Psychophysique

Résumé

La psychophysique est une branche de la psychologie expérimentale qui cherche à déterminer les relations quantitatives qui existent entre un stimulus physique et la perception qu'on en a. La psychophysique s'intéresse aux sens physiologiques tels que la vue, l'ouïe, le toucher (plus rarement l'odorat ou le goût) mais aussi à des sensations comme la perception du temps ou du mouvement. Histoire On considère que Gustav Fechner, auteur du Elemente der Psychophysik en 1860, est le fondateur de la psychophysique. Même si on peut faire remonter ses influences aux physiciens allemands comme Johann Friedrich Herbart, Fechner fut le premier à avoir entrepris une mathématisation systématique de la relation entre stimulation et sensation à partir des travaux de Ernst Weber. Élève de Fechner, influencé par les travaux de Hermann von Helmholtz et les théories de la méthode expérimentale dans les sciences naturelles, le physiologiste allemand Wilhelm Wundt va donner à la psychophysique

Source officielle

https://fr.wikipedia.org/wiki/Psychophysique

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Semantic-Driven Selection of Printer Color Rendering Intents

Albrecht Johannes Lindner, Sabine Süsstrunk

In this paper we introduce a unified framework that automatically selects the optimal color rendering intent for a given print job. We first present how we extract information from both the image features and the semantic information contained in keywords attached to this image. Then we show how our framework unifies the two inputs to select the optimal ICC rendering intent. The framework is evaluated with a psychophysical experiment on an image data set printed with the ICC media-relative colorimetric and perceptual intents using an Océ large format printer. We find that our method is correctly able to predict the observers preferences in 81% of the images tested when the keyword is included compared to 58% when the keyword is not included.

2012

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Models of Evidence Integration in Rapid Decision Making Processes

Nicolas Marcille

Decision making is of crucial interest in many disciplines such as psychology, neuroscience, economics and machine learning. Binary perceptual decision theories relate to situations where an observer (or machine) is confronted with one of two possible noisy stimuli. For example, human readers have to decide whether a handwritten character is an n or a u; a trader has to decide whether to sell or to keep; a monkey has to decide whether dots on a screen are moving to the left or to the right. While engineering and economical decision theories focus on how to compute optimal decisions, psychology and neuroscience investigate the actual decision making process in humans and animals. Humans only need a fraction of a second to recognize objects. This astonishing speed is also evident in sports such as table tennis or soccer requiring rapid reactions to moving balls. In these tasks, the brain has to decide rapidly upon visual information available for only a hundred milliseconds or less. However, most experimental and theoretical work on decision making focuses on stationary paradigms and surprisingly few studies have taken the timing of the stimulus into account. Decisions about noisy stimuli require evidence integration over time. Traditionally, evidence integration and decision making are described as a one-stage process: a decision is made when evidence for the presence of a stimulus crosses a threshold. We will show that this model is incompatible with psychophysical experiments on feature fusion, where two visual stimuli are presented in rapid succession. Paradoxically, the second stimulus biases decisions more strongly than the first one, contrary to predictions of one-stage models and intuition. We present a two-stage model where sensory information is integrated and buffered before it starts driving the diffusion process.

EPFL2011

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The application of Bayesian modeling techniques is increasingly common in neuroscience due to the coherent and principled way in which the paradigm deals with uncertainty. The Bayesian framework is particularly valuable in the context of complex, ill-posed generative problems, in which case the incorporation of prior knowledge is optimal and practical. If one wants to take full advantage of joint imaging and behavioral data, the need for comprehensive generative models is evident. Here, I exploited the versatility afforded by Bayesian modeling approaches to investigate a series of questions from clinical, systems, and cognitive neuroscience. First, I investigated the lateralization of the cortical motor network in Parkinson's disease patients on and off dopaminergic medication with fMRI and dynamic causal modeling (DCM). Group-level model comparison revealed that disease and medication effects differentially involve homotopic cortical motor connections, but medication does not appear to have a restorative effect at the systems level. Our findings suggest the presence of maladaptive mechanisms, resulting from disease progression and long-term dopamine replacement. In a second project, I considered the way in which humans handle uncertainty in the parameters of the generative model of a task at hand. Using a novel psychophysics paradigm, participants' responses to fitting a parabola to a set of noisy points were compared to the predictions of a set of regression models, including Bayesian regression and several sub-optimal models. Only Bayesian regression could explain participants' response distributions across various levels of sensory uncertainty, suggesting that humans process parameter uncertainty in accordance with Bayesian regression. This finding deepens our understanding of the way in which humans learn and generalize from sparse, ambiguous data. The topic investigated in the greatest depth in this thesis was visual crowding - the deterioration of target discrimination due to the presence of flanking objects. Traditionally, vision is modeled as a feedforward, hierarchical network. Thus, crowding is explained as a pooling of neighboring elements' features, leading to a "bottleneck" at the earliest stages of vision. However, additional flankers can paradoxically improve performance (uncrowding). First, I used DCM and fMRI to show that recurrent processing is crucial for crowding and un-crowding alike. Higher visual areas modulate the lower areas, suggesting that explicit object representation plays a key role. I then used population receptive field (pRF) mapping to investigate the effects of context on pRF size. In accordance with pooling model predictions, the pRF size in crowding is larger than in the case of no crowding. However, in uncrowding, the pRF size is smaller than in crowding and no crowding, providing further evidence against purely feedforward models. Overall, my findings provide strong empirical evidence of context-dependent feedback modulation in vision. I propose a possible implementation which integrates the observed findings into a generative model of crowding: context-agnostic feedforward mechanisms transmit the information about the target and flankers retinotopically to higher visual areas; a subsequent context-dependent feedback pRF determines whether the target will be enhanced - by decreasing the pRF size - or suppressed - by enlarging the pRF size to encompass flankers.

EPFL2020

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EPFL2020