Rapid modulation of sensory processing induced by stimulus conflict

Humans are constantly confronted with environmental stimuli that conflict with task goals and can interfere with successful behavior. Prevailing theories propose the existence of cognitive control mechanisms that can suppress the processing of conflicting input and enhance that of the relevant input. However, the temporal cascade of brain processes invoked in response to conflicting stimuli remains poorly understood. By examining evoked electrical brain responses in a novel, hemifield-specific, visual-flanker task, we demonstrate that task-irrelevant conflicting stimulus input is quickly detected in higher level executive regions while simultaneously inducing rapid, recurrent modulation of sensory processing in the visual cortex. Importantly, however, both of these effects are larger for individuals with greater incongruency-related RT slowing. The combination of neural activation patterns and behavioral interference effects suggest that this initial sensory modulation induced by conflicting stimulus inputs reflects performance-degrading attentional distraction because of their incompatibility rather than any rapid task-enhancing cognitive control mechanisms. The present findings thus provide neural evidence for a model in which attentional distraction is the key initial trigger for the temporal cascade of processes by which the human brain responds to conflicting stimulus input in the environment

What May Visualization Processes Optimize?

In this paper, we present an abstract model of visualization and inference processes and describe an information-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of workflows in data analysis and visualization, and identified four levels of typical visualization components, namely disseminative, observational, analytical and model-developmental visualization. We noticed a common phenomenon at different levels of visualization, that is, the transformation of data spaces (referred to as alphabets) usually corresponds to the reduction of maximal entropy along a workflow. Based on this observation, we establish an information-theoretic measure of cost-benefit ratio that may be used as a cost function for optimizing a data visualization process. To demonstrate the validity of this measure, we examined a number of successful visualization processes in the literature, and showed that the information-theoretic measure can mathematically explain the advantages of such processes over possible alternatives.Comment: 10 page

Parameterized complexity of machine scheduling: 15 open problems

Machine scheduling problems are a long-time key domain of algorithms and complexity research. A novel approach to machine scheduling problems are fixed-parameter algorithms. To stimulate this thriving research direction, we propose 15 open questions in this area whose resolution we expect to lead to the discovery of new approaches and techniques both in scheduling and parameterized complexity theory.Comment: Version accepted to Computers & Operations Researc