The effects of number of sample stimuli and number of choices in a detection task on measures of discriminability.
Adding more samples or choices keeps the discriminability measure clean, so you can scale task complexity without spoiling your data.
01Research in Context
What this study did
McConkey et al. (1999) ran pigeons in a detection task.
They changed two things at once: how many sample stimuli birds saw and how many choice keys lit up.
The goal was to see if these extra pieces warp the core measure of stimulus–response discriminability.
What they found
The birds’ ability to tell stimuli apart stayed rock-steady.
Even with more samples and more keys, the discriminability number did not budge.
A simple generalization model still fit the data, so the measure stayed clean.
How this fits with other research
Gardner et al. (1977) saw the opposite: when they raised the number of pecks required before the choice, accuracy dropped.
The two studies differ in what they counted: R counted extra keys, T counted extra work.
Extra keys do not hurt; extra work does.
Newman et al. (1991) showed that reinforcer ratio effects hinge on how different the stimuli look.
R’s finding lines up: discriminability stays fixed once stimulus contrast is set, no matter how many keys you add.
Why it matters
When you build conditional-discrimination programs for learners, you can add foils or extra choices without fear of muddying your data.
Keep stimulus contrast sharp, and the discriminability score stays honest.
Next time you need five comparison pictures instead of three, go ahead—the measure will still tell the truth.
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02At a glance
03Original abstract
Six pigeons were trained on a conditional discrimination task involving the discrimination of various intensities of yellow light. The research asked whether stimulus—response discriminability measures between any pair of stimuli would remain constant when a third or fourth sample and reinforced response were added. The numbers of different sample stimuli presented and different responses reinforced were two (Part 1), three (Parts 2 and 4), and four (Part 3). Across conditions within parts, the ratios of reinforcers obtainable for correct responses were varied over at least five levels. In Part 5, the numbers of sample stimuli and reinforced responses were varied among two, three, and four, and the reinforcer ratio between consecutive remaining samples was constant at 2:1. It was found that once a particular response had been reinforced, subjects continued to emit that response when the conditional stimulus for that response was no longer presented. Data analysis using a generalization‐based detection model indicated that this model was able to describe the data effectively. Four findings were in accord with the theory. First, estimates of stimulus—response discriminability usually decreased as the arranged physical disparity between the sample stimuli decreased. Second, stimulus—response discriminability measures were independent of response—reinforcer discriminability measures, preserving parameter invariance between these measures. Third, stimulus—response discriminability measures for constant pairs of conditional stimuli did not change systematically as conditional stimulus—response alternatives were added. Fourth, log stimulus—response discriminability values between physically adjacent conditional stimuli summed to values that were not significantly different from estimates of the discriminability values for conditional stimuli that were spaced further apart.
Journal of the experimental analysis of behavior, 1999 · doi:10.1901/jeab.1999.72-33