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Regular version of the site

Useful links

Machine learning and vision data sets

Educational Resources

  • Open Science Framework: Open cognitive task tutorials
  • Go Cognitive: Free materials for students, educators, and researchers in cognitive psychology and cognitive neuroscience
  • MILA: Montreal Institute for Learning Algorithms resources
  • Neuronal Dynamics: an online-book on neuronal dynamics, with the description of the leaky integrate-and-fire layer and mathematics underlying LIF-algorythms
  • 3Blue1Brown: a series of lectures on neural networks
  • Michael Freeman: An Introduction to Hierarchical Modeling

Important and/or new materials on attention, vision, and computational models

Varia

Useful code

  • Code for sampling methods. Some of our analysis will be looking at point comparisons along a continuous distribution. In these cases, we might not be able to use tradition comparison methods. These files use sampling methods, and 'bootstrapping' in particular to solve this problem. Many of our statistics assume a normal distribution, but resampling methods allow you to estimate the true mean and standard deviation (and other parameters) by using a smaller subsection (sample) of your data. It makes no assumption about the shape of the underlying distribution. The original source code was provided by Dr. Amelia Hunt, University of Aberdeen (UK).
      bootstrappercent.m
      permutationData.mat
      permutationExample.m

 

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