Implementation of inverted encoding model as described in Scotti, Chen, & Golomb
Python package for easy implementation of inverted encoding modeling as described in Scotti, Chen, & Golomb (in-prep).
Contact: email@example.com (Paul Scotti)
Run the following to install:
pip install inverted-encoding
from inverted_encoding import IEM, permutation, circ_diff import numpy as np predictions, confidences, aligned_at_prediction_recons, aligned_at_zero_recons = IEM(trialbyvoxel,features,stim_max=180,nfolds=num_runs,is_circular=True) # use "help(IEM)" for more information, below is a summary: # trialbyvoxel: your matrix of brain activations, does not necessarily have to be voxels # features: array of your stimulus features (must be integers within range defined by stim_max) # stim_max=180 means that your stimulus space ranges 0-179° degrees # nfolds refers to the K to use for KFold cross-validation. We recommend setting this to the number of runs you have for 1-run-left-out CV. # is_circular=True for a circular stimulus space, False for non-circular stimulus space # predictions: array of predicted stimulus for each trial # confidences: array of goodness of fit values for each trial # aligned_at_prediction_recons: trial-by-trial reconstructions (matrix of num_trials x stim_max) such that # when plotted ideally each reconstruction is centered at the original trial stimulus # aligned_at_zero_recons: trial-by-trial reconstructions aligned at zero, such that when # plotted ideally each reconstruction is centered at zero on the x axis (e.g., plt.plot(aligned_at_zero_recons[trial,:])) ## Compute mean absolute error (MAE) by doing the following, then compare to null distribution: if is_circular: # if your stimulus space is circular, need to compute circular differences mae = np.mean(np.abs(circ_diff(predictions,features,stim_max))) else: mae = np.mean(np.abs(predictions-features)) null_mae_distribution = permutation(features,stim_max=180,num_perm=1000,is_circular=True)
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