The Attentional Blink | mysimulator.uk

RSVP Stream

Lag between T1 and T2 3 items

Item duration (SOA) 100 ms

T1 task demand

Live Readouts

P(T2|T1) — this lag, empirical

no trials yet

P(T2|T1) — theoretical

—

Trials at this lag

Total trials run

Accuracy vs Lag

empirical (this session)theoretical curve

How it works

In a Rapid Serial Visual Presentation (RSVP) stream, letters flash one after another every 60–200 ms. Two targets are embedded: T1 (report it) and T2, appearing lag items later. Raymond, Shapiro & Arnell (1992) found that identifying T1 makes T2 hard to see for roughly 200–500 ms afterward — the attentional blink — except at lag 1, where T2 often "rides along" with T1 (lag‑1 sparing), because the attentional gate hasn't closed yet.

This simulator models the probability of correctly reporting T2 (given T1 was seen) as a parametric "attentional gate" function of the time t since T1 onset, following the resource-depletion / two-stage account of the blink (Chun & Potter, 1995):

t = lag × SOA
gate(t) = 0.95                                    if t < 100 ms   (gate still open: lag-1 sparing)
gate(t) = 0.95 − D·exp(−(t − 300·k)² / (2·(120·k)²))   otherwise

D = 0.55·k        (dip depth)
k = T1-task-demand factor (0.7 low, 1.0 medium, 1.3 high)

Each trial samples a Bernoulli outcome with probability gate(t) — a Monte Carlo draw from the theoretical curve, exactly like a real subject's trial-by-trial report. Run enough trials at a given lag and the empirical bar converges on the orange theoretical mark. Raising T1 task demand deepens and widens the dip, matching the empirical finding that harder T1 tasks produce a bigger blink. The purple particle field in the scene visualizes the same gate(t) value in real time as "how much attentional resource is currently available."

Lag slider — items between T1 and T2 (1 = immediately after T1).
SOA slider — stream speed; converts lag into real elapsed time.
T1 task demand — how attention-hungry the T1 judgement is.
Run 1 Trial — plays the RSVP stream once and reports hit/miss.
Run 50 Trials — samples the model 50× instantly to build up the bar chart.

Attentional gate100%

Distractor T1 target T2 target

⚠ Couldn't load the 3D engineThree.js failed to load from the CDN. Check your connection and reload.