Example Analyses

This page showcases the kind of analysis pyAVS’s fixation-locked MEG data supports, using a figure from a manuscript currently in preparation that analyzes the AVS dataset (distinct from the AVS dataset paper itself, see Citation). It combines a fixation-aligned dynamic representational similarity analysis (dRSA), a per-fixation ANN-to-MEG encoding analysis, and source-projected fixation event-related fields (ERFs).

Important

This section describes a real figure from an in-preparation manuscript, reproduced here as a scientific showcase of what the dataset and pyAVS support – not as a tutorial to be followed step by step. The corresponding analysis code lives in pyavs/scripts/rsa_analysis/, pyavs/scripts/encoding/, and pyavs/scripts/meg_viz/compute_source_erp.py (research pipelines, not part of the documented package API).

Dynamic Representational Similarity Analysis (dRSA)

For each fixation, the MEG time course and the corresponding image crop were extracted. MEG activity was averaged across fixations targeting the same COCO-Stuff object category (see Fixation Object Labeling), yielding category-level neural representations for 171 object categories. Pairwise correlation distances between category-average MEG patterns were computed at each time point to form a time-resolved MEG representational dissimilarity matrix (RDM). In parallel, image crops were passed through a ResNet50 network trained on a cropped version of Ecoset, and pairwise distances between category-average layer embeddings formed a model RDM. Neural and model RDMs were compared using Spearman rank correlation.

Inter-Subject Reliability

The grand-average Spearman correlation between MEG RDMs of held-out subject pairs rises rapidly after fixation onset, peaking within approximately 100-150 ms – benchmarked against an inter-subject noise ceiling computed the same way.

Layer-Wise Alignment

Comparing MEG RDMs against RDMs built from different ResNet50 layers shows that higher, later layers (avgpool, layer3) achieve higher peak representational similarity than earlier layers, consistent with fixation-locked MEG activity reflecting increasingly object-level (rather than purely low-level visual) representations shortly after fixation onset.

Per-Fixation ANN-to-MEG Encoding

A complementary, model-driven approach: the ResNet50 (Ecoset-cropped) embedding of each individual fixation crop was used to predict the simultaneously recorded per-sensor MEG time course via a linear encoding model, fit and evaluated per fixation (not averaged across repeated presentations, since each fixation in active viewing is a unique, unrepeated event). Sensor-wise encoding performance (Pearson r) rises steeply after fixation onset, peaking near 100 ms.

Source-Projected Fixation ERFs

Fixation-locked responses were also projected into source space (see Source Reconstruction). Grand-average (n = 5) cortical source amplitude at 100 ms post-fixation onset shows bilateral occipital activation dominating this early time window; the whole-brain average source amplitude time course peaks shortly after fixation onset before decaying.

Try It Yourself