Using auditory classification images for the identification of fine acoustic cues used in speech perception

International audienceAn essential step in understanding the processes underlying the general mechanism of perceptual categorization is to identify which portions of a physical stimulation modulate the behavior of our perceptual system. More specifically, in the context of speech comprehension, it is still a major open challenge to understand which information is used to categorize a speech stimulus as one phoneme or another, the auditory primitives relevant for the categorical perception of speech being still unknown. Here we propose to adapt a method relying on a Generalized Linear Model with smoothness priors, already used in the visual domain for the estimation of so-called classification images, to auditory experiments. This statistical model offers a rigorous framework for dealing with non-Gaussian noise, as it is often the case in the auditory modality, and limits the amount of noise in the estimated template by enforcing smoother solutions. By applying this technique to a specific two-alternative forced choice experiment between stimuli " aba " and " ada " in noise with an adaptive SNR, we confirm that the second formantic transition is key for classifying phonemes into /b/ or /d/ in noise, and that its estimation by the auditory system is a relative measurement across spectral bands and in relation to the perceived height of the second formant in the preceding syllable. Through this example, we show how the GLM with smoothness priors approach can be applied to the identification of fine functional acoustic cues in speech perception. Finally we discuss some assumptions of the model in the specific case of speech perception

Evolution and Optimality of Similar Neural Mechanisms for Perception and Action during Search

A prevailing theory proposes that the brain's two visual pathways, the ventral and dorsal, lead to differing visual processing and world representations for conscious perception than those for action. Others have claimed that perception and action share much of their visual processing. But which of these two neural architectures is favored by evolution? Successful visual search is life-critical and here we investigate the evolution and optimality of neural mechanisms mediating perception and eye movement actions for visual search in natural images. We implement an approximation to the ideal Bayesian searcher with two separate processing streams, one controlling the eye movements and the other stream determining the perceptual search decisions. We virtually evolved the neural mechanisms of the searchers' two separate pathways built from linear combinations of primary visual cortex receptive fields (V1) by making the simulated individuals' probability of survival depend on the perceptual accuracy finding targets in cluttered backgrounds. We find that for a variety of targets, backgrounds, and dependence of target detectability on retinal eccentricity, the mechanisms of the searchers' two processing streams converge to similar representations showing that mismatches in the mechanisms for perception and eye movements lead to suboptimal search. Three exceptions which resulted in partial or no convergence were a case of an organism for which the targets are equally detectable across the retina, an organism with sufficient time to foveate all possible target locations, and a strict two-pathway model with no interconnections and differential pre-filtering based on parvocellular and magnocellular lateral geniculate cell properties. Thus, similar neural mechanisms for perception and eye movement actions during search are optimal and should be expected from the effects of natural selection on an organism with limited time to search for food that is not equi-detectable across its retina and interconnected perception and action neural pathways

Modelling decision-making biases

Biases are a fundamental aspect of everyday life decision-making. A variety of modelling approaches have been suggested to capture decision-making biases. Statistical models are a means to describe the data, but the results are usually interpreted according to a verbal theory. This can lead to an ambiguous interpretation of the data. Mathematical cognitive models of decision-making outline the structure of the decision process with formal assumptions, providing advantages in terms of prediction, simulation, and interpretability compared to statistical models. We compare studies that used both signal detection theory and evidence accumulation models as models of decision-making biases, concluding that the latter provides a more comprehensive account of the decision-making phenomena by including response time behavior. We conclude by reviewing recent studies investigating attention and expectation biases with evidence accumulation models. Previous findings, reporting an exclusive influence of attention on the speed of evidence accumulation and prior probability on starting point, are challenged by novel results suggesting an additional effect of attention on non-decision time and prior probability on drift rate

The spatial resolutions of stereo and motion perception and their neural basis

PhD ThesisDepth perception requires finding matching features between the two eye’s images to estimate binocular disparity. This process has been successfully modelled using local cross-correlation. The model is based on the known physiology of primary visual cortex (V1) and has explained many aspects of stereo vision including why spatial stereoresolution is low compared to the resolution for luminance patterns, suggesting that the limit on spatial stereoresolution is set in V1. We predicted that this model would perform better at detecting square-wave disparity gratings, consisting of regions of locally constant disparity, than sine-waves which are slanted almost everywhere. We confirmed this through computational modelling and performed psychophysical experiments to test whether human performance followed the predictions of the model. We found that humans perform equally well with both waveforms. This contradicted the model’s predictions raising the question of whether spatial stereoresolution may not be limited in V1 after all or whether changing the model to include more of the known physiology may make it consistent with human performance. We incorporated the known size-disparity correlation into the model, giving disparity detectors with larger preferred disparities larger correlation windows, and found that this modified model explained the new human results. This provides further evidence that spatial stereoresolution is limited in V1. Based on previous evidence that MT neurons respond well to transparent motion in different depth planes we predicted that the spatial resolution of joint motion/disparity perception would be limited by the significantly larger MT receptive field sizes and therefore be much lower than the resolution for pure disparity. We tested this using a new joint motion/disparity grating, designed to require the detection of conjunctions between motion and disparity. We found little difference between the resolutions for disparity and joint gratings, contradicting our predictions and suggesting that a different area than MT was used

A robust framework for medical image segmentation through adaptable class-specific representation

Medical image segmentation is an increasingly important component in virtual pathology, diagnostic imaging and computer-assisted surgery. Better hardware for image acquisition and a variety of advanced visualisation methods have paved the way for the development of computer based tools for medical image analysis and interpretation. The routine use of medical imaging scans of multiple modalities has been growing over the last decades and data sets such as the Visible Human Project have introduced a new modality in the form of colour cryo section data. These developments have given rise to an increasing need for better automatic and semiautomatic segmentation methods. The work presented in this thesis concerns the development of a new framework for robust semi-automatic segmentation of medical imaging data of multiple modalities. Following the specification of a set of conceptual and technical requirements, the framework known as ACSR (Adaptable Class-Specific Representation) is developed in the first case for 2D colour cryo section segmentation. This is achieved through the development of a novel algorithm for adaptable class-specific sampling of point neighbourhoods, known as the PGA (Path Growing Algorithm), combined with Learning Vector Quantization. The framework is extended to accommodate 3D volume segmentation of cryo section data and subsequently segmentation of single and multi-channel greyscale MRl data. For the latter the issues of inhomogeneity and noise are specifically addressed. Evaluation is based on comparison with previously published results on standard simulated and real data sets, using visual presentation, ground truth comparison and human observer experiments. ACSR provides the user with a simple and intuitive visual initialisation process followed by a fully automatic segmentation. Results on both cryo section and MRI data compare favourably to existing methods, demonstrating robustness both to common artefacts and multiple user initialisations. Further developments into specific clinical applications are discussed in the future work section

Human observers have optimal introspective access to perceptual processes even for visually masked stimuli

© 2015, eLife Sciences Publications Ltd. All rights reserved. Many believe that humans can âperceive unconsciouslyâ â that for weak stimuli, briefly presented and masked, above-chance discrimination is possible without awareness. Interestingly, an online survey reveals that most experts in the field recognize the lack of convincing evidence for this phenomenon, and yet they persist in this belief. Using a recently developed bias-free experimental procedure for measuring subjective introspection (confidence), we found no evidence for unconscious perception; participantsâ behavior matched that of a Bayesian ideal observer, even though the stimuli were visually masked. This surprising finding suggests that the thresholds for subjective awareness and objective discrimination are effectively the same: if objective task performance is above chance, there is likely conscious experience. These findings shed new light on decades-old methodological issues regarding what it takes to consider a neurobiological or behavioral effect to beâunconscious,â and provide a platform for rigorously investigating unconscious perception in future studies.Link_to_subscribed_fulltex