Leonard, B., & Stocco, A. (2023). Errors Are The Stepping Stones to Learning: Trial-by-Trial Modeling Reveals Overwhelming Evidence for Medicator Retrievals of Previous Errors in Memory Consolidation [Conference Paper]. Proceedings in the 21st International Conference on Cognitive Modeling. [pdf]
Yang, Y., Sibert, C. L., & Stocco, A. (2023). Reliance on Episodic vs. Procedural Systems in Decision-Making Depends on Individual Differences in Their Relative Neural Efficiency. Preprint available at bioRxiv. https://doi.org/10.1101%2F2023.01.10.523458
Leonard, B., Hake, H., & Stocco, A. (2023). Faulty Memories, Favored Outcomes: How Errors Impact Learning Processes. Proceedings of the Annual Meeting of the Cognitive Science Society, 45. http://dx.doi.org/10.31234/osf.io/fcy32
Hake, H. S., Leonard, B., Ulibarri, S., Grabowski, T., Van Rijn, H., & Stocco, A. (2023). Breaking New Ground in Computational Psychiatry: Model-Based Characterization of Forgetting in Healthy Aging and Mild Cognitive Impairment. Preprint available at medRxiv. https://doi.org/10.1101/2023.05.13.23289941
Lebiere, C., Cranford, E. A., Martin, M., Morrison, D., Stocco, A. (2022). Cognitive Architectures and their Applications. Proceedings of the 2022 IEEE 8th International Conference on Collaboration and Internet Computing. https://doi.org/10.1109/CIC56439.2022.00018
Hake, H. S., Sibert, C., & Stocco, A. (2022). Inferring a Cognitive Architecture from Multitasking Neuroimaging Data: A Data-Driven Test of the Common Model of Cognition Using Granger Causality. Topics in Cognitive Science, 14(4), 845-859. https://doi.org/10.1111/tops.12623
Sibert, C., Hake, H. S., & Stocco, A. (2022). The structured mind at rest: low-frequency oscillations reflect interactive dynamics between spontaneous brain activity and a common architecture for task control. Frontiers in Neuroscience, 16, 832503. https://doi.org/10.3389/fnins.2022.832503
Wapstra, N. J., Ketola, M., Thompson, S., Lee, A., Madhyastha, T., Grabowski, T. J., Stocco, A. (2022). Increased Basal Ganglia Modulatory Effective Connectivity Observed in Resting-State fMRI in Individuals With Parkinson’s Disease. Frontiers in Aging Neuroscience, 14, 719089. https://doi.org/10.3389/fnagi.2022.719089
Yang, Y. C., & Stocco, A. (2022). The Cognitive Substrates of Model-Based Learning: An Integrative Declarative-Procedural Model. Virtual MathPsych/ICCM. [pdf]
Yang, Y. C., & Stocco, A. (2022). A Model of Motivation and Effort Allocation in the ACT-R Cognitive Architecture. Proceedings in the International Conference on Cognitive Modeling. [pdf]
Stocco, A., Smith, B. M., PeConga, E., & Zoellner, L. (2021). Memory, Interrupted: A Retrieval Model of Intrusive Memories, Recovery Trajectories, and Neurobiological Effects in Post-Traumatic Stress Disorder.
Xu, Y., & Stocco, A. (2021). recovering reliable idiographic biological parameters from noisy behavioral data: the case of basal ganglia indices in the probabilistic selection task. Computational Brain & Behavior, 4, 318-334. https://doi.org/10.1007/s42113-021-00102-5
Xu, Y., Prat, C. S., Sense, F., van Rijn, H., Stocco, A. (2021). Distributed Patters of Functional Connectivity Underline Individual Differences in Long-Term Memory Forgetting. Preprint available at bioRxiv. https://doi.org/10.1101/2021.08.04.455133
Stocco, A., Sibert, C., Steine-Hanson, Z., Koh, N., Laird, J. E., Lebiere, C. J., & Rosenbloom, P. (2021). Analysis of the human connectome data supports the notion of a “Common Model of Cogition” for human and human-like intelligence across domains. NeuroImage, 235, 118035. https://doi.org/10.1016/j.neuroimage.2021.118035
Smith, B. M., Thomasson, M., Yang, Y. C., Sibert, C., & Stocco, A. (2021). When fear shrinks the brain: A computational model of the effects of posttraumatic stress on hippocampal volume. Topics in Cognitive Science, 13(3), 499-514. https://doi.org/10.1111/tops.12537
Zhou, P., Sense, F., van Rijn, H., Stocco, A. (2021). Reflections of idiographic long-term memory characteristics in resting-state neuroimaging data. Cognition, 212, 104660. https://doi.org/10.1016/j.cognition.2021.104660
Yang, Y. C., Karmol, A. M., & Stocco, A. (2021). Core Cognitive Mechanisms Underlying Syntactic Priming: A Comparison of Three Alternative Models. Frontiers in Psychology, 12, 662345. https://doi.org/10.3389/fpsyg.2021.662345
Rice, P. J., & Stocco, A. (2021). Estimating individual differences in working memory through ACT-R modeling and resting state connectivity. Proceedings of the 19th International Conference on Cognitive Modeling. [pdf]
Kuo, CH., & Prat, C. S. (2023). Programmers show distinct, language-like brain responses to violations in form and meaning when reading code. Preprint available at Research Square. https://doi.org/10.21203/rs.3.rs-3396298/v1
Seo, R. & Prat, C. S. (2023). Investigating Local and Global Mechanisms in Bilingual Grammatical Processing. Journal of Experimental Psychology: Learning, Memory, and Cognition. Advance online publication. https://doi.org/10.1037/xlm0001251
Prat, C. S., Gallée, J., & Yamasaki, B. L. (2023). Getting language right: Relating individual differences in right hemisphere contributions to language learning and relearning. Brain and Language, 239, 105242. https://doi.org/10.1016/j.bandl.2023.105242
Kuo, CH., Mottarella, M., Haile, T., & Prat, C. S. (2022). Predicting Programming Success: How Intermitternet Knowledge Assessments, Individual Psychometrics, and Resting-State EEG Predictor Python Programming and Debugging Skills. 2022 International Conference on Software, Telecommunications and Computer Networks, 1-6. https://doi.org/10.23919/SoftCOM55329.2022.9911411.
Prat, C. S. (2022) The Neuroscience of You: How Every Brain is Different and How to Understand Yours. Dutton.
Mottarella, M., Yamasaki, B. L., & Prat, C. S. (2021). Relating Individual Differences in Reading Skill to Neural Indices of Proactive Control and Online Filtering During a Working Memory Task. Discourse Processes, 5-6, 569-591. https://doi.org/10.1080/0163853X.2021.1926407
Stocco, A., Prat, C. S., & Graham, L. K. (2021). Indiivdual Differences in Reward-Based Learning Predict Fluid Reasoning Abilities. Cognitive Science, 45(2), e12941. https://doi.org/10.1111/cogs.12941
Yamasaki, B. L., & Prat, C. S. (2021). Predictors and consequences of individual differences in cross-linguistic interactions: A model of second language reading skill. Bilingualism: Language and Cognition, 24(1), 154-166. doi:10.1017/S1366728920000279
MacInnes, J. J., Adcock, R. A., Stocco, A., Prat, C. S., Rao, R. P. N., & Dickerson, K. C. (2020). Pyneal: Open source real-time fMRI software. Frontiers in Neuroscience, 14. https://doi.org/10.3389/fnins.2020.00900
Bice, K., Yamasaki, B. L., & Prat, C. S. (2020). Bilingual language experience shapes resting-state brain rhythms. Neurobiology of Language, 1(3), 288-318. https://doi.org/10.1162/nol_a_00014
Prat, C. S., Madhyastha, T. M., Mottarella, M. J., Kuo, CH. (2020). Relating natural language aptitude to individual differences in learning programming languages. Scientific Reports, 10(1), 3817. https://doi.org/10.1038/s41598-020-60661-8
Bice, K., & Prat, C. S. (2020). Comparing the effects of frontal and temporal neurostimulation on second language learning. In Cognitive Science Society. [pdf]
Haile, T., Prat, C. S., & Stocco, A. (2020). One size doesn’t fit all: Idiographic computational models reveal individual differences in learning and meta-learning strategies. Proceedings of the 18th International Conference on Cognitive Modeling. [pdf]
Ceballos, J. M., Stocco, A., & Prat, C. S. (2020). The role of basal ganglia reinforcement learning in lexical ambiguity resolution. Topics in Cognitive Science, 12(1), 402-416. https://doi.org/10.1111/tops.12488
Zhou, P., Prat, C. S., Yamasaki, B. L., & Stocco, A. (2020). Monitoring of attentional oscillations through Spectral Similarity Analysis predicts reading comprehension. Brain and Language, 197, 104709. https://doi.org/10.1016/j.bandl.2019.104709
Yamasaki, B. L., Stocco, A., Liu, A. S., Prat, C. S. (2019). Effects of bilingual language experience on basal ganglia computations: A dynamics causal modeling test of the conditional routing model. Brain and Language, 197, 104665. https://doi.org/10.1016/j.bandl.2019.104665
Seo, R., & Prat, C. S. (2019). Proactive and Reactive Language Control in the Bilingual Brain. Brain Sciences, 9(7), 161. https://doi.org/10.3390/brainsci9070161
Jiang, L., Stocco, A., Losey, D. M., Abernathy, J. A., Prat, C. S., & Rao, R. P. N. (2019). BraintNet: a multi-person brain-to-brain interface for direct collaboration between brains. Scientific Reports, 9(7), 6615. https://doi.org/10.1038/s41598-019-41895-7
Prat, C. S., Yamasaki, B. L., & Peterson, E. R. (2019). Individual differences in resting-state brain rhythms uniquely predict second language learning rate and willingness to communicate in adults. Journal of Cognitive Neuroscience, 31(1), 78-94. https://doi.org/10.1162/jocn_a_01337
Yamasaki, B. L., Stocco, A., & Prat, C. S. (2018). Relating individual differences in bilingual language experiences to executive attention. Language, Cognition, and Neuroscience, 33(9), 1128-1151. https://doi.org/10.1080/23273798.2018.1448092
Seo, R., Stocco, A., & Prat, C. S. (2018). The bilingual language network: Differential involvement of anterior cingulate, basal ganglia, and prefrontal cortex in preparation, monitoring, and execution. NeuroImage, 174, 44-56. https://doi.org/10.1016/j.neuroimage.2018.02.010
Mehravari, A. S., Emmorey, K., Prat, C. S., Klarman, L., & Osterhout, L. (2017). Brain-based individual difference measures of reading skill in deaf and hearing adults. Neuropsychologia, 101, 153-168. https://doi.org/10.1016/j.neuropsychologia.2017.05.004
Topic: Language
Ceballos, J. M., Stocco, A., & Prat, C.S. (2019). The role of Basal Ganglia reinforcement learning in lexical priming and automatic semantic ambiguity resolution, CogSci, 205-211. [pdf]
Rice, P. & Stocco, A. (2019). The role of dorsal premotor cortex in resolving abstract motor rules: Converging evidence from Transcranial Magnetic Stimulation and cognitive modeling, Topics in Cognitive Science, 11(1), 240-260. [pdf]
Yamasaki, B., Stocco, A., & Prat, C.S. (2018). Relating individual differences in bilingual language experiences to executive attention, Language, Cognition and Neuroscience, 33(9), 1128-1151.
Seo, R., Stocco, A., & Prat, C.S. (2018). The bilingual language network: Differential involvement of anterior cingulate, basal ganglia and prefrontal cortex in preparation, monitoring, and execution. NeuroImage. 174, 44-56.
Prat, C.S., Yamasaki, B., Kleunder, R., & Stocco, A. (2016) Resting-state EEG predicts rate of second language learning in adults. Brain & Language, 157-158, 44-50.
Becker, T. M., Prat, C.S., & Stocco, A. (2016). A network-level analysis of cognitive flexibility reveals a differential influence of the anterior cingulate cortex in bilinguals versus monolinguals. Neuropsychologia, 85, 63-72.
Topic: Direct Brain-to-brain Interfaces
Losey, D. M., Stocco, A., Abernethy, J. A., &. Rao, R. P. N. (2016). Navigating a 2D virtual world using direct brain stimulation. Frontiers in Robotics and Artificial Intelligence, 3, 72. [link]
Stocco, A., Prat. C.S., Losey, D. Cronin, J., Wu, J., Abernethy, J. A., & Rao, R. P. N. (2015). Playing 20 Questions with the Mind: Bi-Directional Communication with a Brain-to-Brain Interface. PLOS ONE, e0137303 [link]
Rao, R. P. N., Stocco, A., Bryan, M., Sarma, D., Youngquist, T., Wu, J., & Prat, C. S. (2014). A direct brain-to-brain interface in humans. PLoS ONE 9(11), e111332. [link]
Rao, R. N. P. & Stocco, A. (2014). When two brains connect. Scientific American Mind, 25(6), 36-39.
Topic: Higher-Level Cognition
Stocco, A. (2014). Coordinate-based meta-analysis of neuroimaging data with R. The R Journal, 6(2), 5-15.
Cole, M. W., Laurent, P., & Stocco, A. (2013). Rapid instructed task learning: A new window into the human brain’s unique capacity for flexible cognitive control. Cognitive, Affective, & Behavioral Neuroscience, 13(1), 1-22. [pdf]
Stocco, A., Yamasaki, B. L., Natalenko, R., & Prat, C. S. (2014). Bilingual brain training: A neurobiological framework of how bilingual experience improves executive function. International Journal of Bilingualism, 18, 66-91. [pdf]
Borst, J. P., Taatgen, N. A., Stocco, A., & van Rijn, H. (2010). The neural correlates of problem states: Testing fMRI predictions of a computational model of multitasking. PloS one, 5(9), e12966. [pdf]
Stocco, A., Fum, D., & Napoli, A. (2009). Dissociable processes underlying decisions in the Iowa Gambling Task: a new integrative framework. Behavioral and Brain Functions, 5(1), 1. [pdf]
Anderson, J. R., Fincham, J. M., Qin, Y., & Stocco, A. (2008). A central circuit of the mind. Trends in cognitive sciences, 12(4), 136-143. [pdf]
Prat, C. S. & Just, M. A. (2008). Brain bases of individual differences in cognition. Psychological Science Agenda, 22(5). [pdf]
Stocco, A., & Anderson, J. R. (2008). Endogenous control and task representation: an fMRI study in algebraic problem-solving. Journal of cognitive neuroscience, 20(7), 1300-1314. [pdf]
Stocco, A., & Fum, D. (2008). Implicit emotional biases in decision making: The case of the Iowa Gambling Task. Brain and cognition, 66(3), 253-259. [pdf]
Fum, D., Missier, F. D., & Stocco, A. (2007). The cognitive modeling of human behavior: Why a model is (sometimes) better than 10,000 words. Cognitive Systems Research, 8(3), 135-142. [pdf]
Long, D. L., & Prat, C. S. (2002). Working memory and Stroop interference: An individual differences investigation. Memory & Cognition, 30, 294-301. [pdf]
Topic: Individual Differences
Stocco, A., Yamasaki, B. L., & Prat, C. S. (2018). Human performance across decision making, selective attention, and working memory tasks: Experimental data and computer simulations. Data in Brief, 17, 907-914.
Stocco, A., Murray, N., L. Yamasaki, B. L., Renno, T., J., Nguyen, J., & Prat, C. S. (2017). Individual differences in the Simon effect are underpinned by differences in competitive dynamics in the basal ganglia: An experimental verification and a computational model. Cognition, 164, 31-45.
Prat, C.S., Yamasaki, B., Kleunder, R., & Stocco, A. (2016) Resting-state qEEG predicts rate of second language learning in adults. Brain & Language, 157-158, 44-50.
Prat, C. S., Stocco, A., Neuhaus, E., & Kleinhans, N. (2016). Basal ganglia impairments lead to abnormal signal routing to prefrontal cortex in Autism Spectrum Disorder. Neuropsychologia, 91, 268-281.
Prat, C.S., Yamasaki, B., Kleunder, R., & Stocco, A. (2016) Resting-state EEG predicts rate of second language learning in adults. Brain & Language, 157-158, 44-50.
Topic: Computational Modeling
Steine-Hanos, Z. K., Koh, N., & Stocco, A. (in press). Refining the Common Model of Cognition Through Large Neuroscience Data. Procedia Computer Science.
Rice, P. J., & Stocco, A. (2018) Mechanisms of rule resolution in premotor cortex: A combined TMS/computational modeling study. In I. Juvina, C. Myers, and J. Houpt (Eds.), Proceedings of the 16th International Conference on Cognitive Modeling, Madison, WI: University of Wisconsin, pp. 108–113
Rice, P. J., & Stocco, A. (2018) Dorsal premotor cortex and conditional rule resolution: A high-frequency TMS investigation. In C. Kalish, M. Rau, J. Zhou, and T. T. Rogers (Eds.), Proceedings of the 40th Annual Meeting of the Cognitive Science Society. Madison, WI: University of Wisconsin, pp. 944-949.
Stocco, A., Laird, J. Lebiere, C., & Rosenbloom, P. (2018). Empirical evidence from neuroimaging data for a Standard Model of the Mind. In C. Kalish, M. Rau, J. Zhou, and T. T. Rogers (Eds.), Proceedings of the 40th Annual Meeting of the Cognitive Science Society. Madison, WI, pp. 1094-1099.
Orr, M. G., Lebiere, C., Stocco, A., Pirolli, P., Pires, B., Kennedy, W. (2018) Multi-scale resolution of cognitive architectures: A paradigm for simulating minds and society. In H. Bisgin, A. Hyder, C. Dancy, & R. Thomson (Eds.) Proceedings of the International Conference SBP-BRiMS 2018, July 10-13, 2018 Washington, DC, Springer, pp. 3-15.
Stocco, A. (2018). A biologically-plausible action selection system for cognitive architectures: Implications of basal ganglia anatomy for learning and decision-making models. Cognitive Science, 42(2), 457-490.
Stocco, A. (2017). An integrated computational framework for attention, reinforcement learning, and working memory. The 2017 AAAI Fall Symposium Series. pp. 470-475. AAAI Press, Palo Alto, California.
Rice, P. J., & Stocco, A. (2017). Basal ganglia-inspired functional constraints improve the robustness of Q-value estimates in model-free reinforcement learning. Proceedings of the 15h International Conference on Cognitive Modeling.
Stocco, A., Murray, N., L. Yamasaki, B. L., Renno, T., J., Nguyen, J., & Prat, C. S. (2017). Individual differences in the Simon effect are underpinned by differences in competitive dynamics in the basal ganglia: An experimental verification and a computational model. Cognition, 164, 31-45.
Stocco, A., & Lebiere, C. (2014). Inhibitory synapses between striatal projection neurons support efficient enhancement of cortical signals: A computational model. Journal of Computational Neuroscience, 37, 65-80.
Prat, C. S., Stocco, A., Neuhaus, E., & Kleinhans, N. (2016). Basal ganglia impairments lead to abnormal signal routing to prefrontal cortex in Autism Spectrum Disorder. Neuropsychologia, 91, 268-281.
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