Abstract Ever since their discovery, mirror neurons have generated much interest and debate. A commonly held view of mirror neuron function is that they transform “visual information into knowledge,” thus enabling action understanding and non-verbal social communication between con-specifics (Rizzolatti & Craighero 2004). This functionality is thought to be so important that it has been argued that mirror neurons must be a result of selective pressure.
Abstract Contextual influences on choice are ubiquitous in ecological settings. Current evidence suggests that subjective values are normalized with respect to the distribution of potentially available rewards. However, how this context-sensitivity is realised in the brain remains unknown. To address this, here we examine functional magnetic resonance imaging (fMRI) data during performance of a gambling task where blocks comprise values drawn from one of two different, but partially overlapping, reward distributions or contexts. At the beginning of each block (when information about context is provided), hippocampus is activated and this response is enhanced when contextual influence on choice increases. In addition, response to value in ventral tegmental area/substantia nigra (VTA/SN) shows context-sensitivity, an effect enhanced with an increased contextual influence on choice. Finally, greater response in hippocampus at block start is associated with enhanced context sensitivity in VTA/SN. These findings suggest that context-sensitive choice is driven by a brain circuit involving hippocampus and dopaminergic midbrain.
Anticipatory skin conductance responses [SCRs] are a widely used measure of aversive conditioning in humans. Here, we describe a dynamic causal model [DCM] of how anticipatory, evoked, and spontaneous skin conductance changes are generated by sudomotor nerve activity. Inversion of this model, using variational Bayes, provides a means of inferring the most likely sympathetic nerve activity, given observed skin conductance responses. In two fear conditioning experiments, we demonstrate the predictive validity of the DCM by showing it has greater sensitivity to the effects of conditioning, relative to alternative (conventional) response estimates. Furthermore, we establish face validity by showing that trial-by-trial estimates of anticipatory sudomotor activity are better predicted by formal learning models, relative to response estimates from peak-scoring approaches. The model furnishes a potentially powerful approach to characterising SCR that exploits knowledge about how these signals are generated.
Can active inference model consciousness? We offer three conditions implying that it can. The first condition is the simulation of a reality or generative world model, which determines what can be known or acted upon; namely an epistemic field. The second is inferential competition to enter the world model. Only the inferences that coherently reduce long-term uncertainty win, evincing a selection for consciousness that we call Bayesian binding. The third is epistemic depth, which is the recurrent sharing of the Bayesian beliefs throughout the system. Due to this recursive loop — in a hierarchical system (such as a brain) — the world model contains the knowledge that it exists. This is distinct from self-consciousness, because the world model knows itself non-locally and continuously evidences this knowing (i.e., field-evidencing). Formally, we propose a hyper-model for precision-control across the entire hierarchy, whose latent states (or parameters) encode and control the overall structure and weighting rules for all layers of inference. This Beautiful Loop Theory is deeply revealing about meditation, psychedelic, and altered states, minimal phenomenal experience, and provides a new vision for conscious artificial intelligence.
Understanding the neural basis of poor impulse control in Internet addiction (IA) is important for understanding the neurobiological mechanisms of this syndrome. The current study investigated how neuronal pathways implicated in response inhibition were affected in IA using a Go-Stop paradigm and functional magnetic resonance imaging (fMRI). Twenty-three control subjects aged 15.2 ± 0.5 years (mean ± S.D.) and eighteen IA subjects aged 15.1 ± 1.4 years were studied. Effective connectivity within the response inhibition network was quantified using (stochastic) dynamic causal modeling (DCM). The results showed that the indirect frontal-basal ganglia pathway was engaged by response inhibition in healthy subjects. However, we did not detect any equivalent effective connectivity in the IA group. This suggests the IA subjects fail to recruit this pathway and inhibit unwanted actions. This study provides a clear link between Internet addiction as a behavioral disorder and aberrant connectivity in the response inhibition network.
In cognitive neuroscience, electrical brain activity is most commonly recorded at the scalp. In order to infer the contributions and connectivity of underlying neuronal sources within the brain, it is necessary to reconstruct sensor data at the source level. Several approaches to this reconstruction have been developed, thereby solving the so-called implicit inverse problem Michel et al. (Clin Neurophysiol 115:2195–2222, 2004). However, a unifying premise against which to validate these source reconstructions is seldom available. The dataset provided in this work, in which brain activity is simultaneously recorded on the scalp (non-invasively) by electroencephalography (EEG) and on the cortex (invasively) by electrocorticography (ECoG), can be of a great help in this direction. These multimodal recordings were obtained from a macaque monkey under wakefulness and sedation. Our primary goal was to establish the connectivity architecture between two sources of interest (frontal and parietal), and to assess how their coupling changes over the conditions. We chose these sources because previous studies have shown that the connections between them are modified by anaesthesia Boly et al. (J Neurosci 32:7082–7090, 2012). Our secondary goal was to evaluate the consistency of the connectivity results when analyzing sources recorded from invasive data (128 implanted ECoG sources) and source activity reconstructed from scalp recordings (19 EEG sensors) at the same locations as the ECoG sources. We conclude that the directed connectivity in the frequency domain between cortical sources reconstructed from scalp EEG is qualitatively similar to the connectivity inferred directly from cortical recordings, using both data-driven (directed transfer function) and biologically grounded (dynamic causal modelling) methods. Furthermore, the connectivity changes identified were consistent with previous findings Boly et al. (J Neurosci 32:7082–7090, 2012). Our findings suggest that inferences about directed connectivity based upon non-invasive electrophysiological data have construct validity in relation to invasive recordings.
This dataset includes skin conductance response (SCR) and pulse time stamp (HB) measurements for each of 60 healthy unmedicated participants (30 males and 30 females aged 23.7 +/- 4.8 years) in response to the 45 most arousing negative, and 45 least arousing neutral IAPS pictures, each presented for 1 s each, while listening to regular or random distractor sounds, as described in Bach et al. (2015). ITI was selected randomly on each trial from 7.65 s, 9 s, or 10.35 s. The experiment was preceded by a 2-minute resting period and divided into 3 blocks, separated by resting periods. Each resting period begins and ends with an event marker in the psychophysiological recordings.
We set out a new general framework for making inferences from neuroimaging data, which includes a standard approach to neuroimaging analysis, statistical parametric mapping (SPM), as a special case. The model offers numerous conceptual and statistical advantages that derive from analyzing data at the "cluster level" rather than the "voxel level" and from explicit modeling of the shape and position of clusters of activation. This provides a natural and principled way to pool data from nearby voxels for parameter and variance-component estimation. The model can also be viewed as performing a spatio-temporal cluster analysis. The parameters of the model are estimated using an expectation maximization (EM) algorithm.
