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courses:rg:predicting_human_brain_activity_associated_with_the_meanings_of_nouns [2011/09/11 02:20]
ufal
courses:rg:predicting_human_brain_activity_associated_with_the_meanings_of_nouns [2011/09/11 10:48]
ufal
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       * they measured brain activation of 9 participants after being exposed to all 60 word - picture combinations       * they measured brain activation of 9 participants after being exposed to all 60 word - picture combinations
   * evaluation   * evaluation
-    * they carried out "leave-two-out" cross validation with all 60 word-fMRI instances+    * they carried out "leave-two-out" cross validation with all 60 examples
       * 58 of them served as a training data       * 58 of them served as a training data
       * for two of them fMRI images were predicted and compared with observed images. On the basis of cosine similarity measure a matching was determined. If the predicted image for the first word matches with its corresponding observed image, one positive point is scored - aggregated over all folds, it forms an accuracy measure       * for two of them fMRI images were predicted and compared with observed images. On the basis of cosine similarity measure a matching was determined. If the predicted image for the first word matches with its corresponding observed image, one positive point is scored - aggregated over all folds, it forms an accuracy measure
-      *  +  experiments and results 
- +    * quantitative measurment 
 +      * matching two unseen words to their seen fMRI images 
 +        * 0.77 averaged over all 9 participants - significantly above a chance level 
 +        * higher activation tends to be predicted in the left hemisphere - it is consistent with the generally held view that left hemisphere is more responsible for semantic representation than the right one 
 +      * prediction for a word in a new semantic category 
 +        * in training stage, they excluded all examples from the same semantic category as either of the two tested words 
 +        * 0.70 averaged over all 9 participants - still above the chance level 
 +      * prediction, when two tested words belong to the same category 
 +        * hard to distinguish 
 +        * 0.62 averaged over all participants - slightly above the chance level 
 +      * ability to distinguish in even more diverse range of words 
 +        * model trained on 59 examples and tested on a remaining example + another 1000 highly frequent words 
 +        * it ranked predicted fMRI images for 1001 words with respect to its similarity to observed fMRI image of the testing example 
 +        * 0.72 over 9 participants 
 +    * 
  
  

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