A widely tunable few electron droplet

Quasi-static transport measurements are employed to characterize a few electron quantum dot electrostatically defined in a GaAs/AlGaAs heterostructure. The gate geometry allows observations on one and the same electron droplet within a wide range of coupling strengths to the leads. The weak coupling regime is described by discrete quantum states. At strong interaction with the leads Kondo phenomena are observed as a function of a magnetic field. By varying gate voltages the electron droplet can, in addition, be distorted into a double quantum dot with a strong interdot tunnel coupling while keeping track of the number of trapped electrons.Comment: 11 pages, 5 figure

Negative frequency tuning of a carbon nanotube nano-electromechanical resonator

A suspended, doubly clamped single wall carbon nanotube is characterized as driven nano-electromechanical resonator at cryogenic temperatures. Electronically, the carbon nanotube displays small bandgap behaviour with Coulomb blockade oscillations in electron conduction and transparent contacts in hole conduction. We observe the driven mechanical resonance in dc-transport, including multiple higher harmonic responses. The data shows a distinct negative frequency tuning at finite applied gate voltage, enabling us to electrostatically decrease the resonance frequency to 75% of its maximum value. This is consistently explained via electrostatic softening of the mechanical mode.Comment: 4 pages, 4 figures; submitted for the IWEPNM 2013 conference proceeding

Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating

Towards tunable consensus clustering for studying functional brain connectivity during affective processing

In the past decades, neuroimaging of humans has gained a position of status within neuroscience, and data-driven approaches and functional connectivity analyses of functional magnetic resonance imaging (fMRI) data are increasingly favored to depict the complex architecture of human brains. However, the reliability of these findings is jeopardized by too many analysis methods and sometimes too few samples used, which leads to discord among researchers. We propose a tunable consensus clustering paradigm that aims at overcoming the clustering methods selection problem as well as reliability issues in neuroimaging by means of first applying several analysis methods (three in this study) on multiple datasets and then integrating the clustering results. To validate the method, we applied it to a complex fMRI experiment involving affective processing of hundreds of music clips. We found that brain structures related to visual, reward, and auditory processing have intrinsic spatial patterns of coherent neuroactivity during affective processing. The comparisons between the results obtained from our method and those from each individual clustering algorithm demonstrate that our paradigm has notable advantages over traditional single clustering algorithms in being able to evidence robust connectivity patterns even with complex neuroimaging data involving a variety of stimuli and affective evaluations of them. The consensus clustering method is implemented in the R package “UNCLES” available on http://cran.r-project.org/web/packages/UNCLES/index.html

Under pressure: Response urgency modulates striatal and insula activity during decision-making under risk

When deciding whether to bet in situations that involve potential monetary loss or gain (mixed gambles), a subjective sense of pressure can influence the evaluation of the expected utility associated with each choice option. Here, we explored how gambling decisions, their psychophysiological and neural counterparts are modulated by an induced sense of urgency to respond. Urgency influenced decision times and evoked heart rate responses, interacting with the expected value of each gamble. Using functional MRI, we observed that this interaction was associated with changes in the activity of the striatum, a critical region for both reward and choice selection, and within the insula, a region implicated as the substrate of affective feelings arising from interoceptive signals which influence motivational behavior. Our findings bridge current psychophysiological and neurobiological models of value representation and action-programming, identifying the striatum and insular cortex as the key substrates of decision-making under risk and urgency

The Affective Impact of Financial Skewness on Neural Activity and Choice

Few finance theories consider the influence of “skewness” (or large and asymmetric but unlikely outcomes) on financial choice. We investigated the impact of skewed gambles on subjects' neural activity, self-reported affective responses, and subsequent preferences using functional magnetic resonance imaging (FMRI). Neurally, skewed gambles elicited more anterior insula activation than symmetric gambles equated for expected value and variance, and positively skewed gambles also specifically elicited more nucleus accumbens (NAcc) activation than negatively skewed gambles. Affectively, positively skewed gambles elicited more positive arousal and negatively skewed gambles elicited more negative arousal than symmetric gambles equated for expected value and variance. Subjects also preferred positively skewed gambles more, but negatively skewed gambles less than symmetric gambles of equal expected value. Individual differences in both NAcc activity and positive arousal predicted preferences for positively skewed gambles. These findings support an anticipatory affect account in which statistical properties of gambles—including skewness—can influence neural activity, affective responses, and ultimately, choice

Benthic pH gradients across a range of shelf sea sediment types linked to sediment characteristics and seasonal variability

This study used microelectrodes to record pH profiles in fresh shelf sea sediment cores collected across a range of different sediment types within the Celtic Sea. Spatial and temporal variability was captured during repeated measurements in 2014 and 2015. Concurrently recorded oxygen microelectrode profiles and other sedimentary parameters provide a detailed context for interpretation of the pH data. Clear differences in profiles were observed between sediment type, location and season. Notably, very steep pH gradients exist within the surface sediments (10–20 mm), where decreases greater than 0.5 pH units were observed. Steep gradients were particularly apparent in fine cohesive sediments, less so in permeable sandier matrices. We hypothesise that the gradients are likely caused by aerobic organic matter respiration close to the sediment–water interface or oxidation of reduced species at the base of the oxic zone (NH4+, Mn2+, Fe2+, S−). Statistical analysis suggests the variability in the depth of the pH minima is controlled spatially by the oxygen penetration depth, and seasonally by the input and remineralisation of deposited organic phytodetritus. Below the pH minima the observed pH remained consistently low to maximum electrode penetration (ca. 60 mm), indicating an absence of sub-oxic processes generating H+ or balanced removal processes within this layer. Thus, a climatology of sediment surface porewater pH is provided against which to examine biogeochemical processes. This enhances our understanding of benthic pH processes, particularly in the context of human impacts, seabed integrity, and future climate changes, providing vital information for modelling benthic response under future climate scenarios

Functional brain networks involved in gaze and emotional processing

Eye-gaze direction plays a fundamental role in the perception of facial features and particularly the processing of emotional facial expressions. Yet, the neural underpinnings of the integration of eye gaze and emotional facial cues are not well understood. The primary aim of this study was to delineate the functional networks that subserve the recognition of emotional expressions as a function of eye gaze. Participants were asked to identify happy, angry, or neutral faces, displayed with direct or averted gaze, while their neural responses were measured with fMRI. The results showed that recognition of happy expressions, irrespective of eye-gaze direction, engaged the critical nodes of the default mode network. Recognition of angry faces, on the other hand, was gaze-dependent, engaging the critical nodes of the salience network when presented with direct gaze, but fronto-parietal areas when presented with averted gaze. Functional connectivity analysis further showed gaze-dependent engagement of a large-scale network connected to bilateral amygdala during the recognition of angry expressions. This study provides important insights into the functional connectivity between the amygdala and other critical social-cognitive brain nodes, which are essential in processing of ambiguous, potentially threatening social signals. These findings have implications for psychiatric disorders, such as post-traumatic stress disorder, which are characterized by aberrant limbic connectivity