MLDS: Maximum Likelihood Difference Scaling in R

The MLDS package in the R programming language can be used to estimate perceptual scales based on the results of psychophysical experiments using the method of difference scaling. In a difference scaling experiment, observers compare two supra-threshold differences (a,b) and (c,d) on each trial. The approach is based on a stochastic model of how the observer decides which perceptual difference (or interval) (a,b) or (c,d) is greater, and the parameters of the model are estimated using a maximum likelihood criterion. We also propose a method to test the model by evaluating the self-consistency of the estimated scale. The package includes an example in which an observer judges the differences in correlation between scatterplots. The example may be readily adapted to estimate perceptual scales for arbitrary physical continua

MLDS: Maximum Likelihood Difference Scaling in R

The MLDS package in the R programming language can be used to estimate perceptual scales based on the results of psychophysical experiments using the method of difference scaling. In a difference scaling experiment, observers compare two supra-threshold differences (a,b) and (c,d) on each trial. The approach is based on a stochastic model of how the observer decides which perceptual difference (or interval) (a,b) or (c,d) is greater, and the parameters of the model are estimated using a maximum likelihood criterion. We also propose a method to test the model by evaluating the self-consistency of the estimated scale. The package includes an example in which an observer judges the differences in correlation between scatterplots. The example may be readily adapted to estimate perceptual scales for arbitrary physical continua.

Detection and identification of mirror-image letter pairs in central and peripheral vision

AbstractReading performance is poorer in the peripheral than in the central visual field, even after size-scaling to compensate for differences in visual acuity at the different eccentricities. Since several studies have indicated that the peripheral retina is deficient with respect to spatial phase discrimination, we compared the psychometric functions for detection (D) and identification (I) of size-scaled, mirror-symmetric letters (i.e. letters differing in the phase spectra of their odd symmetric components) at three inferior field eccentricities (0, 4, and 7.5 deg) using a two-alternative, temporal, forced-choice procedure and retinal image stabilization to control retinal locus. Each subject's data were fit with Weibull functions and tested for goodness-of-fit under several hypotheses. This analysis revealed that while the psychometric functions were of constant shape across eccentricity for the respective tasks, they showed statistically significant variations in the D/I threshold ratios. However, these variations were so small that poorer reading outside the fovea is unlikely to be due to reduced letter discriminability that might occur secondary to a loss of peripheral field phase sensitivity

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

Study of Systematic Chromatic Changes in Color Space to Model Color Transparency

Several studies have suggested that translation and convergence in a linear trichromatic color space lead to the perception of transparency, but other transformations, such as shear and rotation,do not. We have designed a psychophysical experiment to study thelimits of such systemic chromatic changes, adding categories such as different luminance levels and vector lengths. The number of our stimuli and the number of observations provide strong statistical support for D'Zmura's model. Our main results show that for vectors exceeding a minimal length, convergence and translation (except in the equiluminant plane) lead to the perception of transparency, while shear and divergence do not. However, our results reveal that small shears and divergences also appear transparent. We also found that large translations in the equiluminant plane tend to be less often judged as transparent

Hierarchical and nonhierarchical features of the mouse visual cortical network

Neocortical computations underlying vision are performed by a distributed network of functionally specialized areas. Mouse visual cortex, a dense interareal network that exhibits hierarchical properties, comprises subnetworks interconnecting distinct processing streams. To determine the layout of the mouse visual hierarchy, we have evaluated the laminar patterns formed by interareal axonal projections originating in each of ten areas. Reciprocally connected pairs of areas exhibit feedforward/feedback relationships consistent with a hierarchical organization. Beta regression analyses, which estimate a continuous hierarchical distance measure, indicate that the network comprises multiple nonhierarchical circuits embedded in a hierarchical organization of overlapping levels. Single-unit recordings in anaesthetized mice show that receptive field sizes are generally consistent with the hierarchy, with the ventral stream exhibiting a stricter hierarchy than the dorsal stream. Together, the results provide an anatomical metric for hierarchical distance, and reveal both hierarchical and nonhierarchical motifs in mouse visual cortex

Spatial embedding and wiring cost constrain the functional layout of the cortical network of rodents and primates

Mammals show a wide range of brain sizes, reflecting adaptation to diverse habitats. Comparing interareal cortical networks across brains of different sizes and mammalian orders provides robust information on evolutionarily preserved features and species-specific processing modalities. However, these networks are spatially embedded, directed, and weighted, making comparisons challenging. Using tract tracing data from macaque and mouse, we show the existence of a general organizational principle based on an exponential distance rule (EDR) and cortical geometry, enabling network comparisons within the same model framework. These comparisons reveal the existence of network invariants between mouse and macaque, exemplified in graph motif profiles and connection similarity indices, but also significant differences, such as fractionally smaller and much weaker long-distance connections in the macaque than in mouse. The latter lends credence to the prediction that long-distance cortico-cortical connections could be very weak in the much-expanded human cortex, implying an increased susceptibility to disconnection syndromes such as Alzheimer disease and schizophrenia. Finally, our data from tracer experiments involving only gray matter connections in the primary visual areas of both species show that an EDR holds at local scales as well (within 1.5 mm), supporting the hypothesis that it is a universally valid property across all scales and, possibly, across the mammalian class

The nonhuman primate neuroimaging and neuroanatomy project

Multi-modal neuroimaging projects such as the Human Connectome Project (HCP) and UK Biobank are advancing our understanding of human brain architecture, function, connectivity, and their variability across individuals using high-quality non-invasive data from many subjects. Such efforts depend upon the accuracy of non-invasive brain imaging measures. However, ‘ground truth’ validation of connectivity using invasive tracers is not feasible in humans. Studies using nonhuman primates (NHPs) enable comparisons between invasive and non-invasive measures, including exploration of how “functional connectivity” from fMRI and “tractographic connectivity” from diffusion MRI compare with long-distance connections measured using tract tracing. Our NonHuman Primate Neuroimaging & Neuroanatomy Project (NHP_NNP) is an international effort (6 laboratories in 5 countries) to: (i) acquire and analyze high-quality multi-modal brain imaging data of macaque and marmoset monkeys using protocols and methods adapted from the HCP; (ii) acquire quantitative invasive tract-tracing data for cortical and subcortical projections to cortical areas; and (iii) map the distributions of different brain cell types with immunocytochemical stains to better define brain areal boundaries. We are acquiring high-resolution structural, functional, and diffusion MRI data together with behavioral measures from over 100 individual macaques and marmosets in order to generate non-invasive measures of brain architecture such as myelin and cortical thickness maps, as well as functional and diffusion tractography-based connectomes. We are using classical and next-generation anatomical tracers to generate quantitative connectivity maps based on brain-wide counting of labeled cortical and subcortical neurons, providing ground truth measures of connectivity. Advanced statistical modeling techniques address the consistency of both kinds of data across individuals, allowing comparison of tracer-based and non-invasive MRI-based connectivity measures. We aim to develop improved cortical and subcortical areal atlases by combining histological and imaging methods. Finally, we are collecting genetic and sociality-associated behavioral data in all animals in an effort to understand how genetic variation shapes the connectome and behavior