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courses:rg:predicting_human_brain_activity_associated_with_the_meanings_of_nouns [2011/09/11 11:59] ufal |
courses:rg:predicting_human_brain_activity_associated_with_the_meanings_of_nouns [2011/09/11 12:20] ufal |
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| * convenience of selected verbs as a basis for features | * 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 | * 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 | + | * 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 ===== | ||