Brianna Pritchett

Georgia Institute of Technology | GT · Human-Computer Interaction

Prior work decoding linguistic meaning from imaging data has been largely limited to concrete nouns, using similar stimuli for training and testing, from a relatively small number of semantic categories. Here we present a new approach for building a brain decoding system in which words and sentences are represented as vectors in a semantic space co...

A set of left frontal, temporal, and parietal brain regions respond robustly during language comprehension and production (e.g., Fedorenko et al. 2010; Menenti et al. 2011). These regions have been further shown to be selective for language relative to other cognitive processes, including arithmetic, aspects of executive function, and music percept...

Socioeconomic status (SES) has a measurable impact on many educational outcomes and likely also influences computer science (CS) achievement. We present a novel model to account for the observed connections between SES and CS achievement. We examined possible mediating variables between SES and CS achievement, including spatial ability and access t...

The neural processes that underlie your ability to read and understand this sentence are unknown. Sentence comprehension occurs very rapidly, and can only be understood at a mechanistic level by discovering the precise sequence of underlying computational and neural events. However, we have no continuous and online neural measure of sentence proces...

Several different groups have demonstrated the feasibility of building forward models of functional MRI data in response to concrete stimuli such as pictures or video, and of using these models to decode or reconstruct stimuli shown while acquiring test fMRI data. In this paper, we introduce an approach for building forward models of conceptual sti...