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eLife-2017-data-and-analysis-scripts

These Matlab scripts & data files can be used to reproduce the figures from Garvert et al., eLife 2017, https://elifesciences.org/content/6/e17086

Dependencies:

  • Matlab (we used 2014a but it should all work with previous versions)
  • bioinformatics toolbox (for script graphallshortestpaths.m in different scripts, e.g. generate_Fig3C.m)

Data files: All neural data files come in the following format: subjects x runs x object trial t-1 x object trial t

  • ROI1.mat: parameter estimates extracted from entorhinal ROI1 (contrast used to define ROI: associated < non-associated)
  • ROI2.mat: parameter estimates extracted from entorhinal ROI2 (contrast used to define ROI: length 2 < length 3)
  • ROI3.mat: parameter estimates extracted from entorhinal ROI3 (contrast used to define ROI: Chadwick et al. peak coordinate)
  • ROI4.mat: parameter estimates extracted from entorhinal ROI depicted in Figure 4B
  • ROI5.mat: parameter estimates extracted from anatomically defined region of interest depicted in Figure 2 – figure supplement 1.
  • lOFC.mat: parameter estimates extracted from OFC ROI (contrast used to define ROI: associated < non-associated)
  • subgenual.mat: parameter estimates extracted from OFC ROI (contrast used to define ROI: associated < non-associated)
  • visual.mat: parameter estimates extracted from OFC ROI (contrast used to define ROI: main effect of object onset)
  • RT.mat: log-transformed and demeaned response times, format: subj x runs x object on trial t-1 x object on trial t

behaviour.mat:

  • rt_train: response times in the training blocks (subjects x training blocks)
  • cr_train: % correct in the training blocks (subjects x training blocks)
  • rt_object: response times of cover task during the scan for the seven different objects (subjects x runs x object)
  • rt_object: % correct of cover task during the scan for the seven different objects (subjects x runs x object)
  • rt_trans: Average response time across blocks for the different object transitions (subjects x object on trial t-1 x object on trial t)

exp_settings.mat:

  • path_dist: time: average number of objects during training between any pair of objects (subjects x object A x object B)
  • transition_count: number of times a transition between two objects was experienced during training (subjects x object A x object B). Note this contains transitions between all 12 objects on the full graph.

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These Matlab scripts & data files can be used to reproduce the figures from Garvert et al., eLife 2017

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