Disentangling syntax and semantics in the brain with deep networks
Authors: Charlotte Caucheteux, Alexandre Gramfort, Jean-Remi King
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Functional MRI dataset. We analyze the Narratives public dataset (Nastase et al., 2020), which contains the f MRI measurements of 345 unique subjects listening to narratives. |
| Researcher Affiliation | Collaboration | 1Inria, Saclay, France 2Facebook AI Research, Paris, France 3 Ecole normale sup erieure, PSL University, CNRS, Paris, France. |
| Pseudocode | No | The paper describes methods through text and figures (e.g., Figure 2 for method to isolate syntactic representations) but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a direct link to a source code repository or an explicit statement about the release of their own source code for the methodology described. |
| Open Datasets | Yes | Functional MRI dataset. We analyze the Narratives public dataset (Nastase et al., 2020), which contains the f MRI measurements of 345 unique subjects listening to narratives. |
| Dataset Splits | Yes | f g was fitted on Itrain = 99% of the dataset, and evaluated on Itest = 1% of the left out-data (2.5 min of audio). ...We repeat the procedure 100 times with a 100-fold cross-validation, using scikit-learn KFold without shuffling (Pedregosa et al., 2011). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions several software packages used, such as 'spa Cy', 'Supar', 'Gector', 'scikit-learn', and 'MNE-Python', but it does not specify their version numbers, which is required for reproducibility. |
| Experiment Setup | Yes | We use the linear ridge regression from scikit-learn (Pedregosa et al., 2011), with penalization parameters chosen among 10 values log-spaced between 10 1 and 108 and g was a finite impulse response (FIR) model with 5 delays, following (Huth et al., 2016)." and "To isolate the syntactic representations of GPT-2 , we synthesize, for each sentence of each story, k = 10 sentences with the same syntactic structures (Figure 2). |