Biology subjects

Akbarinia, A.

Publications and source records attributed to Akbarinia, A..

4 recordsLinked to original sources

bioRxiv2024

Exploring the Categorical Nature of Colour Perception: Insights from Deep Networks

This study delves into the categorical aspects of colour perception, employing the odd-one-out paradigm on artificial neural networks. We reveal a significant alignment between human data and unimodal vision networks (e.g., ImageNet object recognition). Vision-language models (e.g., CLIP text-image matching) account for the remaining unexplained data even in non-linguistic experiments. These results suggest that categorical colour perception is a language-independent representation, albeit partly shaped by linguistic colour terms during its development. Exploring the ubiquity of colour categories in Taskonomy unimodal vision networks highlights the task-dependent nature of colour categories, predominantly in semantic and 3D tasks, with a notable absence in low-level tasks. To explain this difference, we analysed kernels responses before the winnertaking-all, observing that networks with mismatching colour categories align in continuous representations. Our findings quantify the dual influence of visual signals and linguistic factors in categorical colour perception, thereby formalising a harmonious reconciliation of the universal and relative debates.

animal behavior and cognition↗

bioRxiv2023

Contrast Sensitivity Function in Deep Networks

The contrast sensitivity function (CSF) is a fundamental signature of the visual system that has been measured extensively in several species. It is defined by the visibility threshold for sinusoidal gratings at all spatial fre-quencies. Here, we investigated the CSF in deep neural networks using the same 2AFC contrast detection paradigm as in human psychophysics. We examined 240 networks pretrained on several tasks. To obtain their corre-sponding CSFs, we trained a linear classifier on top of the extracted features from frozen pretrained networks. The linear classifier is exclusively trained on a contrast discrimination task with natural images. It has to find which of the two input images has higher contrast. The networks CSF is measured by detecting which one of two images contains a sinusoidal grating of varying orientation and spatial frequency. Our results demonstrate char-acteristics of the human CSF are manifested in deep networks both in the luminance channel (a band-limited inverted U-shaped function) and in the chromatic channels (two low-pass functions of similar properties). The exact shape of the networks CSF appears to be task-dependent. The human CSF is better captured by networks trained on low-level visual tasks such as image-denoising or autoencoding. However, human-like CSF also emerges in mid- and high-level tasks such as edge detection and object recognition. Our analysis shows that human-like CSF appears in all architectures but at different depths of processing, some at early layers, while others in intermediate and final layers. Overall, these results suggest that (i) deep networks model the human CSF faithfully, making them suitable candidates for applications of image quality and compression, (ii) efficient/purposeful processing of the natural world drives the CSF shape, and (iii) visual representation from all levels of visual hierarchy contribute to the tuning curve of the CSF, in turn implying a function which we intuitively think of as modulated by low-level visual features may arise as a consequence of pooling from a larger set of neurons at all levels of the visual system.

animal behavior and cognition↗

bioRxiv2022

Color and gloss constancy under diverse lighting environments

When we look at an object, we simultaneously see how glossy or matte it is, how light or dark, and what color. Yet, at each point on the objects surface, both diffuse and specular reflections are mixed in different proportions, resulting in substantial spatial chromatic and luminance variations. To further complicate matters, this pattern changes radically when the object is viewed under different lighting conditions. The purpose of this study was to simultaneously measure our ability to judge color and gloss using an image set capturing diverse object and illuminant properties. Participants adjusted the hue, lightness, chroma, and specular reflectance of a reference object so that it appeared to be made of the same material as a test object. Critically, the two objects were presented under different lighting environments. We found that hue matches were highly accurate, except for under a chromatically atypical illuminant. Chroma and lightness constancy were generally poor, but these failures correlated well with simple image statistics. Gloss constancy was particularly poor, and these failures were only partially explained by reflection contrast. Importantly, across all measures, participants were highly consistent with one another in their deviations from constancy. Although color and gloss constancy hold well in simple conditions, the variety of lighting and shape in the real world presents significant challenges to our visual systems ability to judge intrinsic material properties.

neuroscience↗

bioRxiv2021

Emergent Color Categorization in a Neural Network trained for Object Recognition

Color is a prime example of categorical perception, yet it is unclear why and how color categories emerge. While prelinguistic infants and animals treat color categorically, several recent modeling endeavors have successfully utilized communicative concepts to predict color categories. Rather than modeling categories directly, we investigate the potential emergence of color categories as a result of acquiring visual skills. Specifically, whether color is represented categorically in a convolutional neural network (CNN) trained to recognize objects in natural images. Systematically training new output layers to the CNN for a color classification task, we find clear borders between new (non-training) colors that are largely invariant to the training colors. Using an evolutionary algorithm that relies on the principle of categorical perception we verify these border locations. These results provide strong evidence that color categorization emerges as a function of basic visual skills and provide a new basis for uncovering how they emerge.

animal behavior and cognition↗