We seek to understand the principles of development.
Research Focus
The primary focus of our lab is on elucidating the algorithms employed by the brain to process sensory signals, and the learning programs that allow for the extraction of regularities in sensory data. Our experimental work, using techniques of psychophysics, electrophysiology and neuroimaging, probes how these algorithms develop in humans and our computational efforts seek to mimic these processes in artificial systems. In addition to investigating processing strategies used by the typically-developing brain we are also studying deviations from these strategies caused by atypical sensory experiences such as congenital blindness and neurological conditions like autism.
Project Prakash
In 2005, we launched Project Prakash. The project identifies congenitally, but curably, blind children in India who have so far remained untreated due to poor medical access. By providing them corrective surgery, we gain a powerful vantage point to study the development of vision. Following these children longitudinally yields insights into the process by which they develop visual skills. We have examined various aspects of vision including acuity, contrast sensitivity, recognition and imagery. The data have revealed significant developmental changes and are helping define the landscape of neural plasticity late in life; its extent as well as limitations, with implications for basic science and clinical practice.
A prominent theme that emerges from Project Prakash is the importance of temporally changing information for perceptual organization.
Autism
Interestingly, this idea also plays a central role in our work on autism. According to a theory that we have recently proposed, several symptoms of autism may arise as a consequence of differences in the extraction of temporal dependencies between events, making it hard to predict how a dynamic situation will unfold. A key goal for our lab now is to rigorously test this theory by examining whether prediction is indeed compromised in autism. This work holds the potential of enhancing our understanding of autism and also elucidating important aspects of predictive processing in the non-autistic brain.
To sign up for an autism study, please visit Autism Research at MIT.
Computation
Our computational experimentation serves a useful role in testing our hypotheses regarding empirical data from humans and, conversely, generating predictions that can then be tested via experimentation with humans. Much of our computational work has focused on examining how different kinds of training regimens (biomimetic or otherwise) and varied network architectures can help explain the remarkable robustness humans exhibit in classifying sensory inputs.
Selected Publications*
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Once blind and now they see (Scientific American, 2013)
Surgery in blind children from India allows them to see for the first time and reveals how vision works in the brain. Surgery often proved a success, even for some of those well into their 20s. The procedure also provided the scientists on the Sinha’s team with a new understanding of the functioning of the visual system. These findings are beginning to give us a sense of the landscape of what can and cannot be achieved when a child gains vision at a late age. On the one hand, visual functions do not fade irretrievably if eyes and brain areas for visual processing are not subject to intensive use during the “critical period” that is believed to last for the first few years of childhood. On the other hand, early visual experience is undeniably important for the normal development of abilities such as high-resolution vision.
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Potential Downside to High Initial Acuity (PNAS, 2019)
Children who are treated for congenital cataracts later exhibit impairments in configural face analysis. This has been explained in terms of a critical period for the acquisition of normal face processing. Here, we consider a more parsimonious account according to which deficits in configural analysis result from the abnormally high initial retinal acuity that children treated for cataracts experience, relative to typical newborns. According to this proposal, the initial period of low retinal acuity characteristic of normal visual development induces extended spatial processing in the cortex that is important for configural face judgments. As a computational test of this hypothesis, we examined the effects of training with high-resolution or blurred images, and staged combinations, on the receptive fields and performance of a convolutional neural network. The results show that commencing training with blurred images creates receptive fields that integrate information across larger image areas and leads to improved performance and better generalization across a range of resolutions. These findings offer an explanation for the observed face recognition impairments after late treatment of congenital blindness, suggest an adaptive function for the acuity trajectory in normal development, and provide a scheme for improving the performance of computational face recognition systems.
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Autism as a disorder of prediction (PNAS, 2014)
A rich collection of empirical findings accumulated over the past three decades attests to the diversity of traits that constitute the autism phenotypes. It is unclear whether subsets of these traits share any underlying causality. This lack of a cohesive conceptualization of the disorder has complicated the search for broadly effective therapies, diagnostic markers, and neural/genetic correlates. In this paper, we describe how theoretical considerations and a review of empirical data lead to the hypothesis that some salient aspects of the autism phenotype may be manifestations of an underlying impairment in predictive abilities. With compromised prediction skills, an individual with autism inhabits a seemingly “magical” world wherein events occur unexpectedly and without cause. Immersion in such a capricious environment can prove overwhelming and compromise one’s ability to effectively interact with it. If validated, this hypothesis has the potential of providing unifying insights into multiple aspects of autism, with attendant benefits for improving diagnosis and therapy.
*Visit the Publications page for a more comprehensive list.