Dynamic Causal Modelling of Active Vision

Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task

Introduction

Learning to self-regulate brain activity by neurofeedback has been shown to lead to changes in the brain and behavior, with beneficial clinical and non-clinical outcomes. Neurofeedback uses a brain-computer interface to guide participants to change some feature of their brain activity. However, the neural mechanism of self-regulation learning remains unclear, with only 50% of the participants succeeding in achieving it. To bridge this knowledge gap, our study delves into the neural mechanisms of self-regulation learning via neurofeedback and investigates the brain processes associated with successful brain self-regulation.

Methods

We study the neural underpinnings of self-regulation learning by employing dynamical causal modeling (DCM) in conjunction with real-time functional MRI data. The study involved a cohort of 18 participants undergoing neurofeedback training targeting the supplementary motor area. A critical focus was the comparison between top-down hierarchical connectivity models proposed by Active Inference and alternative bottom-up connectivity models like reinforcement learning.

Results

Our analysis revealed a crucial distinction in brain connectivity patterns between successful and non-successful learners. Particularly, successful learners evinced a significantly stronger top-down effective connectivity towards the target area implicated in self-regulation. This heightened top-down network engagement closely resembles the patterns observed in goal-oriented and cognitive control studies, shedding light on the intricate cognitive processes intertwined with self-regulation learning.

Discussion

The findings from our investigation underscore the significance of cognitive mechanisms in the process of self-regulation learning through neurofeedback. The observed stronger top-down effective connectivity in successful learners indicates the involvement of hierarchical cognitive control, which aligns with the tenets of Active Inference. This study contributes to a deeper understanding of the neural dynamics behind successful self-regulation learning and provides insights into the potential cognitive architecture underpinning this process.

Revealing Whole-Brain Causality Networks During Guided Visual Searching

In our daily lives, we use eye movements to actively sample visual information from our environment ("active vision"). However, little is known about how the underlying mechanisms are affected by goal-directed behavior. In a study of 31 participants, magnetoencephalography was combined with eye-tracking technology to investigate how interregional interactions in the brain change when engaged in two distinct forms of active vision: freely viewing natural images or performing a guided visual search. Regions of interest with significant fixation-related evoked activity (FRA) were identified with spatiotemporal cluster permutation testing. Using generalized partial directed coherence, we show that, in response to fixation onset, a bilateral cluster consisting of four regions (posterior insula, transverse temporal gyri, superior temporal gyrus, and supramarginal gyrus) formed a highly connected network during free viewing. A comparable network also emerged in the right hemisphere during the search task, with the right supramarginal gyrus acting as a central node for information exchange. The results suggest that all four regions are vital to visual processing and guiding attention. Furthermore, the right supramarginal gyrus was the only region where activity during fixations on the search target was significantly negatively correlated with search response times. Based on our findings, we hypothesize that, following a fixation, the right supramarginal gyrus supplies the right supplementary eye field (SEF) with new information to update the priority map guiding the eye movements during the search task.

Musicianship and melodic predictability enhance neural gain in auditory cortex during pitch deviance detection

When listening to music, pitch deviations are more salient and elicit stronger prediction error responses when the melodic context is predictable and when the listener is a musician. Yet, the neuronal dynamics and changes in connectivity underlying such effects remain unclear. Here, we employed dynamic causal modeling (DCM) to investigate whether the magnetic mismatch negativity response (MMNm)-and its modulation by context predictability and musical expertise-are associated with enhanced neural gain of auditory areas, as a plausible mechanism for encoding precision-weighted prediction errors. Using Bayesian model comparison, we asked whether models with intrinsic connections within primary auditory cortex (A1) and superior temporal gyrus (STG)-typically related to gain control-or extrinsic connections between A1 and STG-typically related to propagation of prediction and error signals-better explained magnetoencephalography responses. We found that, compared to regular sounds, out-of-tune pitch deviations were associated with lower intrinsic (inhibitory) connectivity in A1 and STG, and lower backward (inhibitory) connectivity from STG to A1, consistent with disinhibition and enhanced neural gain in these auditory areas. More predictable melodies were associated with disinhibition in right A1, while musicianship was associated with disinhibition in left A1 and reduced connectivity from STG to left A1. These results indicate that musicianship and melodic predictability, as well as pitch deviations themselves, enhance neural gain in auditory cortex during deviance detection. Our findings are consistent with predictive processing theories suggesting that precise and informative error signals are selected by the brain for subsequent hierarchical processing.

Robust optical axis control of monocular active gazing based on pan-tilt mirrors for high dynamic targets

The real-time and stability performance are both crucial for the active vision system (AVS) to gaze the high dynamic targets (HDTs). This study focused on the robust optical axis control mechanism of monocular AVS based on pan-tilt mirrors. We proposed an adaptive self-window to accommodate the HDTs within the region of interest. The minimum-envelope-ellipse and unscented-Kalman-filter methods were proposed to compensate and predict the angle of optical axis when the HDTs were blocked. The static and dynamic compensation error rates were less than 1.46% and 2.71%, prediction error rate was less than 13.88%, improving the gazing stability while ensuring real-time performance.

Binocular rivalry reveals an out-of-equilibrium neural dynamics suited for decision-making

In ambiguous or conflicting sensory situations, perception is often ‘multistable’ in that it perpetually changes at irregular intervals, shifting abruptly between distinct alternatives. The interval statistics of these alternations exhibits quasi-universal characteristics, suggesting a general mechanism. Using binocular rivalry, we show that many aspects of this perceptual dynamics are reproduced by a hierarchical model operating out of equilibrium. The constitutive elements of this model idealize the metastability of cortical networks. Independent elements accumulate visual evidence at one level, while groups of coupled elements compete for dominance at another level. As soon as one group dominates perception, feedback inhibition suppresses supporting evidence. Previously unreported features in the serial dependencies of perceptual alternations compellingly corroborate this mechanism. Moreover, the proposed out-of-equilibrium dynamics satisfies normative constraints of continuous decision-making. Thus, multistable perception may reflect decision-making in a volatile world: integrating evidence over space and time, choosing categorically between hypotheses, while concurrently evaluating alternatives.

Eye movements during visual imagery and perception show spatial correspondence but have unique temporal signatures

Eye fixation patterns during mental imagery are similar to those during perception of the same picture, suggesting that oculomotor mechanisms play a role in mental imagery (i.e., the "looking at nothing" effect). Previous research has focused on the spatial similarities of eye movements during perception and mental imagery. The primary aim of this study was to assess whether the spatial similarity translates to the temporal domain. We used recurrence quantification analysis (RQA) to assess the temporal structure of eye fixations in visual perception and mental imagery and we compared the temporal as well as the spatial characteristics in mental imagery with perception by means of Bayesian hierarchical regression models. We further investigated how person and picture-specific characteristics contribute to eye movement behavior in mental imagery. Working memory capacity and mental imagery abilities were assessed to either predict gaze dynamics in visual imagery or to moderate a possible correspondence between spatial or temporal gaze dynamics in perception and mental imagery. We were able to show the spatial similarity of fixations between visual perception and imagery and we provide first evidence for its moderation by working memory capacity. Interestingly, the temporal gaze dynamics in mental imagery were unrelated to those in perception and their variance between participants was not explained by variance in visuo-spatial working memory capacity or vividness of mental images. The semantic content of the imagined pictures was the only meaningful predictor of temporal gaze dynamics. The spatial correspondence reflects shared spatial structure of mental images and perceived pictures, while the unique temporal gaze behavior could be driven by generation, maintenance and protection processes specific to visual imagery. The unique temporal gaze dynamics offer a window to new insights into the genuine process of mental imagery independent of its similarity to perception.

The predictive global neuronal workspace: A formal active inference model of visual consciousness

The global neuronal workspace (GNW) model has inspired over two decades of hypothesis-driven research on the neural basis of consciousness. However, recent studies have reported findings that are at odds with empirical predictions of the model. Further, the macro-anatomical focus of current GNW research has limited the specificity of predictions afforded by the model. In this paper we present a neurocomputational model - based on Active Inference - that captures central architectural elements of the GNW and is able to address these limitations. The resulting 'predictive global workspace' casts neuronal dynamics as approximating Bayesian inference, allowing precise, testable predictions at both the behavioural and neural levels of description. We report simulations demonstrating the model's ability to reproduce: 1) the electrophysiological and behavioural results observed in previous studies of inattentional blindness; and 2) the previously introduced four-way taxonomy predicted by the GNW, which describes the relationship between consciousness, attention, and sensory signal strength. We then illustrate how our model can reconcile/explain (apparently) conflicting findings, extend the GNW taxonomy to include the influence of prior expectations, and inspire novel paradigms to test associated behavioural and neural predictions.

Inferring What to Do (And What Not to)

In recent years, the "planning as inference" paradigm has become central to the study of behaviour. The advance offered by this is the formalisation of motivation as a prior belief about "how I am going to act". This paper provides an overview of the factors that contribute to this prior. These are rooted in optimal experimental design, information theory, and statistical decision making. We unpack how these factors imply a functional architecture for motivated behaviour. This raises an important question: how can we put this architecture to work in the service of understanding observed neurobiological structure? To answer this question, we draw from established techniques in experimental studies of behaviour. Typically, these examine the influence of perturbations of the nervous system-which include pathological insults or optogenetic manipulations-to see their influence on behaviour. Here, we argue that the message passing that emerges from inferring what to do can be similarly perturbed. If a given perturbation elicits the same behaviours as a focal brain lesion, this provides a functional interpretation of empirical findings and an anatomical grounding for theoretical results. We highlight examples of this approach that influence different sorts of goal-directed behaviour, active learning, and decision making. Finally, we summarise their implications for the neuroanatomy of inferring what to do (and what not to).