Biology subjects

Koenig-Robert, R.

Publications and source records attributed to Koenig-Robert, R..

6 recordsLinked to original sources

bioRxiv2023

Movement trajectories as a window into the dynamics of emerging neural representations.

Transforming sensory inputs into meaningful neural representations is critical to adaptive behaviour in everyday environments. While non-invasive neuroimaging methods are the de-facto method for investigating neural representations, they remain expensive, not widely available, time-consuming, and restrictive in terms of the experimental conditions and participant populations they can be used with. Here we show that movement trajectories collected in online behavioural experiments can be used to measure the emergence and dynamics of neural representations with fine temporal resolution. By combining online computer mouse-tracking and publicly available neuroimaging (MEG and fMRI) data via Representational Similarity Analysis (RSA), we show that movement trajectories track the evolution of visual representations over time. We used a time constrained face/object categorization task on a previously published set of images containing human faces, illusory faces and objects to demonstrate that time-resolved representational structures derived from movement trajectories correlate with those derived from MEG, revealing the unfolding of category representations in comparable temporal detail (albeit delayed) to MEG. Furthermore, we show that movement-derived representational structures correlate with those derived from fMRI in most task-relevant brain areas, faces and objects selective areas in this proof of concept. Our results highlight the richness of movement trajectories and the power of the RSA framework to reveal and compare their information content, opening new avenues to better understand human perception.

neuroscience↗

bioRxiv2020

Decoding non-conscious thought representations during successful thought suppression

Controlling our own thoughts is central to mental wellbeing and its failure is at the crux of a number of mental disorders. Paradoxically, behavioural evidence shows that thought-suppression often fails. Despite the broad importance of understanding the mechanisms of thought control, little is known about the fate of neural representations of suppressed thoughts. Using functional MRI, we investigated the brain areas involved in controlling visual thoughts and tracked suppressed thought representations using multi-voxel pattern analysis (MVPA). Participants were asked to either visualize a vegetable/fruit or suppress any visual thoughts about those objects. Surprisingly, the content (object identity) of successfully suppressed thoughts was still decodable in visual and executive areas with algorithms trained on perception or imagery. This suggests that pictorial representations of the suppressed thoughts are still present despite individuals reporting they are not. Thought generation was associated with the left hemisphere, whereas thought suppression with right hemisphere engagement. Further, GLM analyses showed that subjective success in thought suppression was correlated with engagement of executive areas, while thought-suppression failure was associated with engagement of visual and memory related areas. These results reveal that the content of suppressed thoughts exist hidden from awareness, seemingly without an individuals knowledge, providing a compelling reason why thought suppression is so ineffective. These data inform models of unconscious thought production and could be used to develop new treatment approaches to disorders involving maladaptive thoughts.

neuroscience↗

bioRxiv2018

Expectation and attention increase the integration of top-down and bottom-up signals in perception through different pathways

Perception results from the integration of incoming sensory information with pre-existing information available in the brain. In this EEG (electroencephalography) study we utilised the Hierarchical Frequency Tagging method to examine how such integration is modulated by expectation and attention. Using intermodulation (IM) components as a measure of non-linear signal integration, we show in three different experiments that both expectation and attention enhance integration between top-down and bottom-up signals. Based on multispectral phase coherence, we present two direct physiological measures to demonstrate the distinct yet related mechanisms of expectation and attention. Specifically, our results link expectation to the modulation of prediction signals and the integration of top-down and bottom-up information at lower levels of the visual hierarchy. Meanwhile, they link attention to the propagation of ascending signals and the integration of information at higher levels of the visual hierarchy. These results are consistent with the predictive coding account of perception.

neuroscience↗

bioRxiv2018

Thought Control Failure: Sensory Determinants and Functional Effects

The ability to control ones thoughts is important for mental wellbeing, attention, focus, future planning and ideation. While there is a long history of research into thought control, the inherent subjectivity of thoughts has made objective investigation, and thus mechanistic understanding difficult. Here, we report a novel method to empirically investigate thought control success and failure by objectively measuring the sensory strength of visual thoughts. We use the perceptual illusion binocular rivalry to assess emergent images in mind during two common thought control strategies: thought suppression and thought substitution. Thought suppression was ineffective, suppressed thoughts primed subsequent rivalry dominance at the same level as intentionally imagined thoughts. This priming effect was disrupted by concurrent uniform luminance and changes in retinotopic location, suggesting early visual representations/traces. While individuals showed some metacognition of thought suppression, strikingly, the perceptual effects remained even when thoughts were reported as successfully suppressed, indicating these thoughts may exist outside of reportable awareness. In contrast, thought substitution was more effective in controlling the perceptual effects and showed good metacognition. A thought control index predicted greater levels of trait mindfulness, while high levels of anxiety and schizotypy were related to poor thought control. Overall, our findings offer a novel method to track thoughts before and after they emerge into awareness and suggest that non-reportable and involuntary thoughts form visual representations pivotal to thought control failure.

neuroscience↗

bioRxiv2016

Decoding the nonconscious dynamics of thought generation

Much of economics, psychology and neuroscience have focused on thought dynamics and how they control our behavior, from individual moral choices to the irrationality of market dynamics. However, how much of our thoughts we actually control when we feel we make deliberate choices remains unknown. Here we show that the content of thoughts can be decoded from activity patterns as early as 11 seconds before individuals report having formed the volitional thought. Participants freely chose which of two differently oriented and colored gratings to think about. Using functional magnetic resonance imaging (fMRI) and pattern classification methods, we consistently classified the contents of thoughts using activity patterns recorded before and after the thought was reported. We found that activity patterns were predictive as far as 11 seconds before the conscious thought, in visual, frontal and subcortical areas. These predictive patterns contained similar information to the responses evoked by unattended perceptual gratings and were evident in individual visual areas. Interestingly, neural information present before the decision was associated with the vividness of future thoughts, suggesting that preceding nonconscious sensory-like representations can impact the content and strength of future conscious thoughts. Our results suggest that thoughts and their strength can be biased by prior spontaneous nonconscious perception-like representations, advancing theories of free will and models of intrusive and repetitive thought production.

neuroscience↗

bioRxiv2016

Hierarchical Frequency Tagging reveals neural markers of predictive coding under varying uncertainty

Understanding the integration of top-down and bottom-up signals is essential for the study of perception. Current accounts of predictive coding describe this in terms of interactions between state units encoding expectations or predictions, and error units encoding prediction error. However, direct neural evidence for such interactions has not been well established. To achieve this, we combined EEG methods that preferentially tag different levels in the visual hierarchy: Steady State Visual Evoked Potential (SSVEP at 10Hz, tracking bottom-up signals) and Semantic Wavelet-Induced Frequency Tagging (SWIFT at 1.3Hz tracking top-down signals). Importantly, we examined intermodulation components (IM, e.g., 11.3Hz) as a measure of integration between these signals. To examine the influence of expectation and predictions on the nature of such integration, we constructed 50-second movie streams and modulated expectation levels for upcoming stimuli by varying the proportion of images presented across trials. We found SWIFT, SSVEP and IM signals to differ in important ways. SSVEP was strongest over occipital electrodes and was not modified by certainty. Conversely, SWIFT signals were evident over temporo- and parieto-occipital areas and decreased as a function of increasing certainty levels. Finally, IMs were evident over occipital electrodes and increased as a function of certainty. These results link SSVEP, SWIFT and IM signals to sensory evidence, predictions, prediction errors and hypothesis-testing - the core elements of predictive coding. These findings provide neural evidence for the integration of top-down and bottom-up information in perception, opening new avenues to studying such interactions in perception while constraining neuronal models of predictive coding.\n\nSIGNIFICANCE STATEMENTThere is a growing understanding that both top-down and bottom-up signals underlie perception. But how do these signals interact? And how does this process depend on the signals probabilistic properties? Predictive coding theories of perception describe this in terms how well top-down predictions fit with bottom-up sensory input. Identifying neural markers for such signal integration is therefore essential for the study of perception and predictive coding theories in particular. The novel Hierarchical Frequency Tagging method simultaneously tags top-down and bottom-up signals in EEG recordings, while obtaining a measure for the level of integration between these signals. Our results suggest that top-down predictions indeed integrate with bottom-up signals in a manner that is modulated by the predictability of the sensory input.

neuroscience↗