BerryPy

What does BerryPy stand for?

Basic error and response time analyses in Python

What is BerryPy?

A Jupyter notebook for computing individual RTs and error rates, and collating these data across participants.

What is needed to run BerryPy?

• Jupyter Notebook (Python 3.x)
• shutil
• numpy
• pandas
• glob
• os
• errno
• re
• matplotlib
• seaborn
• sys

What can BerryPy do?

• Reject extreme values (below/above a given RT threshold)
• Reject outlier RTs (based on SDs, trimming, or median absolute deviations)
• Define conditions based on information in the input file
• Compute mean or median RTs
• Compute accuracies/error rates
  • Note: BerryPy rejects extreme values before calculating accuracies

BerryPy reads input files produced by an experiment control programme (e.g., PsychoPy), and produces a .csv output file that can be imported into SPSS or other statistical software packages.

BerryPy looks for its input files in the folder input (located in the same folder as the notebook itself), and writes its output to the folder output.

Aims and objectives

Aims	Objectives
Easy to use	Typical user interaction via text configuration files
Flexible	Defining conditions quite powerful out of the box
Extensible	Open source – modify code, add code

Additional aims from a teaching perspective	Objectives
Think about preprocessing steps	Need to define parameters in config files
No unnecessary user interaction	Automatically processes all .csv files in input directory
Visualise individual data	Creates Markdown output file with RT distributions and plausibility checks

Limitations

BerryPy works best with standard PsychoPy output files. For example:

• BerryPy expects the input files to have a header
• BerryPy expects one trial per line

All of this can of course be adapted, but this will require some Python knowledge.

How does BerryPy work?

Please note that the description below assumes that you know how to open a Jupyter notebook.

1. Download and unzip BerryPy. Change your working directory to the newly created BerryPy folder.
2. Add raw data to the folder input (by default, BerryPy expects .csv files, but this can be changed in the notebook)
3. Edit configuration files (see below for details)
4. Open the notebook
5. Use Cell → Run All to run the notebook
6. Results are written to the folder output (details below)

Please note that there will be no centrally installed BerryPy package. Instead, add BerryPy to each project folder.

The configuration files

Note that the configuration files address the most common options. If the analysis needs to go beyond these options, extra functions can be added to the notebook (the notebook contains an example function that has been commented out to illustrate this).

General settings

• File name: config_general_settings.txt
• Contains info about relevant column names in input files (e.g. response.corr for accuracy of response)
• Set thresholds for extreme values (e.g., exclude anything below 100 ms and above 4000 ms)
• Outlier rejection using SD, trimmed mean, or median absolute deviation
• Option to list IDs of participants to exclude from analysis
• Option to define columns that should be kept for the output file (e.g., age or gender)

RT analysis

• File name: config_rts.txt
• Name and define conditions for output file
• Define measure of central tendency (i.e., mean or median)
• Basic syntax:
  • <columnName>: [operator]<columnValue>, where <columnName> is the column name in the input file (e.g., response.corr) and <columnValue> is the value (e.g., 0 for incorrect)
• Operators
  • equal (default): =
  • does not equal: !=
  • below: <
  • below or equal: <=
  • above: >
  • above or equal: >=

Error analysis

• File name: config_errors.txt
• Same syntax rules and operators as for RTs
• Major difference to RT configuration file: need to define ratio using /
  • Basic syntax: <trialsOfInterest>/<allTrials>

Examples

Minimal input file example:

condition	response.corr	response.rt	trials.thisN	stimVisibility	stimLocation
congruent	1	0.624	0	high	left
incongruent	0	0.683	1	low	left
incongruent	1	0.727	2	high	right
congruent	1	0.386	3	low	centre
incongruent	1	0.495	4	low	centre
incongruent	1	0.528	5	high	right
incongruent	0	0.581	6	high	centre
congruent	0	0.669	7	low	right
congruent	1	0.421	8	high	left
...
incongruent	1	0.484	99	low	centre

RT analysis

Example content of RT configuration file

# compute mean congruent and incongruent RTs for correct trials only
# conRT and inconRT will be the column headers in the output file
# not necessary to add default operator equal
conRT; condition: congruent; response.corr: 1; onColumn: response.rt; measure: mean
inconRT; condition: incongruent; response.corr: 1; onColumn: response.rt; measure: mean

# as above, but exclude the first 20 trials (i.e., trial numbers 0 to 19)
conRT; condition: congruent; response.corr: 1; trials.thisN: [>=]20; onColumn: response.rt; measure: mean
inconRT; condition: incongruent; response.corr: 1; trials.thisN: [>=]20; onColumn: response.rt; measure: mean

# mean con RTs separately for low and high visibility trials
conRT_lowVis; condition: congruent; response.corr: 1; stimVisibility: low; onColumn: response.rt; measure: mean
conRT_highVis; condition: congruent; response.corr: 1; stimVisibility: high; onColumn: response.rt; measure: mean

# mean con RTS for lateralised stimuli
conRT_lateral; condition: congruent; response.corr: 1; stimLocation: left,right; onColumn: response.rt; measure: mean

# alternatively
conRT_lateral; condition: congruent; response.corr: 1; stimLocation: [!=]centre; onColumn: response.rt; measure: mean

# to compute medians instead of means
conRT; condition: congruent; response.corr: 1; onColumn: response.rt; measure: median
inconRT; condition: incongruent; response.corr: 1; onColumn: response.rt; measure: median

Error analysis

Example content of error configuration file

# accuracies for congruent and incongruent trials
conAcc; condition: congruent; response.corr: 1/0,1
inconAcc; condition: incongruent; response.corr: 1/0,1

# alternatively, using error rates
conErr; condition: congruent; response.corr: 0/0,1
inconAcc; condition: incongruent; response.corr: 0/0,1

# accuracies for high-vis trials only
conAcc_highVis; condition: congruent; response.corr: 1/0,1; stimVisibility: high
inconAcc_highVis; condition: incongruent; response.corr: 1/0,1; stimVisibility: high

The values provided can also be strings, allowing the computation of ratios more generally:

# calculate the percentage of congruent trials
percentCongr; congruency: congruent/congruent,incongruent

The output

• In the folder output, there will be a file called output.csv: This file contains the means or medians (dependent on what you chose in the general settings file) and error rates (if you asked for them) for every participant and condition
• In the same folder, there will be a Markdown file called report.md
  • It provides basic sanity checks for every participant (e.g., overall percentage of timeouts and errors)
  • It provides information about the distribution of RTs
  • It tells you how many trials were rejected as outliers
• You can view the Markdown output file using a dedicated Markdown viewer, or a Markdown extension for your browser