Attentional Blink Magnitude

Compute T2 accuracy conditional on correct T1 by lag and subtract from a caller-selected baseline lag; preserves facilitation/negative deficits and does not infer a blink window.

Description

Attentional-blink summaries often condition second-target accuracy on correct first-target identification.

Attentional-blink summaries often condition second-target accuracy on correct first-target identification. Grouping by lag preserves the timing dependence.

The tool returns uniqueLags (unique lags); conditionalSecondTargetFractions (conditional second target fractions); baselineMinusLagFractions (baseline minus lag fractions). Input measurements and model parameters are supplied by the caller; the tool computes the stated summary rather than acquiring those measurements.

When to use Attentional Blink Magnitude

Use Attentional Blink Magnitude when you already have lags, firstTargetCorrectFlags, secondTargetCorrectFlags, baselineLag and want uniqueLags, conditionalSecondTargetFractions, baselineMinusLagFractions. This is useful for checking a supplied calculation, documenting a measurement convention, or comparing observations prepared on the same basis.

How to use Attentional Blink Magnitude and understand its results

Enter lags, firstTargetCorrectFlags, secondTargetCorrectFlags, baselineLag using the field table below. Match the units, ordering and data boundaries described in the calculation.
Run the tool after checking the stated bounds and matching paired observations or categories. A malformed request gives a tool error rather than a usable measurement.
Read uniqueLags, conditionalSecondTargetFractions, baselineMinusLagFractions. Use the calculation and interpretation below to distinguish amounts, ratios, signs and decision flags where applicable.
Review the supplied parameters and assumptions before using the result in a report or comparing it with another observation.

Input field	Meaning	Value type and bounds	Requirement
lags	Lags	list of number	Required
firstTargetCorrectFlags	First target correct flags	list of number	Required
secondTargetCorrectFlags	Second target correct flags	list of number	Required
baselineLag	Baseline lag	integer	Required

Use positive integer lags and binary outcomes. Each observed lag needs a correct-T1 trial, and the supplied baseline lag must occur.

P(T2 correct|T1 correct,lag) = correctBoth/firstCorrect; deficit = baselineFraction−lagFraction

The symbols refer to the supplied quantities or generated values described above; unit conversions and comparison boundaries follow the stated implementation.

json

{
  "input": {
    "lags": [
      1,
      1,
      2,
      2,
      5,
      5
    ],
    "firstTargetCorrectFlags": [
      1,
      0,
      1,
      1,
      1,
      1
    ],
    "secondTargetCorrectFlags": [
      0,
      1,
      1,
      0,
      1,
      1
    ],
    "baselineLag": 5
  },
  "output": {
    "uniqueLags": [
      1,
      2,
      5
    ],
    "conditionalSecondTargetFractions": [
      0,
      0.5,
      1
    ],
    "baselineMinusLagFractions": [
      1,
      0.5,
      0
    ]
  }
}

Limitations and assumptions

The baseline is caller-selected. The tool does not construct trials, adjust uncertainty or infer a neural mechanism.

Numerical inputs must be finite. The tool checks its declared input shape and calculation constraints; those checks do not verify the quality, calibration or representativeness of the supplied observations.

Alternative or Complementary methods

Digit Memory Span: Score supplied digit-span trials by caller-defined successful-trial count per sequence length; this does not implement a licensed test or normative interpretation. Signal Detection d Prime: Compute d′ from trial counts with an explicit Hautus loglinear half-count correction, preserving finite extreme-count estimates; assumes equal-variance signal detection.

Methodological background: The psychometric function: fitting, sampling, and goodness of fit.^[1]

References

The psychometric function: fitting, sampling, and goodness of fit — Wichmann and Hill; Perception & Psychophysics

Similar or alternative tools

Digit Memory Span
Score supplied digit-span trials by caller-defined successful-trial count per sequence length; this does not implement a licensed test or normative interpretation.
Signal Detection d Prime
Compute d′ from trial counts with an explicit Hautus loglinear half-count correction, preserving finite extreme-count estimates; assumes equal-variance signal detection.