# Developing Concepts for Neuroscience: A Philosophical Toolkit

## TL;DR

This paper explores how the success of concepts in neuroscience depends on the goals scientists aim to achieve, using philosophical insights to evaluate and improve scientific terminology.

## Contribution

The paper introduces a philosophical framework to assess the success of neuroscience concepts based on their alignment with epistemic goals.

## Key findings

- The success of neuroscience concepts depends on the specific epistemic goals they are intended to achieve.
- Examples like 'default mode network' and 'cortical column' illustrate how concept development can succeed or fail based on these goals.
- Clarifying epistemic goals can improve the evaluation and refinement of scientific concepts in neuroscience.

## Abstract

Alongside models and methods, concepts are crucial tools to study and understand the brain. They help us pursue various goals, such as describing phenomena based on patterns in the data or explaining why these phenomena occur. Yet while terms such as “action potential” or “network” guide our efforts to reach these goals, other concepts have failed to advance our understanding of the brain. In this paper, we draw on recent work from philosophy of science to show that the success or failure of concepts in neuroscience depends on the epistemic goals the field aims to achieve. Looking at cases such as “default mode network,” “cortical column,” and “hierarchy,” we formulate conditions under which introducing, refining, or replacing a concept succeeds or fails. These cases suggest that to better evaluate our concepts, we should make explicit which goals we aim to achieve when using them.

Whether and how concepts should be developed depends on the phenomena that neuroscientists aim to describe, classify, and explain. These epistemic goals shape when introducing novel terms like “default mode network,” refining existing terms such as “hierarchy,” and retiring or replacing outdated ones such as “cortical columns” succeeds or fails to advance our scientific understanding of how the brain works.