J. Michael Herrmann

The neural representation of time: An information-theoretic perspective

A prominent finding in psychophysical experiments on time perception is Webers law, the linear scaling of timing errors with duration. The ability to reproduce this scaling has been taken as a criterion for the validity of neurocomputational models of time perception. However, the origin of Webers law remains unknown, and currently only a few models generi- cally reproduce it. Here, we use an information-theoretical framework that considers the neuronal mechanisms of time perception as stochastic processes to investigate the statistical origin of Webers law in time perception and also its frequently observed deviations. Under the assumption that the brain is able to compute optimal estimates of time, we find that Webers law only holds exactly if the estimate is based on temporal changes in the variance of the process. In contrast, the timing errors scale sublinearly with time if the systematic changes in the mean of a process are used for estimation, as is the case in the majority of time perception models, while estimates based on temporal correlations result in a superlinear scaling. This hierarchy of temporal information is preserved if several sources of temporal information are available. Furthermore, we consider the case of multiple stochastic processes and study the examples of a covariance-based model and a model based on synfire chains. This approach reveals that existing neurocomputational models of time perception can be classified as mean-, variance- and correlation-based processes and allows predictions about the scaling of the resulting timing errors.

Variants of guided self-organization for robot control

Autonomous robots can generate exploratory behavior by self-organization of the sensorimotor loop. We show that the behavioral manifold that is covered in this way can be modified in a goal-dependent way without reducing the self-induced activity of the robot. We present three strategies for guided self-organization, namely by using external rewards, a problem-specific error function, or assumptions about the symmetries of the desired behavior. The strategies are analyzed for two different robots in a physically realistic simulation.

Response-retrieval and negative priming: Encoding- and Retrieval-Specific Effects

The existence of across-notation automatic numerical processing of two-digit (2D) numbers was explored using size comparisons tasks. Participants were Arabic speakers, who use two sets of numerical symbols—Arabic and Indian. They were presented with pairs of 2D numbers in the same or in mixed notations. Responses for a numerical comparison task were affected by decade difference and unit-decade compatibility and global distance in both conditions, extending previous findings with Arabic digits (Nuerk, Weger, & Willmes, 2001). Responses for a physical comparison task were affected by congruency with the numerical size, as indicated by the size congruency effect (SiCE). The SiCE was affected by unit-decade compatibility but not by global distance, thus suggesting that the units and decades digits of the 2D numbers, but not the whole number value were automatically translated into a common representation of magnitude. The presence of similar results for same- and mixed-notation pairs supports the idea of an abstract representation of magnitude.

Event-related brain potential correlates of identity negative priming from overlapping pictures.

Event-related potentials (ERPs) were obtained from an identity priming task, where a green target had to be selected against a superimposed red distractor. Several priming conditions were realized in a mix of control (CO), negative priming (NP), and positive priming (PP) trials. PP and NP effects in reaction times (RTs) were significant. ERP results conceptually replicate earlier findings of left-posterior P300 reduction in PP and NP trials compared to CO. This ERP effect may reflect the detection of prime-probe similarity corresponding to the concept of a retrieval cue. A novel finding concerned amplitude increase of the frontal late positive complex (LPC) in the order NP, CO, and PP. NP therefore seemed to induce brain activity related to cognitive control and/or memory processes, with reduced LPC amplitude indicating effortful processing. Overall, retrieval-based explanations of identity NP are supported.

Compositionality of arm movements can be realized by propagating synchrony

We present a biologically plausible spiking neuronal network model of free monkey scribbling that reproduces experimental findings on cortical activity and the properties of the scribbling trajectory. The model is based on the idea that synfire chains can encode movement primitives. Here, we map the propagation of activity in a chain to a linearly evolving preferred velocity, which results in parabolic segments that fulfill the two-thirds power law. Connections between chains that match the final velocity of one encoded primitive to the initial velocity of the next allow the composition of random sequences of primitives with smooth transitions. The model provides an explanation for the segmentation of the trajectory and the experimentally observed deviations of the trajectory from the parabolic shape at primitive transition sites. Furthermore, the model predicts low frequency oscillations (<10xa0Hz) of the motor cortex local field potential during ongoing movements and increasing firing rates of non-specific motor cortex neurons before movement onset.

Recurrence-Based Estimation of Time-Distortion Functions for ERP Waveform Reconstruction

We introduce an approach to compensate for temporal distortions of repeated measurements in event-related potential research. The algorithm uses a combination of methods from nonlinear time-series analysis and is based on the construction of pairwise registration functions from cross-recurrence plots of the phase-space representations of ERP signals. The globally optimal multiple-alignment path is approximated by hierarchical cluster analysis, i.e. by iteratively combining pairs of trials according to similarity. By the inclusion of context information in form of externally acquired time markers (e.g. reaction time) into a regularization scheme, the extracted warping functions can be guided near paths that are implied by the experimental procedure. All parameters occurring in the algorithm can be optimized based on the properties of the data and there is a broad regime of parameter configurations where the algorithm produces good results. Simulations on artificial data and the analysis of ERPs from a psychophysical study demonstrate the robustness and applicability of the algorithm.

Homeokinetic proportional control of myoelectric prostheses

Self-organized control of myoelectric prostheses aims at an automatic selection of communication channels between a prosthetic device and its user. During training, the patient is instructed to generate control signals that follow the observed autonomous movements of the prosthesis. At the same time, the prosthetic controller maximizes both the diversity of movements and the coincidences of prosthetic movements and human control signals by varying the sensory features and control actions. This dual control algorithm is derived from the homeokinetic principle for robot control and is tested in a proportional control task for a hand prostheses.

Homeokinetic reinforcement learning

In order to find a control policy for an autonomous robot by reinforcement learning, the utility of a behaviour can be revealed locally through a modulation of the motor command by probing actions. For robots with many degrees of freedom, this type of exploration becomes inefficient such that it is an interesting option to use an auxiliary controller for the selection of promising probing actions. We suggest here to optimise the exploratory modulation by a self-organising controller. The approach is illustrated by two control tasks, namely swing-up of a pendulum and walking in a simulated hexapod. The results imply that the homeokinetic approach is beneficial for high complexity problems.

Identity Negative Priming: A Phenomenon of Perception, Recognition or Selection?

The present study addresses the problem whether negative priming (NP) is due to information processing in perception, recognition or selection. We argue that most NP studies confound priming and perceptual similarity of prime-probe episodes and implement a color-switch paradigm in order to resolve the issue. In a series of three identity negative priming experiments with verbal naming response, we determined when NP and positive priming (PP) occur during a trial. The first experiment assessed the impact of target color on priming effects. It consisted of two blocks, each with a different fixed target color. With respect to target color no differential priming effects were found. In Experiment 2 the target color was indicated by a cue for each trial. Here we resolved the confounding of perceptual similarity and priming condition. In trials with coinciding colors for prime and probe, we found priming effects similar to Experiment 1. However, trials with a target color switch showed such effects only in trials with role-reversal (distractor-to-target or target-to-distractor), whereas the positive priming (PP) effect in the target-repetition trials disappeared. Finally, Experiment 3 split trial processing into two phases by presenting the trial-wise color cue only after the stimulus objects had been recognized. We found recognition in every priming condition to be faster than in control trials. We were hence led to the conclusion that PP is strongly affected by perception, in contrast to NP which emerges during selection, i.e., the two effects cannot be explained by a single mechanism.

Inhibition in the Dynamics of Selective Attention: An Integrative Model for Negative Priming

We introduce a computational model of the negative priming (NP) effect that includes perception, memory, attention, decision making, and action. The model is designed to provide a coherent picture across competing theories of NP. The model is formulated in terms of abstract dynamics for the activations of features, their binding into object entities, their semantic categorization as well as related memories and appropriate reactions. The dynamic variables interact in a connectionist network which is shown to be adaptable to a variety of experimental paradigms. We find that selective attention can be modeled by means of inhibitory processes and by a threshold dynamics. From the necessity of quantifying the experimental paradigms, we conclude that the specificity of the experimental paradigm must be taken into account when predicting the nature of the NP effect.