Dynamic Computerized Tumbling-E Testing for Temporal Reliability of Human Sequential Perceptual Decisions

Abstract

OBJECTIVES: Visual acuity and tumbling-E tasks are often treated as static threshold measures, yet sequential perceptual decisions unfold over time. A computerized tumbling-E task preserves response latency, timeouts, and stimulus-size adaptation, creating a temporal reliability dataset rather than only a chart-line score. This matters for human-AI comparison because the Temporal Hallucination Index (THI) shows how static accuracy can obscure delays, drift, persistence, and unstable convergence. METHODS: We curated trial-level human data from a computerized dynamic tumbling-E task. On each trial, a single E optotype appeared in one of four orientations, participants selected the perceived direction or timed out, and stimulus size was automatically adjusted through an adaptive staircase. Primary outcomes were reaction time, timeout rate, delay rate above a 3-second budget, and observable THI based on delay and timeout components. RESULTS: The final dataset included 1,154 valid trials from 21 human identifiers across 77 sessions. There were 1,078 non-timeout responses and 76 timeouts, giving a 6.6% timeout rate. Non-timeout reaction times centered near 1.5 seconds (mean 1546 ms; median 1506 ms; IQR 1306-1713 ms), with only 3 responses exceeding 3,000 ms. Adaptation was dominated by smaller-next-stimulus transitions (89.2%). Mean arcminutes declined from 29.42 at trial 0 to 5.04 at trial 19, supporting convergence near a 20/20-level optotype without clinical acuity diagnosis. CONCLUSIONS: This dataset converts a tumbling-E visual task into a temporally resolved human perceptual-decision benchmark. Its novel contribution is automatic capture of staircase behavior, response timing, timeouts, and trial-level reliability signals. The human data show fast timing and smooth adaptation toward threshold, establishing a human-only baseline for future comparison with artificial agents.