Bayesian Causal Inference (BCI) models multisensory perception as inference about two causal structures: either the sensory data stems from several separate causes or from a common cause. If the sensory evidence supports a common cause, the multimodal sensory input is integrated into a single percept. BCI has been applied to the rubber hand illusion, in which the subject integrates tactile stimulation with seen brush strokes on a rubber hand and experiences said hand as their own. A model of body ownership is relevant for virtual reality design, especially for strengthening avatar embodiment. In previous work, we have criticized the applied BCI model for its extraordinarily wide priors. Here we investigate whether the priors can be narrowed by increasing the sensory evidence. We try to accomplish this by extending the model across time. Preliminary results look encouraging, but further research, especially psychophysical experiments to inform the parameter choices, is needed.

Expectations play a critical role in human perception, cognition, and decision-making. There has been a recent surge in modelling such expectations and the resulting behaviour when they are violated. One prominent recent proposal is the ViolEx model. To move the model & the literature forward, we identified three areas of concern and addressed two in this study - Lack of formalization and implementation. Specifically, we aim to provide the first implementation of ViolEx using the Active Inference formalism by leveraging the rich overlap between them. As a result, we successfully simulated all expectation violation coping strategies of ViolEx and analyzed their potential mediators. Through this analysis, we identified novel predictors of immunization & assimilation and documented the computational mechanism of their mediation. We see this as the first step in developing a formal research framework to study expectation violations and hope to serve as a base for future ViolEx studies.

Active Inference and its accompanying Bayesian Mechanics (BM) are important psychological and cognitive science theories. While there is a strong interaction between the theories and the philosophical realm, it needs to be clarified what its metaphysical commitments are. We tease out these commitments while looking from the perspective of the psychology of goals. We find that Active Inference cannot account for the dynamic growth of goals, primarily because of its closed generative model. We trace the reason for this through the extrinsic `ontological constraint' of BM, characteristic of all mechanistic models which follow the `logic of machines.' Finally, we ground our arguments in the necessity of external relations in substance metaphysics and its incompatibility with internal relations and impredicativity. Thus we argue that Active Inference implicitly presupposes a Substance metaphysics, yielding the theory no resource to model novelty, growth, and development observed in human psychology. We briefly sketch a powerful alternative grounded in process metaphysics to model biological and cognitive systems.

Statistical descriptions of reaction times are central components of quantitative attention models. It is often assumed that total reaction time is comprised of various components, e.g. sensory delays, decision making and motor execution contributions. We use machine learning to decompose observed total reaction times into sensorimotor and decision components, and evaluate which model assumptions maximize approximate Bayesian model evidence (free energy or evidence lower bound). We find that an inverse Gaussian decision time distribution combined with a very narrow Gaussian sensorimotor distribution can best explain human reaction time data. We also model outliers explicitly by a uniform background distribution. We find that the model assigns a small fraction of datapoints to this outlier distribution.

Computational cognitive theories of Autism Spectrum Disorder have received renewed attention in recent years. Consistent with the predictive processing framework, ASD has been re-conceptualized as a disorder of aberrant prediction and learning-rate estimation involving multiple levels of a putative cognitive computational hierarchy. Specifically, behavioral symptoms of individuals with ASD might manifest due to an aberrant overestimation of the volatility of environmental contingencies (i.e. tendency of change in cue-outcome probabilities) which in turn might induce a dysfunctional setting of learning rates. In this work, we attempted to conceptually replicate computational modeling analyses of an impactful study of the recent ASD modeling literature in an independent sample of subjects. We were not able to replicate some prior reported effects likely due to differences in model architecture and cognitive task setup. We found statistical trends in similar directions.

Attention can be biased by the previous learning and experience. We present an algorithmic-level model of this bias in visual attention that predicts quantitatively how bottom-up, top-down and selection history compete to control attention. In the model, the output of saliency maps as bottom-up guidance interacts with a history map that encodes learning effects and a top-down task control to prioritize visual features. We test the model on a reaction-time (RT) data set from the experiment presented in (Feldmann-Wustefeld, Uengoer, & Schubö, 2015). The model accurately predicts parameters of reaction time distributions from an integrated priority map that is comprised of an optimal, weighted combination of separate maps. Analysis of the weights confirms learning history effects on attention guidance.

Navigation is a fundamental cognitive function of virtually all moving animals. Several navigation strategies require an estimate of the current travelling direction that is updated continuously. In the central complex of the insect brain, multimodal cues are fused into a compass-like head direction representation. Based on the proposed connectivity of columnar neurons in the central complex of the desert locust we designed a computational model to examine how these neurons could maintain a stable representation of heading direction and how shifts occur by optic flow signals when the animal turns. Our proposed model architecture shows that the activity of head direction-encoding CL1a neurons remains stable if the activity of a second class of columnar neurons, CL2, is exactly the same. Shifts occur via modulation of the network connectivity. Our model can be used to deduce testable hypotheses where data are lacking, inspiring new avenues of experimental investigations.

Although expectancy effects induced by placebo treatment are reported to attenuate depressive symptoms in the long run, mechanisms underlying situational dynamics are not well understood. Improved reward learning has been discussed as a candidate mediator for effects of positive expectancies on more positive mood. Here, we fitted a series of Q-learning models to measure the effect of sham antidepressant treatment vs. open-label placebo in a probabilistic reinforcement learning task. Treatment effects were observed mainly in those Q-learning models justified by the task structure. Additionally, interindividual variability remained the largest origin of unexplained variance in predictive match across models. These findings provide further support for the role of expectancies in reward learning. They also highlight the need for unraveling individual differences in cognitive mechanisms that account for differences in reward learning, and obtaining reliable estimates for them.

This review explores the complex relationship between Fear of Falling (FoF) and Fear of Heights (FoH), and their impact on human postural control. FoF encompasses a spectrum of psychological and physiological responses that dynamically influence postural control, while FoH involves perceptual distortions and heightened physiological arousal in response to elevated environments. Through a comprehensive literature review, we examine the research methods and findings of studies on FoF and FoH. We further propose that Optimal Feedback Control (OFC) theory is a suitable framework to understand the computational aspects of how these fears modify postural control. We aim to provide a nuanced understanding of FoF and FoH, not only as psychological phenomena but as complex, dynamic interactions of cognitive, physiological, and motor processes influencing an individual's interaction with their environment.