Exploration, inference and prediction in neuroscience and biomedicine

TL;DR

This paper discusses the contrasting philosophies of traditional statistical inference and pattern-learning algorithms in neuroscience, emphasizing the importance of selecting analysis tools based on research goals like mechanistic understanding or predictive accuracy.

Contribution

It advocates for a goal-driven approach to choosing quantitative methods in neuroscience, moving beyond categorical labels like 'statistics' or 'machine learning'.

Findings

Highlights tension between inference and prediction approaches.

Recommends aligning analysis tools with research objectives.

Discourages method selection based solely on categories.

Abstract

The last decades saw dramatic progress in brain research. These advances were often buttressed by probing single variables to make circumscribed discoveries, typically through null hypothesis significance testing. New ways for generating massive data fueled tension between the traditional methodology, used to infer statistically relevant effects in carefully-chosen variables, and pattern-learning algorithms, used to identify predictive signatures by searching through abundant information. In this article, we detail the antagonistic philosophies behind two quantitative approaches: certifying robust effects in understandable variables, and evaluating how accurately a built model can forecast future outcomes. We discourage choosing analysis tools via categories like 'statistics' or 'machine learning'. Rather, to establish reproducible knowledge about the brain, we advocate prioritizing tools in view of the core motivation of each quantitative analysis: aiming towards mechanistic insight, or optimizing predictive accuracy.