Tyeson Davies-Barton

This article examines the significance of what Edmund Husserl calls “the paradox of human subjectivity” for the metaphysical realism-idealism debate in transcendental phenomenology. I distinguish two plausible interpretations of the paradox that are relevant to this debate. I term the first a vicious circle of mutual dependence: the world is characterized as being ontologically dependent on one causally dependent part of the world. I term the second a paradox of bodily self-constitution: the transcendental subject is defined in such a way that it would be impossible for it to constitute itself as an empirical subject, and yet, in order to constitute the world, it must undergo such a process of self-objectification. I focus on one prominent version of phenomenology from each side of the realism-idealism debate, treating them as counterparts insofar as they share three core commitments. I argue that while the main accounts of the subject available to this idealist version generate at least one such paradox, the realist version can explain how one and the same subject is both transcendental and empirical without generating either paradox. My argument therefore provides a substantial prima facie reason to favour this realist version of phenomenology over its idealist counterpart for those who accept their shared commitments and take transcendental phenomenology to have metaphysical implications for the realism-idealism debate.

Cognitive scientists and neuroscientists typically understand the brain as a complex communication/information-processing system. A limitation of this framework is that it requires cognitive systems to have prior knowledge about their environment to successfully perform some of their basic functions, such as perceiving. It is unclear how the source of such knowledge can be explained from within this framework. Drawing on Dennett (1981), we refer to this as the loans of intelligence problem. Recent advances in machine learning have resulted in the development of a family of algorithms, including the class known as autoencoders, that seem to provide a way for the information-processing framework to avoid this problem: cognitive systems do not require loans of intelligence, but instead acquire the knowledge necessary for perception through a process of unsupervised learning. This paper argues that although autoencoders do avoid the loans of intelligence problem, how they do so should not be understood from within the information-processing framework. Instead, their success should be interpreted as a proof of concept of how neural networks can attune to Gibsonian information. We thus propose that autoencoders belong to a class of algorithms for modeling the brain that have recently been dubbed direct fit algorithms.