RaCCooNS

class pymovements.datasets.RaCCooNS(name: str = 'RaCCooNS', long_name: str = 'Radboud Coregistration Corpus of Narrative Sentences', resources: ResourceDefinitions = <factory>, experiment: Experiment = <factory>, *, mirrors: dict[str, Sequence[str]] = <factory>, extract: dict[str, bool] | None = None, custom_read_kwargs: dict[str, dict[str, Any]] | None = None, column_map: dict[str, str] | None = None, trial_columns: list[str] | None = None, time_column: str | None = None, time_unit: str | None = None, pixel_columns: list[str] | None = None, position_columns: list[str] | None = None, velocity_columns: list[str] | None = None, acceleration_columns: list[str] | None = None, distance_column: str | None = None)

RaCCooNS dataset [Frank and Aumeistere, 2024].

The Radboud Coregistration Corpus of Narrative Sentences (RaCCooNS) dataset consists simultaneously recorded eye-tracking and EEG data from Dutch sentence reading, aimed at studying human sentence comprehension and evaluating computational language models. The dataset includes 37 participants reading 200 narrative sentences, with eye movements and brain activity recorded to analyze reading behavior and neural responses. The dataset provides both raw and preprocessed data, including fixation-related potentials, enabling comparisons between cognitive and neural processes.

Check the respective paper [Frank and Aumeistere, 2024] for details.

Warning

This dataset currently cannot be fully processed by pymovements due to an error during parsing of individual files.

See issue #1401 for reference.

name

The name of the dataset.

Type:

str

long_name

The full name of the dataset.

Type:

str

resources

A tuple of dataset gaze_resources. Each list entry must be a dictionary with the following keys: - resource: The url suffix of the resource. This will be concatenated with the mirror. - filename: The filename under which the file is saved as. - md5: The MD5 checksum of the respective file.

Type:

ResourceDefinitions

experiment

The experiment definition.

Type:

Experiment

Examples

Initialize your Dataset object with the RaCCooNS definition:

>>> import pymovements as pm
>>>
>>> dataset = pm.Dataset("RaCCooNS", path='data/RaCCooNS')

Download the dataset resources:

>>> dataset.download()

Load the data into memory:

>>> dataset.load()

Methods

__init__([name, long_name, resources, ...])
from_yaml(path)	Load a dataset definition from a YAML file.
to_dict(*[, exclude_private, exclude_none])	Return dictionary representation.
to_yaml(path, *[, exclude_private, exclude_none])	Save a dataset definition to a YAML file.

Attributes

acceleration_columns
column_map
custom_read_kwargs
distance_column
extract
filename_format	Regular expression, which will be matched before trying to load the file.
filename_format_schema_overrides	Specifies datatypes of named groups in the filename pattern.
has_resources	Checks for resources in resources.
long_name
name
pixel_columns
position_columns
time_column
time_unit
trial_columns
velocity_columns
resources
experiment
mirrors