Biology subjects

Desender, K.

Publications and source records attributed to Desender, K..

17 recordsLinked to original sources

bioRxiv2026

Explaining temporally clustered errors with an autocorrelated Drift Diffusion Model

A widely used framework for studying the computational mechanisms of decision making is the Drift Diffusion Model (DDM). To account for the presence of both fast and slow errors in empirical data, the DDM incorporates across-trial variability in parameters such as the drift rate and the starting point. Although these variability parameters enable the model to reproduce both fast and slow errors, they rely on the assumption that over trials each parameter is independently sampled. As a result, the DDM effectively predicts that errors-- whether fast or slow--occur randomly over time. However, in empirical data this assumption is violated, as error responses are often temporally clustered. To address this limitation, we introduce the autocorrelated DDM, in which trial-to-trial fluctuations in drift rate, starting point, and boundary evolve according to first-order autoregressive (AR1) processes. Using simulations, we demonstrate that, unlike the across-trial variability DDM, the autocorrelated DDM naturally accounts for temporal clustering of errors. We further show that model parameters can be reliably recovered using Amortized Bayesian Inference, even with as few as 500 trials. Finally, fits to empirical data indicate that the autocorrelated DDM provides the best account of error clustering, highlighting that computational parameters fluctuate over time, despite typically being estimated as fixed across trials.

neuroscience↗

bioRxiv2025

Trial-by-trial fluctuations in decision criterion shape confidence

Many of the choices we make are accompanied by a sense of confidence. Within classical Signal Detection Theory (SDT), confidence is conceptualized as the absolute distance between a decision variable and a decision criterion. The decision criterion is traditionally modelled as being stable over an experimental session. However, recent work challenges the notion of a static decision criterion, suggesting instead that the criterion undergoes trial-by-trial fluctuations. Combining SDT theory and model simulations, we predict that fluctuations in the decision criterion shape confidence. In 15 human decision-making datasets, trial-by-trial estimates of decision criterion were obtained with the Hierarchical Model for Fluctuations in Criterion (hMFC). Across all datasets, we confirmed our pre-registered hypothesis that confidence is shaped by single-trial criterion state. This effect was found in 14 out of 15 individual datasets, indicating a robust pattern across a variety of task paradigms and confidence reporting scales. Going beyond self-report, the shaping of confidence by criterion fluctuations was replicated in an implicit measure of confidence, RTs, and in two key neurophysiological markers, pupil-linked arousal and a neural signature of confidence. Our results demonstrate that variability in confidence, which has traditionally been treated as noise, actually reflects genuine sensitivity to the current state of the (fluctuating) decision criterion.

neuroscience↗

bioRxiv2025

Neurally-informed modelling unravels a single evidence accumulation process for choices and subsequent confidence reports

How is the content of our decision making processes translated into subjective confidence? Current computational models designed to account for choice- and confidence-reporting behaviour disagree on two fundamental questions: do choice and confidence reports arise from shared versus distinct evaluation processes and what stopping-rule is applied to evidence sampling for confidence reports? Here, we employed a neurally-informed modelling approach in which competing models were compared both based on their fits to behaviour and their ability to recapitulate human electrophysiological signatures of evidence accumulation. Participants made self-paced confidence reports after indicating perceptual choices on a random dot motion task. Model comparison based on behavioural data alone showed that boundary-based stopping rules for confidence reporting provided superior fits than time-based stopping rules. However, results were inconclusive when comparing models with a single evidence accumulation process dictating choice and confidence reports compared to models invoking distinct processes. Importantly, the single-process model markedly outperformed the other models in its ability to account for the observed neural evidence accumulation patterns, including the fact that signal amplitudes predicted reported confidence several hundred milliseconds before the initial choice had been reported. Our study demonstrates that choice behaviour, subjective confidence, and associated neural decision signals can be jointly explained by a model invoking a single process of evidence accumulation with separate boundaries for the initial choice and subsequent confidence reports. Significance StatementHow does the brain generate representations of subjective confidence from categorical decisions? While some models suggest that confidence arises from a read-out of the same information that determined the initial choice, others propose that confidence is determined by a distinct computational process. Definitively testing these alternative accounts has proven difficult because they often make similar predictions for behavioural data. Here, we employ a novel neurally-informed modelling method that evaluates models based on their ability to reproduce key neurophysiological signatures of choice and confidence in addition to behaviour. Our results indicate that choices and subjective confidence are determined by a shared sensory evidence accumulation process.

neuroscience↗

bioRxiv2025

Common Neural Choice Signals reflect Accumulated Evidence, not Confidence

Centro-parietal EEG signals (CPP and Pe) correlate with the reported level of confidence. According to recent computational work these signals reflect evidence which feeds into the computation of confidence, not directly confidence. To test this prediction, we causally manipulated prior beliefs to selectively affect confidence, while leaving objective task performance unaffected. Behaviorally, we found that manipulating prior beliefs causally affected confidence without corresponding changes in accuracy and a negligible effect on reaction times. The EEG data showed a monotonic relation between the reported level of confidence and both CPP and Pe amplitudes. Importantly, this finding is compatible both with the theory that these signals track confidence as well as with the alternative theory that they track accumulated evidence. Critically, both neural signals were insensitive to the influence of prior beliefs on confidence, showing that they reflect the accumulated evidence that is used by the system to compute confidence, not directly confidence. Likewise, oscillatory activity in alpha and beta band was insensitive to the influence of prior beliefs on confidence. Decoding analyses revealed that the brain does hold shared representations for prior beliefs and confidence, and we identified a frontal signal that is sensitive to both confidence and prior beliefs.

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.

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bioRxiv2024

A Bayesian Hierarchical Model of Trial-to-Trial Fluctuations in Decision Criterion

Classical decision models assume that the parameters giving rise to choice behavior are stable, yet emerging research suggests these parameters may fluctuate over time. Such fluctuations, observed in neural activity and behavioral strategies, have significant implications for understanding decision-making processes. However, empirical studies on fluctuating human decision-making strategies have been limited due to the extensive data requirements for estimating these fluctuations. Here, we introduce hMFC (Hierarchical Model for Fluctuations in Criterion), a Bayesian framework designed to estimate slow fluctuations in the decision criterion from limited data. We first showcase the importance of considering fluctuations in decision criterion: incorrectly assuming a stable criterion gives rise to apparent history effects and underestimates perceptual sensitivity. We then present a hierarchical estimation procedure capable of reliably recovering the underlying state of the fluctuating decision criterion with as few as 500 trials per participant, offering a robust tool for researchers with typical human datasets. Critically, hMFC does not only accurately recover the state of the underlying decision criterion, it also effectively deals with the confounds caused by criterion fluctuations. Lastly, we provide code and a comprehensive demo at www.github.com/robinvloeberghs/hMFC to enable widespread application of hMFC in decision-making research.

animal behavior and cognition↗

bioRxiv2023

The Temporal Dynamics of Metacognitive Experiences Track Rational Adaptations in Task Performance

Human task performance elicits diverse subjective metacognitive experiences, such as boredom, effort, fatigue and frustration, which are thought to play important roles in the monitoring and regulation of cognitive processes. Yet, their specific contributions to task performance remain poorly understood. Therefore, we investigated the temporal dynamics underlying these metacognitive experiences and the latent cognitive processes supporting task performance. We used a time-on-task design using a conflict Flanker task, and analyzed the data using a comprehensive approach encompassing behavioral, model-based, subjective, and neural measures. Our results show that the temporal dynamics in cognitive processes can be understood as a rational attempt to optimize task performance and that distinct metacognitive experiences track different aspects of this rational endeavor. These findings suggest that metacognitive experiences act as tools for humans to gain insights into the optimality of their cognitive performance.

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

Modelling Speed-Accuracy Tradeoffs in the Stopping Rule for Confidence Judgments

Making a decision and reporting confidence in the accuracy of that decision are thought to be driven by the same mechanism: the accumulation of evidence. Previous research has shown that choices and reaction times are well accounted for by a computational model assuming noisy accumulation of evidence until crossing a decision boundary (e.g., the drift diffusion model). Decision confidence can be derived from the amount of evidence following post-decision evidence accumulation. Currently, the stopping rule for post-decision evidence accumulation is underspecified. In the current work, we quantitatively and qualitatively compare the ability of four prominent models of confidence couched within evidence accumulation to account for this stopping rule. In two experiments, participants were instructed to make fast or accurate decisions, and to give fast or carefully considered confidence judgments. We then compared the different models in their ability to capture the speed-accuracy effects on confidence. Both qualitatively and quantitatively, the data were best accounted for by our newly proposed Flexible Collapsing Boundaries model, in which post-decision accumulation terminates once it reaches one of two opposing slowly collapsing confidence boundaries. Inspection of the parameters of this model revealed that instructing participants to make fast versus accurate decisions influenced the height of the decision boundaries, while instructing participants to make fast versus careful confidence judgments influenced height of the confidence boundaries. Our data show that the stopping rule for confidence judgments can be well described as an accumulation-to-bound process, and that the height of these confidence boundaries are under strategic control.

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↗

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

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↗

bioRxiv2020

Dynamic expressions of confidence within an evidence accumulation framework

Human observers can reliably report their confidence in the choices they make. An influential framework conceptualizes decision confidence as the probability of a decision being correct, given the choice made and the evidence on which it was based. This framework accounts for three diagnostic signatures of human confidence reports, including an opposite dependence of confidence on evidence strength for correct and error trials. However, the framework does not account for the temporal evolution of these signatures, because it only describes the transformation of a static representation of evidence into choice and the associated confidence. Here, we combine this framework with another influential framework: dynamic accumulation of evidence over time, and build on the notion that confidence reflects the probability of being correct, given the choice and accumulated evidence up until that point. Critically, we show that such a dynamic model predicts that the diagnostic signatures of confidence depend on time; most critically, it predicts a stronger opposite dependence of confidence on evidence strength and choice correctness as a function of time. We tested, and confirmed, these predictions in human behaviour during random dot motion discrimination, in which confidence judgments were queried at different points in time. We conclude that human confidence reports reflect the dynamics of the probability of being correct given the accumulated evidence and choice.

neuroscience↗

bioRxiv2020

Temporal expectation hastens sensory encoding but does not affect evidence quality

The ability to predict the timing of forthcoming events, known as temporal expectation, has a strong impact on human information processing. Although there is growing consensus that temporal expectations enhance the speed and accuracy of perceptual decisions, it remains unclear whether they affect the decision process itself, or non-decisional (sensory / motor) processes. Here, healthy human participants (N = 21; 18 female) used predictive auditory cues to anticipate the timing of low-contrast visual stimuli they were required to detect. Modelling of the behavioral data using a prominent sequential sampling model indicated that temporal expectations speeded up non-decisional processes but had no effect on decision formation. Electrophysiological recordings confirmed and extended this result: temporal expectations hastened the onset of a neural signature of decision formation, but had no effect on its build-up rate. Anticipatory alpha-band power was modulated by temporal expectation, and co-varied with intrinsic trial-by-trial variability in behavioral and neural signatures of the onset latency of the decision process. These findings highlight how temporal predictions optimize our interaction with unfolding sensory events. SIGNIFICANCE STATEMENTTemporal expectation enhances performance, but the locus of this effect remains debated. Here, we contrasted the two dominant accounts: enhancement through (1) expedited decision onset, or (2) an increase in the quality of sensory evidence. We manipulated expectations about the onset of a dim visual target using a temporal cueing paradigm, and probed the locus of the expectation effect with two complementary approaches: drift diffusion modeling of behavior, and estimation of the onset and progression of the decision process from a supramodal accumulation-to-bound signal in simultaneously measured EEG signals. Behavioral modeling and neural data provided strong, converging evidence for an account in which temporal expectations enhance perception by speeding up decision onset, without affecting evidence quality.

neuroscience↗

bioRxiv2019

Metacognitive awareness of difficulty in action selection: the role of the cingulo-opercular network

The question whether and how we are able to monitor our own cognitive states (metacognition) has been a matter of debate for decades. Do we have direct access to our cognitive processes or can we only infer them indirectly based on their consequences? In the current study, we wanted to investigate the brain circuits that underlie the metacognitive experience of fluency in action selection. To manipulate action-selection fluency we used a subliminal response priming paradigm. On each trial, both male and female human participants additionally engaged in the metacognitive process of rating how hard they felt it was to respond to the target stimulus. Despite having no conscious awareness of the prime, results showed that participants rated incompatible trials (during which subliminal primes interfered with the required response) to be more difficult than compatible trials (where primes facilitated the required response) reflecting metacognitive awareness of difficulty. This increased sense of subjective difficulty was mirrored by increased activity in the rostral cingulate zone (RCZ) and the anterior insula, two regions that are functionally closely connected. Importantly, this reflected activations that were unique to subjective difficulty ratings and were not explained by reaction times or prime-response compatibility. We interpret these findings in light of a possible grounding of the metacognitive judgement of fluency in action selection in interoceptive signals resulting from increased effort.

neuroscience↗

bioRxiv2018

Post-decisional sense of confidence shapes speed-accuracy tradeoff for subsequent choices

When external feedback about decision outcomes is lacking, agents need to adapt their decision policies based on an internal estimate of the correctness of their choices (i.e., decision confidence). We hypothesized that agents use confidence to continuously update the tradeoff between the speed and accuracy of their decisions: When confidence is low in one decision, the agent needs more evidence before committing to a choice in the next decision, leading to slower but more accurate decisions. We tested this hypothesis by fitting a bounded accumulation decision model to behavioral data from three different perceptual choice tasks. Decision bounds indeed depended on the reported confidence on the previous trial, independent of objective accuracy. This increase in decision bound was predicted by a centro-parietal EEG component sensitive to confidence. We conclude that the brain uses internally computed confidence signals for the ongoing adjustment of decision policies.

neuroscience↗

bioRxiv2018

A post-decisional neural marker of confidence predicts information-seeking

Theoretical work predicts that decisions made with low confidence should lead to increased information-seeking. This is an adaptive strategy because it can increase the quality of a decision, and previous behavioral work has shown that decision-makers engage in such confidence-driven information seeking. The present study aimed to characterize the neural markers that mediate the relationship between confidence and information-seeking. A paradigm was used in which human participants made an initial perceptual decision, and then decided whether or not they wanted to sample more evidence before committing to a final decision and confidence judgment. Pre-decisional and post-decisional ERP components were similarly modulated by the level of confidence and by information-seeking choices. Time-resolved multivariate decoding of scalp EEG signals first revealed that information-seeking choices could be decoded from the time of the initial decision to the time of the subsequent information-seeking choice (within-condition decoding). No above-chance decoding was visible in the pre-response time window. Crucially, a classifier trained to decode high versus low confidence predicted information-seeking choices after the initial perceptual decision (across-condition decoding). This time window corresponds to that of a post-decisional neural marker of confidence. Collectively, our findings demonstrate for the first time that neural indices of confidence are functionally involved in information-seeking decisions.

neuroscience↗