1,932,949 research outputs found
Effects of noise upon human information processing
Studies of noise effects upon human information processing are described which investigated whether or not effects of noise upon performance are dependent upon specific characteristics of noise stimulation and their interaction with task conditions. The difficulty of predicting noise effects was emphasized. Arousal theory was considered to have explanatory value in interpreting the findings of all the studies. Performance under noise was found to involve a psychophysiological cost, measured by vasoconstriction response, with the degree of response cost being related to scores on a noise annoyance sensitivity scale. Noise sensitive subjects showed a greater autonomic response under noise stimulation
Contextual Feedback to Superficial Layers of V1
Neuronal cortical circuitry comprises feedforward, lateral, and feedback projections, each of which terminates in distinct cortical layers [1-3]. In sensory systems, feedforward processing transmits signals from the external world into the cortex, whereas feedback pathways signal the brain's inference of the world [4-11]. However, the integration of feedforward, lateral, and feedback inputs within each cortical area impedes the investigation of feedback, and to date, no technique has isolated the feedback of visual scene information in distinct layers of healthy human cortex. We masked feedforward input to a region of V1 cortex and studied the remaining internal processing. Using high-resolution functional brain imaging (0.8 mm(3)) and multivoxel pattern information techniques, we demonstrate that during normal visual stimulation scene information peaks in mid-layers. Conversely, we found that contextual feedback information peaks in outer, superficial layers. Further, we found that shifting the position of the visual scene surrounding the mask parametrically modulates feedback in superficial layers of V1. Our results reveal the layered cortical organization of external versus internal visual processing streams during perception in healthy human subjects. We provide empirical support for theoretical feedback models such as predictive coding [10, 12] and coherent infomax [13] and reveal the potential of high-resolution fMRI to access internal processing in sub-millimeter human cortex
Human Computation and Convergence
Humans are the most effective integrators and producers of information,
directly and through the use of information-processing inventions. As these
inventions become increasingly sophisticated, the substantive role of humans in
processing information will tend toward capabilities that derive from our most
complex cognitive processes, e.g., abstraction, creativity, and applied world
knowledge. Through the advancement of human computation - methods that leverage
the respective strengths of humans and machines in distributed
information-processing systems - formerly discrete processes will combine
synergistically into increasingly integrated and complex information processing
systems. These new, collective systems will exhibit an unprecedented degree of
predictive accuracy in modeling physical and techno-social processes, and may
ultimately coalesce into a single unified predictive organism, with the
capacity to address societies most wicked problems and achieve planetary
homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added
references to page 1 and 3, and corrected typ
Modeling Visual Information Processing in Brain: A Computer Vision Point of View and Approach
We live in the Information Age, and information has become a critically
important component of our life. The success of the Internet made huge amounts
of it easily available and accessible to everyone. To keep the flow of this
information manageable, means for its faultless circulation and effective
handling have become urgently required. Considerable research efforts are
dedicated today to address this necessity, but they are seriously hampered by
the lack of a common agreement about "What is information?" In particular, what
is "visual information" - human's primary input from the surrounding world. The
problem is further aggravated by a long-lasting stance borrowed from the
biological vision research that assumes human-like information processing as an
enigmatic mix of perceptual and cognitive vision faculties. I am trying to find
a remedy for this bizarre situation. Relying on a new definition of
"information", which can be derived from Kolmogorov's compexity theory and
Chaitin's notion of algorithmic information, I propose a unifying framework for
visual information processing, which explicitly accounts for the perceptual and
cognitive image processing peculiarities. I believe that this framework will be
useful to overcome the difficulties that are impeding our attempts to develop
the right model of human-like intelligent image processing.Comment: That is a journal version of a paper that in 2007 has been submitted
to 15 computer vision conferences and was discarded by 11 of the
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