Human Attention Benchmark vs. Synthetic Metrics in Model Performance Correlation

Abstract

Abstract: This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How does the human attention benchmark compare to existing synthetic attention evaluation metrics in terms of correlation with model performance on downstream tasks. Many computational models of visual attention have been created from a wide variety of different approaches to predict where people look in images. Each model is usually introduced by demonstrating performances on new images, and it is hard to make immediate comparisons between. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the human attention benchmark compare to existing synthetic attention evaluation metrics in terms of correlation with model performance on downstream tasks?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance