Hierarchical predictive coding suggests that attention in humans emerges from increased precision in probabilistic inference, whereas expectation biases attention in favor of contextually anticipated stimuli. We test these notions within auditory perception by independently manipulating top-down expectation and attentional precision alongside bottom-up stimulus predictability. Our findings support an integrative interpretation of commonly observed electrophysiological signatures of neurodynamics, namely mismatch negativity (MMN), P300, and contingent negative variation (CNV), as manifestations along successive levels of predictive complexity. Early firstlevel processing indexed by the MMN was sensitive to stimulus predictability: here, attentional precision enhanced early responses, but explicit top-down expectation diminished it. This pattern was in contrast to later, second-level processing indexed by the P300: although sensitive to the degree of predictability, responses at this level were contingent on attentional engagement and in fact sharpened by top-down expectation. At the highest level, the drift of the CNV was a fine-grained marker of top-down expectation itself. Source reconstruction of high-density EEG, supported by intracranial recordings, implicated temporal and frontal regions differentially active at early and late levels. The cortical generators of the CNV suggested that it might be involved in facilitating the consolidation of contextsalient stimuli into conscious perception. These results provide convergent empirical support to promising recent accounts of attention and expectation in predictive coding.

There is increasing evidence that human perception is realized by a hierarchy of neural processes in which predictions sent backward from higher levels result in prediction errors that are fed forward from lower levels, to update the current model of the environment. Moreover, the precision of prediction errors is thought to be modulated by attention. Much of this evidence comes from paradigms in which a stimulus differs from that predicted by the recent history of other stimuli (generating a so-called "mismatch response"). There is less evidence from situations where a prediction is not fulfilled by any sensory input (an "omission" response). This situation arguably provides a more direct measure of "top-down" predictions in the absence of confounding "bottom-up" input. We applied Dynamic Causal Modeling of evoked electromagnetic responses recorded by EEG and MEG to an auditory paradigm in which we factorially crossed the presence versus absence of "bottom-up" stimuli with the presence versus absence of "top-down" attention. Model comparison revealed that both mismatch and omission responses were mediated by increased forward and backward connections, differing primarily in the driving input. In both responses, modeling results suggested that the presence of attention selectively modulated backward "prediction" connections. Our results provide new model-driven evidence of the pure top-down prediction signal posited in theories of hierarchical perception, and highlight the role of attentional precision in strengthening this prediction.

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Techniques based on electroencephalography (EEG) measure the electric potentials on the scalp and process them to infer the location, distribution, and intensity of underlying neural activity. Accuracy in estimating these parameters is highly sensitive to uncertainty in the conductivities of the head tissues. Furthermore, dissimilarities among individuals are ignored when standarized values are used. In this paper, we apply the maximum-likelihood and maximum a posteriori (MAP) techniques to simultaneously estimate the layer conductivity ratios and source signal using EEG data. We use the classical 4-sphere model to approximate the head geometry, and assume a known dipole source position. The accuracy of our estimates is evaluated by comparing their standard deviations with the Cramér-Rao bound (CRB). The applicability of these techniques is illustrated with numerical examples on simulated EEG data. Our results show that the estimates have low bias and attain the CRB for sufficiently large number of experiments. We also present numerical examples evaluating the sensitivity to imprecise assumptions on the source position and skull thickness. Finally, we propose extensions to the case of unknown source position and present examples for real data.

In a forced-choice task, we asked human participants to discriminate by touch alone glass plates from transparent polymethyl methacrylate (PMMA) plastic plates. While the surfaces were flat and did not exhibit geometric features beyond a few tens of nanometres, the materials differed by their molecular structures. They produced similar coefficients of friction and thermal effects were controlled. Most participants performed well above chance and participants with dry fingers discriminated the materials especially well. Current models of tactile surface perception appeal to surface topography and cannot explain our results. A correlation analysis between detailed measurements of the interfacial forces and discrimination performance suggested that the perceptual task depended on the transitory contact phase leading to full slip. This result demonstrates that differences in interfacial mechanics between the finger and a material can be sensed by touch and that the evanescent mechanics that take place before the onset of steady slip have perceptual value.

When we touch an object or explore a texture, frictional strains are induced by the tactile interactions with the surface of the object. Little is known about how these interactions are perceived, although it becomes crucial for the nascent industry of interactive displays with haptic feedback (e.g. smartphones and tablets) where tactile feedback based on friction modulation is particularly relevant. To investigate the human perception of frictional strains, we mounted a high-fidelity friction modulating ultrasonic device on a robotic platform performing controlled rubbing of the fingertip and asked participants to detect induced decreases of friction during a forced-choice task. The ability to perceive the changes in friction was found to follow Weber's Law of just noticeable differences, as it consistently depended on the ratio between the reduction in tangential force and the pre-stimulation tangential force. The Weber fraction was 0.11 in all conditions demonstrating a very high sensitivity to transient changes in friction. Humid fingers experienced less friction reduction than drier ones for the same intensity of ultrasonic vibration but the Weber fraction for detecting changes in friction was not influenced by the humidity of the skin.

We compare three global configuration search methods on a scalable model problem to measure relative performance over a range of molecule sizes. Our model problem is a 2-D polymer composed of atoms connected by rigid rods in which all pairs of atoms interact via Lennard-Jones potentials. The global minimum energy can be calculated analytically. The search methods are all hybrids combining a global sampling algorithm with a local refinement technique. The sampling methods are simulated annealing (sA), genetic algorithms (GA), and random search. Each of these uses a conjugate gradient (cc) routine to perform the local refinement. Both GA and SA perform progressively better relative to random search as the molecule size increases. We also test two other local refinement techniques in addition to CG, coupled to random search as the global method. These are simplex followed by CG and simplex followed by block-truncated Newton. For small problems, the addition of the intermediate simplex step improved the performance of the overall hybrid method.

We propose to assess the process of learning a task using electroencephalographic (EEG) measurements. In particular, we quantify changes in brain activity associated to the progression of the learning experience through the functional analysis-of-variances (FANOVA) estimators of the EEG power spectral density (PSD). Such functional estimators provide a sense of the effect of training in the EEG dynamics. For that purpose, we implemented an experiment to monitor the process of learning to type using the Colemak keyboard layout during a twelve-lessons training. Hence, our aim is to identify statistically significant changes in PSD of various EEG rhythms at different stages and difficulty levels of the learning process. Those changes are taken into account only when a probabilistic measure of the cognitive state ensures the high engagement of the volunteer to the training. Based on this, a series of statistical tests are performed in order to determine the personalized frequencies and sensors at which changes in PSD occur, then the FANOVA estimates are computed and analyzed. Our experimental results showed a significant decrease in the power of b and c rhythms for ten volunteers during the learning process, and such decrease happens regardless of the difficulty of the lesson. These results are in agreement with previous reports of changes in PSD being associated to feature binding and memory encoding.