Richard Aslin, Ph.D.

Richard Aslin's picture
300 George Street, Suite 900, New Haven, CT 06511
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After 33 years on the faculty in the Department of Psychology and then Brain and Cognitive Sciences at the University of Rochester, Richard Aslin has joined Haskins Laboratories as a senior scientist.  He will be re-establishing a BabyLab to carry on the outstanding tradition of developmental research at Haskins, complementing the on-going studies of older infants and young children by other Haskins scientists.

Research Overview

During the course of development, human infants gather information about the external world without the benefit of an extensive base of knowledge that adults automatically bring to bear on perceptual, motor, cognitive, and language tasks. What mechanisms allow infants to acquire this initial level of information and how does that information guide subsequent learning? Clearly, most learning that occurs in infancy, and a substantial amount of learning in adulthood, is performed without instruction—it is implicit and based on an analysis of the distributional properties of environmental stimulation.

For over two decades, my research has been directed at exploring and understanding these implicit learning mechanisms, which are typically referred to as “statistical learning”. Although initially studied in the task of word segmentation from fluent speech, statistical learning has been extended to other domains, such as musical tones, phonetic categories, sequences of visual shapes, sequences of motor responses, and combinations of objects (or object parts) in complex visual scenes. An important goal of these studies is to reveal the computational constraints that enable statistical learning to be tractable given the complexity of the input and the infinite number of statistical computations that are possible over any set of inputs. Initial computational models of statistical learning focused on bi-gram statistics and conditional probabilities, but more recent work has broadened to include Bayesian ideal learning models. Empirical studies of statistical learning have also evolved to explore order effects in learning multiple structures and to understand how statistical patterns trigger the formation of categories.

A related line of research focuses on spoken word recognition in both infants, toddlers, and adults using eye-tracking and EEG methods. Once an auditory word-form has been extracted from fluent speech, how does the infant map that sequence of sounds onto meaning? Recent and on-going studies have examined how infants and toddlers recognize the meaning of the unfolding speech signal, for both previously known and recently learned words, as well as for mispronounced words or words preceded by a disfluency. Most of these studies employ table-top eye-trackers, while others use a head-camera or head-mounted eye-tracker in combination with a LENA audio-recording and analysis system. Studies of adults employ an artificial lexicon paradigm and the visual world eye-tracking paradigm to carefully control variables such as word frequency and acoustic similarity (neighborhood structure).

In the past few years, my research has moved toward studies of brain function in adults and infants using fMRI and optical imaging, respectively. We have shown that activations in LIFG are correlated with statistical learning and that functional connectivity in a network of brain regions changes as new statistical patterns are available in the input.  A 48-channel optical imaging system (Hitachi ETG-4000) provides a measure of hemodynamic activity in the superficial layers of cortex while infants are being presented with controlled stimulation. This fNIRS system has enabled us to study how the brain makes predictions in how basic aspects of neural coding change during development.  The 142-channel Shimadzu fNIRS system in the Hirsch lab is being outfitted with infant-friendly optical fibers to further enhance our on-going studies.

Grant Support

NSF (BCS-1514351), “EAGER: Developmental mechanisms of perception and language in the infant brain”, R. Aslin PI, C. Nelson co-PI (Boston Children’s Hospital)

NIH (HD-088731), “Probabilistic computation in the cortex of the developing human brain”, R. Aslin PI, J. Fiser co-I (Central European University, Budapest)

James S. McDonnell Foundation (220020494), “Planning grant for statistical learning center”, R. Aslin PI (Ram Frost, Ken Pugh, and Jay Rueckl, co-I’s)

Students and Staff

Laurie Bayet, postdoctoral fellow (Boston Children’s Hospital)

Benjamin Zinszer, postdoctoral fellow (University of Texas)

Claire Kabdebon, postdoctoral fellow (Haskins Laboratories)

Recent Publications

Bergelson, E. and Aslin, R. N. (Published online 6/30/2017).  Semantic specificity in one-year-olds’ word comprehension.  Language Learning & Development.  DOI:10.1080/15475441.2017.1324308

Emberson, L. L., Zinszer, B. D., Raizada, R. D. S., and Aslin, R. N. (2017).  Decoding the Infant Mind: Multichannel Pattern Analysis (MCPA) using fNIRS.  PLoS ONE, April 20, 12(4):e0172500. 

Emberson, L. L., Boldin, A., Riccio, J. E., Guillet, R., & Aslin, R. N. (2017).  Deficits in top-down, sensory prediction in infants at-risk due to premature birth.  Current Biology, 27, 431-436. 

Emberson, L. L., Cannon, G., Palmeri, H., Richard, J. E., & Aslin, R. N. (2017).  Using fNIRS to examine occipital and temporal responses to stimulus repetition in young infants: Evidence of selective frontal cortex involvement.  Developmental Cognitive Neuroscience, 23, 26-38. 

Karuza, E. A., Li, P., Weiss, D. J., Bulgarelli, F., Zinszer, B., and Aslin, R. N.  (2016).  Sampling over non-uniform distributions: A neural efficiency account of the primacy effect in statistical learning..  Journal of Cognitive Neuroscience, 28, 484-500.

Emberson, L. L., Richards, J. E., and Aslin, R. N. (2015). Top-down modulation in the infant brain: Learning-induced expectations rapidly affect the sensory cortex at 6 months. Proceedings of the National Academy of Sciences, 112, 9585-9590.

Aslin, R. N., Shukla, M, & Emberson, L. L. (2015). Hemodynamic correlates of cognition in human infants. Annual Review of Psychology, 66, 349–79.

Aslin, R. N. (2014). Infant learning: Historical, conceptual, and methodological challenges.. Infancy, 19, 2-27. 

Aslin, R. N. (2014).  Phonetic category learning and its influence on speech production.  Ecological Psychology, 26, 4-15. 

Aslin, R. N. and Newport, E. L. (2014).  Distributional language learning: Mechanisms and models of category formation.  Language Learning, 64: Cognitive Neuroscience Supplement 2, 86–105.  

Kidd, C., Piantadosi, S. T. and Aslin, R. N. (2014).  The Goldilocks Effect in infant auditory attention.  Child Development, 85, 1795–1804 

Karuza, E. A., Newport, E. L., Aslin, R. N., Starling, S. J., Tivarus, M. E., and Bavelier, D.  (2013).  The neural correlates of statistical learning in a word segmentation task: An fMRI study.  Brain and Language, 127, 46-54.

Reeder, P. A., Newport, E. L, and Aslin, R. N. (2013).  From shared contexts to syntactic categories: The role of distributional information in learning linguistic form-classes.  Cognitive Psychology, 66, 30-54.

Aslin, R. N. (2012).  Questioning the questions that have been asked about the infant brain using NIRS.  Cognitive Neuropsychology, 29, 7-33.

Aslin, R. N. and Newport, E. L. (2012). Statistical learning: From acquiring specific items to forming general rules.  Current Directions in Psychological Science, 21, 170-176. 

Bejjanki, V. R., Clayards, M., Knill, D. C. and Aslin, R. N. (2011).  Cue integration in categorical tasks: Insights from audio-visual speech perception.  PLoS One, 6, e19812. 

Shukla, M., White, K. S., and Aslin, R. N. (2011).  Prosody guides the rapid mapping of auditory word forms onto visual objects in 6-mo-old infants.  Proceedings of the National Academy of Sciences, 108, 6038-6043.

White, K. S. and Aslin, R. N. (2011).  Adaptation to novel accents in toddlers.  Developmental Science, 14, 372-384.