DS005189#

Search Superiority Recollection Familiarity

Access recordings and metadata through EEGDash.

Citation: Jason Helbing, Dejan Draschkow, Melissa L.-H. Võ (2024). Search Superiority Recollection Familiarity. 10.18112/openneuro.ds005189.v1.0.1

Modality: eeg Subjects: 30 Recordings: 185 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005189

dataset = DS005189(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = DS005189(cache_dir="./data", subject="01")

Advanced query

dataset = DS005189(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds005189,
  title = {Search Superiority Recollection Familiarity},
  author = {Jason Helbing and Dejan Draschkow and Melissa L.-H. Võ},
  doi = {10.18112/openneuro.ds005189.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005189.v1.0.1},
}

About This Dataset#

In this experiment, participants searched for objects in some scenes and intentionally memorized others. We then tested their memory of these objects, finding stronger (quantitative difference) and different (qualitative difference: recollection benefit) memory representations for search targets.

We recorded both EEG and eye movements. Behavioral data is split into encoding (Encode_beh) and memory testing (Test_beh).

Analysis scripts and preprocessed data as well as additional materials are available on the OSF at https://osf.io/esr5q/.

Project Abstract:

Most memory is not formed deliberately but as a by-product of natural behavior. These incidental representations, when generated during visual search, can be stronger than intentionally memorized content (search superiority effect). In this study, we investigate whether this effect is purely quantitative (stronger memory) or also due to qualitative memory differences; more precisely, differences in recollection and familiarity, two processes supporting recognition memory. In an EEG study with eye tracking, 30 participants searched for objects in scenes and intentionally memorized others before completing a surprise recognition memory test. We find that compared to new objects, both search targets and intentionally memorized objects elicit a more positive-going mid-frontal negativity peaking at around 400 ms post stimulus onset (FN400), which is associated with familiarity, as well as a more positive-going parietal late component (LPC), indicative of recollection. Both components show no differences between tasks, indicating equal contributions of recollection and familiarity to remembering searched and memorized objects. Behavioral data from remember–know judgments and receiver operating characteristics (ROCs), however, contrasts with the EEG findings: Search targets are more often reported as recollected and their ROCs show higher intercepts, indicating more recollection, whereas there are essentially no behavioral differences in familiarity between tasks. These results indicate that search superiority relies on increased recollection rather than familiarity. The absent LPC effect despite the behavioral task difference challenges existing assumptions about the neural correlates of recognition memory, raising the question whether they hold when investigated using real-world scenes and incidental encoding during naturalistic tasks.

Dataset Information#

Dataset ID	`DS005189`
Title	Search Superiority Recollection Familiarity
Year	2024
Authors	Jason Helbing, Dejan Draschkow, Melissa L.-H. Võ
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005189.v1.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 30
Recordings: 185
Tasks: 1

Channels & sampling rate

Channels: 61
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS005189 class to access this dataset programmatically.

class eegdash.dataset.DS005189(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005189. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 30; recordings: 30; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005189 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005189

Examples

>>> from eegdash.dataset import DS005189
>>> dataset = DS005189(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

save(path, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None