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

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

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

A feature-binding model with localized excitations

We study a model of feature binding in prefrontal cortex which defers pecific perceptual information to lower areas and merely maintains the identity of the combination. The model consists of three layers of pulse-coupled leaky integrate-and-fire neurons. Features are encoded by the location of sustained activity in the subordinate layers. The feature layers are excitatorily coupled to a superordinate layer that represents combinations of features by means of an oscillatory dynamics. The model accounts for effects such as the memorization of an object that was perceived only for a short period, illusory binding of simultaneous stimuli, and the limit of attentional capacity. The present paper discusses conditions for localized excitations in networks of integrate-and-fire neurons and considers the application to a dynamic link architecture

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feature-binding model with localized excitation

Distinct transition in flow statistics and vortex dynamics between low- and high-extent turbulent drag reduction in polymer fluids

Flexible polymer additives are known to reduce the energy dissipation and friction drag in turbulent flows. As the fluid elasticity increases, the flow undergoes several stages of transitions. Much attention in the area has been focused on the onset of drag reduction (DR) and the eventual convergence to the maximum drag reduction (MDR) asymptote. Between the onset and MDR, recent experimental and numerical observations prompted the need to further distinguish the low- and high-extent drag reduction (LDR and HDR). Fundamental knowledge of this transition will be important for understanding turbulent dynamics in the presence of polymers, as well as for inspiring new flow control strategies for efficient friction reduction. We use direct numerical simulation (DNS) to explore all these transitions in the parameter space and, in particular, demonstrate that the LDR HDR transition is not merely a quantitative effect of the level of drag reduction, but a qualitative transition into a different stage of turbulence. A number of sharp changes in flow statistics are found to accompany the transition and at HDR, turbulence becomes localized with vortices forming clusters. These observations suggest that polymer-induced drag reduction follows two distinct stages. The first starts at the onset of drag reduction, where the coil-stretch transition of polymers causes an overall suppression of turbulent fluctuations. The second starts at the LDR HDR transition, where flow statistics become fundamentally changed in the log-law layer and turbulence localization is observed. A mechanism is then proposed for the latter based on the changing vortex regeneration dynamics between LDR and HDR