DOI: 10.5061/dryad.5tb2rbp3z
title: Larger capacity for unconscious versus conscious episodic memory

description: Episodic memory is the memory for experienced events. A peak competence of
 episodic memory is the mental combination of events to infer
 commonalities. Inferring commonalities may proceed with and without
 consciousness of events. Yet, what distinguishes conscious from
 unconscious inference? This question inspired nine experiments that
 featured strongly and weakly masked cartoon clips presented for
 unconscious and conscious inference. Each clip featured a scene with a
 visually impenetrable hiding place. Five animals crossed the scene
 one-by-one consecutively. One animal trajectory represented one event. The
 animals moved through the hiding place, where they might linger or not.
 Participants’ task was to observe the animals’ entrances and exits to
 maintain a mental record of which animals hid simultaneously. We
 manipulated information load to explore capacity limits. Memory of
 inferences was tested immediately, 3.5 or 6 minutes following encoding.
 Participants retrieved inferences well when encoding was conscious. When
 encoding was unconscious, participants needed to respond intuitively. Only
 habitually intuitive decision-makers exhibited a significant delayed
 retrieval of inferences drawn unconsciously. Their unconscious retrieval
 performance did not drop significantly with increasing information load,
 while conscious retrieval performance dropped significantly. A working
 memory network, including hippocampus, was activated during both conscious
 and unconscious inference and correlated with retrieval success. An
 episodic retrieval network, including hippocampus, was activated during
 both conscious and unconscious retrieval of inferences and correlated with
 retrieval success. Only conscious encoding/retrieval recruited additional
 brain regions outside these networks. Hence, levels of consciousness
 influenced the memories’ behavioral impact, memory capacity, and the
 neural representational code.
description: See related publication
 https://doi.org/10.1016/j.cub.2021.06.012
description: <strong>Behavioral Data</strong>
 Raw behavioral data for retrieval- and stimulus awareness tasks
 are provided as CSV files. Variable descriptions are provided in text
 files. <strong>fMRI</strong>
 Functional and structural volumes are provided as raw data in
 NIfTI-format, and still need to be pre-processed before they can be used
 in statistical models that prerequisite standardized (i.e. slice-time
 corrected, realigned, coregistrered, normalized and potentially segmented
 or smoothed) data. Structural volumes have been skull-stripped to ensure
 anonymity of participants. Scanner parameters are
 provided in ScannerParams.PDF Session structure:
 functional volumes were collected in 12 sessions per Subject. Every
 session encompasses an encoding, pause, and retrieval part. The MR Scanner
 was continuously acquiring images throughout sessions, resulting in images
 of the different parts (encoding, pause, retrieval) being grouped together
 within each session folder. This means that onsets and movement regressors
 for pause and retrieval parts need to be adjusted if statistical models
 are to only contain these parts (isolated from the volumes outside these
 parts). This was done in the related publication
 (CURRENT-BIOLOGY-D-20-01249), where encoding and retrieval parts are
 modelled separately. Adjusted onsets, as well as scan indices for encoding
 and retrieval parts are included in this repository in the MATLAB
 datastructure model_data.mat. Encoding and retrieval are named
 "tasks" in this datastructure, not "parts". Detailed
 variable descriptions and data access documentation is provided in the
 text file "model_data_description.TXT"
 Statistical masks: due to a rapid TR of 1220 ms, combined with a
 64-channel head coil, functional images exhibited a drop in signal
 intensity around the center of the brain. This is a general physical
 drawback of multichannel acquisitions. The signal collapses towards the
 center of the brain. The implicit statistical masking of SPM under default
 parameters may thus lead to the exclusion of large parts of these regions
 of interest from the analysis. Therefore, a custom explicit inclusive
 statistical mask has been constructed for the related publication, based
 on SPM's implicit mask, but replacing missing areas with normalized
 automated anatomical labeling (AAL) areas. Additionally, white matter was
 excluded from this custom explicit mask. To this aim, the average
 normalized structural (T1) image was segmented per group and experiment
 (experiment with strong masking; experiment with weak masking) using SPM’s
 built-in segmentation function. The resulting white matter tissue map was
 then inverted and multiplied with the custom explicit inclusive
 statistical mask to create the final custom explicit mask. The resulting
 masks (spatially normalized to SPM's MNI305 T1 template) are included
 in this repository. Retrieval of weakly masked
 clips (conscious encoding) was on average more easy for participants than
 retrieval of strongly masked clips, leading to participants giving only
 correct responses in the following sessions of the weak masking condition
 (Note: VPxxxx are subject IDs): VP6048<br>    
 Session 6<br>     Session 7<br>     Session 8<br>    
 Session 11<br>     Session 12 VP6191<br>  
   Session 4<br>     Session 9<br>     Session 11
 VP6207<br>     Session 1<br>     Session 8<br>
     Session 11<br>     Session 12
 VP6302<br>     Session 3
 VP6349<br>     Session 1
 VP6457<br>     Session 3<br>     Session 9<br>
     Session 11 VP6581<br>     Session 2<br>
     Session 5<br>     Session 6<br>     Session 8<br>  
   Session 11 VP6650<br>     Session 4<br>  
   Session 12 VP6685<br>     Session 5<br>  
   Session 7<br>     Session 11 VP6690<br>  
   Session 6<br>     Session 7<br>     Session 10<br>    
 Session 12 VP7010<br>     Session 6<br>    
 Session 11<br>     Session 12 VP7018<br>  
   Session 1<br>     Session 8 VP7040<br>  
   Session 4<br>     Session 7 VP7111<br>  
   Session 3<br>     Session 6<br>     Session 7<br>    
 Session 9<br>     Session 12 VP7206<br>    
 Session 3<br>     Session 5<br>     Session 8<br>    
 Session 11 VP7452<br>     Session 8
 VP7469<br>     Session 4
 VP7474<br>     Session 1<br>     Session 3<br>
     Session 6<br>     Session 7<br>     Session 8<br>  
   Session 9<br>     Session 10<br>     Session 12
 Therefore, for these subjects & sessions, no onsets for
 regressor "R_Experiment_incorrect" can be provided in the table
 model_data.weak_masking.retrieval(subject).onsets{session}.
 For second level brain-behavior correlations, additional
 regressor-files (CSV) are provided containing reaction times and retrieval
 accuracies (as used in the related publication), aggregated per subject.
 File names are: RT_differences_for_fMRI_analyses.CSV
 and Accuracies_for_fMRI_analyses.CSV