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Compute RAC (Response Time-Accuracy Characteristic) Data

Source: R/rac_functions.R
make_racdata.Rd

Computes response time-accuracy data for lineup identifications following the approach described in Seale-Carlisle et al. (2019) and implemented in pyWitness (Mickes et al., 2024). RAC analysis shows the relationship between response time and accuracy for suspect identifications.

Usage

make_racdata(
  data,
  lineup_size = 6,
  time_bins = NULL,
  innocent_suspect = "auto"
)

Arguments

data

A dataframe with the following columns:

  • target_present: Logical. TRUE if guilty suspect in lineup

  • identification: Character. "suspect", "filler", or "reject"

  • response_time: Numeric. Response time in milliseconds (or seconds)

lineup_size

Integer. Number of people in lineup (default = 6)

time_bins

Numeric vector of bin edges for grouping response times (e.g., c(0, 5000, 10000, 15000, 20000) creates bins in milliseconds). If NULL, uses individual response time values (not recommended for continuous data).

innocent_suspect

How innocent-suspect identifications in target-absent lineups are obtained. "designated": the lineups had a designated innocent suspect, so target-absent "suspect" rows are counted directly, and zero such rows means a false-ID rate of zero. "estimated": there was no designated innocent suspect, so the innocent-suspect count is estimated as filler IDs divided by lineup_size. "auto" (the default) chooses "designated" when any target-absent row is a suspect ID or the data have a non-missing suspect_pos column, and "estimated" otherwise, with a message.

Value

A list containing:

  • rac_data: Dataframe with response time bins and accuracy

  • n_ids_per_bin: Number of suspect IDs in each time bin

  • overall_accuracy: Overall accuracy across all suspect IDs

Details

RAC analysis computes, for each response time bin: $$Accuracy = \frac{Correct Suspect IDs}{Correct Suspect IDs + Incorrect Suspect IDs}$$

Explicit target-absent suspect IDs are used when present. Otherwise, target-absent filler IDs are divided by lineup size to estimate incorrect suspect IDs. The two estimators are never added together. The bin mean time uses the same suspect-ID observations as accuracy; estimated-mode TA filler times receive weight \(1/k\). Bin edges must cover all supplied observations. Count accuracy depends on TP/TA sampling fractions and does not adjust to a different population base rate. The displayed binomial SE is approximate, especially for estimated counts.

RAC analysis is useful for examining the speed-accuracy tradeoff in eyewitness identifications. Faster responses typically indicate stronger memory, and RAC curves often show higher accuracy for faster response times.

References

Seale-Carlisle, T. M., Colloff, M. F., Flowe, H. D., Wells, W., Wixted, J. T., & Mickes, L. (2019). Confidence and response time as indicators of eyewitness identification accuracy in the lab and in the real world. Journal of Applied Research in Memory and Cognition, 8(4), 420-428.

Mickes, L., Seale-Carlisle, T. M., Chen, X., & Boogert, S. (2024). pyWitness 1.0: A python eyewitness identification analysis toolkit. Behavior Research Methods, 56, 1533-1550.

Examples

data <- create_example_lineup_data(n_trials = 200,
                                   include_response_time = TRUE,
                                   seed = 123)
rac <- make_racdata(data, time_bins = c(0, 4000, 8000, 12000, 20000))
rac$rac_data
#> # A tibble: 4 × 7
#>   response_time mean_time n_correct n_incorrect n_total accuracy      se
#>   <chr>             <dbl>     <int>       <int>   <int>    <dbl>   <dbl>
#> 1 [0,4000]          2767.        24           4      28    0.857  0.0661
#> 2 (4000,8000]       5798.        35           7      42    0.833  0.0575
#> 3 (8000,12000]      8873.         3           1       4    0.75   0.217 
#> 4 (12000,20000]       NA          0           0       0   NA     NA