Neuroscience of Consciousness最新文献

Our thoughts are inherently dynamic and often wander far from our current situation (mind wandering, MW). Although previous research revealed that brain regions involved in arousal regulation modulate neural dynamics to facilitate the transition from MW to the awareness of its occurrence, the specific physiological states and afferent signals underlying this process remain unclear. In this study, we examined electroencephalography, electrocardiography, and respiration data before participants were aware of MW during a task in which they focused on external or internal stimuli (tones or their breath). We showed that the transition to awareness of MW was characterized by decreased alpha and beta activity and a suppression of the parietal later component of the heartbeat-evoked potential (HEP), a modulation pattern identified in this study as a marker of enhanced central processing based on its consistent appearance across internal attention conditions. Furthermore, when participants were instructed to focus on their breath, they were more likely to be in the exhalation phase during the transition to awareness of MW and in the inhalation phase when MW was reported. This respiratory pattern was accompanied by changes in cardiac activity and HEP amplitudes. Based on these findings, we propose that the transition from MW to the awareness of its occurrence is associated with diverse neural activity, including the enhanced processing of bodily signals that co-occurs with specific cardiac and respiratory dynamics.

Understanding the neural correlates of consciousness remains a central challenge in neuroscience. In this study, we explore the potential of neural field theory (NFT) as a computational framework for representing consciousness states. While prior research has validated NFT's capacity to differentiate between normal and pathological states of consciousness, the relationship of its parameters to the representation of consciousness states remains unclear. Here, we fitted a corticothalamic NFT model to the electroencephalography (EEG) data collected from healthy individuals and patients with disorders of consciousness. We then comprehensively explored the correlations between the fitted NFT parameters and features extracted from both experimental and simulated EEG data across various states of consciousness. The identified correlations not only highlight the model's ability to differentiate between healthy and impaired states of consciousness, but also shed light on the physiological bases of these states, pinpointing potential biomarkers. Our results provide valuable insights into how consciousness levels are represented within the NFT framework and into the dynamics of brain activity across normal and pathological states of consciousness. This underscores the potential of NFT as a useful tool for consciousness research, facilitating in-silico experimentation.

Top-down attentional amplification is often assumed to affect 'what' we see, that is, the contents of conscious experience. Previously, this claim has been examined by studies that manipulated attention and characterized conscious perception in binary categorical labels (e.g. seen versus unseen). However, these categorical judgments are not powerful enough to characterize the quality of conscious perception, or 'how' we see, or qualia, for short. To address this, we introduce a similarity rating paradigm to consciousness research that tries to characterize the attentional effects on the structure of the quality of experience, or qualia structures for short. Under the dual-task paradigm, participants rated the similarity of stimulus pairs in the periphery. We used three stimulus sets, the rotated letters 'L' and 'T' (N = 14), rotated red/green bisected disks (N = 14) or greyscale faces (N = 13). The similarity ratings of all the pairs described the phenomenological relationships between the stimuli, and served as a proxy for the qualia structure of conscious experience of the stimuli; which we characterized with dimension reduction and an unsupervised optimal transport alignment technique. We found that alignment accuracy remained high for face qualia structures under both full and poor attention. Withdrawal of attention collapsed qualia structures for letters and disks. Extending previous dual-task approaches from binary categorizations to relational judgments, our approach establishes a novel pathway to elucidate qualia structures.

This paper uses simple arguments to derive a negative conclusion: that a computer cannot be conscious. If the brain is only a neural computer, brains cannot be conscious. Consciousness implies that there is something else happening in the brain, besides computation. In a running computer, information about outside events is encoded, to enable physical computation. The information required to decode the information (e.g. to interpret volts as bits, or neuron spike trains as numbers) is not inside the computer. The meaning of any computation is not defined inside the computer; it is only defined by some external entity which decodes the results. Without decoding information, a computer contains no information about outside events. In the same way, the meaning of any book is not defined inside the book; the book requires outside knowledge to read it. Consciousness contains meaningful information about external events. If the brain is only a computer, without decoding (which requires external information) it contains no information about external events. If the brain is only a computer, consciousness cannot be realised by events inside the brain. This conclusion is compared with philosophical positions on computational functionalism, representation, and intentionality. Something more than neural computing must be happening in the brain. One suggestion is that the something could be an analogue model of 3-D space. An analogue model contains information which requires little or no decoding. Hence, an analogue model of reality in the brain might be the source of consciousness. This merits further investigation.

[This corrects the article DOI: 10.1093/nc/niaf048.].

The motivational force of pain is undeniable. But what pain commands us to do, how we might satisfy this command, and if our experience of pain is inherently linked to suffering are far murkier topics. This paper brings together empirical studies of pain reprocessing during advanced meditation, the rise of allostatic paradigms to account for biological self-regulation, and the philosophy of pain in the classical Sanskrit philosophical tradition of Pratyabhijñā Śaivism to argue that pain is an allostatic imperative to adapt a part of one's body. We theorize two components of an allostatic response, heterostatic and homeostatic, that work in tandem to address pain as an allostatic command. Homeostatic responses are error-corrective in that they seek to protect an organism by returning to a previously stable steady state. Heterostatic responses are anticipatory in that they seek to better prepare an organism to meet future challenges by proactively shifting to a new steady state. We note that an organism's successful adaptation to its environment depends not just on error-correction, but also on anticipatory change. We theorize that a broad range of affect properly accompanies pain. We propose potential directions for empirically developing this model. We also note the possibility that this model could be extended to account for mental pain.

[This corrects the article DOI: 10.1093/nc/niaf019.].

During an acute episode of depersonalization/derealization (DP/DR), people report a complex and idiosyncratic change in their perceptual experience. Specifically, derealisation describes the experience of detachment from the external world and altered visual perception in which the surroundings look faded, foggy, or dream-like. Whilst some have argued that there may not be genuine perceptual changes in derealization, this proposal is yet to be tested empirically. Thus, we set out to investigate the potential perceptual changes in derealization. In this Registered Report, we conducted two online experiments to reveal the impact of DP/DR symptoms measured via the state version of the Cambridge Depersonalisation Scale (CDS) on how people evaluate (Experiment 1, N = 200, CDS-state mean: 32.43 ± 29.94 SD) and adjust (Experiment 2, N = 125, CDS-state mean: 29.38 ± 30.47 SD) naturalistic scene images with different levels of saturation and contrast. Participants were asked to rate how real the presented images look compared to their everyday experience (in Experiment 1) and to adjust the contrast or saturation level of images to match their everyday visual experience (in Experiment 2). We tested the effect of CDS-state scores on these subjective ratings via model comparison with Bayes Factors. In both experiments, we found strong evidence supporting the null models, suggesting that DP/DR symptoms did not affect realness ratings or vividness adjustments. These results provide empirical support for theories suggesting that self-reported altered vividness experience in derealization does not reflect genuine perceptual changes, instead they signify the (meta-)cognitive interpretation of these experiences. We discuss pros and cons of the current research practices when assessing derealization and highlight key avenues for the future investigation.

While the beneficial impacts of meditation are increasingly acknowledged, its underlying neural mechanisms remain poorly understood. We examined the electrophysiological brain signals of expert Buddhist monks during two established meditation methods known as Samatha and Vipassana, which employ focused attention and open-monitoring technique. By combining source-space magnetoencephalography with advanced signal processing and machine learning tools, we provide an unprecedented assessment of the role of brain oscillations, complexity, and criticality in meditation. In addition to power spectral density, we computed long-range temporal correlations (LRTC), deviation from criticality coefficient (DCC), Lempel-Ziv complexity, 1/f slope, Higuchi fractal dimension, and spectral entropy. Our findings indicate increased levels of neural signal complexity during both meditation practices compared to the resting state, alongside widespread reductions in gamma-band LRTC and 1/f slope. Importantly, the DCC analysis revealed a separation between Samatha and Vipassana, suggesting that their distinct phenomenological properties are mediated by specific computational characteristics of their dynamic states. Furthermore, in contrast to most previous reports, we observed a decrease in oscillatory gamma power during meditation, a divergence likely due to the correction of the power spectrum by the 1/f slope, which could reduce potential confounds from broadband 1/f activity. We discuss how these results advance our comprehension of the neural processes associated with focused attention and open-monitoring meditation practices.

Measuring awareness on a trial-by-trial basis might impose a multi-task cost on the observed effect. Here, we examined this potential cost, asking if it can be mitigated by training. In two experiments, one group of participants reported awareness offline, in a post-test, and another reported it online, in each trial. To test the effect of training, all participants completed two sessions on separate days. When analyzing all trials, we found overall slower reaction times (RTs) in the online group, suggesting a multi-task cost, but no interaction with the priming effect. Notably, this difference was smaller in the second session, implying that the multi-task cost is reduced by training. Critically however, this analysis yielded no convincing evidence for unconscious priming (due to potential threat of regression to the mean). We accordingly analyzed only trials where RTs were fast. Convincing response priming was found, as well as an interaction between priming and session. This suggests that training did increase priming. We also exploratorily tested for individual differences in priming and found between-session consistency mostly for the offline condition. Taken together, our results indicate that although multi-tasking adds noise and prolongs RTs, it does not necessarily diminish unconscious response priming for fast trials, which in turn can be enhanced by training. Costs and benefits of these methodological choices should thus be considered in future studies, as well as targeting only fast responses, where the effects were more compelling. Future work should also test if these patterns apply to other types of priming.