.. _interface: ************************ Developer Clarifications ************************ The Following documentation stores commonly-asked developer questions. We hope this will be useful to anyone interested in contributing to Brain-Score's codebase or scientific workings. 1. **For a given model, are activations different on each benchmark? How?** Activations per model are generated based on benchmark stimuli; not every benchmark has unique stimuli. For most model-benchmark pairs, activations will be different because stimuli will be different. The exceptions to this are the benchmarks that use the same stimuli, such as the `MajajHong20215` family of benchmarks. 2. **Result Caching** Result Caching is a Brain-Score `repo `_ that allows model activations (and other functions) to be cached to disk, in order to speed up the process of rescoring models. It contains a decorator that can be attached to a function right before it is defined. On the first run of that function, `result_caching` will save to disk the result of tha function and will load that result from disk in future calls with the same parameters. All files are saved in the user's `~/result_caching` folder, and they are persistent, as there is no garbage collection built in. You can deactivate `result_caching` by simply setting the environment flag `RESULTCACHING_DISABLE` to `1`. Please see the link above for more detailed documentation. 3. **Model Mapping Procedure** In general, there are different methods that are used in the Brain-Score code to instruct the model to "begin recording", observe stimuli, and to generate scores. Models follow the `ModelCommitment` to conform to the `BrainModel` API. A `BrainModel` is any model that has a `region_layer_map`. This allows the layers in the model to be mapped to layers in the ventral visual stream, and is chosen by scoring models on the public version of a benchmark (the private benchmark data is heldout for the BrainModel to be scored on). See the more technical docs `here `_ for additional notes.