Are mental properties supervenient on brain properties?

TL;DR

This paper introduces a rigorous statistical framework called epsilon-supervenience to investigate whether mental properties can be inferred from brain connectomes, enabling experimental testing of the mind-brain relationship.

Contribution

It proposes a novel, testable statistical formulation of supervenience and demonstrates its application using pattern recognition on brain connectivity data.

Findings

Epsilon-supervenience can determine if mental properties are inferable from brain data within a specified error rate.

The approach bridges neuroscience and statistics for empirical testing of the mind-brain link.

Provides a foundation for future cross-disciplinary research on mental property inference.

Abstract

The "mind-brain supervenience" conjecture suggests that all mental properties are derived from the physical properties of the brain. To address the question of whether the mind supervenes on the brain, we frame a supervenience hypothesis in rigorous statistical terms. Specifically, we propose a modified version of supervenience (called epsilon-supervenience) that is amenable to experimental investigation and statistical analysis. To illustrate this approach, we perform a thought experiment that illustrates how the probabilistic theory of pattern recognition can be used to make a one-sided determination of epsilon-supervenience. The physical property of the brain employed in this analysis is the graph describing brain connectivity (i.e., the brain-graph or connectome). epsilon-supervenience allows us to determine whether a particular mental property can be inferred from one's connectome to within any given positive misclassification rate, regardless of the relationship between the two. This may provide motivation for cross-disciplinary research between neuroscientists and statisticians.