The term "minimal phenomenal selfhood" describes the basic, pre-reflective experience of being a self (Blanke & Metzinger, 2009). Theoretical accounts of the minimal self have long recognized the importance and the ambivalence of the body as both part of the physical world, and the enabling condition for being in this world (Gallagher, 2005; Grafton, 2009). A recent account of minimal phenomenal selfhood (MPS, Metzinger, 2004a) centers on the consideration that minimal selfhood emerges as the result of basic self-modeling mechanisms, thereby being founded on pre-reflective bodily processes. The free energy principle (FEP, Friston, 2010) is a novel unified theory of cortical function that builds upon the imperative that self-organizing systems entail hierarchical generative models of the causes of their sensory input, which are optimized by minimizing free energy as an approximation of the log-likelihood of the model. The implementation of the FEP via predictive coding mechanisms and in particular the active inference principle emphasizes the role of embodiment for predictive self-modeling, which has been appreciated in recent publications. In this review, we provide an overview of these conceptions and illustrate thereby the potential power of the FEP in explaining the mechanisms underlying minimal selfhood and its key constituents, multisensory integration, interoception, agency, perspective, and the experience of mineness. We conclude that the conceptualization of MPS can be well mapped onto a hierarchical generative model furnished by the free energy principle and may constitute the basis for higher-level, cognitive forms of self-referral, as well as the understanding of other minds.

Behavioural data obtained with perceptual decision making experiments are typically analysed with the drift-diffusion model. This parsimonious model accumulates noisy pieces of evidence towards a decision bound to explain the accuracy and reaction times of subjects. Recently, Bayesian models have been proposed to explain how the brain extracts information from noisy input as typically presented in perceptual decision making tasks. It has long been known that the drift-diffusion model is tightly linked with such functional Bayesian models but the precise relationship of the two mechanisms was never made explicit. Using a Bayesian model, we derived the equations which relate parameter values between these models. In practice we show that this equivalence is useful when fitting multi-subject data. We further show that the Bayesian model suggests different decision variables which all predict equal responses and discuss how these may be discriminated based on neural correlates of accumulated evidence. In addition, we discuss extensions to the Bayesian model which would be difficult to derive for the drift-diffusion model. We suggest that these and other extensions may be highly useful for deriving new experiments which test novel hypotheses.

Both animal and human studies on numerosity have shown the importance of the parietal cortex for numerosity processing. However, most studies have focused on the perceptual processing of numerosity. Still, it is unclear how and where numerosity information is coded when this information is retained during a working memory delay phase. Such temporal storage could be realized by the same structures as perceptual processes, or be transformed to a more abstract representation, potentially involving prefrontal regions. FMRI decoding studies allow the identification of brain areas that exhibit multi‐voxel activation patterns specific to the content of working memory. Here, we used an assumption‐free searchlight‐decoding approach to test where numerosity‐specific codes can be found during a 12 s retention period. Participants (n = 24) performed a retro‐cue delayed match‐to‐sample task, in which numerosity information was presented as visual dot arrays. We found mnemonic numerosity‐specific activation in the right lateral portion of the intraparietal sulcus; an area well‐known for perceptual processing of numerosity. The applied retro‐cue design dissociated working memory delay activity from perceptual processes and showed that the intraparietal sulcus also maintained working memory representation independent of perception.

Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that allows the modulation of the excitability and plasticity of the human brain. Focalized tDCS setups use specific electrode arrangements to constrain the current flow to circumscribed brain regions. However, the effectiveness of focalized tDCS can be compromised by electrode positioning errors on the scalp, resulting in significant reductions of the current dose reaching the target brain regions for tDCS. Electrode placement guided by neuronavigation based on the individual's head and brain anatomy derived from structural magnetic resonance imaging (MRI) data may be suited to improve positioning accuracy. This protocol describes the method of neuronavigated electrode placement for a focalized tDCS setup, which is suitable for concurrent administration during functional MRI (fMRI). We also quantify the accuracy of electrode placement and investigate electrode drift in a concurrent tDCS-fMRI experiment. Critical steps involve the optimization of electrode positions based on current modeling that considers the individual's head and brain anatomy, the implementation of neuronavigated electrode placement on the scalp, and the administration of optimized and focal tDCS during fMRI. The regional precision of electrode placement is quantified using the Euclidean norm (L2 Norm) to determine deviations of the actual from the intended electrode positions during a concurrent tDCS-fMRI study. Any potential displacement of electrodes (drift) during the experiment is investigated by comparing actual electrode positions before and after the fMRI acquisition. In addition, we directly compare the placement accuracy of neuronavigated tDCS to that achieved by a scalp-based targeting approach (a 10-20 Electroencephalography (EEG) system). These analyses demonstrate superior placement accuracy for neuronavigation compared to scalp-based electrode placement and negligible electrode drift across a 20 min scanning period.

Working memory (WM) representations are generally known to be influenced by task demands, but it is not clear whether this extends to the somatosensory domain. One way to investigate the influence of task demands is with categorization paradigms, wherein either a single stimulus or an associated category is maintained in WM. In the somatosensory modality, category representations have been identified in the premotor cortex (PMC) and the intraparietal sulcus (IPS). In this study we used multivariate-pattern-analysis with human fMRI data to investigate whether the WM representations in the PMC, IPS or other regions are influenced by changing task demands. We ensured the task-dependent, categorical WM information was decorrelated from stimulus features by (1) teaching participants arbitrary, non-rule based stimulus groupings and (2) contrasting identical pairs of stimuli across experimental conditions, where either a single stimulus or the associated group was maintained in WM. Importantly, we also decoupled the decision and motor output from the WM representations. With these experimental manipulations, we were able to pinpoint stimulus-specific WM information to the left frontal and parietal cortices and context-dependent, group-specific WM information to the left IPS. By showing that grouped stimuli are represented more similarly in the Group condition than in the Stimulus condition, free from stimulus and motor output confounds, we provide novel evidence for the adaptive nature of somatosensory WM representations in the IPS with changing task-demands.