Predictive coding in a multisensory path integration task: An fMRI study

The neural network of sensory attenuation: A neuroimaging meta-analysis

Sensory attenuation refers to the reduction in sensory intensity resulting from self-initiated actions compared to stimuli initiated externally. A classic example is scratching oneself without feeling itchy. This phenomenon extends across various sensory modalities, including visual, auditory, somatosensory, and nociceptive stimuli. The internal forward model proposes that during voluntary actions, an efferent copy of the action command is sent out to predict sensory feedback. This predicted sensory feedback is then compared with the actual sensory feedback, leading to the suppression or reduction of sensory stimuli originating from self-initiated actions. To further elucidate the neural mechanisms underlying sensory attenuation effect, we conducted an extensive meta-analysis of functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) studies. Utilizing activation likelihood estimation (ALE) analysis, our results revealed significant activations in a prominent cluster encompassing the right superior temporal gyrus (rSTG), right middle temporal gyrus (rMTG), and right insula when comparing external-generated with self-generated conditions. Additionally, significant activation was observed in the right anterior cerebellum when comparing self-generated to external-generated conditions. Further analysis using meta-analytic connectivity modeling (MACM) unveiled distinct brain networks co-activated with the rMTG and right cerebellum, respectively. Based on these findings, we propose that sensory attenuation arises from the suppression of reflexive inputs elicited by self-initiated actions through the internal forward modeling of a cerebellum-centered action prediction network, enabling the "sensory conflict detection" regions to effectively discriminate between inputs resulting from self-induced actions and those originating externally.

Contemporary neurocognitive models of memory: A descriptive comparative analysis

The great complexity involved in the study of memory has given rise to numerous hypotheses and models associated with various phenomena at different levels of analysis. This has allowed us to delve deeper in our knowledge about memory but has also made it difficult to synthesize and integrate data from different lines of research. In this context, this work presents a descriptive comparative analysis of contemporary models that address the structure and function of multiple memory systems. The main goal is to outline a panoramic view of the key elements that constitute these models in order to visualize both the current state of research and possible future directions. The elements that stand out from different levels of analysis are distributed neural networks, hierarchical organization, predictive coding, homeostasis, and evolutionary perspective.

Neural correlates of visual and tactile path integration and their task related modulation

Self-motion induces sensory signals that allow to determine travel distance (path integration). For veridical path integration, one must distinguish self-generated from externally induced sensory signals. Predictive coding has been suggested to attenuate self-induced sensory responses, while task relevance can reverse the attenuating effect of prediction. But how is self-motion processing affected by prediction and task demands, and do effects generalize across senses? In this fMRI study, we investigated visual and tactile self-motion processing and its modulation by task demands. Visual stimuli simulated forward self-motion across a ground plane. Tactile self-motion stimuli were delivered by airflow across the subjects' forehead. In one task, subjects replicated a previously observed distance (Reproduction/Active; high behavioral demand) of passive self-displacement (Reproduction/Passive). In a second task, subjects travelled a self-chosen distance (Self/Active; low behavioral demand) which was recorded and played back to them (Self/Passive). For both tasks and sensory modalities, Active as compared to Passive trials showed enhancement in early visual areas and suppression in higher order areas of the inferior parietal lobule (IPL). Contrasting high and low demanding active trials yielded supramodal enhancement in the anterior insula. Suppression in the IPL suggests this area to be a comparator of sensory self-motion signals and predictions thereof.

Being active over one's own motion: Considering predictive mechanisms in self-motion perception

Self-motion perception is a key element guiding pilots' behavior. Its importance is mostly revealed when impaired, leading in most cases to spatial disorientation which is still today a major factor of accidents occurrence. Self-motion perception is known as mainly based on visuo-vestibular integration and can be modulated by the physical properties of the environment with which humans interact. For instance, several studies have shown that the respective weight of visual and vestibular information depends on their reliability. More recently, it has been suggested that the internal state of an operator can also modulate multisensory integration. Interestingly, the systems' automation can interfere with this internal state through the loss of the intentional nature of movements (i.e., loss of agency) and the modulation of associated predictive mechanisms. In this context, one of the new challenges is to better understand the relationship between automation and self-motion perception. The present review explains how linking the concepts of agency and self-motion is a first approach to address this issue.

Seeing and extrapolating motion trajectories share common informative activation patterns in primary visual cortex

The natural environment is dynamic and moving objects become constantly occluded, engaging the brain in a challenging completion process to estimate where and when the object might reappear. Although motion extrapolation is critical in daily life-imagine crossing the street while an approaching car is occluded by a larger standing vehicle-its neural underpinnings are still not well understood. While the engagement of low-level visual cortex during dynamic occlusion has been postulated, most of the previous group-level fMRI-studies failed to find evidence for an involvement of low-level visual areas during occlusion. In this fMRI-study, we therefore used individually defined retinotopic maps and multivariate pattern analysis to characterize the neural basis of visible and occluded changes in motion direction in humans. To this end, participants learned velocity-direction change pairings (slow motion-upwards; fast motion-downwards or vice versa) during a training phase without occlusion and judged the change in stimulus direction, based on its velocity, during a following test phase with occlusion. We find that occluded motion direction can be predicted from the activity patterns during visible motion within low-level visual areas, supporting the notion of a mental representation of motion trajectory in these regions during occlusion.

Introduction to special issue on "Prediction in Perception and Action"

The wide diversity of articles in this issue reveals an explosion of evidence for the mechanisms of prediction in the visual system. When thought of as visual priors, predictive mechanisms can be seen as tightly interwoven with incoming sensory data. Prediction is thus a fundamental and essential aspect not only of visual perception but of the actions that are guided by perception.