A common model of representational spaces in human cortex
Multivariate pattern analysis affords investigation of fine-grained patterns of neural activity that carry fine-grained distinctions in the information they represent. These patterns of brain activity in different brains can be recast as vectors in a common high-dimensional representational space with basis functions that have tuning profiles and patterns of connectivity that are common across brains. We derive transformation matrices that rotate individual anatomical spaces into the common model space with searchlight-based, whole cortex hyperalignment. Transformation matrices can be derived based on patterns of response to a rich, naturalistic stimulus, such as a movie, or on patterns of functional connectivity. Basing hyperalignment on functional connectivity makes it possible to hyperalign brains based on fMRI data obtained in the resting state as well as during movie viewing. The common model provides a common structure that captures fine-grained distinctions among cortical patterns of response that are not modeled well by current brain atlases. The model also captures coarse-scale features of cortical topography, such as retinotopy and category-selectivity, and provides a computational account for both coarse-scale and fine-scale topographies with multiplexed topographic basis functions.
What Variability in Children's Language Reveals about the Psychological and Neurological Processes of Language Acquisition: Evidence from Typical Children and Children with Autism Spectrum Disorder
Language acquisition research usually focuses on when/at what age children "know", for example, the grammatical constructions and lexical principles of their language. Variability in child performance has been considered to be noise, and/or a signal that better tasks/stimuli are needed. In this talk, I will argue that such variability is actually quite revealing of the processes of children's language acquisition, in both typically developing children and children with Autism Spectrum Disorder (ASD). Language and communication impairments are considered to be an important deficit of ASD; however, it is not clear when during development these impairments emerge, nor the extent to which they can be attributed to impairments in core aspects of language per se vs. impairments in other social or cognitive processes. For the past decade, I have conducted a longitudinal study assessing the language development of a group of children recently diagnosed with an ASD, whose language comprehension was assessed using an innovative method for this population, Intermodal Preferential Looking (IPL). In this talk, I will discuss IPL and speech data examining these children's acquisition of grammar and vocabulary; I exploit their widespread variability to illuminate possible roles for social, linguistic input, and (most recently) neurological factors in their language development. I conclude by discussing the implications of these findings for advancing our understanding of autism and of language.