My broad scientific research aims to understand how the dynamic neurocognitive networks in human brain support memory, learning and knowledge representations in multi-domains by combining both computational modeling and neuroimaging approaches. My current projects at Stanford focus on developing computational frameworks to account for both behavioral and neuroimaging evidence on how children learn pivotal cognitive skills, naming language and math, and how the representations, processes and even structures in the neurocognitive brain networks change during the learning and development.

Honors & Awards

  • Glushko Dissertation Prize, Cognitive Science Society (2016, June)
  • University Fellowship, University of Wisconsin-Madison (2008-2014)
  • Rumelhart Memorial Travel Award, Stanford University and 13th NCPW (2012)

Boards, Advisory Committees, Professional Organizations

  • Membership, SFN (2015 - Present)
  • Membership, OHBM (2014 - Present)
  • Membership, Cognitive Science Society (2009 - Present)
  • Membership, CNS (2008 - Present)

Professional Education

  • Bachelor of Science, Beijing Normal University (2005)
  • Doctor of Philosophy, University of Wisconsin Madison (2014)

Stanford Advisors

Research & Scholarship

Lab Affiliations


All Publications

  • Mechanisms of interactive specialization and emergence of functional brain circuits supporting cognitive development in children. NPJ science of learning Battista, C., Evans, T. M., Ngoon, T. J., Chen, T., Chen, L., Kochalka, J., Menon, V. 2018; 3: 1


    Cognitive development is thought to depend on the refinement and specialization of functional circuits over time, yet little is known about how this process unfolds over the course of childhood. Here we investigated growth trajectories of functional brain circuits and tested an interactive specialization model of neurocognitive development which posits that the refinement of task-related functional networks is driven by a shared history of co-activation between cortical regions. We tested this model in a longitudinal cohort of 30 children with behavioral and task-related functional brain imaging data at multiple time points spanning childhood and adolescence, focusing on the maturation of parietal circuits associated with numerical problem solving and learning. Hierarchical linear modeling revealed selective strengthening as well as weakening of functional brain circuits. Connectivity between parietal and prefrontal cortex decreased over time, while connectivity within posterior brain regions, including intra-hemispheric and inter-hemispheric parietal connectivity, as well as parietal connectivity with ventral temporal occipital cortex regions implicated in quantity manipulation and numerical symbol recognition, increased over time. Our study provides insights into the longitudinal maturation of functional circuits in the human brain and the mechanisms by which interactive specialization shapes children's cognitive development and learning.

    View details for PubMedID 30631462

  • Positive Attitude Toward Math Supports Early Academic Success: Behavioral Evidence and Neurocognitive Mechanisms. Psychological science Chen, L., Bae, S. R., Battista, C., Qin, S., Chen, T., Evans, T. M., Menon, V. 2018: 956797617735528


    Positive attitude is thought to impact academic achievement and learning in children, but little is known about its underlying neurocognitive mechanisms. Using a large behavioral sample of 240 children, we found that positive attitude toward math uniquely predicted math achievement, even after we accounted for multiple other cognitive-affective factors. We then investigated the neural mechanisms underlying the link between positive attitude and academic achievement in two independent cohorts of children (discovery cohort: n = 47; replication cohort: n = 28) and tested competing hypotheses regarding the differential roles of affective-motivational and learning-memory systems. In both cohorts, we found that positive attitude was associated with increased engagement of the hippocampal learning-memory system. Structural equation modeling further revealed that, in both cohorts, increased hippocampal activity and more frequent use of efficient memory-based strategies mediated the relation between positive attitude and higher math achievement. Our study is the first to elucidate the neurocognitive mechanisms by which positive attitude influences learning and academic achievement.

    View details for PubMedID 29364780

  • Sleep Benefits Memory for Semantic Category Structure While Preserving Exemplar-Specific Information SCIENTIFIC REPORTS Schapiro, A. C., McDevitt, E. A., Chen, L., Norman, K. A., Mednick, S. C., Rogers, T. T. 2017; 7: 14869


    Semantic memory encompasses knowledge about both the properties that typify concepts (e.g. robins, like all birds, have wings) as well as the properties that individuate conceptually related items (e.g. robins, in particular, have red breasts). We investigate the impact of sleep on new semantic learning using a property inference task in which both kinds of information are initially acquired equally well. Participants learned about three categories of novel objects possessing some properties that were shared among category exemplars and others that were unique to an exemplar, with exposure frequency varying across categories. In Experiment 1, memory for shared properties improved and memory for unique properties was preserved across a night of sleep, while memory for both feature types declined over a day awake. In Experiment 2, memory for shared properties improved across a nap, but only for the lower-frequency category, suggesting a prioritization of weakly learned information early in a sleep period. The increase was significantly correlated with amount of REM, but was also observed in participants who did not enter REM, suggesting involvement of both REM and NREM sleep. The results provide the first evidence that sleep improves memory for the shared structure of object categories, while simultaneously preserving object-unique information.

    View details for PubMedID 29093451

  • Distinct influences of affective and cognitive factors on children's non-verbal and verbal mathematical abilities. Cognition Wu, S. S., Chen, L., Battista, C., Smith Watts, A. K., Willcutt, E. G., Menon, V. 2017; 166: 118-129


    Individual differences in children's math performance have been associated with math anxiety, attention problems, working memory (WM), and reading skills, but the mechanisms by which these factors jointly contribute to children's math achievement are unknown. Here, we use structural equation modeling to characterize the relation between these factors and their influence on non-verbal Numerical Operations (NO) and verbal Math Reasoning (MR) in 330 children (M=8.34years). Our findings indicate that WM plays a central role in both non-verbal NO and verbal MR, whereas math anxiety and reading comprehension have unique and more pronounced influences on MR, compared to NO. Our study elucidates how affective and cognitive factors distinctly influence non-verbal and verbal mathematical problem solving.

    View details for DOI 10.1016/j.cognition.2017.05.016

    View details for PubMedID 28558312

  • A unified model of human semantic knowledge and its disorders. Nature human behaviour Chen, L., Lambon Ralph, M. A., Rogers, T. T. 2017; 1 (3)


    How is knowledge about the meanings of words and objects represented in the human brain? Current theories embrace two radically different proposals: either distinct cortical systems have evolved to represent different kinds of things, or knowledge for all kinds is encoded within a single domain-general network. Neither view explains the full scope of relevant evidence from neuroimaging and neuropsychology. Here we propose that graded category-specificity emerges in some components of the semantic network through joint effects of learning and network connectivity. We test the proposal by measuring connectivity amongst cortical regions implicated in semantic representation, then simulating healthy and disordered semantic processing in a deep neural network whose architecture mirrors this structure. The resulting neuro-computational model explains the full complement of neuroimaging and patient evidence adduced in support of both domain-specific and domain-general approaches, reconciling long-standing disputes about the nature and origins of this uniquely human cognitive faculty.

    View details for DOI 10.1038/s41562-016-0039

    View details for PubMedID 28480333

    View details for PubMedCentralID PMC5417358

  • A Model of Emergent Category-specific Activation in the Posterior Fusiform Gyrus of Sighted and Congenitally Blind Populations JOURNAL OF COGNITIVE NEUROSCIENCE Chen, L., Rogers, T. T. 2015; 27 (10): 1981-1999


    Theories about the neural bases of semantic knowledge tend between two poles, one proposing that distinct brain regions are innately dedicated to different conceptual domains and the other suggesting that all concepts are encoded within a single network. Category-sensitive functional activations in the fusiform cortex of the congenitally blind have been taken to support the former view but also raise several puzzles. We use neural network models to assess a hypothesis that spans the two poles: The interesting functional activation patterns reflect the base connectivity of a domain-general semantic network. Both similarities and differences between sighted and congenitally blind groups can emerge through learning in a neural network, but only in architectures adopting real anatomical constraints. Surprisingly, the same constraints suggest a novel account of a quite different phenomenon: the dyspraxia observed in patients with semantic impairments from anterior temporal pathology. From this work, we suggest that the cortical semantic network is wired not to encode knowledge of distinct conceptual domains but to promote learning about both conceptual and affordance structure in the environment.

    View details for DOI 10.1162/jocn_a_00834

    View details for Web of Science ID 000360665400008

    View details for PubMedID 26042499

  • Remediation of Childhood Math Anxiety and Associated Neural Circuits through Cognitive Tutoring. journal of neuroscience Supekar, K., Iuculano, T., Chen, L., Menon, V. 2015; 35 (36): 12574-12583


    Math anxiety is a negative emotional reaction that is characterized by feelings of stress and anxiety in situations involving mathematical problem solving. High math-anxious individuals tend to avoid situations involving mathematics and are less likely to pursue science, technology, engineering, and math-related careers than those with low math anxiety. Math anxiety during childhood, in particular, has adverse long-term consequences for academic and professional success. Identifying cognitive interventions and brain mechanisms by which math anxiety can be ameliorated in children is therefore critical. Here we investigate whether an intensive 8 week one-to-one cognitive tutoring program designed to improve mathematical skills reduces childhood math anxiety, and we identify the neurobiological mechanisms by which math anxiety can be reduced in affected children. Forty-six children in grade 3, a critical early-onset period for math anxiety, participated in the cognitive tutoring program. High math-anxious children showed a significant reduction in math anxiety after tutoring. Remarkably, tutoring remediated aberrant functional responses and connectivity in emotion-related circuits anchored in the basolateral amygdala. Crucially, children with greater tutoring-induced decreases in amygdala reactivity had larger reductions in math anxiety. Our study demonstrates that sustained exposure to mathematical stimuli can reduce math anxiety and highlights the key role of the amygdala in this process. Our findings are consistent with models of exposure-based therapy for anxiety disorders and have the potential to inform the early treatment of a disability that, if left untreated in childhood, can lead to significant lifelong educational and socioeconomic consequences in affected individuals.Math anxiety during early childhood has adverse long-term consequences for academic and professional success. It is therefore important to identify ways to alleviate math anxiety in young children. Surprisingly, there have been no studies of cognitive interventions and the underlying neurobiological mechanisms by which math anxiety can be ameliorated in young children. Here, we demonstrate that intensive 8 week one-to-one cognitive tutoring not only reduces math anxiety but also remarkably remediates aberrant functional responses and connectivity in emotion-related circuits anchored in the amygdala. Our findings are likely to propel new ways of thinking about early treatment of a disability that has significant implications for improving each individual's academic and professional chances of success in today's technological society that increasingly demands strong quantitative skills.

    View details for DOI 10.1523/JNEUROSCI.0786-15.2015

    View details for PubMedID 26354922

    View details for PubMedCentralID PMC4563039

  • Revisiting domain- general accounts of category specificity in mind and brain WILEY INTERDISCIPLINARY REVIEWS-COGNITIVE SCIENCE Chen, L., Rogers, T. T. 2014; 5 (3): 327-344

    View details for DOI 10.1002/wcs.1283

    View details for Web of Science ID 000334511800008

  • Developmental trajectories of reading development and impairment from ages 3 to 8 years in Chinese children JOURNAL OF CHILD PSYCHOLOGY AND PSYCHIATRY Lei, L., Pan, J., Liu, H., McBride-Chang, C., Li, H., Zhang, Y., Chen, L., Tardif, T., Liang, W., Zhang, Z., Shu, H. 2011; 52 (2): 212-220


    Early prediction of reading disabilities in Chinese is important for early remediation efforts. In this 6-year longitudinal study, we investigated the early cognitive predictors of reading skill in a statistically representative sample of Chinese children from Beijing.Two hundred sixty-one (261) native Chinese children were administered seven language-related skills over three years between the ages of 3 and 6 years. Performances on these skills were then examined in relation to subsequent word reading accuracy and fluency. Individual differences in developmental profiles across tasks were then estimated using growth mixture modeling.Four developmental trajectories were classified - the typical (control), catch-up (with low initial cognitive performances but adequate subsequent reading), literacy-related-cognitive-delay (with difficulties in morphological awareness, phonological awareness, and speeded naming and subsequent word recognition), and language-delay (relatively low across all tasks) groups.Findings suggest that the combination of phonological awareness, rapid naming and morphological awareness are essential in the early prediction of later reading difficulties in Chinese children.

    View details for DOI 10.1111/j.1469-7610.2010.02311.x

    View details for Web of Science ID 000286207700013

    View details for PubMedID 20854364

  • Structure and meaning in Chinese: An ERP study of idioms JOURNAL OF NEUROLINGUISTICS Liu, Y., Li, P., Shu, H., Zhang, Q., Chen, L. 2010; 23 (6): 615-630
  • Monolingual and bilingual recognition of regular and irregular English verbs: Sensitivity to form similarity varies with first language experience JOURNAL OF MEMORY AND LANGUAGE Basnight-Brown, D. M., Chen, L., Hua, S., Kostic, A., Feldman, L. B. 2007; 57 (1): 65-80
  • ERP signatures of subject-verb agreement in L2 learning BILINGUALISM-LANGUAGE AND COGNITION Chen, L., Shu, H., Liu, Y., Zhao, J., Li, P. 2007; 10 (2): 161-174