Biology subjects

Hess, A. J.

Publications and source records attributed to Hess, A. J..

6 recordsLinked to original sources

bioRxiv2026

Modelling Metacognition: A Joint Prediction-Confidence Model for Predictive Inference Task Data

Metacognition is the ability to reflect on and evaluate our own cognitive processes. It is often altered in psychopathology. Yet, the computational mechanisms underlying these alterations remain unclear. In this work, we extend Hierarchical Gaussian Filter (HGF) models to jointly fit trial-by-trial predictions and confidence ratings in a predictive inference task, providing an individualised characterisation on metacognitive processing. Applying our cognitive computational model to a large subclinical open dataset (N=430), we are able to achieve, on average, excellent fit of prediction responses [Formula] and a moderate to good fit of confidence ratings [Formula]. Analysis of experimental change-points revealed that our model accurately captures confidence self-reports dynamics around these change-points. Posterior parameter estimates reveal a negative effect of sensory input prediction errors and a positive effect of sensory input prediction precision on confidence ratings, respectively. In addition, we replicate state-of-the-art findings related to compulsivity as measured by a transdiagnostic factor score, such as inflated confidence and a decoupling of action updates (here, prediction errors) and confidence in compulsivity. These results demonstrate the robustness of our methodology and the potential of joint prediction-confidence modelling to uncover latent metacognitive alterations in psychopathology.

neuroscience↗

bioRxiv2026

Perceived Control Over Breathing Perturbations Depends on Interosensory Input and Prediction Error

Interoception and homeostatic/allostatic control are not only fundamental for survival but play a key role for maintaining somatic and mental health. Furthermore, metacognitive evaluations of these processes, such as perceived failure of allostatic regulation, have been proposed to underlie the experience of chronic symptoms, such as chronic fatigue. A central question in this regard is what mechanisms the brain uses to evaluate control over bodily states. A prominent computational proposal posits that this is achieved by monitoring of interoceptive prediction errors (PEs). However, to date, this proposal has not been tested experimentally. Here, we tested this hypothesis by applying computational process models of perceived control to data from a novel behavioural paradigm, the Respiratory Metacognition of Control Task (RMCT). The RMCT manipulates control over breathing by changing inspiratory resistive loads as a function of control achieved in a gamified prediction task. We developed and compared trial-by-trial generative models of perceived explicit control in the RMCT, using data from 50 volunteers in a pre-registered analysis. Bayesian model selection suggested that perceived control over breathing is best explained as a function of both trial-wise interoceptive outcomes (breathing with or without an inspiratory resistive load) and trial-wise PEs about respiratory resistance. These results support a longstanding computational proposal of how the brain detects failures of bodily regulation and provide a mechanistic model for understanding inter-individual differences in perceived control over bodily states.

neuroscience↗

bioRxiv2024

Bayesian Workflow for Generative Modeling in Computational Psychiatry

Computational (generative) modelling of behaviour has considerable potential for clinical applications. In order to unlock the potential of generative models, reliable statistical inference is crucial. For this, Bayesian workflow has been suggested which, however, has rarely been applied in Translational Neuromodeling and Computational Psychiatry (TN/CP) so far. Here, we present a worked example of Bayesian workflow in the context of a typical application scenario for TN/CP. This application example uses Hierarchical Gaussian Filter (HGF) models, a family of computational models for hierarchical Bayesian belief updating. When equipped with a suitable response model, HGF models can be fit to behavioural data from cognitive tasks; these data frequently consist of binary responses and are typically univariate. This poses challenges for statistical inference due to the limited information contained in such data. We present a novel set of response models that allow for simultaneous inference from multivariate (here: two) behavioural data types. Using both simulations and empirical data from a speed-incentivised associative reward learning (SPIRL) task, we show that harnessing information from two different data streams (binary responses and continuous response times) improves the accuracy of inference (specifically, identifiability of parameters and models). Moreover, we find a linear relationship between log-transformed response times in the SPIRL task and participants uncertainty about the outcome. Our analysis illustrates the benefits of Bayesian workflow for a typical use case in TN/CP. We argue that adopting Bayesian workflow for generative modelling helps increase the transparency and robustness of results, which in turn is of fundamental importance for the long-term success of TN/CP.

neuroscience↗

bioRxiv2023

The type of DNA damage response after Decitabine treatment depends on the level of DNMT activity

Decitabine and Azacytidine are considered as epigenetic drugs that induce DNA- methyltransferase (DNMT)-DNA crosslinks, resulting in DNA-hypomethylation and -damage. Although they are applied against myeloid cancers, important aspects of their mode of action remain unknown, which highly limits their clinical potential. Using a combinatorial approach, we reveal that the efficacy profile of both compounds primarily depends on the level of induced DNA-damage. Under low DNMT-activity, only Decitabine has a substantial impact. Conversely, when DNMT-activity is high, toxicity and cellular response to both compounds are dramatically increased, but do not primarily depend on DNA-hypomethylation or RNA-associated processes, contradicting an RNA-dependent effect of Azacytidine. By applying spatial proteomics, we show that Decitabine induces a strictly DNMT-dependent multifaceted DNA- damage response based on chromatin-recruitment of various repair-associated proteins. The choice of DNA-repair pathway herby depends on the severity of Decitabine-induced DNA- lesions. While mismatch (MMR) and base-excision DNA repair (BER) as well as RAD50- dependent DNA double-strand break repair are always activated in response to Decitabine, Fanconi anemia-dependent DNA-repair combined with homologous recombination is only activated when DNMT-activity is moderate. In contrast, high DNMT-activity and therefore immense replication stress, induce DNA repair by non-homologous and alternative end-joining.

biochemistry↗

bioRxiv2023

Incorporating uncertainty within dynamic interoceptive learning

Interoception, the perception of the internal state of the body, has been shown to be closely linked to emotions and mental health. Of particular interest are interoceptive learning processes that capture associations between environmental cues and body signals as a basis for making homeostatically relevant predictions about the future. Here we extended an interoceptive Breathing Learning Task (BLT) to incorporate continuous measures of prediction certainty, and tested its application using a Rescorla Wagner (RW) associative learning model. Sixteen healthy participants completed the continuous version of the BLT, where they were asked to predict the likelihood of breathing resistances. The task was modified from a previous version and required continuous, rather than binary predictions, in order to include a more precise measure of prediction certainty. The RW model was used to fit a learning rate to each participants continuous and binarised predictions, and was additionally extended to test whether learning rates differed according to stimuli valence. The empirical task data demonstrated excellent replicability compared to previously collected data using binary predictions, and the continuous model fits closely captured participant behaviour at the group level. The model extension to estimate different learning rates for negative (i.e. breathing resistance) and positive (i.e. no breathing resistance) trials indicated that learning rates did not significantly differ according to stimuli nature. Furthermore, examining the relationship between estimates of prediction certainty and learning rates with interoceptive and mental health questionnaires demonstrated that fatigue severity was related to both prediction certainty and learning rate, and anxiety sensitivity was related to prediction certainty. The updated task and model show promise for future investigations into interoceptive learning and potential links to mental health.

neuroscience↗

bioRxiv2021

Interoception of breathing and its relationship with anxiety

Interoception, the perception of internal bodily states, is thought to be inextricably linked to affective qualities such as anxiety. While interoception spans sensory to metacognitive processing, it is not clear whether anxiety is differentially related to these processing levels. Here we investigated this question in the domain of breathing, using computational modelling and high-field (7 Tesla) fMRI to assess brain activity relating to dynamic changes in inspiratory resistance of varying predictability. Notably, the anterior insula was associated with both breathing-related prediction certainty and prediction errors, suggesting an important role in representing and updating models of the body. Individuals with low vs. moderate anxiety traits showed differential anterior insula activity for prediction certainty. Multimodal analyses of data from fMRI, computational assessments of breathing-related metacognition, and questionnaires demonstrated that anxiety-interoception links span all levels from perceptual sensitivity to metacognition, with strong effects seen at higher levels of interoceptive processes.

neuroscience↗