The structure of experience, the nature of the visual, and type 2 blindsight

Unlike those with type 1 blindsight, people who have type 2 blindsight have some sort of consciousness of the stimuli in their blind field. What is the nature of that consciousness? Is it visual experience? I address these questions by considering whether we can establish the existence of any structural—necessary—features of visual experience. I argue that it is very difficult to establish the existence of any such features. In particular, I investigate whether it is possible to visually, or more generally perceptually, experience form or movement at a distance from our body, without experiencing colour. The traditional answer, advocated by Aristotle, and some other philosophers, up to and including the present day, is that it is not and hence colour is a structural feature of visual experience. I argue that there is no good reason to think that this is impossible, and provide evidence from four cases—sensory substitution, achomatopsia, phantom contours and amodal completion—in favour of the idea that it is possible. If it is possible then one important reason for rejecting the idea that people with type 2 blindsight do not have visual experiences is undermined. I suggest further experiments that could be done to help settle the matter

Does Phenomenal Consciousness Overflow Attention? An Argument from Feature-Integration

In the past two decades a number of arguments have been given in favor of the possibility of phenomenal consciousness without attentional access, otherwise known as phenomenal overflow. This paper will show that the empirical data commonly cited in support of this thesis is, at best, ambiguous between two equally plausible interpretations, one of which does not posit phenomenology beyond attention. Next, after citing evidence for the feature-integration theory of attention, this paper will give an account of the relationship between consciousness and attention that accounts for both the empirical data and our phenomenological intuitions without positing phenomenal consciousness beyond attention. Having undercut the motivations for accepting phenomenal overflow along with having given reasons to think that phenomenal overflow does not occur, I end with the tentative conclusion that attention is a necessary condition for phenomenal consciousness

Embodied Cognition and Perception: Dewey, Science and Skepticism

This article examines how Modern theories of mind remain even in some materialistic and hence ontologically anti-dualistic views; and shows how Dewey, anticipating Merleau-Ponty and 4E cognitive scientists, repudiates these theories. Throughout I place Dewey’s thought in the context of scientific inquiry, both recent and historical and including the cognitive as well as traditional sciences; and I show how he incorporated sciences of his day into his thought, while also anticipating enactive cognitive science. While emphasizing Dewey’s continued relevance, my main goal is to show how his scientifically informed account of perception and cognition combats skepticism propagated by certain scientific visions, exacerbated by commonplace notions about mind, that jointly suggest that human beings lack genuine access to reality

Learning Aerial Image Segmentation from Online Maps

This study deals with semantic segmentation of high-resolution (aerial) images where a semantic class label is assigned to each pixel via supervised classification as a basis for automatic map generation. Recently, deep convolutional neural networks (CNNs) have shown impressive performance and have quickly become the de-facto standard for semantic segmentation, with the added benefit that task-specific feature design is no longer necessary. However, a major downside of deep learning methods is that they are extremely data-hungry, thus aggravating the perennial bottleneck of supervised classification, to obtain enough annotated training data. On the other hand, it has been observed that they are rather robust against noise in the training labels. This opens up the intriguing possibility to avoid annotating huge amounts of training data, and instead train the classifier from existing legacy data or crowd-sourced maps which can exhibit high levels of noise. The question addressed in this paper is: can training with large-scale, publicly available labels replace a substantial part of the manual labeling effort and still achieve sufficient performance? Such data will inevitably contain a significant portion of errors, but in return virtually unlimited quantities of it are available in larger parts of the world. We adapt a state-of-the-art CNN architecture for semantic segmentation of buildings and roads in aerial images, and compare its performance when using different training data sets, ranging from manually labeled, pixel-accurate ground truth of the same city to automatic training data derived from OpenStreetMap data from distant locations. We report our results that indicate that satisfying performance can be obtained with significantly less manual annotation effort, by exploiting noisy large-scale training data.Comment: Published in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSIN

Estimating position & velocity in 3D space from monocular video sequences using a deep neural network

This work describes a regression model based on Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) networks for tracking objects from monocular video sequences. The target application being pursued is Vision-Based Sensor Substitution (VBSS). In particular, the tool-tip position and velocity in 3D space of a pair of surgical robotic instruments (SRI) are estimated for three surgical tasks, namely suturing, needle-passing and knot-tying. The CNN extracts features from individual video frames and the LSTM network processes these features over time and continuously outputs a 12-dimensional vector with the estimated position and velocity values. A series of analyses and experiments are carried out in the regression model to reveal the benefits and drawbacks of different design choices. First, the impact of the loss function is investigated by adequately weighing the Root Mean Squared Error (RMSE) and Gradient Difference Loss (GDL), using the VGG16 neural network for feature extraction. Second, this analysis is extended to a Residual Neural Network designed for feature extraction, which has fewer parameters than the VGG16 model, resulting in a reduction of ~96.44 % in the neural network size. Third, the impact of the number of time steps used to model the temporal information processed by the LSTM network is investigated. Finally, the capability of the regression model to generalize to the data related to "unseen" surgical tasks (unavailable in the training set) is evaluated. The aforesaid analyses are experimentally validated on the public dataset JIGSAWS. These analyses provide some guidelines for the design of a regression model in the context of VBSS, specifically when the objective is to estimate a set of 1D time series signals from video sequences.Peer ReviewedPostprint (author's final draft

Drosophila tan Encodes a Novel Hydrolase Required in Pigmentation and Vision

Many proteins are used repeatedly in development, but usually the function of the protein is similar in the different contexts. Here we report that the classical Drosophila melanogaster locus tan encodes a novel enzyme required for two very different cellular functions: hydrolysis of N-β-alanyl dopamine (NBAD) to dopamine during cuticular melanization, and hydrolysis of carcinine to histamine in the metabolism of photoreceptor neurotransmitter. We characterized two tan-like P-element insertions that failed to complement classical tan mutations. Both are inserted in the 5′ untranslated region of the previously uncharacterized gene CG12120, a putative homolog of fungal isopenicillin-N N-acyltransferase (EC 2.3.1.164). Both P insertions showed abnormally low transcription of the CG12120 mRNA. Ectopic CG12120 expression rescued tan mutant pigmentation phenotypes and caused the production of striking black melanin patterns. Electroretinogram and head histamine assays indicated that CG12120 is required for hydrolysis of carcinine to histamine, which is required for histaminergic neurotransmission. Recombinant CG12120 protein efficiently hydrolyzed both NBAD to dopamine and carcinine to histamine. We conclude that D. melanogaster CG12120 corresponds to tan. This is, to our knowledge, the first molecular genetic characterization of NBAD hydrolase and carcinine hydrolase activity in any organism and is central to the understanding of pigmentation and photoreceptor function