The Mismeasure of Consciousness: A problem of coordination for the Perceptual Awareness Scale

As for most measurement procedures in the course of their development, measures of consciousness face the problem of coordination, i.e., the problem of knowing whether a measurement procedure actually measures what it is intended to measure. I focus on the case of the Perceptual Awareness Scale to illustrate how ignoring this problem leads to ambiguous interpretations of subjective reports in consciousness science. In turn, I show that empirical results based on this measurement procedure might be systematically misinterpreted

Methodological Artefacts in Consciousness Science

Consciousness is scientifically challenging to study because of its subjective aspect. This leads researchers to rely on report-based experimental paradigms in order to discover neural correlates of consciousness (NCCs). I argue that the reliance on reports has biased the search for NCCs, thus creating what I call 'methodological artefacts'. This paper has three main goals: first, describe the measurement problem in consciousness science and argue that this problem led to the emergence of methodological artefacts. Second, provide a critical assessment of the NCCs put forward by the global neuronal workspace theory. Third, provide the means of dissociating genuine NCCs from methodological artefacts

Consciousness Science Underdetermined: A short history of endless debates

Consciousness scientists have not reached consensus on two of the most central questions in their field: first, on whether consciousness overflows reportability; second, on the physical basis of consciousness. I review the scientific literature of the 19th century to provide evidence that disagreement on these questions has been a feature of the scientific study of consciousness for a long time. Based on this historical review, I hypothesize that a unifying explanation of disagreement on these questions, up to this day, is that scientific theories of consciousness are underdetermined by the evidence, namely, that they can be preserved “come what may” in front of (seemingly) disconfirming evidence. Consciousness scientists may have to find a way of solving the persistent underdetermination of theories of consciousness to make further progress

A new empirical challenge for local theories of consciousness

Local theories of consciousness state that one is conscious of a feature if it is adequately represented and processed in sensory brain areas, given some background conditions. We challenge the core prediction of local theories based on recently discovered long-lasting postdictive effects demonstrating that features can be represented for hundreds of milliseconds in perceptual areas without being consciously perceived. Unlike previous empirical data aimed against local theories, proponents of local theories cannot explain these effects away by conjecturing that subjects are phenomenally conscious of features that they cannot report. Only a strong and counterintuitive version of this claim can account for long-lasting postdictive effects. Although possible, we argue that adopting this strong version of the “overflow hypothesis” would have the effect of nullifying the weight of the evidence taken to support local theories of consciousness in the first place. We also discuss several alternative explanations that proponents of local theories could offer

Using the SkelCL Library for High-Level GPU Programming of 2D Applications

Application programming for GPUs (Graphics Processing Units) is complex and error-prone, because the popular approaches — CUDA and OpenCL — are intrinsically low-level and offer no special support for systems consisting of multiple GPUs. The SkelCL library offers pre-implemented recurring computation and communication patterns (skeletons) which greatly simplify programming for single- and multi-GPU systems. In this paper, we focus on applications that work on two-dimensional data. We extend SkelCL by the matrix data type and the MapOverlap skeleton which specifies computations that depend on neighboring elements in a matrix. The abstract data types and a high-level data (re)distribution mechanism of SkelCL shield the programmer from the low-level data transfers between the system’s main memory and multiple GPUs. We demonstrate how the extended SkelCL is used to implement real-world image processing applications on two-dimensional data. We show that both from a productivity and a performance point of view it is beneficial to use the high-level abstractions of SkelCL