Description Usage Arguments Value Author(s) Examples

Computes Signal Detection Theory indices (d', beta, A', B”D, c).

1 2 |

`n_hit` |
Number of hits. |

`n_fa` |
Number of false alarms. |

`n_miss` |
Number of misses. |

`n_cr` |
Number of correct rejections. |

`n_targets` |
Number of targets (n_hit + n_miss). |

`n_distractors` |
Number of distractors (n_fa + n_cr). |

`adjusted` |
Should it use the Hautus (1995) adjustments for extreme values. |

Calculates the d', the beta, the A' and the B”D based on the signal detection theory (SRT). See Pallier (2002) for the algorithms.

Returns a list containing 4 objects:

**dprime (d')**: The sensitivity. Reflects the distance between the two distributions: signal, and signal+noise and corresponds to the Z value of the hit-rate minus that of the false-alarm rate.**beta**: The bias (criterion). The value for beta is the ratio of the normal density functions at the criterion of the Z values used in the computation of d'. This reflects an observer's bias to say 'yes' or 'no' with the unbiased observer having a value around 1.0. As the bias to say 'yes' increases (liberal), resulting in a higher hit-rate and false-alarm-rate, beta approaches 0.0. As the bias to say 'no' increases (conservative), resulting in a lower hit-rate and false-alarm rate, beta increases over 1.0 on an open-ended scale.**aprime (A')**: Non-parametric estimate of discriminability. An A' near 1.0 indicates good discriminability, while a value near 0.5 means chance performance.**bppd (B”D)**: Non-parametric estimate of bias. A B”D equal to 0.0 indicates no bias, positive numbers represent conservative bias (i.e., a tendency to answer 'no'), negative numbers represent liberal bias (i.e. a tendency to answer 'yes'). The maximum absolute value is 1.0.**c**: Another index of bias. the number of standard deviations from the midpoint between these two distributions, i.e., a measure on a continuum from "conservative" to "liberal".

Note that for d' and beta, adjustement for extreme values are made following the recommandations of Hautus (1995).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
library(psycho)
n_hit <- 9
n_fa <- 2
n_miss <- 1
n_cr <- 7
indices <- psycho::dprime(n_hit, n_fa, n_miss, n_cr)
df <- data.frame(Participant = c("A", "B", "C"),
n_hit = c(1, 2, 5),
n_fa = c(6, 8, 1))
indices <- psycho::dprime(n_hit=df$n_hit,
n_fa=df$n_fa,
n_targets=10,
n_distractors=10,
adjusted=FALSE)
``` |

neuropsychology/psycho.R documentation built on Nov. 17, 2018, 2:14 p.m.

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