How to optimize neuroscience data utilization and experiment design for advancing brain models of visual and linguistic cognition?

TL;DR

This paper discusses strategies for optimizing data collection and experimental design in neuroscience to improve artificial neural network models of visual and linguistic brain functions, highlighting debates and future directions.

Contribution

It provides a comprehensive analysis of current controversies and proposes directions for integrating experimental design with model development in neuroscience.

Findings

Qualitative insights can complement raw data in model training.

Model-free and model-based approaches each have advantages and limitations.

Sharing data and models is crucial for advancing brain research.

Abstract

In recent years, neuroscience has made significant progress in building large-scale artificial neural network (ANN) models of brain activity and behavior. However, there is no consensus on the most efficient ways to collect data and design experiments to develop the next generation of models. This article explores the controversial opinions that have emerged on this topic in the domain of vision and language. Specifically, we address two critical points. First, we weigh the pros and cons of using qualitative insights from empirical results versus raw experimental data to train models. Second, we consider model-free (intuition-based) versus model-based approaches for data collection, specifically experimental design and stimulus selection, for optimal model development. Finally, we consider the challenges of developing a synergistic approach to experimental design and model building, including encouraging data and model sharing and the implications of iterative additions to existing models. The goal of the paper is to discuss decision points and propose directions for both experimenters and model developers in the quest to understand the brain.