Delayed Free Recall of Word Lists
…a collaboration with the Computational Memory Lab at the University of Pennsylvania…
- Citations
- 2
- Size
- 576 GB
- Updated
- Jun 25, 2026
100 results for "verbal memory encoding" · page 3 of 10 · ranked by relevance
…a collaboration with the Computational Memory Lab at the University of Pennsylvania…
…a collaboration with the Computational Memory Lab at the University of Pennsylvania…
…Matlab (psychtoolbox 3) script for Working Memory (WorkMem) and one-back control…
…Backward Span Trainings on Working Memory: Evidence from a Randomized, Controlled Trial…
…To better understand masking’s effects on the subcortical neural encoding of…
…This data collection provides a suitable benchmark to large-scale encoding and…
A synchronized multimodal neuroimaging dataset containing concurrent fMRI and MEG recordings from 12 Mandarin Chinese speakers during naturalistic story listening, supplemented with high-resolution structural imaging, diffusion MRI, and resting-state fMRI. The dataset includes rich linguistic annotations of stimuli encompassing word frequencies, syntactic structures, temporal alignments, and embeddings from multiple pre-trained language models, enabling comprehensive investigation of neural mechanisms underlying language processing.
[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on003694-blue…
This dataset contains neuroimaging data from a cued recall task investigating sustained neural representations of personally familiar people and places. Participants were cued to recall specific individuals and locations while MEG data were recorded, enabling investigation of how the brain maintains and represents autobiographical memories of social and spatial information.
Brain Treebank is a large-scale intracranial EEG dataset comprising 43 hours of iEEG recordings from 10 epilepsy patients watching naturalistic Hollywood movies, with 1,688 electrodes sampled at 2048 Hz. The dataset includes time-aligned linguistic annotations with word-level transcripts and Universal Dependencies syntax trees, providing a unique resource for studying neural language processing during naturalistic stimulation.