Visual Search Slope
Aggregate correct-trial visual-search RTs by set size and fit equally weighted condition means; signed slopes are retained and no serial/parallel search diagnosis is asserted.
Description
Visual-search slope relates mean correct-trial reaction time to set size.
Visual-search slope relates mean correct-trial reaction time to set size. Grouping first gives each tested set size equal weight in the regression.
The tool returns uniqueSetSizes (unique set sizes); meanCorrectReactionTimesMs (mean correct reaction times ms); slopeMsPerItem (slope ms per item); interceptMs (intercept ms); fitRSquared (fit r squared). Input measurements and model parameters are supplied by the caller; the tool computes the stated summary rather than acquiring those measurements.
When to use Visual Search Slope
Use Visual Search Slope when you already have setSizes, reactionTimesMs, correctFlags and want uniqueSetSizes, meanCorrectReactionTimesMs, slopeMsPerItem, interceptMs, fitRSquared. This is useful for checking a supplied calculation, documenting a measurement convention, or comparing observations prepared on the same basis.
How to use Visual Search Slope and understand its results
- Enter setSizes, reactionTimesMs, correctFlags 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 uniqueSetSizes, meanCorrectReactionTimesMs, slopeMsPerItem, interceptMs, fitRSquared. 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 |
|---|---|---|---|
| setSizes | Set sizes | list of number | Required |
| reactionTimesMs | Reaction times ms | list of number | Required |
| correctFlags | Correct flags | list of number | Required |
Use positive integer set sizes, positive RTs and binary correctness. Every observed set size needs a correct trial; at least two distinct sizes are needed.
meanRT[size] = mean(correct RTs at size); slope = OLS slope(meanRT versus size) The symbols refer to the supplied quantities or generated values described above; unit conversions and comparison boundaries follow the stated implementation.
{
"input": {
"setSizes": [
2,
2,
4,
4,
6,
6
],
"reactionTimesMs": [
200,
220,
300,
320,
400,
420
],
"correctFlags": [
1,
1,
1,
1,
1,
1
]
},
"output": {
"uniqueSetSizes": [
2,
4,
6
],
"meanCorrectReactionTimesMs": [
210,
310,
410
],
"slopeMsPerItem": 50,
"interceptMs": 110,
"fitRSquared": 1
}
}Limitations and assumptions
Incorrect trials are excluded without speed-accuracy modelling. The slope does not alone identify a serial or parallel search 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
Posner Cueing Effect: Measure invalid-minus-valid Posner cueing benefit after correct-trial and caller-supplied RT-window exclusions; both conditions must remain observed. Mental Rotation Slope: Regress mental-rotation RT on shortest unsigned circular angular disparity, explicitly mapping 270° to 90°; task correctness filtering must precede input.
Methodological background: Least squares estimation.1
References
-
Least squares estimation — NIST/SEMATECH
Similar or alternative tools
- Posner Cueing Effect
Measure invalid-minus-valid Posner cueing benefit after correct-trial and caller-supplied RT-window exclusions; both conditions must remain observed.
- Mental Rotation Slope
Regress mental-rotation RT on shortest unsigned circular angular disparity, explicitly mapping 270° to 90°; task correctness filtering must precede input.