About

Jason Zevin is an Associate Professor of Psychology and Linguistics at USC Dornsife. He holds a Ph.D. in Neuroscience from the University of Southern California, obtained in August 2003, and completed postdoctoral training as an NRSA Fellow at the Sackler Institute for Developmental Psychobiology from September 2003 to August 2006. His research specialties include psycholinguistics, cognitive neuroscience, and computational modeling, focusing on understanding the neural and cognitive mechanisms underlying language processing and learning. Zevin has held academic appointments at Weill Cornell Medical College, serving as an Assistant Professor of Psychology in Psychiatry from September 2006 to August 2012, and as an Associate Professor from September 2012 to December 2013. He has also worked as a Senior Scientist at Haskins Laboratories since January 2011. His research contributions include studies on the limits of song reacquisition in adult zebra finches exposed to white noise, published in the Journal of Neuroscience. His work integrates insights from psychology, neuroscience, and computational approaches to advance understanding of language and cognition.

Research topics

• Linguistics
• Cognitive psychology
• Psychology
• Computer Science
• Developmental psychology

Selected publications

• Statistical learning of syllable sequences as trajectories through a perceptual similarity space

  Cognition · 2024-01-13 · 2 citations

  articleSenior authorCorresponding
  PublisherDOI

• Models of Word Reading

  2022-05-13 · 26 citations

  otherSenior author
  PublisherDOI

• Statistical learning of syllable sequences as trajectories through a perceptual similarity space

  2022-07-15

  preprintOpen access1st authorCorresponding

  Learning from sequential statistics is a general capacity common to many model systems. One form of statistical learning (SL) – learning to segment “words’ from continuous streams of speech syllables in which the only segmentation cue is ostensibly the transitional (or conditional) probability from one syllable to the next – has been studied in great detail. Typically, this phenomenon is modeled as the calculation of probabilities over discrete, featureless units. Here we present an alternative model, in which sequences are learned as trajectories through a similarity space. A simple recurrent network that coded syllables using representations capture the similarity relations among syllables correctly simulated the result of a classic SL study. We then used the simulations to identify a set of “words” that produces the reverse of the typical SL, i.e., part-words are predicted to be more familiar than words. Results from two experiments are consistent with simulation results.

  PublisherOA PDFDOI

• Perceptual similarity and learning from sequential statistics.

  eScholarship (California Digital Library) · 2021-01-01

  articleOpen access1st authorCorresponding

  Most models of statistical learning do not consider the perceptual properties of the units that make up a sequence. Here, we manipulate the similarity of units in a modified version of classic statistical learning paradigm (Aslin et al., 1998). After a 3-minute familiarization stream, we asked the participants to rate their familiarity with words and part-words, with the addition of non-words (trisyllabic words without any previous exposure). We developed a simple recurrent neural network that used distributed representations for the syllables and produced similar results to the human data. We then explored how perceptual similarity impacted learning of different familiarization streams. Specifically, greater similarity relations among units were predicted to lead to poorer discrimination between words and part-words. Based on the model’s results, we created a new familiarization sequence of trisyllabic words and tested for impacts on perceptual similarity on human performance.

  Publisher

• Neural correlates of phonology-to-orthography mapping consistency effects on Chinese spoken word recognition

  Brain and Language · 2021-05-06 · 9 citations

  article
  PublisherDOI

• How you read affects what you gain: Individual differences in the functional organization of the reading system predict intervention gains in children with reading disabilities.

  Journal of Educational Psychology · 2021 · 18 citations

  • Psychology
  • Cognitive psychology
  • Developmental psychology

  graders) rely on two sources of information during an oral word reading task - print-speech correspondences and semantic imageability - before and after a phonologically-weighted intervention. We show that children who relied more on print-speech regularities and less on imageability pre-intervention had better intervention gains. In parallel, children who over the course of the intervention exhibited greater increases in their reliance on print-speech correspondences and greater decreases in their reliance on imageability had better intervention outcomes. Importantly, these two factors were differentially related to specific reading task outcomes, with greater reliance on print-speech correspondences associated with pseudoword naming, while (lesser) reliance on imageability related to word reading and comprehension. We discuss the implications of these findings for theoretical models of reading acquisition and educational practice.

  PublisherDOI

• Top-down grouping affects adjacent dependency learning

  Psychonomic Bulletin & Review · 2020-06-15 · 8 citations

  articleOpen access
  PublisherOA PDFDOI

• Individual differences in learning the regularities between orthography, phonology and semantics predict early reading skills

  Journal of Memory and Language · 2020 · 72 citations

  • Computer Science
  • Psychology
  • Linguistics
  PublisherOA PDFDOI

• Statistical Learning of Unfamiliar Sounds as Trajectories Through a Perceptual Similarity Space

  Cognitive Science · 2019-08-01 · 10 citations

  articleOpen accessSenior author

  In typical statistical learning studies, researchers define sequences in terms of the probability of the next item in the sequence given the current item (or items), and they show that high probability sequences are treated as more familiar than low probability sequences. Existing accounts of these phenomena all assume that participants represent statistical regularities more or less as they are defined by the experimenters-as sequential probabilities of symbols in a string. Here we offer an alternative, or possibly supplementary, hypothesis. Specifically, rather than identifying or labeling individual stimuli discretely in order to predict the next item in a sequence, we need only assume that the participant is able to represent the stimuli as evincing particular similarity relations to one another, with sequences represented as trajectories through this similarity space. We present experiments in which this hypothesis makes sharply different predictions from hypotheses based on the assumption that sequences are learned over discrete, labeled stimuli. We also present a series of simulation models that encode stimuli as positions in a continuous two-dimensional space, and predict the next location from the current location. Although no model captures all of the data presented here, the results of three critical experiments are more consistent with the view that participants represent trajectories through similarity space rather than sequences of discrete labels under particular conditions.

  PublisherOA PDFDOI

• Speech perception with temporally patterned noise maskers

  OSF Preprints (OSF Preprints) · 2019-01-01

  articleOpen access1st authorCorresponding

  Patterned adverse listening conditions for word recognition tasks

  PublisherOA PDFDOI

Recent grants

• NIH Grant P01HD001994

  NIH · $49.1M · 2017

• Predictors of Non- Response in Young Dyslexic Children with Co-Morbid ADHD

  NIH · $9.1M · 2019

• NIH Grant F32DC006352

  NIH · $124k · 2006

• NIH Grant R21DC008969

  NIH · $455k · 2011

Frequent coauthors

• Felix Hao Wang

  22 shared

• Toben H. Mintz

  21 shared

• Jianfeng Yang

  Shaanxi Normal University

  19 shared

• Kenneth R. Pugh

  16 shared

• Hua Shu

  Huazhong University of Science and Technology

  15 shared

• Bruce D. McCandliss

  Stanford University

  15 shared

• W. Einar Mencl

  Haskins Laboratories

  14 shared

• Jay G. Rueckl

  10 shared

Labs

• USC DornsifePI

Education

• PhD, Graduate Program in Neuroscience

  University of Southern California

  2003

• AB, Psychology

  Washington University in St. Louis

  1998