sumstats_garner: Perform a summary statistics analysis of data from a Garner...

In fsotoc/grtools: General Recognition Theory tools for the analysis of perceptual independence

Description

Performs an analysis of data from a 2x2 Garner filtering experiment, based on summary statistics (see Ashby & Maddox, 1994).

Usage

sumstats_garner(trial_data, test_incorrect = F)

Arguments

trial_data	Trial-by-trial data from a single participant in a Garner filtering experiment. See "Details" for instructions on the correct format for this data frame.
test_incorrect	If TRUE, the function runs tests on response times for both correct and incorrect trials. The default is FALSE.

Details

A 2x2 Garner filtering experiment involves stimuli that vary in two dimensions, each with two levels, 1 and 2. The task of the participant is to classify stimuli according to their level in one of these dimensions (the relevant dimension), while ignoring variation in the other dimension (the irrelevant dimension).

There are two block types in the task. During baseline blocks, the irrelevant dimension is fixed to a specific level. During interference blocks, the irrelevant dimension is not fixed, but varies across trials. Both accuracy and response times are gathered during the task.

The data from a single participant in this task should be ordered in a data frame with rows representing individual trials and columns with the following format:

• Column 1: block type, with a value of 1 for baseline blocks and a value of 2 for interference blocks

• Column 2: Level of the relevant dimension, with values 1 and 2

• Column 3: Level of the irrelevant dimension, with values 1 and 2

• Column 4: Accuracy, with a value of 0 for incorrect trials and 1 for correct trials

• Column 5: Response time

To see an example data frame, type data(garner_data) in the R console. The data will be available as a data.frame named garner_data

Value

An object of class "sumstats_garner"

The function summary is used to obtain a summary of conclusions from the analysis about separability. Note that any reported violations of separability can be due to either violations of perceptual or decisional separability, which cannot be dissociated through a Garner filtering experiment.

References

Ashby, F. G., & Maddox, W. T. (1994). A response time theory of separability and integrality in speeded classification. Journal of Mathematical Psychology, 38(4), 423-466.

Examples

# Load example data frame and see the first 10 rows
data(garner_data)
garner_data[1:10,]
  
# Run the analysis
garner_results <- sumstats_garner(garner_data)
  
# See a summary of results
summary(garner_results)
  
# Print to screen the details of each test
garner_results
  

fsotoc/grtools documentation built on Nov. 15, 2020, 5:14 a.m.