Package 'preproc.iquizoo'

Title: Utility Functions for Data Processing of Iquizoo Games
Description: Several couples of games are developed by IQUIZOO.COM. Here are the functions used to do data processing for all of those games.
Authors: Liang Zhang [aut, cre]
Maintainer: Liang Zhang <[email protected]>
License: MIT + file LICENSE
Version: 2.8.2
Built: 2024-11-17 04:39:56 UTC
Source: https://github.com/psychelzh/preproc.iquizoo

Help Index

Attention Network Test

Description

The indices for ANT task are calculated.

Usage

ant_orient(data, .by = NULL, .input = NULL, .extra = NULL)

ant_alert(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

Executive function score (prefix cong_eff), total orienting scores (prefix orient), endogenous orienting scores (prefix orient_endo), exogenous orienting scores (prefix orient_exo), total alerting scores (prefix alert), audio alerting scores (prefix alert_aud) and visual alerting scores (prefix alert_vis) for the following performances:

pc

Percent of correct.
mrt

Mean reaction time.
ies

Inverse efficiency score.
rcs

Rate correct score.
lisas

Linear integrated speed-accuracy score.

Balloon Analogue Risk Task

Description

This task is deemed as a measure of impulsivity.

Usage

bart(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

mean_pumps

Mean of hits for balloons not exploded.
mean_pumps_raw

Mean of hits for all balloons.
num_explosion

Number of exploded balloons.

Behavioral Pattern Separation (BPS) task

Description

This function mainly calculates the "BPS score" developed by Stark et al. (2013).

Usage

bps(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

pc

Percent of correct responses.
p_sim_target

Percent of similar responses for "target" stimuli.
p_sim_lure

Percent of similar responses for "lure" stimuli.
p_sim_foil

Percent of similar responses for "foil" stimuli.
bps_score

BPS score.

Visual Arrays

Description

The visual arrays task is used to measure working memory capacity. Here we calculate the capacity from data. Note this is used when the whole visual arrays are to be detected.

Usage

capacity(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

k

The mean capacity in all conditions.
k3

The capacity in condition of 3 arrays.
k5

The capacity in condition of 5 arrays.
k7

The capacity in condition of 7 arrays.
k9

The capacity in condition of 9 arrays.

Adaptive Filtering

Description

This is an self-adaptive version (item number is adaptive to user's ability) of filtering task. Only two conditions are included, i.e., condition of no distractor and condition of two distractors.

Usage

condstairs(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

capacity0

The mean number of target in condition of no distractors.
capacity2

The mean number of target in condition of 2 distractors.
capacity

The mean number of target in both conditions.
efficiency

The filtering efficiency, .i.e, difference between condition of no distractors and two distractors.

Count Correct Responses

Description

These functions count the number of correct responses. countcorrect() counts the correct responses regardless of errors, countcorrect2() subtracts the number of errors from number of correct responses, sumweighted() counts the correct responses by giving a weight for different responses, sumscore() adds up the score for each response.

Usage

countcorrect(data, .by = NULL, .input = NULL, .extra = NULL)

countcorrect2(data, .by = NULL, .input = NULL, .extra = NULL)

sumweighted(data, .by = NULL, .input = NULL, .extra = NULL)

sumscore(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

nc

Count of correct responses. For countcorrect().
nc_cor

Corrected count of correct responses (subtracting number of errors). For countcorrect2().
nc_weighted

Count of weighted correct responses. For sumweighted().
nc_score

Sum of scores. For sumscore().

Continuous Performance Test

Description

Continuous Performance Test (CPT) is a classical test for attention. There are many methods used to calculate the performance index of this task, and here only includes those common ones.

Usage

cpt(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

nc

Count of correct responses.
mrt

Mean reaction time of hits.
rtsd

Standard deviation of reaction times of hits.
dprime

Sensitivity (d').
commissions

Number of errors caused by action.
omissions

Number of errors caused by inaction.

Driving Test

Description

A test measuring impulsivity originally developed by Gardner et al (2005).

Usage

driving(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

still_ratio

The ratio of still duration in yellow light state.

Deese-Roediger-McDermott (DRM) paradigm

Description

This is a classical false memory test. Here calculates the effect size of false memory.

Usage

drm(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

tm_dprime

Sensitivity (d') of true memory (against "foil" stimuli).
tm_bias

Bias of true memory (against "foil" stimuli).
fm_dprime

Sensitivity (d') of false memory.
fm_bias

Bias of false memory.
memory_score

Memory score (tm_dprime - fm_dprime).

Fit a Simple Numerosity Model

Description

This model assumes the distribution of mental representation for a given number/count k is N(k, (w * k) ^ 2).

Usage

fit_numerosity(data, name_bigset, name_smallset, name_acc, n_fit = 5, seed = 1)

Arguments

data

Raw data of class data.frame.
name_bigset, name_smallset

Variable name in data indicates bigger and smaller set.
name_acc

Variable name in data indicates user's response is correct or not.
n_fit

Number of fits to try to find the best estimate.
seed

Random seed. Default is 1 so that results can be reproduced.

Value

A list() with structure the same as optim().

Face Name Task

Description

This is used to do face name task indicator calculations. Current version integrates a occupation memory task.

Usage

fname(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Details

Following Amariglio et al. (2012), we will calculate three scores: FN-N, FN-O and FN-Total.

Value

An object with the same class as data contains following values:

fnn

The overall number of correct face name pairs.
fno

The overall number of correct face occupation pairs.
fntotal

The overall number of correct face name and occupation pairs.

References

Amariglio, R. E., Frishe, K., Olson, L. E., Wadsworth, L. P., Lorius, N., Sperling, R. A., & Rentz, D. M. (2012). Validation of the Face Name Associative Memory Exam in cognitively normal older individuals. Journal of Clinical and Experimental Neuropsychology, 34(6), 580–587. https://doi.org/10.1080/13803395.2012.666230

Iowa Gambling Task (modified)

Description

A classical test on decision making. Read more details on wikipedia. This modified version uses pools to simulate cards, but the essential ideas are the same.

Usage

igt(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

sum_outcome

The total outcome over all trials.
perc_good

The number of choices on "good" pools.

Judgment of Line Orientation

Description

This test is about visuo-spatial skills. For more details, read this introduction.

Usage

jlo(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

nc

Count of correct responses.
mean_ang_err

Mean of the response angle errors.
mean_log_err

Mean of the log-transformed (of base 2) response angle errors.

Location Memory

Description

Several tests are based on subject's spatial acuity, so typically a distance error is collected and scores are calculated based on that error. locmem() deal with the distance condition only. locmem2() deals with a special case when the response order and distance both matter.

Usage

locmem(data, .by = NULL, .input = NULL, .extra = NULL)

locmem2(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

nc_loc

Count of correct responses for location.
mean_dist_err

Mean of the response distance errors.
mean_log_err

Mean of the log-transformed (of base ee) response distance errors.
nc_order

Count of correct responses for order. For locmem2() only.

London Tower

Description

A classical test on problem solving.

Usage

london(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

prop_perfect

Proportion of responses with minimal moves.
mrt_init

Mean initial response time.

Multiple Sensory Integration

Description

There will typically be some speed advantage if there are more than one sensory inputs to be employed. This function calculates this advantage.

Usage

multisense(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

mrt_image

Mean reaction time of Image stimuli.
mrt_sound

Mean reaction time of Sound stimuli.
mrt_mixed

Mean reaction time of Mixed stimuli.
mrt_mixadv

Mean reaction decrease of Mixed stimuli compared to other two types of stimuli.

N Back Paradigm

Description

A classical working memory test.

Usage

nback(data, .by = NULL, .input = NULL, .extra = NULL)

dualnback(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Details

Note for dual n-back, the returned indices include those for each modal and both modals so they are tripled with additional suffix after each index name.

Value

An object with the same class as data contains following values (tripled for dual n-back):

pc

Percentage of correct responses.
mrt

Mean reaction time.
ies

Inverse efficiency score.
rcs

Rate correct score.
lisas

Linear integrated speed-accuracy score.
dprime

Sensitivity index.

Number Line Estimation

Description

A classical test on subject's numerical estimation skills.

Usage

nle(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

mean_abs_err

Mean absolute error.
mean_log_err

Mean log absolute error.

Non-symbolic Number Comparison

Description

A classical test on subject's counting estimation skills.

Usage

nsymncmp(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

pc

Percentage of correct responses.
mrt

Mean reaction time.
w

Weber fraction.

See Also

symncmp() for symbolic number comparison.

Calculate Performance Indices

Description

Accepts a data frame containing raw data and calculates performance indices using a user-defined function.

Usage

preproc_data(
  data,
  fn,
  ...,
  col_raw_parsed = "raw_parsed",
  pivot_results = TRUE,
  pivot_names_to = "index_name",
  pivot_values_to = "score"
)

Arguments

data

A data.frame contains raw data.
fn

This can be a function or formula. See rlang::as_function() for more details.
...

Additional arguments passed to fn.
col_raw_parsed

The column name in which stores user's raw data in format of a list of data.frames.
pivot_results

Whether to pivot the calculated indices. If TRUE, the calculated indices are pivoted into long format, with each index name stored in the column of pivot_names_to, and each index value stored in the column of pivot_values_to. If FALSE, the calculated indices are stored in the same format as returned by fn.
pivot_names_to, pivot_values_to

The column names used to store index names and values if pivot_results is TRUE. See tidyr::pivot_longer() for more details.

Details

Observations with empty raw data (empty vector, e.g. NULL, in col_raw_parsed column) are removed before calculating indices. If no observations left after removing, a warning is signaled and NULL is returned.

Value

A data.frame contains the calculated indices.

NeuroRacer Modified

Description

This is a modified version of NeuroRacer game.

Usage

racer(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

mean_score

Mean overlap score.
dprime

Sensitivity index of detection task.

Raven's Advanced Progressive Matrices

Description

This test contains two sets, namely set I and set II, and set I is a practice set, whereas set II is the test set. So scores for each set and whole set are calculated here.

Usage

rapm(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

nc_prac

Number of correct items for set I.
nc_test

Number of correct items for set II.
nc_total

Number of correct items for whole set.

Spatial Reference Frame

Description

Typically, two classes of spatial frames of reference: "egocentric" and "allocentric". The spatial acuity for both classes are calculated.

Usage

refframe(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

mean_dist_err_allo/mean_dist_err_ego

Mean of the response distance errors for allocentric and egocentric conditions respectively.
mean_log_err_allo/mean_log_err_ego

Mean of the log-transformed (of base ee) response distance errors for allocentric and egocentric conditions respectively.

Probability Reinforcement Learning

Description

A classical reinforcement learning test.

Usage

reinf(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

pc_learn

The total percent of correct in the learn phase.
pc_test

The total percent of correct in the test phase.
pc_approach

The percent of correct for approach trials.
pc_avoid

The percent of correct for avoid trials.

Reaction Times

Description

Choice Reaction Time (CRT) and Simple Reaction Time (SRT) are classical tests of human reaction times. These functions calculates the mean and standard deviation of reaction times. In addition, subjects can commit errors in CRT tests, so the number of correct responses is also calculated in crt().

Usage

crt(data, .by = NULL, .input = NULL, .extra = NULL)

srt(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

nc

Count of correct responses. Only for crt().
mrt

Mean reaction time.
rtsd

Standard deviation of reaction times.
ies

Inverse efficiency score. Only for crt().
rcs

Rate correct score. Only for crt().
lisas

Linear integrated speed-accuracy score. Only for crt().

Span (spatial or verbal)

Description

There is a bunch of tests measuring working memory span or attention span.

Usage

span(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

nc

Count of correct responses.
max_span

Maximal span.
mean_span_pcu

Mean span using partial credit unit score.
mean_span_anu

Mean span using all-or-nothing unit score.

Threshold estimation from staircase method

Description

A very simple method is used here, i.e., averaging all the levels in the last block.

Usage

staircase(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Details

This is under the assumption that the last block is stable enough so that an average of the levels is just the convergence of the threshold.

Value

An object with the same class as data contains following values:

thresh_peak_valley

The mean threshold of peaks and valleys.
thresh_last_block

The mean threshold of the last block.

Stop Signal Paradigm

Description

A classical test on inhibition skills. The index calculation is now based on https://doi.org/10.7554/eLife.46323.

Usage

stopsignal(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

A tibble with the following variables:

pc_all

Percent of correct for all the responses.
pc_go

Percent of correct for the go trials only.
pc_stop

Percent of correct for the stop trials only.
rt_nth

Percentile go reaction time (ms) based on pc_stop.
mean_ssd

Mean of stop signal delay (ms).
ssrt

Stop signal reaction time (ms).

Task Switching and Stroop-like paradigm

Description

In task switching paradigms, two types of tasks switch between each other, so the "switch cost" can be calculated (using switchcost()). Similarly, in Stroop-like tasks, stimuli are classified into two conditions (i.e., "congruent" and "incongruent"), so the "congruence effect" can be calculated (using congeff()). There are also special types of tests where congruence effect and switch cost both exist, from which complexswitch() calculates both.

Usage

complexswitch(data, .by = NULL, .input = NULL, .extra = NULL)

congeff(data, .by = NULL, .input = NULL, .extra = NULL)

switchcost(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

A tibble with the following variables:

For the total task:

pc

Percent of correct.
mrt

Mean reaction time.

For congruence effect and switch cost, the following indices will be included (including diffs and value for each condition):

pc

Percent of correct.
mrt

Mean reaction time.
ies

Inverse efficiency score.
rcs

Rate correct score.
lisas

Linear integrated speed-accuracy score.

Symbolic Number Comparison

Description

Several values including percentage of correct responses (pc), mean reaction time (mrt), distance effect (dist_effect) and adjusted distance effect (dist_effect_cor).

Usage

symncmp(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

pc

Percentage of correct responses.
mrt

Mean reaction time.
dist_eff

Distance effect.

See Also

nsymncmp() for non-symbolic number comparison.

SynWin Test

Description

This is a multi-task game designed by Elsmore (1994).

Usage

synwin(data, .by = NULL, .input = NULL, .extra = NULL)

Arguments

data

Raw data of class data.frame.
.by

The column name(s) in data used to be grouped by. If set to NULL (default), all data will be treated as from one subject and there will be no grouping columns in the value returned.
.input, .extra

Each is a list() containing all the input variable names and special values for certain variables. See more in the details section.

Value

An object with the same class as data contains following values:

score_total

Total score. Sum of the three sub-tests.
score_mem

Score in the memory sub-test.
score_vis

Score in visual monitoring sub-test.
score_aud

Score in auditory monitoring sub-test.