What Simulations Teach Us About Ordinary Objects

Abstract

Under the label of scientific metaphysics, many naturalist metaphysicians are moving away from a priori conceptual analysis and instead seek scientific explanations that will help bring forward a unified understanding of the world. This paper first reviews how our classical assumptions about ordinary objects fail to be true in light of quantum mechanics. The paper then explores how our experiences of ordinary objects arise by reflecting on how our neural system operates algorithmically. Contemporary models and simulations in computational neuroscience are shown to provide a theoretical framework that does not conflict with existing fundamental physical theories, and nonetheless helps us make sense of the manifest image. It is argued that we must largely explain how the manifest image arises in algorithmic terms, so that we can pursue a metaphysics about ordinary objects that is scientifically well founded.

The Cognitive Basis of the Conditional Probability Solution to the Value Problem for Reliabilism

“…This is because each possible feature would have to be hard-wired into our brains over the course of evolution. As we saw, PP tells a different story, namely, that these features come about by means of the process of discrimination, as mentioned above, whereby a population of neurons successively refine the differences in the sense data to which they are sensible (see, e.g., Schwaninger, 2019). At the primary level, these features will typically correspond to simple gradients of light and dark on the retina, but on higher levels, such simple building blocks will be composed to represent meaningful larger structures like trees and animals.…”

“…Schwaninger (2019) makes this point explicit by identifying how hyper-parameters can be maladapted to a certain environment such that cognitive learning processes fail to build internal representations of the data, including representations that allows for linguistic communication about the world.…”

The Cognitive Philosophy of Communication

“…Each of these networks includes a recognition as well as a generative model that allows the agents to build internal representations of pears and The downstream flow of information is given by a generative model that generates images from the internal representation in order to predict the activity of lower cortical regions. This allows a human-like communication process of two agents A and B to be modelled in a computer simulation as illustrated in Figure 3 [97]. Both agents are represented by a neural network that was trained on similar data, in this case a set of images of pears.…”

“…However, not much is left of these problems within a framework that does not assume a world that is shaped prior to human perception. The problems simply disappear if objects are conceived of as constructions in the Kantian framework [97].…”