Can synchronization explain representational content? : A reply to Caspar M. Schwiedrzik

Multivariate decoding provides an important tool for studying the representation and transformation of mental contents in the human brain. Specifically, decoding can be used to identify the neural correlates of contents of consciousness (NCCCs). Decoding of functional magnetic resonance imaging (fMRI) signals has so far mostly revealed content-selectivity in sensory brain regions, but not in prefrontal cortex. The limitations of fMRI-decoding only permit cautious conclusions because fMRI signals are only indirectly related to neural coding. However, the role of prefrontal cortex in visual awareness is also questioned by other findings, reviewed in Schwiedrzik (this collection). Neural synchronization might offer an alternative to solving the binding problem by providing a computational means of integrating information encoded in distributed brain regions. However, it is unclear whether synchronization in itself serves as a coding dimension for visual features. Furthermore, other alternatives to synchronization, especially the role of spatial codes, need to be considered as potential solutions to the feature binding problem

An information-based approach to consciousness : mental state decoding

The debate on the neural correlates of visual consciousness often focuses on the question of which additional processing has to happen for a visual representation to enter consciousness. However, a related question that has only rarely been addressed is which brain regions directly encodespecific contents of consciousness. The search for these core neural correlates of contents of consciousness (NCCCs) requires establishing a mapping between sensory experiences and population measures of brain activity in specific brain regions. One approach for establishing this mapping is multivariate decoding. Using this technique, several properties of NCCCs have been investigated. Masking studies have revealed that information about sensory stimuli can be decoded from the primary visual cortex, even if the stimuli cannot be consciously identified by a subject. This suggests that information that does not reach awareness can be encapsulated in early visual stages of processing. Visual imagery representations and veridical perception share similar neural representations in higher-level visual regions, suggesting that these regions are directly related to the encoding of conscious visual experience. But population signals in these higher-level visual regions cannot be the sole carriers of visual experiences because they are invariant to low-level visual features. We found no evidence for increased encoding of sensory information in the prefrontal cortex when a stimulus reaches awareness. In general, we found no role of the prefrontal cortex in encoding sensory experiences at all. However, the improved discrimination of sensory information during perceptual learning could be explained by an improved read-out by the prefrontal cortex. One possible implication is that prefrontal cortical regions do not participate in the encoding of sensory features per se. Instead they may be relevant in making decisions about sensory features, without exhibiting a re-representation of sensory information

Free will beliefs are better predicted by dualism than determinism beliefs across different cultures

Most people believe in free will. Whether this belief is warranted or not, free will beliefs (FWB) are foundational for many legal systems and reducing FWB has effects on behavior from the motor to the social level. This raises the important question as to which specific FWB people hold. There are many different ways to conceptualize free will, and some might see physical determinism as a threat that might reduce FWB, while others might not. Here, we investigate lay FWB in a large, representative, replicated online survey study in the US and Singapore (n = 1800), assessing differences in FWB with unprecedented depth within and between cultures. Specifically, we assess the relation of FWB, as measured using the Free Will Inventory, to determinism, dualism and related concepts like libertarianism and compatibilism. We find that libertarian, compatibilist, and dualist, intuitions were related to FWB, but that these intuitions were often logically inconsistent. Importantly, direct comparisons suggest that dualism was more predictive of FWB than other intuitions. Thus, believing in free will goes hand-in-hand with a belief in a non-physical mind. Highlighting the importance of dualism for FWB impacts academic debates on free will, which currently largely focus on its relation to determinism. Our findings also shed light on how recent (neuro)scientific findings might impact FWB. Demonstrating physical determinism in the brain need not have a strong impact on FWB, due to a wide-spread belief in dualism

The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data

The multivariate analysis of brain signals has recently sparked a great amount of interest, yet accessible and versatile tools to carry out decoding analyses are scarce. Here we introduce The Decoding Toolbox (TDT) which represents a user-friendly, powerful and flexible package for multivariate analysis of functional brain imaging data. TDT is written in Matlab and equipped with an interface to the widely used brain data analysis package SPM. The toolbox allows running fast whole-brain analyses, region-of-interest analyses and searchlight analyses, using machine learning classifiers, pattern correlation analysis, or representational similarity analysis. It offers automatic creation and visualization of diverse cross-validation schemes, feature scaling, nested parameter selection, a variety of feature selection methods, multiclass capabilities, and pattern reconstruction from classifier weights. While basic users can implement a generic analysis in one line of code, advanced users can extend the toolbox to their needs or exploit the structure to combine it with external high-performance classification toolboxes. The toolbox comes with an example data set which can be used to try out the various analysis methods. Taken together, TDT offers a promising option for researchers who want to employ multivariate analyses of brain activity patterns.DFG, GRK 1589, Verarbeitung sensorischer Informationen in neuronalen SystemenBMBF, 01GQ1006, Modulation von Bewertungsprozessen beim menschlichen Entscheidungsverhalten: ein neurocomputationaler Ansat

Ulcerative C2 neurocutaneous dysesthesia (trigeminal trophic syndrome in an alternative distribution)

Trigeminal trophic syndrome is an uncommon condition characterized by paresthesia, itch, and self-inflicted wounds following the trigeminal dermatome(s). Similar processes adhering to cervical nerve distributions have been reported, calling into question the specificity of trigeminal trophic syndrome for the trigeminal network. Herein, we report patient with trigeminal trophic syndrome adhering to the C2 dermatome, a previously unreported distribution

Neocortical substrates of feelings evoked with music in the ACC, insula, and somatosensory cortex

Neurobiological models of emotion focus traditionally on limbic/paralimbic regions as neural substrates of emotion generation, and insular cortex (in conjunction with isocortical anterior cingulate cortex, ACC) as the neural substrate of feelings. An emerging view, however, highlights the importance of isocortical regions beyond insula and ACC for the subjective feeling of emotions. We used music to evoke feelings of joy and fear, and multivariate pattern analysis (MVPA) to decode representations of feeling states in functional magnetic resonance (fMRI) data of n = 24 participants. Most of the brain regions providing information about feeling representations were neocortical regions. These included, in addition to granular insula and cingulate cortex, primary and secondary somatosensory cortex, premotor cortex, frontal operculum, and auditory cortex. The multivoxel activity patterns corresponding to feeling representations emerged within a few seconds, gained in strength with increasing stimulus duration, and replicated results of a hypothesis-generating decoding analysis from an independent experiment. Our results indicate that several neocortical regions (including insula, cingulate, somatosensory and premotor cortices) are important for the generation and modulation of feeling states. We propose that secondary somatosensory cortex, which covers the parietal operculum and encroaches on the posterior insula, is of particular importance for the encoding of emotion percepts, i.e., preverbal representations of subjective feeling.publishedVersio

Switch-independent task representations in frontal and parietal cortex

Alternating between two tasks is effortful and impairs performance. Previous fMRI studies have found increased activity in frontoparietal cortex when task switching is required. One possibility is that the additional control demands for switch trials are met by strengthening task representations in the human brain. Alternatively, on switch trials, the residual representation of the previous task might impede the buildup of a neural task representation. This would predict weaker task representations on switch trials, thus also explaining the performance costs. To test this, male and female participants were cued to perform one of two similar tasks, with the task being repeated or switched between successive trials. Multivoxel pattern analysis was used to test which regions encode the tasks and whether this encoding differs between switch and repeat trials. As expected, we found information about task representations in frontal and parietal cortex, but there was no difference in the decoding accuracy of task-related information between switch and repeat trials. Using cross-classification, we found that the frontoparietal cortex encodes tasks using a generalizable spatial pattern in switch and repeat trials. Therefore, task representations in frontal and parietal cortex are largely switch independent. We found no evidence that neural information about task representations in these regions can explain behavioral costs usually associated with task switching