Function Reference

# DetectionTheory.dprimeABX — Method.

Compute d' for an ABX task from 'hit' and 'false alarm' rates.

dprimeABX(H, FA, method)

Arguments

H::Real: Hit rate.
FA::Real : False alarm rate.
method::String: 'diff' for differencing strategy or 'IO' for independent observations strategy.

Returns

dprime::Real: d' value

References

Macmillan, N. A., & Creelman, C. D. (2004). Detection Theory: A User’s Guide (2nd ed.). London: Lawrence Erlbraum Associates.

Examples

    #independent observations model
    dp = dprimeABX(0.7, 0.2, "IO")
    #differencing model
    dp = dprimeABX(0.7, 0.2, "diff")

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# DetectionTheory.dprimeMAFC — Method.

Compute d' corresponding to a certain proportion of correct responses in m-AFC tasks.

dprimeMAFC(pc, m)

Arguments

Pc::Real: Proportion of correct responses.
m::Integer: Number of alternatives.

Returns

dprime::Real: d' value

References

Green, D. M., & Swets, J. A. (1988). Signal Detection Theory and Psychophysics. Los Altos, California: Peninsula Publishing.
Green, D. M., & Dai, H. P. (1991). Probability of being correct with 1 of M orthogonal signals. Perception & Psychophysics, 49(1), 100–101.

Examples

    dp = dprimeMAFC(0.7, 3)

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# DetectionTheory.dprimeOddity — Method.

Compute d' for an odd-one-out task.

dprimeOddity(pc, method)

Arguments

pc::Real: Proportion of correct responses.
method::String: 'diff' for differencing strategy or 'IO' for independent observations strategy.

Returns

dprime::Real: d' value

References

Macmillan, N. A., & Creelman, C. D. (2004). Detection Theory: A User’s Guide (2nd ed.). London: Lawrence Erlbraum Associates.
Versfeld, N. J., Dai, H., & Green, D. M. (1996). The optimum decision rules for the oddity task. Perception & Psychophysics, 58(1), 10–21.

Examples

    dp = dprimeOddity(0.7, "diff")
    dp = dprimeOddity(0.7, "IO")

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# DetectionTheory.dprimeSD — Method.

Compute d' for one interval same/different task from 'hit' and 'false alarm' rates.

dprimeSD(H, FA, method)

Arguments

H::Real: Hit rate.
FA::Real : False alarm rate.
method::String: 'diff' for differencing strategy or 'IO' for independent observations strategy.

Returns

dprime::Real: d' value

References

Macmillan, N. A., & Creelman, C. D. (2004). Detection Theory: A User’s Guide (2nd ed.). London: Lawrence Erlbraum Associates.
Kingdom, F. A. A., & Prins, N. (2010). Psychophysics: A Practical Introduction. Academic Press.

Examples

    #independent observations model
    dp = dprimeSD(0.7, 0.2, "IO")
    #differencing model
    dp = dprimeSD(0.7, 0.2, "diff")

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# DetectionTheory.dprimeYesNo — Method.

Compute d' for one interval "yes/no" type tasks from hits and false alarm rates.

dprimeYesNo(H, FA)

Arguments

H::Real: Hit rate.
FA::Real: False alarms rate.

Returns

dprime::Real: d' value

References

Green, D. M., & Swets, J. A. (1988). Signal Detection Theory and Psychophysics. Los Altos, California: Peninsula Publishing.
Macmillan, N. A., & Creelman, C. D. (2004). Detection Theory: A User’s Guide (2nd ed.). London: Lawrence Erlbraum Associates.

Examples

    dp = dprimeYesNo(0.7, 0.2)

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