In this study we tested predictions of two important theories of visual coding, contrast energy and sparse coding theory, on the dependence of population activity level and metabolic demands on spatial structure of the visual input. With carefully calibrated displays we find that in humans neither the V1 blood oxygenation level dependent (BOLD) response nor the initial visually evoked fields in magnetoencephalography (MEG) are sensitive to phase perturbations in photographs of natural scenes. As a control, we quantitatively show that the applied phase perturbations decrease sparseness (kurtosis) of our stimuli but preserve their root mean square (RMS) contrast. Importantly, we show that the lack of sensitivity of the V1 population response level to phase perturbations is not due to a lack of sensitivity of our methods because V1 responses were highly sensitive to variations of image RMS contrast. Our results suggest that the transition from a sparse to a distributed neural code in the early visual system induced by reducing image sparseness has negligible consequences for population metabolic cost. This result imposes a novel and important empirical constraint on quantitative models of sparse coding: Population metabolic rate and population activation level is sensitive to second order statistics (RMS contrast) of the input but not to its spatial phase and fourth order statistics (kurtosis).
The temporal maintenance and subsequent retrieval of information that no longer exists in the environment is called working memory. It is believed that this type of memory is controlled by the persistent activity of neuronal populations, including the prefrontal, temporal, and parietal cortex. For a long time, it has been controversially discussed whether, in working memory, the PFC stores past sensory events or, instead, its activation is an extramnemonic source of top-down control over posterior regions. Recent animal studies suggest that specific information about the contents of working memory can be decoded from population activity in prefrontal areas. However, it has not been shown whether the contents of working memory during the delay periods can be decoded from EEG recordings in the human brain. We show that by analyzing the nonlinear dynamics of EEG oscillatory patterns it is possible to noninvasively decode with high accuracy, during encoding and maintenance periods, the contents of visual working memory information within high-gamma oscillations in the human PFC. These results are thus in favor of an active storage function of the human PFC in working memory; this, without ruling out the role of PFC in top-down processes. The ability to noninvasively decode the contents of working memory is promising in applications such as brain computer interfaces, together with computation of value function during planning and decision making processes.
The primary motor cortex (M1) is the main effector structure implicated in the generation of voluntary movements and is directly involved in motor learning. The intrinsic horizontal neuronal connections of M1 exhibit short-term and long-term plasticity, which is a strong substrate for learning-related map reorganization. Transcranial direct current stimulation (tDCS) applied for few minutes over M1 has been shown to induce relatively long-lasting plastic alterations and to modulate motor performance. Here we test the hypothesis that the relatively long-lasting synaptic modification induced by tDCS over M1 results in the alteration of associations among populations of M1 neurons which may be reflected in changes of its functional architecture. fMRI resting-state datasets were acquired immediately before and after 10 minutes of tDCS during rest, with the anode/cathode placed over the left M1. For each functional dataset, grey-matter voxels belonging to Brodmann area 4 (BA4) were labelled and afterwards BA4 voxel-based synchronization matrices were calculated and thresholded to construct undirected graphs. Nodal network parameters which characterize the architecture of functional networks (connectivity degree, clustering coefficient and characteristic path-length) were computed, transformed to volume maps and compared before and after stimulation. At the dorsolateral-BA4 region cathodal tDCS boosted local connectedness, while anodal-tDCS enhanced long distance functional communication within M1. Additionally, the more efficient the functional architecture of M1 was at baseline, the more efficient the tDCS-induced functional modulations were. In summary, we show here that it is possible to non-invasively reorganize the intrinsic functional architecture of M1, and to image such alterations.
Transcranial direct current stimulation (tDCS) and transcranial random noise stimulation (tRNS) consist in the application of electrical current of small intensity through the scalp, able to modulate perceptual and motor learning, probably by changing brain excitability. We investigated the effects of these transcranial electrical stimulation techniques in the early and later stages of visuomotor learning, as well as associated brain activity changes using functional magnetic resonance imaging (fMRI). We applied anodal and cathodal tDCS, low-frequency and high-frequency tRNS (lf-tRNS, 0.1-100 Hz; hf-tRNS 101-640 Hz, respectively) and sham stimulation over the primary motor cortex (M1) during the first 10 minutes of a visuomotor learning paradigm and measured performance changes for 20 minutes after stimulation ceased. Functional imaging scans were acquired throughout the whole experiment. Cathodal tDCS and hf-tRNS showed a tendency to improve and lf-tRNS to hinder early learning during stimulation, an effect that remained for 20 minutes after cessation of stimulation in the late learning phase. Motor learning-related activity decreased in several regions as reported previously, however, there was no significant modulation of brain activity by tDCS. In opposition to this, hf-tRNS was associated with reduced motor task-related-activity bilaterally in the frontal cortex and precuneous, probably due to interaction with ongoing neuronal oscillations. This result highlights the potential of lf-tRNS and hf-tRNS to differentially modulate visuomotor learning and advances our knowledge on neuroplasticity induction approaches combined with functional imaging methods.
Philosophers, psychologists, and economists have long argued that certain decision rights carry not only instrumental value but may also be valuable for their own The ideas of autonomy, freedom, and liberty derive their intuitive appeal - partly - from an assumed positive intrinsic value of decision rights. Providing evidence for the existence of this intrinsic value and measuring its size, however, intricate. Here, we develop a method capable of achieving these goals. The data that the large majority of our subjects intrinsically value decision rights beyond their strumental benefit. The intrinsic valuation of decision rights has potentially important consequences for corporate governance, human resource management, and optimal job design: it may explain why managers value power, why employees appreciate with task discretion, why individuals sort into self-employment, and why the realloca- tion of decision rights is often very difficult and cumbersome. Our method and may also prove useful in developing an empirical revealed preference foundation concepts such as "freedom of choice" and "individual autonomy."
