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Internal noise, External noise paradigms and Observer models| old_uid | 5202 |
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| title | Internal noise, External noise paradigms and Observer models |
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| start_date | 2008/07/03 |
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| schedule | 16h |
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| online | no |
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| summary | Signal detection theory (SDT) provides a precise language and general framework for analyzing decision making in the presence of uncertainty by postulating noisy internal representations of external stimuli and separating response criterion from signal-noise discriminability. The functional relationship between the internal representations and the physical characteristics of external stimuli, necessary for predicting human performance in various stimulus conditions, is however unspecified in the SDT framework. The issue has been addressed by the external noise approach. By constructing and specifying observer models with various intrinsic properties that do not vary with stimulus conditions, the approach provides a framework to compute the internal responses and predict performance in new stimulus conditions. Recently, the approach has been used to assay mechanisms of attention, perceptual learning, object recognition, adaptation, and various visual deficits.
A number of components, derived from both sensory psychology and physiology, have been used to construct observer models, including perceptual template, non-linear transducer, additive noise, multiplicative noise, contrast-gain control, and decision uncertainty. To identify the optimal model for the range of empirical data, we have derived properties of five observer models in the literature in relation to three external noise paradigms: the equivalent input noise method, the triple TvC method, and the double-pass procedure. The models are compared in light of existing data in the literature as well as their ability to fit the data in a new experiment. We conclude that the optimal model consists of five components: a perceptual template, a non-linear transducer function, internal additive noise, internal multiplicative noise, and a decision structure.
In this talk, I will first present a review of the observer models. I will then describe a Bayesian adaptive procedure, the "qTvC" method, developed to efficiently estimate threshold versus external noise contrast (TvC) functions at three performance criterion levels. Finally, I will talk about attentional modulation of BOLD contrast response functions in early and mid visual cortical areas. |
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| responsibles | Information non disponible |
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