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Spring Course: Blogging about Cognitive Science

This spring, Prof. Seana Coulson & grad student Rose Hendricks will lead a seminar on Cognitive Science blogging. (more)

Keown, C.L., Berletch, J.B., Castanon, R., Nery, J.R., Disteche, C.M., Ecker, J.R., Mukamel, E.A. (2017) Allele-specific non-CG DNA methylation marks domains of active chromatin in female mouse brain. PNAS. 2017; Early Edition.
DNA methylation at gene promoters in a CG context is associated with transcriptional repression, including at genes silenced on the inactive X chromosome in females. Non-CG methylation (mCH) is a distinct feature of the neuronal epigenome that is differentially distributed between males and females on the X chromosome. However, little is known about differences in mCH on the active (Xa) and inactive (Xi) X chromosomes because stochastic X-chromosome inactivation (XCI) confounds allele-specific epigenomic profiling. We used whole-genome bisulfite sequencing in a mouse model with nonrandom XCI to examine allele-specific DNA methylation in frontal cortex. Xi was largely devoid of mCH, whereas Xa contained abundant mCH similar to the male X chromosome and the autosomes. In contrast to the repressive association of DNA methylation at CG dinucleotides (mCG), mCH accumulates on Xi in domains with transcriptional activity, including the bodies of most genes that escape XCI and at the X-inactivation center, validating this epigenetic mark as a signature of transcriptional activity. Escape genes showing CH hypermethylation were the only genes with CG-hypomethylated promoters on Xi, a well-known mark of active transcription. Finally, we found extensive allele-specific mCH and mCG at autosomal imprinted regions, some with a negative correlation between methylation in the two contexts, further supporting their distinct functions. Our findings show that neuronal mCH functions independently of mCG and is a highly dynamic epigenomic correlate of allele-specific gene regulation.
Cooperrider, K., Marghetis, T., and Núñez, R. (2017). Where does the Ordered Line Come From? Evidence From a Culture of Papua New Guinea. Psychological Science.
Number lines, calendars, and measuring sticks all represent order along some dimension (e.g., magnitude) as position on a line. In high-literacy, industrialized societies, this principle of spatial organization—linear order—is a fixture of visual culture and everyday cognition. But what are the principle’s origins, and how did it become such a fixture? Three studies investigated intuitions about linear order in the Yupno, members of a culture of Papua New Guinea that lacks conventional representations involving ordered lines, and in U.S. undergraduates. Presented with cards representing differing sizes and numerosities, both groups arranged them using linear order or sometimes spatial grouping, a competing principle. But whereas the U.S. participants produced ordered lines in all tasks, strongly favoring a left-to-right format, the Yupno produced them less consistently, and with variable orientations. Conventional linear representations are thus not necessary to spark the intuition of linear order—which may have other experiential sources—but they nonetheless regiment when and how the principle is used.

Featured Classes
Fall 2017:
  • COGS181: Neural Networks/Deep Learning
  • DSGN90: Understanding/Designers Search
    In this one week course, the research goal will be to answer the question: How do designers (in many different fields) solve design problems with search engines? We know (from earlier work) that search is actually fairly important in the day-to-day for designers. Do they use search engines to find inspiration? Do they use them to help solve prosaic problems that are a pain to solve otherwise? Do they use them to find services that they need to help solve tough problems? Instructor will be in touch with when the course will meet. Questions about the course details can be directed to Daniel Russell <>; for administrative questions about the course to contact

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Philip Guo featured in MIT EECS Connector

Computer science alum Philip Guo aims to lower the barriers to learning programming and data science. MIT EECS Alumni Magazine

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