Name
Abstract | ||
|---|---|---|
Bayesian probabilistic analysis offers a new approach to characterize semantic representations by inferring the most likely feature structure directly from the patterns of brain activity. In this study, infinite latent feature models [1] are used to recover the semantic features that give rise to the brain activation vectors when people think about properties associated with 60 concrete concepts. The semantic features recovered by ILFM are consistent with the human ratings of the shelter, manipulation, and eating factors that were recovered by a previous factor analysis. Furthermore, different areas of the brain encode different perceptual and conceptual features. This neurally-inspired semantic representation is consistent with some existing conjectures regarding the role of different brain areas in processing different semantic and perceptual properties. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-34713-9_30 | MLINI |
Keywords | Field | DocType |
Brain Activity,Semantic Representation,Semantic Feature,Latent Feature,Human Rating | Computer science,Brain activity and meditation,Natural language processing,Probabilistic latent semantic analysis,Artificial intelligence,Semantic similarity,Pattern recognition,Feature structure,Semantic feature,Perception,Machine learning,Pattern recognition (psychology),Bayesian probability | Conference |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chang, Kai-min | 1 | 143 | 16.84 |
| Brian Murphy | 2 | 0 | 0.34 |
| Marcel Just | 3 | 476 | 75.67 |