psycho (version 0.6.2)

dprime: Dprime (d') and Other Signal Detection Theory indices.

Description

Computes Signal Detection Theory indices, including d', beta, A', B''D and c.

Usage

dprime(
  n_hit,
  n_fa,
  n_miss = NULL,
  n_cr = NULL,
  n_targets = NULL,
  n_distractors = NULL,
  adjusted = TRUE
)

Value

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 the following indices:

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.

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".

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.

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

Arguments

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.

Author

Dominique Makowski

Examples

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
)