Uncovering the computational mechanisms underlying many-alternative choice

This repository contains all code and data for:

Thomas, A. W., Molter, F., & Krajbich, I. (2021). Uncovering the computational mechanisms underlying many-alternative choice. Elife, 10, e57012. doi.org/10.7554/eLife.57012

All code underlying this project is contained in src/, with one jupyter notebook for each figure of the manuscript.

Local installation

1. Clone and switch to this repository:

git clone https://github.com/athms/many-item-choice.git
cd many-item-choice

2. Install all dependencies:

I recommend setting up a new Python environment (e.g., with the miniconda installer).

To recreate our environment use the following command:

conda env create -f environment.yml

This will create a new Python 3.6 environment with the name many-item-choice that includes all required dependencies.

You can then activate this environment as follows:

conda activate many-item-choice

3. Start the Jupyter notebook server:

jupyter notebook

The data

All data of this project are included in the data repository.

There you will find two main file types:

Aggregate data files (contained in data/summary_files/) and
Individual subject data files (contained in data/subject_files/).

Note that all files are separated by choice set size (9, 16, 25, and 36).

Aggregate data contain one row for each trial with the following information:

{setsize, subject, trial} (int): indicators for the choice set size, subject, and trial
rt (float): response time (in ms)
rt_choice_indication (float): choice indication time (in ms; defined as time between space bar press and click on an item image; see the manuscript for details)
choice (int): chosen item
gaze_count (float): overall number of gazes in that trial (a gaze is defined as all consecutive and uninterrupted fixations to an item)
seen_items_count (float): number of items that the subject looked at in that trial
returning_gaze_count (float): overall number of returning gazes in that trial
best_chosen (float [0, 1]): whether the subject choose the item with the highest value in that trial (1) or not (0)?
best_seen_chosen (float [0, 1]): whether the subject choose the item with the highest value that they have seen in that trial (1) or not (0)?
longest_chosen (float [0, 1]): whether the subject chose the item that they have looked at longest (1) or not (0) in that trial?
item_value_{0-setsize} (float): value of each choice set item (as indicated by the liking rating)
cumulative_gaze_{0-setsize} (float): cumulative gaze of each choice set item (defined as the fraction of trial time that the subject spent looking at the item)
stimulus_{0-setsize} (string): filename of respective snack food stimulus (files are stored in data/stimuli)
gaze_onset_{0-setsize} (float): time point (in ms) at which the item was first looked at in that trial

Note that item numbers start with 0 and increase from left-to-right and top-to-bottom. The item in the top left thus has number 0 while the item in the bottom right has the highest number in the choice set.

There are two types of individual subject data files:

Gaze files contain one row for each gaze with the following information:

{setsize, subject, trial, item} (int): indicators for the choice set size, subject, trial, and looked-at item
dur (float): duration of the gaze (in ms)
onset (float): onset of the gaze (in ms; relative to the trial onset)
stimulus (string): filename of the snack food stimulus (stimulus files are stored in data/stimuli)
gaze_num (float): gaze number in trial
is_returning (float [0, 1]): is this a returning gaze (1) or not (0)?
returning_gaze_count (float): counter for returning gazes to this item
is_{first,last} (float [0, 1]): is this the first/last gaze of the trial?
item_value (float): value of the item (as indicated by its liking rating)
choice (int): chosen item
is_last_to_choice (float [0, 1]): whether chosen item was looked at last in that trial (1) or not (0)?

Liking rating files contain one row per rating trial with the following information:

{subject, trial} (int): indicators for the subject and trial
rt (float): response time (in ms)
stimulus (string): filename of the snack food stimulus
rating (float): liking rating

The folder data/stimulus_positions further contains the item positions on the experiment screen for each choice set size.

A few notes before computing the notebooks

This project involves many computationally intensive analyses, which can take several days to compute (depending on your machine). By default, many results are therefore read from the repository, if the respective output files exist in the results/ directory. Thus, if you want to re-compute a specific result (or all of them), delete (or rename) the respective output files or folder.

1. Fitting the individual models:

To fit the three model types (PSM, IAM, and GLAM) for one subject in one choice set size, run:

cd src
python fit_subject-setsize-models.py <subject> <set size>

This script will fit the passive- and active-gaze variants of the three models for the specified subject and choice set size.

To iteratively fit all models for all subjects in all set size conditions, run:

cd src
for setsize in 9 16 25 36; do
  for subject in $(seq 0 48); do
    python fit_subject-setsize-models.py $subject $setsize;
  done;
done

2. Running the model recovery:

The model recovery can be computed by the use of src/model-recovery.py:

cd src
python model-recovery.py

Note that the model recovery is based on the individual model fits for the 9-item choice sets and can thus only be computed if these fits exist in results/posterior_traces!