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courses:rg:predicting_human_brain_activity_associated_with_the_meanings_of_nouns [2011/09/11 00:27] ufal vytvořeno |
courses:rg:predicting_human_brain_activity_associated_with_the_meanings_of_nouns [2011/09/11 10:48] ufal |
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| ===== Comments ===== | ===== Comments ===== | ||
| - | + | ==== Summary | |
| - | + | * authors present a computational model, which predicts the functional magnetic resonance imaging (fMRI) of neural activation associated with words for which no fMRI data are available | |
| - | + | * fMRI prediction for a word '' | |
| - | ===== Suggested Additional Reading ===== | + | - compute a vector of semantic features over a huge corpus |
| - | + | * 25 features are defined in terms of co-occurrence of '' | |
| + | - predict neural fMRI activation as a weighted sum of semantic features | ||
| + | * weights for every voxel (3D pixel) and feature are estimated using multiple regression | ||
| + | * fMRI data | ||
| + | * they created 60 representative fMRI images | ||
| + | * word - picture combination from 12 semantic categories | ||
| + | * they measured brain activation of 9 participants after being exposed to all 60 word - picture combinations | ||
| + | * evaluation | ||
| + | * they carried out " | ||
| + | * 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 | ||
| + | * 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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| ===== What do we dislike about the paper ===== | ===== What do we dislike about the paper ===== | ||
| + | * authors selected 25 sensory-motor verbs as a basis for their co-occurence features. But they did not sufficiently explain what led them to pick exactly these ones. | ||
| + | * | ||
| Written by Michal Novák | Written by Michal Novák | ||