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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 11:59] 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 " | + | * they carried out " |
| * 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 | ||
| - | | + | |
| - | + | * quantitative measurments | |
| - | + | * 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 | ||
| + | * examination of learnt basis set of fMRI signatures for 25 verb-based signatures | ||
| + | * ' | ||
| + | * though for other verbs these correspondences between the function and brain regions is not present across all participants | ||
| + | * convenience of selected verbs as a basis for features | ||
| + | * they generated 115 random sets of 25 features constructed from 5000 highly frequent words (excluding 25 verbs used in an original setting) in corpus and trained system using these feature sets | ||
| + | * accuracy of prediction fMRI ranged from 0.46 to 0.68 (with mean equal to 0.60) => compared with 0.77 in setting using | ||
| ===== What do we like about the paper ===== | ===== What do we like about the paper ===== | ||