Bayesian Brain Theories Research Articles

Animal affect, welfare and the Bayesian brain.

According to the Bayesian brain hypothesis, the brain can be viewed as a predictive machine, such that predictions (or expectations) affect how sensory inputs are integrated. This means that in many cases, affective responses may depend more on the subject's perception of the experience (driven by expectations built on past experiences) rather than on the situation itself. Little research to date has applied this concept to affective states in animals. The aim of this paper is to explore how the Bayesian brain hypothesis can be used to understand the affective experiences of animals and to develop a basis for novel predictions regarding animal welfare. Drawing from the literature illustrating how predictive processes are important to human well-being, and are often impaired in affective disorders, we explore whether the Bayesian brain theories may help understanding animals' affective responses and whether deficits in predictive processes may lead to previously unconsidered welfare consequences. We conclude that considering animals as predictive entities can improve our understanding of their affective responses, with implications for basic research and for how to provide animals a better life.

Microgravity induces overconfidence in perceptual decision-making

Does gravity affect decision-making? This question comes into sharp focus as plans for interplanetary human space missions solidify. In the framework of Bayesian brain theories, gravity encapsulates a strong prior, anchoring agents to a reference frame via the vestibular system, informing their decisions and possibly their integration of uncertainty. What happens when such a strong prior is altered? We address this question using a self-motion estimation task in a space analog environment under conditions of altered gravity. Two participants were cast as remote drone operators orbiting Mars in a virtual reality environment on board a parabolic flight, where both hyper- and microgravity conditions were induced. From a first-person perspective, participants viewed a drone exiting a cave and had to first predict a collision and then provide a confidence estimate of their response. We evoked uncertainty in the task by manipulating the motion’s trajectory angle. Post-decision subjective confidence reports were negatively predicted by stimulus uncertainty, as expected. Uncertainty alone did not impact overt behavioral responses (performance, choice) differentially across gravity conditions. However microgravity predicted higher subjective confidence, especially in interaction with stimulus uncertainty. These results suggest that variables relating to uncertainty affect decision-making distinctly in microgravity, highlighting the possible need for automatized, compensatory mechanisms when considering human factors in space research.

Pertinence of Predictive Models as Regards the Behavior of Observed Biological and Artificial Phenomena

In this assessment, we have made an effort of synthesis on the role of theoretical and observational investigations in the analysis of the concepts and functioning of different natural biological and artificial phenomena. In this context, we pursued the objective of examining published works relating to the behavioral prediction of phenomena associated with its observation. We have examined examples from the literature concerning phenomena with known behaviors that associated to knowledge uncertainty as well as cases concerning phenomena with unknown and changing random behaviors linked to random uncertainty. The concerned cases are relative to brain functioning in neuroscience, modern smart industrial devices, and health care predictive endemic protocols. As predictive modeling is very concerned by the problematics relative to uncertainties that depend on the degree of matching in the link prediction-observation, we investigated first how to improve the model to match better the observation. Thus, we considered the case when the observed behavior and its model are contrasting, that implies the development of revised or amended models. Then we studied the case concerning the practice of modeling for the prediction of future behaviors of a phenomenon that is well known, and owning identified behavior. For such case, we illustrated the situation of prediction matched to observation operated in two cases. These are the Bayesian Brain theory in neuroscience and the Digital Twins industrial concept. The last investigated circumstance concerns the use of modeling for the prediction of future behaviors of a phenomenon that is not well known, or owning behavior varying arbitrary. For this situation, we studied contagion infections with an unknown mutant virus where the prediction task is very complicated and would be constrained only to adjust the principal clinical observation protocol. Keywords: prediction, observation, Bayesian, neuroscience, brain functioning, mutant virus

Mental States: A Key Point in Scam Compliance and Warning Compliance in Real Life.

The internet’s convenience and anonymity have facilitated different types of covert fraud, resulting in economic, mental, and social harm to victims. Understanding why people are deceived and implementing appropriate interventions is critical for fraud reduction. Based on the Bayesian brain theory, individuals’ mental states may be a key point in scam compliance and warning compliance. Fraud victims with different mental states may construct various hypotheses and explanations about the fraud they are exposed to, causing different cognition and behavior patterns. Therefore, we first conducted a semi-structured in-depth interview with online fraud victims to investigate the individual and social factors that affect victims’ mental states. Grounded theory analysis showed five core factors influencing scam compliance: psychological traits, empirical factors, motivation, cognitive biases, and emotional imbalance. Based on our findings of psychological processes and deception’s influential factors, we then designed warnings to inform victims of fraud, particularly for those involving novel types of scams. Tested on a real-life setting, our designed warnings effectively enhanced warning compliance, allowing more fraud victims to avoid financial losses.

L’esprit predictif : introduction à la théorie du cerveau bayésien

The question of how the mind works is at the heart of cognitive science. It aims to understand and explain the complex processes underlying perception, decision-making and learning, three fundamental areas of cognition. Bayesian Brain Theory, a computational approach derived from the principles of Predictive Processing (PP), offers a mechanistic and mathematical formulation of these cognitive processes. This theory assumes that the brain encodes beliefs (probabilistic states) to generate predictions about sensory input, then uses prediction errors to update its beliefs. In this paper, we present an introduction to the fundamentals of Bayesian Brain Theory. We show how this innovative theory hybridizes concepts inherited from the philosophy of mind and experimental data from neuroscience, and how it translates complex cognitive processes such as perception, action, emotion, or belief, or even the psychiatric symptomatology.

Blind image quality prediction with hierarchical feature aggregation

Blind image quality assessment (BIQA) aims to evaluate the perceptual quality of image with no pristine image for comparison, which attracts extensive attention and is of wide applications. Research on human visual system (HVS) indicates visual perception is classically modeled as a hierarchical process. Meanwhile, empirical evidence shows that different levels of distortion generate individual degradation on hierarchical features. Although previous BIQA methods exploited different levels of feature, the degradations and correlations on hierarchical features are not detailedly analyzed. In this paper, hierarchical degradation is systematically analyzed by quantitative and qualitative experiments using interpretable hierarchical features. Additionally, as there exist strong relationships, these features cannot be simply concatenated for quality prediction. Inspired by the Bayesian brain theory, correlations among hierarchical features are thoroughly deduced with joint probability. Guided by the mathematical derivation, a new feature aggregation network (FANet) is designed to eliminate correlations for hierarchical features fusion. Finally, a novel end-to-end BIQA framework based on hierarchical feature aggregation is proposed, which eminently analyzes the degradations and correlations on hierarchical features. Experimental results on five benchmark databases show that the proposed method achieves state-of-the-art performance. Furthermore, cross-database evaluations demonstrate the generalization capability and robustness of the proposed method.

The two kinds of free energy and the Bayesian revolution.

The concept of free energy has its origins in 19th century thermodynamics, but has recently found its way into the behavioral and neural sciences, where it has been promoted for its wide applicability and has even been suggested as a fundamental principle of understanding intelligent behavior and brain function. We argue that there are essentially two different notions of free energy in current models of intelligent agency, that can both be considered as applications of Bayesian inference to the problem of action selection: one that appears when trading off accuracy and uncertainty based on a general maximum entropy principle, and one that formulates action selection in terms of minimizing an error measure that quantifies deviations of beliefs and policies from given reference models. The first approach provides a normative rule for action selection in the face of model uncertainty or when information processing capabilities are limited. The second approach directly aims to formulate the action selection problem as an inference problem in the context of Bayesian brain theories, also known as Active Inference in the literature. We elucidate the main ideas and discuss critical technical and conceptual issues revolving around these two notions of free energy that both claim to apply at all levels of decision-making, from the high-level deliberation of reasoning down to the low-level information processing of perception.

A Bayesian brain model of adaptive behavior: an application to the Wisconsin Card Sorting Task.

Adaptive behavior emerges through a dynamic interaction between cognitive agents and changing environmental demands. The investigation of information processing underlying adaptive behavior relies on controlled experimental settings in which individuals are asked to accomplish demanding tasks whereby a hidden regularity or an abstract rule has to be learned dynamically. Although performance in such tasks is considered as a proxy for measuring high-level cognitive processes, the standard approach consists in summarizing observed response patterns by simple heuristic scoring measures. With this work, we propose and validate a new computational Bayesian model accounting for individual performance in the Wisconsin Card Sorting Test (WCST), a renowned clinical tool to measure set-shifting and deficient inhibitory processes on the basis of environmental feedback. We formalize the interaction between the task’s structure, the received feedback, and the agent’s behavior by building a model of the information processing mechanisms used to infer the hidden rules of the task environment. Furthermore, we embed the new model within the mathematical framework of the Bayesian Brain Theory (BBT), according to which beliefs about hidden environmental states are dynamically updated following the logic of Bayesian inference. Our computational model maps distinct cognitive processes into separable, neurobiologically plausible, information-theoretic constructs underlying observed response patterns. We assess model identification and expressiveness in accounting for meaningful human performance through extensive simulation studies. We then validate the model on real behavioral data in order to highlight the utility of the proposed model in recovering cognitive dynamics at an individual level. We highlight the potentials of our model in decomposing adaptive behavior in the WCST into several information-theoretic metrics revealing the trial-by-trial unfolding of information processing by focusing on two exemplary individuals whose behavior is examined in depth. Finally, we focus on the theoretical implications of our computational model by discussing the mapping between BBT constructs and functional neuroanatomical correlates of task performance. We further discuss the empirical benefit of recovering the assumed dynamics of information processing for both clinical and research practices, such as neurological assessment and model-based neuroscience.

Pragmatic Association of the Two Evaluation Concepts of Operational Observation and Mathematical Modeling

Recent developments in several theoretical and industrial concepts are closely associated to the relation of operational observation to mathematical modeling. The present work investigates first the interdependence of these two evaluation notions. An assessment of these notions is performed involving different analyses based on philosophical aspects as phenomenology and structural research. These analyses are also supported by illustrations from physics and quantum science. These analyses examine the autonomy limits of each of both concepts and their interdependence. The associate resulting from such interdependence is therefore studied. This involves different aspects characterizing such associate (couple) as its managing in time and its rulings. The immersion of the couple "observation-theory" is subsequently considered through the exploration of different representing cases showing the nature of the interdependence in this couple. The corroborating interdependence is illuminated in the case of coupled amended models. The matching interdependence is illustrated in the cases of the industrial digital twins concept and Bayesian brain theory in neuroscience. Finally, the imitating interdependence is pointed out in quantum and neuromorphic computing technologies. The conclusion of the paper underlines that mathematical modeling needs operational observation simply to be credible and that the second needs the first for deeper research. Additionally, the interdependence of this associate is valuable to the ideas of several research and industrial innovative concepts. Keywords: observation-modeling associate, phenomenology, structural research, Bayesian concepts, quantum science, matching, imitating, corroborating.

Predictive processing simplified: The infotropic machine

On a traditional view of cognition, we see the agent acquiring stimuli, interpreting these in some way, and producing behavior in response. An increasingly popular alternative is the predictive processing framework. This sees the agent as continually generating predictions about the world, and responding productively to any errors made. Partly because of its heritage in the Bayesian brain theory, predictive processing has generally been seen as an inherently Bayesian process. The ‘hierarchical prediction machine’ which mediates it is envisaged to be a specifically Bayesian device. But as this paper shows, a specification for this machine can also be derived directly from information theory, using the metric of predictive payoff as an organizing concept. Hierarchical prediction machines can be built along purely information-theoretic lines, without referencing Bayesian theory in any way; this simplifies the account to some degree. The present paper describes what is involved and presents a series of working models. An experiment involving the conversion of a Braitenberg vehicle to use a controller of this type is also described.

Why fish pain cannot and should not be ruled out

Do fish consciously feel pain? Addressing this question, Key (2016) asks whether the neural mechanisms underlying conscious pain reports in humans can be identified in fish. This strategy fails in three ways. First, non-mammalian consciousness — if it exists — may depend on different mechanisms. Second, accumulating neurophysiological and behavioural evidence, evolutionary considerations, and emerging Bayesian brain theories suggest that if fish can feel at all, they can feel pain. Finally, the qualitative nature of pain and suffering obliges us, via the precautionary principle, to accommodate the possibility of its existence where doubt remains.brain theories suggest that if fish can feel at all, they can feel pain. Finally, the qualitative nature of pain and suffering obliges us, via the precautionary principle,to accommodate the possibility of its existence where doubt remains.

Hierarchical Prediction Errors in Midbrain and Basal Forebrain during Sensory Learning

In Bayesian brain theories, hierarchically related prediction errors (PEs) play a central role for predicting sensory inputs and inferring their underlying causes, e.g., the probabilistic structure of the environment and its volatility. Notably, PEs at different hierarchical levels may be encoded by different neuromodulatory transmitters. Here, we tested this possibility in computational fMRI studies of audio-visual learning. Using a hierarchical Bayesian model, we found that low-level PEs about visual stimulus outcome were reflected by widespread activity in visual and supramodal areas but also in the midbrain. In contrast, high-level PEs about stimulus probabilities were encoded by the basal forebrain. These findings were replicated in two groups of healthy volunteers. While our fMRI measures do not reveal the exact neuron types activated in midbrain and basal forebrain, they suggest a dichotomy between neuromodulatory systems, linking dopamine to low-level PEs about stimulus outcome and acetylcholine to more abstract PEs about stimulus probabilities.