we refer to as v-ratio, in all of the three studies there was no effect of speed-accuracy tradeoff. These findings have important implications for research on metacognition, such as its measurement, domain-generality, individual differences, and neural correlates.

neuroscience↗

bioRxiv2020

Neural representations of task context and temporal order during action sequence execution

Since routine action sequences can share a great deal of similarity in terms of their stimulus response mappings, their correct execution relies crucially on the ability to preserve contextual and temporal information (Lashley, 1951). However, there are few empirical studies on the neural mechanism and the brain areas maintaining such information. To address this gap in the literature, we recently recorded the blood-oxygen level dependent (BOLD) response in a newly developed coffee-tea making task (Holroyd et al., 2018). The task involves the execution of 4 action sequences that each feature 6 decision states. Here we report a reanalysis of this dataset using a data-driven approach, namely multivariate pattern analysis (MVPA), that examines context-dependent neural activity across several predefined regions of interest. Results highlight involvement of the inferior-temporal gyrus and lateral prefrontal cortex in maintaining temporal and contextual information for the execution of hierarchically-organized action sequences. Furthermore, temporal information seems to be more strongly encoded in areas over the left hemisphere.

neuroscience↗

bioRxiv2020

Theta oscillations shift towards optimal frequency for cognitive control

Cognitive control is supported by theta band (4-7Hz) neural oscillations coordinating neural populations for task implementation. Task performance has been shown to depend on theta amplitude but a second critical aspect of theta oscillations, its peak frequency, has mostly been overlooked. Using modelling, behavioral and electrophysiological recordings, we show that theta oscillations adapt to task demands by shifting towards the optimal frequency.

neuroscience↗