Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision Next revision Both sides next revision | ||
|
courses:rg:predicting_human_brain_activity_associated_with_the_meanings_of_nouns [2011/09/11 10:48] ufal |
courses:rg:predicting_human_brain_activity_associated_with_the_meanings_of_nouns [2011/09/11 12:20] ufal |
||
|---|---|---|---|
| Line 21: | Line 21: | ||
| * 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 | * experiments and results | ||
| - | * quantitative | + | * quantitative |
| * matching two unseen words to their seen fMRI images | * matching two unseen words to their seen fMRI images | ||
| * 0.77 averaged over all 9 participants - significantly above a chance level | * 0.77 averaged over all 9 participants - significantly above a chance level | ||
| Line 35: | Line 35: | ||
| * it ranked predicted fMRI images for 1001 words with respect to its similarity to observed fMRI image of the testing example | * 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 | * 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 25 manually selected verbs it suggest that these 25 designed features are distinctive in capturing regularities in the neural activation encoding of the semantic content of words | ||
| + | * conclusion | ||
| + | * this work presented a predictive relationship between word co-occurrence statistics and neural activation | ||
| + | * high accuracy of selected 25 features shows that neural representation of concrete words is to a large extent grounded in sensory-motor features | ||
| + | * it shows that semantic features share commonalities across individuals and may help to predict neural representations across individuals, | ||
| + | * the model captures semantic, rather than visual aspect of words | ||
| ===== What do we like about the paper ===== | ===== What do we like about the paper ===== | ||