Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence.

Area of Science:

• Neuroscience and Cognitive Science
• Computational Neuroscience
• Artificial Intelligence

Background:

• Current models explain limited aspects of intelligence, focusing on individual tasks or brain regions.
• A gap exists in unifying findings across diverse experimental studies and laboratories.
• Advances in data availability (neural, anatomical, behavioral) and modeling offer new opportunities.

Purpose of the Study:

• To advocate for the integration of experimental results into comprehensive benchmarking platforms.
• To promote the development of ambitious, unified mechanistic models of intelligence.
• To address challenges and propose steps for creating such integrative platforms, using visual intelligence as a case study.

Main Methods:

• Proposing the creation of integrative benchmarking platforms.
• Discussing the advantages and challenges of unifying diverse data.
• Highlighting the development of a platform called Brain-Score for visual intelligence.

Main Results:

• The abstract is a perspective piece, outlining a proposed approach rather than presenting empirical results.
• It identifies the need for and potential benefits of integrative benchmarking.
• It suggests Brain-Score as a model system for this approach.

Conclusions:

• Integrating experimental data into benchmarks is crucial for developing holistic mechanistic models of intelligence.
• Such platforms will incentivize the creation of more ambitious and unified models.
• The proposed approach, exemplified by Brain-Score, can drive progress in understanding cognitive domains.