Date of Award

12-2012

Embargo Period

4-5-2014

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Center for the Neural Basis of Cognition

Advisor(s)

Tom Mitchell

Second Advisor

Dean Pomerleau

Third Advisor

Michael Tarr

Fourth Advisor

Riitta Salmelin

Abstract

Most of the work investigating the representation of concrete nouns in the brain has focused on the locations that code the information. We present a model to study the contributions of perceptual and semantic features to the neural code representing concepts over time and space. The model is evaluated using magnetoencephalography data from different paradigms and not only corroborates previous findings regarding a distributed code, but provides further details about how the encoding of different subcomponents varies in the space-time spectrum. The model also successfully generalizes to novel concepts that it has never seen during training, which argues for the combination of specific properties in forming the meaning of concrete nouns in the brain. The results across paradigms are in agreement when the main differences among the experiments (namely, the number of repetitions of the stimulus, the task the subjects performed, and the type of stimulus provided) were taken into consideration. More specifically, these results suggest that features specific to the physical properties of the stimuli, such as word length and right-diagonalness, are encoded in posterior regions of the brain in the first hundreds of milliseconds after stimulus onset. Then, properties inherent to the nouns, such as is it alive? and can you pick it up?, are represented in the signal starting at about 250 ms, focusing on more anterior parts of the cortex. The code for these different features was found to be distributed over time and space, and it was common for several regions to simultaneously code for a particular property. Moreover, most anterior regions were found to code for multiple features, and a complex temporal profile could be observed for the majority of properties. For example, some features inherent to the nouns were encoded earlier than others, and the extent of time in which these properties could be decoded varied greatly among them. These findings complement much of the work previously described in the literature, and offer new insights about the temporal aspects of the neural encoding of concrete nouns. This model provides a spatiotemporal signature of the representation of objects in the brain. Paired with data from carefully-designed paradigms, the model is an important tool with which to analyze the commonalities of the neural code across stimulus modalities and tasks performed by the subjects.

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