I am a computational neuroscientist and currently focus on understanding brain dynamics at rest as well as during learning. The overarching goal of my research is to develop reliable computational methods that will allow for characterizing and modeling temporal dynamics of brain activity, without averaging data in either space or time. I firmly believe that the spatiotemporal richness in brain activity might hold the key to finding the person- and disorder-centric biomarkers. Funded by a career development award (K99/R00; NIMH) and a young investigator award (NARSAD; Brain & Behavior Foundation), I am currently developing methods to model the temporal dynamics of brain activity in individuals with fragile X syndrome and healthy controls. The application of computational modeling to neuroscience and psychiatry is nascent in its development but holds significant promise to affect public health positively. I have a strong interdisciplinary background in (1) computational sciences, (2) neuroscience as well as (3) psychiatry. Integrating neuroscience, psychiatry, and mathematical modeling represents the new frontier in applications and analysis of large neuroimaging datasets and has the potential to revolutionize our understanding of dynamical brain organization in healthy controls and individuals with psychiatric disorders.

Honors & Awards

  • NIH New Innovator Award (DP2), National Institute of Health (2018-2023)
  • NARSAD Young Investigator Grant, Brain & Behavior Research Foundation (2016-2018)
  • NIH Career Development Award (K99/R00), National Institute of Mental Health (2015-2020)
  • Child Health Research Institute (CHRI) Postdoctoral Grant, Lucile Packard Foundation for Children’s Health (LPFCH) (2013-2014)
  • Seed-grant Award, Stanford’s Center for Cognitive and Neurobiological Imaging (CNI). (2012-2013)
  • Francisco J. Varela Memorial Grant Award, Mind and Life Institute (2006-2011)
  • Merit Scholarship, Indian Institute of Information Technology, Allahabad (IIIT-A), India (2001-2005)

Boards, Advisory Committees, Professional Organizations

  • Editorial Board Member, Scientific Reports (Nature Research Journal) (2017 - Present)

Professional Education

  • Postdoctoral Fellowship, Stanford University School of Medicine, Psychiatry (2014)
  • Doctor of Philosophy, University of Texas at Austin, Computer Science (2011)
  • Master of Science, University of Texas at Austin, Computer Science (2009)
  • Bachelors in Technology, Indian Institute of Information Technology, Information Technology (2005)

Research & Scholarship

Current Research and Scholarly Interests

I am a computational neuroscientist and currently focus on understanding brain dynamics at rest as well as during learning. The overarching goal of my research is to develop reliable computational methods that will allow for characterizing and modeling temporal dynamics of brain activity, without averaging data in either space or time. I strongly believe that the spatiotemporal richness in brain activity might hold the key to finding the person- and disorder-centric biomarkers. Funded by a career development award (K99/R00; NIMH) and a young investigator award (NARSAD; Brain & Behavior Foundation), I am currently developing methods to model the temporal dynamics of brain activity in individuals with fragile X syndrome and healthy controls. The application of computational modeling to neuroscience and psychiatry is nascent in its development but holds significant promise to positively affect public health. I have a strong interdisciplinary background in (1) computational sciences, (2) neuroscience as well as (3) psychiatry. Integrating neuroscience, psychiatry, and mathematical modeling represents the new frontier in applications and analysis of large neuroimaging datasets and has the potential to revolutionize our understanding of dynamical brain organization in healthy controls and in individuals with psychiatric disorders.


2018-19 Courses

Stanford Advisees

  • Postdoctoral Faculty Sponsor
    Hua Xie


All Publications

  • Towards a new approach to reveal dynamical organization of the brain using topological data analysis NATURE COMMUNICATIONS Saggar, M., Sporns, O., Gonzalez-Castillo, J., Bandettini, P. A., Carlsson, G., Glover, G., Reiss, A. L. 2018; 9: 1399


    Little is known about how our brains dynamically adapt for efficient functioning. Most previous work has focused on analyzing changes in co-fluctuations between a set of brain regions over several temporal segments of the data. We argue that by collapsing data in space or time, we stand to lose useful information about the brain's dynamical organization. Here we use Topological Data Analysis to reveal the overall organization of whole-brain activity maps at a single-participant level-as an interactive representation-without arbitrarily collapsing data in space or time. Using existing multitask fMRI datasets, with the known ground truth about the timing of transitions from one task-block to next, our approach tracks both within- and between-task transitions at a much faster time scale (~4-9 s) than before. The individual differences in the revealed dynamical organization predict task performance. In summary, our approach distills complex brain dynamics into interactive and behaviorally relevant representations.

    View details for DOI 10.1038/s41467-018-03664-4

    View details for Web of Science ID 000429689800022

    View details for PubMedID 29643350

    View details for PubMedCentralID PMC5895632

  • Creativity in the Twenty-first Century: The Added Benefit of Training and Cooperation DESIGN THINKING RESEARCH: MAKING DISTINCTIONS: COLLABORATION VERSUS COOPERATION Mayseless, N., Saggar, M., Hawthorne, G., Reiss, A., Plattner, H., Meinel, C., Leifer, L. 2018: 239–49
  • Altered Brain Network Segregation in Fragile X Syndrome Revealed by Structural Connectomics CEREBRAL CORTEX Bruno, J. L., Hosseini, S. M., Saggar, M., Quintin, E., Raman, M. M., Reiss, A. L. 2017; 27 (3): 2249-2259
  • Compensatory Hyperconnectivity in Developing Brains of Young Children With Type 1 Diabetes DIABETES Saggar, M., Tsalikian, E., Mauras, N., Mazaika, P., White, N. H., Weinzimer, S., Buckingham, B., Hershey, T., Reiss, A. L. 2017; 66 (3): 754-762

    View details for DOI 10.2337/db16-0414

    View details for Web of Science ID 000394634100020

  • X-Chromosome Effects on Attention Networks: Insights from Imaging Resting-State Networks in Turner Syndrome. Cerebral cortex (New York, N.Y. : 1991) Green, T., Saggar, M., Ishak, A., Hong, D. S., Reiss, A. L. 2017: 1–8


    Attention deficit hyperactivity disorder (ADHD) is strongly affected by sex, but sex chromosomes' effect on brain attention networks and cognition are difficult to examine in humans. This is due to significant etiologic heterogeneity among diagnosed individuals. In contrast, individuals with Turner syndrome (TS), who have substantially increased risk for ADHD symptoms, share a common genetic risk factor related to the absence of the X-chromosome, thus serving as a more homogeneous genetic model. Resting-state functional MRI was employed to examine differences in attention networks between girls with TS (n = 40) and age- sex- and Tanner-matched controls (n = 33). We compared groups on resting-state functional connectivity measures from data-driven independent components analysis (ICA) and hypothesis-based seed analysis. Using ICA, reduced connectivity was observed in both frontoparietal and dorsal attention networks. Similarly, using seeds in the bilateral intraparietal sulcus (IPS), reduced connectivity was observed between IPS and frontal and cerebellar regions. Finally, we observed a brain-behavior correlation between IPS-cerebellar connectivity and cognitive attention measures. These findings indicate that X-monosomy contributes affects to attention networks and cognitive dysfunction that might increase risk for ADHD. Our findings not only have clinical relevance for girls with TS, but might also serve as a biological marker in future research examining the effects of the intervention that targets attention skills.

    View details for DOI 10.1093/cercor/bhx188

    View details for PubMedID 28981595

  • Changes in Brain Activation Associated with Spontaneous Improvization and Figural Creativity After Design-Thinking-Based Training: A Longitudinal fMRI Study. Cerebral cortex Saggar, M., Quintin, E., Bott, N. T., Kienitz, E., Chien, Y., Hong, D. W., Liu, N., Royalty, A., Hawthorne, G., Reiss, A. L. 2016


    Creativity is widely recognized as an essential skill for entrepreneurial success and adaptation to daily-life demands. However, we know little about the neural changes associated with creative capacity enhancement. For the first time, using a prospective, randomized control design, we examined longitudinal changes in brain activity associated with participating in a five-week design-thinking-based Creative Capacity Building Program (CCBP), when compared with Language Capacity Building Program (LCBP). Creativity, an elusive and multifaceted construct, is loosely defined as an ability to produce useful/appropriate and novel outcomes. Here, we focus on one of the facets of creative thinking-spontaneous improvization. Participants were assessed pre- and post-intervention for spontaneous improvization skills using a game-like figural Pictionary-based fMRI task. Whole-brain group-by-time interaction revealed reduced task-related activity in CCBP participants (compared with LCBP participants) after training in the right dorsolateral prefrontal cortex, anterior/paracingulate gyrus, supplementary motor area, and parietal regions. Further, greater cerebellar-cerebral connectivity was observed in CCBP participants at post-intervention when compared with LCBP participants. In sum, our results suggest that improvization-based creative capacity enhancement is associated with reduced engagement of executive functioning regions and increased involvement of spontaneous implicit processing.

    View details for PubMedID 27307467

  • Sex differences in neural and behavioral signatures of cooperation revealed by fNIRS hyperscanning SCIENTIFIC REPORTS Baker, J. M., Liu, N., Cui, X., Vrticka, P., Saggar, M., Hosseini, S. M., Reiss, A. L. 2016; 6


    Researchers from multiple fields have sought to understand how sex moderates human social behavior. While over 50 years of research has revealed differences in cooperation behavior of males and females, the underlying neural correlates of these sex differences have not been explained. A missing and fundamental element of this puzzle is an understanding of how the sex composition of an interacting dyad influences the brain and behavior during cooperation. Using fNIRS-based hyperscanning in 111 same- and mixed-sex dyads, we identified significant behavioral and neural sex-related differences in association with a computer-based cooperation task. Dyads containing at least one male demonstrated significantly higher behavioral performance than female/female dyads. Individual males and females showed significant activation in the right frontopolar and right inferior prefrontal cortices, although this activation was greater in females compared to males. Female/female dyad's exhibited significant inter-brain coherence within the right temporal cortex, while significant coherence in male/male dyads occurred in the right inferior prefrontal cortex. Significant coherence was not observed in mixed-sex dyads. Finally, for same-sex dyads only, task-related inter-brain coherence was positively correlated with cooperation task performance. Our results highlight multiple important and previously undetected influences of sex on concurrent neural and behavioral signatures of cooperation.

    View details for DOI 10.1038/srep26492

    View details for Web of Science ID 000377330900001

    View details for PubMedID 27270754

    View details for PubMedCentralID PMC4897646

  • Surface-based morphometry reveals distinct cortical thickness and surface area profiles in Williams syndrome AMERICAN JOURNAL OF MEDICAL GENETICS PART B-NEUROPSYCHIATRIC GENETICS Green, T., Fierro, K. C., Raman, M. M., Saggar, M., Sheau, K. E., Reiss, A. L. 2016; 171 (3): 402-413
  • Understanding the influence of personality on dynamic social gesture processing: An fMRI study. Neuropsychologia Saggar, M., Vrticka, P., Reiss, A. L. 2016; 80: 71-78


    This fMRI study aimed at investigating how differences in personality traits affect the processing of dynamic and natural gestures containing social versus nonsocial intent. We predicted that while processing gestures with social intent extraversion would be associated with increased activity within the reticulothalamic-cortical arousal system (RTCS), while neuroticism would be associated with increased activity in emotion processing circuits. The obtained findings partly support our hypotheses. We found a positive correlation between bilateral thalamic activity and extraversion scores while participants viewed social (versus nonsocial) gestures. For neuroticism, the data revealed a more complex activation pattern. Activity in the bilateral frontal operculum and anterior insula, extending into bilateral putamen and right amygdala, was moderated as a function of actor-orientation (i.e., first versus third-person engagement) and face-visibility (actor faces visible versus blurred). Our findings point to the existence of factors other than emotional valence that can influence social gesture processing in particular, and social cognitive affective processing in general, as a function of personality.

    View details for DOI 10.1016/j.neuropsychologia.2015.10.039

    View details for PubMedID 26541443

  • Estimating individual contribution from group-based structural correlation networks. NeuroImage Saggar, M., Hosseini, S. M., Bruno, J. L., Quintin, E., Raman, M. M., Kesler, S. R., Reiss, A. L. 2015; 120: 274-284


    Coordinated variations in brain morphology (e.g., cortical thickness) across individuals have been widely used to infer large-scale population brain networks. These structural correlation networks (SCNs) have been shown to reflect synchronized maturational changes in connected brain regions. Further, evidence suggests that SCNs, to some extent, reflect both anatomical and functional connectivity and hence provide a complementary measure of brain connectivity in addition to diffusion weighted networks and resting-state functional networks. Although widely used to study between-group differences in network properties, SCNs are inferred only at the group-level using brain morphology data from a set of participants, thereby not providing any knowledge regarding how the observed differences in SCNs are associated with individual behavioral, cognitive and disorder states. In the present study, we introduce two novel distance-based approaches to extract information regarding individual differences from the group-level SCNs. We applied the proposed approaches to a moderately large dataset (n=100) consisting of individuals with fragile X syndrome (FXS; n=50) and age-matched typically developing individuals (TD; n=50). We tested the stability of proposed approaches using permutation analysis. Lastly, to test the efficacy of our method, individual contributions extracted from the group-level SCNs were examined for associations with intelligence scores and genetic data. The extracted individual contributions were stable and were significantly related to both genetic and intelligence estimates, in both typically developing individuals and participants with FXS. We anticipate that the approaches developed in this work could be used as a putative biomarker for altered connectivity in individuals with neurodevelopmental disorders.

    View details for DOI 10.1016/j.neuroimage.2015.07.006

    View details for PubMedID 26162553

  • Neural Correlates of Self-Injurious Behavior in Prader-Willi Syndrome HUMAN BRAIN MAPPING Klabunde, M., Saggar, M., Hustyi, K. M., Hammond, J. L., Reiss, A. L., Hall, S. S. 2015; 36 (10): 4135-4143


    Individuals with Prader-Willi syndrome (PWS), a genetic disorder caused by mutations to the q11-13 region on chromosome 15, commonly show severe skin-picking behaviors that can cause open wounds and sores on the body. To our knowledge, however, no studies have examined the potential neural mechanisms underlying these behaviors. Seventeen individuals with PWS, aged 6-25 years, who showed severe skin-picking behaviors, were recruited and scanned on a 3T scanner. We used functional magnetic resonance imaging (fMRI) while episodes of skin picking were recorded on an MRI-safe video camera. Three participants displayed skin picking continuously throughout the scan, three participants did not display skin picking, and the data for one participant evidenced significant B0 inhomogeneity that could not be corrected. The data for the remaining 10 participants (six male, four female) who displayed a sufficient number of picking and nonpicking episodes were subjected to fMRI analysis. Results showed that regions involved in interoceptive, motor, attention, and somatosensory processing were activated during episodes of skin-picking behavior compared with nonpicking episodes. Scores obtained on the Self-Injury Trauma scale were significantly negatively correlated with mean activation within the right insula and left precentral gyrus. These data indicate that itch and pain processes appear to underlie skin-picking behaviors in PWS, suggesting that interoceptive disturbance may contribute to the severity and maintenance of abnormal skin-picking behaviors in PWS. Implications for treatments are discussed. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc.

    View details for DOI 10.1002/hbm.22903

    View details for Web of Science ID 000364219100031

    View details for PubMedID 26173182

  • Examining the neural correlates of emergent equivalence relations in fragile X syndrome PSYCHIATRY RESEARCH-NEUROIMAGING Klabunde, M., Saggar, M., Hustyi, K. M., Kelley, R. G., Reiss, A. L., Hall, S. S. 2015; 233 (3): 373-379
  • Mean-field thalamocortical modeling of longitudinal EEG acquired during intensive meditation training NEUROIMAGE Saggar, M., Zanesco, A. P., King, B. G., Bridwell, D. A., MacLean, K. A., Aichele, S. R., Jacobs, T. L., Wallace, B. A., Saron, C. D., Miikkulainen, R. 2015; 114: 88-104


    Meditation training has been shown to enhance attention and improve emotion regulation. However, the brain processes associated with such training are poorly understood and a computational modeling framework is lacking. Modeling approaches that can realistically simulate neurophysiological data while conforming to basic anatomical and physiological constraints can provide a unique opportunity to generate concrete and testable hypotheses about the mechanisms supporting complex cognitive tasks such as meditation. Here we applied the mean-field computational modeling approach using the scalp-recorded electroencephalogram (EEG) collected at three assessment points from meditating participants during two separate 3-month-long shamatha meditation retreats. We modeled cortical, corticothalamic, and intrathalamic interactions to generate a simulation of EEG signals recorded across the scalp. We also present two novel extensions to the mean-field approach that allow for: (a) non-parametric analysis of changes in model parameter values across all channels and assessments; and (b) examination of variation in modeled thalamic reticular nucleus (TRN) connectivity over the retreat period. After successfully fitting whole-brain EEG data across three assessment points within each retreat, two model parameters were found to replicably change across both meditation retreats. First, after training, we observed an increased temporal delay between modeled cortical and thalamic cells. This increase provides a putative neural mechanism for a previously observed reduction in individual alpha frequency in these same participants. Second, we found decreased inhibitory connection strength between the TRN and secondary relay nuclei (SRN) of the modeled thalamus after training. This reduction in inhibitory strength was found to be associated with increased dynamical stability of the model. Altogether, this paper presents the first computational approach, taking core aspects of physiology and anatomy into account, to formally model brain processes associated with intensive meditation training. The observed changes in model parameters inform theoretical accounts of attention training through meditation, and may motivate future study on the use of meditation in a variety of clinical populations.

    View details for DOI 10.1016/j.neuroimage.2015.03.073

    View details for Web of Science ID 000355002900007

    View details for PubMedID 25862265

  • Pictionary-based fMRI paradigm to study the neural correlates of spontaneous improvisation and figural creativity SCIENTIFIC REPORTS Saggar, M., Quintin, E., Kienitz, E., Bott, N. T., Sun, Z., Hong, W., Chien, Y., Liu, N., Dougherty, R. F., Royalty, A., Hawthorne, G., Reiss, A. L. 2015; 5


    A novel game-like and creativity-conducive fMRI paradigm is developed to assess the neural correlates of spontaneous improvisation and figural creativity in healthy adults. Participants were engaged in the word-guessing game of Pictionary(TM), using an MR-safe drawing tablet and no explicit instructions to be "creative". Using the primary contrast of drawing a given word versus drawing a control word (zigzag), we observed increased engagement of cerebellum, thalamus, left parietal cortex, right superior frontal, left prefrontal and paracingulate/cingulate regions, such that activation in the cingulate and left prefrontal cortices negatively influenced task performance. Further, using parametric fMRI analysis, increasing subjective difficulty ratings for drawing the word engaged higher activations in the left pre-frontal cortices, whereas higher expert-rated creative content in the drawings was associated with increased engagement of bilateral cerebellum. Altogether, our data suggest that cerebral-cerebellar interaction underlying implicit processing of mental representations has a facilitative effect on spontaneous improvisation and figural creativity.

    View details for DOI 10.1038/srep10894

    View details for Web of Science ID 000355548100001

    View details for PubMedID 26018874

    View details for PubMedCentralID PMC4446895

  • Early signs of anomalous neural functional connectivity in healthy offspring of parents with bipolar disorder BIPOLAR DISORDERS Singh, M. K., Chang, K. D., Kelley, R. G., Saggar, M., Reiss, A. L., Gotlib, I. H. 2014; 16 (7): 678-689


    Bipolar disorder (BD) has been associated with dysfunctional brain connectivity and with family chaos. It is not known whether aberrant connectivity occurs before illness onset, representing vulnerability for developing BD amidst family chaos. We used resting-state functional magnetic resonance imaging (fMRI) to examine neural network dysfunction in healthy offspring living with parents with BD and healthy comparison youth.Using two complementary methodologies [data-driven independent component analysis (ICA) and hypothesis-driven region-of-interest (ROI)-based intrinsic connectivity], we examined resting-state fMRI data in 8-17-year-old healthy offspring of a parent with BD (n = 24; high risk) and age-matched healthy youth without any personal or family psychopathology (n = 25; low risk).ICA revealed that, relative to low-risk youth, high-risk youth showed increased connectivity in the ventrolateral prefrontal cortex (VLPFC) subregion of the left executive control network (ECN), which includes frontoparietal regions important for emotion regulation. ROI-based analyses revealed that high-risk versus low-risk youth had decreased connectivities between the left amygdala and pregenual cingulate, between the subgenual cingulate and supplementary motor cortex, and between the left VLPFC and left caudate. High-risk youth showed stronger connections in the VLPFC with age and higher functioning, which may be neuroprotective, and weaker connections between the left VLPFC and caudate with more family chaos, suggesting an environmental influence on frontostriatal connectivity.Healthy offspring of parents with BD show atypical patterns of prefrontal and subcortical intrinsic connectivity that may be early markers of resilience to or vulnerability for developing BD. Longitudinal studies are needed to determine whether these patterns predict outcomes.

    View details for DOI 10.1111/bdi.12221

    View details for Web of Science ID 000344373100002

  • Revealing the neural networks associated with processing of natural social interaction and the related effects of actor-orientation and face-visibility NEUROIMAGE Saggar, M., Shelly, E. W., Lepage, J., Hoeft, F., Reiss, A. L. 2014; 84: 648-656


    Understanding the intentions and desires of those around us is vital for adapting to a dynamic social environment. In this paper, a novel event-related functional Magnetic Resonance Imaging (fMRI) paradigm with dynamic and natural stimuli (2s video clips) was developed to directly examine the neural networks associated with processing of gestures with social intent as compared to nonsocial intent. When comparing social to nonsocial gestures, increased activation in both the mentalizing (or theory of mind) and amygdala networks was found. As a secondary aim, a factor of actor-orientation was included in the paradigm to examine how the neural mechanisms differ with respect to personal engagement during a social interaction versus passively observing an interaction. Activity in the lateral occipital cortex and precentral gyrus was found sensitive to actor-orientation during social interactions. Lastly, by manipulating face-visibility we tested whether facial information alone is the primary driver of neural activation differences observed between social and nonsocial gestures. We discovered that activity in the posterior superior temporal sulcus (pSTS) and fusiform gyrus (FFG) was partially driven by observing facial expressions during social gestures. Altogether, using multiple factors associated with processing of natural social interaction, we conceptually advance our understanding of how social stimuli is processed in the brain and discuss the application of this paradigm to clinical populations where atypical social cognition is manifested as a key symptom.

    View details for DOI 10.1016/j.neuroimage.2013.09.046

    View details for Web of Science ID 000328868600059

    View details for PubMedID 24084068

  • Creativity training enhances goal-directed attention and information processing THINKING SKILLS AND CREATIVITY Bott, N., Quintin, E., Saggar, M., Kienitz, E., Royalty, A., Hong, D. W., Liu, N., Chien, Y., Hawthorne, G., Reiss, A. L. 2014; 13: 120-128
  • Targeted intervention to increase creative capacity and performance: A randomized controlled pilot study THINKING SKILLS AND CREATIVITY Kienitz, E., Quintin, E., Saggar, M., Bott, N. T., Royalty, A., Hong, D. W., Liu, N., Chien, Y., Hawthorne, G., Reiss, A. L. 2014; 13: 57-66
  • Intensive training induces longitudinal changes in meditation state-related EEG oscillatory activity FRONTIERS IN HUMAN NEUROSCIENCE Saggar, M., King, B. G., Zanesco, A. P., MacLean, K. A., Aichele, S. R., Jacobs, T. L., Bridwell, D. A., Shaver, P. R., Rosenberg, E. L., Sahdra, B. K., Ferrer, E., Tang, A. C., Mangun, G. R., Wallace, B. A., Miikkulainen, R., Saron, C. D. 2012; 6


    The capacity to focus one's attention for an extended period of time can be increased through training in contemplative practices. However, the cognitive processes engaged during meditation that support trait changes in cognition are not well characterized. We conducted a longitudinal wait-list controlled study of intensive meditation training. Retreat participants practiced focused attention (FA) meditation techniques for three months during an initial retreat. Wait-list participants later undertook formally identical training during a second retreat. Dense-array scalp-recorded electroencephalogram (EEG) data were collected during 6 min of mindfulness of breathing meditation at three assessment points during each retreat. Second-order blind source separation, along with a novel semi-automatic artifact removal tool (SMART), was used for data preprocessing. We observed replicable reductions in meditative state-related beta-band power bilaterally over anteriocentral and posterior scalp regions. In addition, individual alpha frequency (IAF) decreased across both retreats and in direct relation to the amount of meditative practice. These findings provide evidence for replicable longitudinal changes in brain oscillatory activity during meditation and increase our understanding of the cortical processes engaged during meditation that may support long-term improvements in cognition.

    View details for DOI 10.3389/fnhum.2012.00256

    View details for Web of Science ID 000309107100001

    View details for PubMedID 22973218

  • Behavioral, neuroimaging, and computational evidence for perceptual caching in repetition priming BRAIN RESEARCH Saggar, M., Miikkulainen, R., Schnyer, D. M. 2010; 1315: 75-91


    Repetition priming (RP) is a form of learning, whereby classification or identification performance is improved with item repetition. Various theories have been proposed to understand the basis of RP, including alterations in the representation of an object and associative stimulus-response bindings. There remain several aspects of RP that are still poorly understood, and it is unclear whether previous theories only apply to well-established object representations. This paper integrates behavioral, neuroimaging, and computational modeling experiments in a new RP study using novel objects. Behavioral and neuroimaging results were inconsistent with existing theories of RP, thus a new perceptual memory-based caching mechanism is formalized using computational modeling. The model instantiates a viable neural mechanism that not only accounts for the pattern seen in this experiment but also provides a plausible explanation for previous results that demonstrated residual priming after associative linkages were disrupted. Altogether, the current work helps advance our understanding of how brain utilizes repetition for faster information processing.

    View details for DOI 10.1016/j.brainres.2009.11.074

    View details for Web of Science ID 000275131300009

    View details for PubMedID 20005215

  • Memory Processes in Perceptual Decision Making Proceedings of the 30th Annual Conference of the Cognitive Science Society, Nashville, TN Saggar M., Miikkulainen R., Schnyer D. M. 2008
  • A computational model of the motivation-learning interface Proceedings of the 29th Annual Conference of the Cognitive Science Society, Nashville, TN Saggar M., Markman A.B., Maddox W.T., Miikkulainen R. 2007
  • Autonomous learning of stable quadruped locomotion ROBOCUP 2006: ROBOT SOCCER WORLD CUP X Saggar, M., D'Silva, T., Kohl, N., Stone, P. 2007; 4434: 98-109
  • System identification for the Hodgkin-Huxley model using artificial neural networks 2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6 Saggar, M., Mericli, T., Andoni, S., Miikkulainen, R. 2007: 2239-2244
  • Optimization of association rule mining using improved genetic algorithms IEEE International Conference on Systems, Man and Cybernetics Saggar M, Agrawal, A.K. , Lad, A. 2004; 4434/2007: 3725 - 3729