Andrea Stocco, Ph.D.

Assistant Professor

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Bio

Andrea Stocco is an Assistant Professor at the Department of Psychology and the Institute for Learning and Brain Sciences (I-LABS), and co-director of the Cognition and Cortical Dynamics Laboratory. He was born in Palmanova, Italy, and received both his Master’s degrees and his Ph.D. from the University of Trieste, Italy.

Dr. Stocco’s research concerns how human use abstract mental representations (like, rules, instructions, and plans) to perform complex tasks. He uses computational and mathematical models, neuroimaging techniques, and brain stimulation methods determine and predict how these mental representations are encoded in the brain, how they are transformed into behavior, and how this knowledge can be used to improve learning and skill acquisition.

In 2013, together with Dr. Rajesh Rao and Dr. Chantel Prat, Dr. Stocco has demonstrated the possibility of transferring simple information directly between two brains using non-invasive brain stimulation devices. This pilot study has been extensively covered by the media.

CV

Education

Ph. D. in Psychology, University of Trieste, Italy, April 2005.
“Laurea” (equivalent to M. S.) in Communication Sciences, University of Trieste, Italy, November 2001

 

Academic Positions Held

09/2019 – Present Associate Professor, Department of Psychology, University of Washington
09/2019 – Present Adjunct Associate Professor, Paul G. Allen School of Computer Science and Engineering
06/2015 – 09/2019 Assistant Professor, Department of Psychology, University of Washington
07/2012 – 05/2015 Research Assistant Professor, Department of Psychology, University of Washington
September 2010 – present Research Scientist, Institute for Learning & Brain Sciences, University of Washington.
January 2009 — September 2010 Special Research Faculty, Department of Psychology, Carnegie Mellon University
September. 2008 — December 2008 Postdoctoral Research Associate, Department of Psychiatry, University of Pittsburgh School of Medicine
September 2005 — September 2008 Postdoctoral Research Fellow, Department of Psychology, Carnegie Mellon University

 

Professional Memberships

Cognitive Science Society, Cognitive Neuroscience Society, Association for the Advancement of Artificial Intelligence (AAAI), Biologically Inspired Cognitive Architectures Society (BICA).

 

Professional Services

Founder: Biologically Inspired Cognitive Architectures Society

Director: Biologically Inspired Cognitive Architectures Society (2010-2011)

Reviewer: Artificial Intelligence, Behavioral and Brain Functions, Cognition, Cognitive Science, Biologically Inspired Cognitive Architectures, Cortex, Journal of Artificial General Intelligence, Journal of Cognitive Neuroscience, Journal of Cognitive Systems Research; Neuropsychologia, Proceedings of the National Academy of Sciences, Psychological Review, PLoS ONE.

Action Editor: Journal of Cognitive Systems Research (2007)

Program Committee: Annual Meeting of the Cognitive Science Society (2009), International Conference on Cognitive Modeling (2006-present), European Conference on Artificial Intelligence (2006).

Chairing/Organizing: International Conference on Cognitive Modeling (2006), Twelfth ACT-R Workshop, Trieste, Italy (2005).

Founder and Director: Biologically Inspired Cognitive Architectures Society (2010-2011).

 

Patents

2017 Stocco, A. Losey, D. M., M., Abernethy, J. A., & Rao, R. P. N. Sensory Input Through Non-Invasive Brain Stimulation. US Patent 20170113056.

 

Awards

2012 - Invited Faculty, International College, Spring 2012 (IK2012).

2009 - Carnegie Mellon University, Pittsburgh, PA: Brain Imaging Research Center Young Investigator Pilot Study Award.
2001 - University of Trieste, Trieste, Italy: “Dignita’ di stampa” (rarely conceded only to outstanding research dissertations)

 

Selected Media Coverage

  • “Brain-to-Brain Communication Is Closer Than You Think”, Popular Mechanics, June 7, 2016, http://www.popularmechanics.com/science/a21220/brain-brain-communication
  • “I’m creating telepathy technology to get brains talking”, New Scientist, March 2nd, 2016, https://goo.gl/eYXiL7
  • “Learning a second language trains your brain for math”, Pacific Standard, September 28, 2014, http://goo.gl/fbcm8P
  • “Mind meld? Scientist uses his brain to control another guy's finger”, NBC, August 27, 2013, goo.gl/M7p4nL
  • “Researcher remotely controls colleague's body with brain”, USA Today, August 27, 2013 (Front page, A1), goo.gl/ANpWQR
  • “Mind Melds Made Real" (#13 in the Top 100 science stories of the year), Discover Magazine, Jan/Feb 2014, http://goo.gl/57xSKQ
  • “Mind melds” (#6 on the "Top 10 ideas to change the world") CNN, http://goo.gl/YWdUP0
  • “The Human Brain-to-Brain Interface” (#1 in the list of “Top 5 Neuroscience Breakthroughs of the Year”), The Connectome, http://goo.gl/LHpT2o
 

Download PDF format CV via this link.

Publications

Edited Books
 
Fum, D., Del Missier, F., & Stocco, A. (2006) Proceedings of the Seventh International Conference on Cognitive Modelling. Trieste, Italy: Edizioni Goliardiche.
 
Peer-Reviewed Journals Papers
 
MacInnes, J. J., Adcock, R. A., Stocco A. Prat, C. S., Rao, R. P. N., & Dickerson, K. C. (under revision) Pyneal: Open Source Real-Time fMRI Software.
 
Stocco, A., Sibert. C., Steine-Hanson, Z., Koh, N., Laird, J. E., Lebiere, C. J., & Rosenbloom, P. S. (under revision). A Common Architecture for Human and Artificial Cognition Explains Brain Activity Across Domains.
 
Stocco, A., Prat, C. S. Graham, L. K., & (revision submitted). Individual Differences in Reward Learning Processes Predict Fluid Reasoning Abilities.
 
Schimek, N., Burke-Conte, Z., Abernethy, J. A., Schimek, M., Burke-Conte, C., Bobola, M., Stocco, A., & Mourad, P. M. (2020). Repeated Application of Transcranial Diagnostic Ultrasound Towards the Visual Cortex Induced Illusory Visual Percepts in Healthy Participants. Frontiers in Human Neuroscience, 14, 66.
 
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.
 
Zhou, P., Prat, C., Yamasaki, B., & Stocco, A. (2020). Monitoring of attentional oscillations through spectral similarity analysis predicts reading comprehension. Brain & Language, 200, 104709.
 
Yamasaki, B. L., Liu, A., Stocco, A. & Prat, C. S., (2019). Effects of bilingual language experience on basal ganglia computations: A dynamic causal modeling test of the conditional routing model. Brain & Language, 197, 104665
 
Jiang, L., Stocco, A., Losey, D. M., Abernethy, J. A., Prat, C. S., & Rao, R. P. N. (2019). BrainNet: A Multi-Person Brain-to-Brain Interface for Collaborative Problem Solving. Scientific Reports, 9:6115.
 
Orr, M., Lebiere. C., Stocco, A., Pirolli, P. Pires, B., Kennedy, W. G. (2019). Multi-Scale Resolution of Neural, Cognitive and Social Systems. Computational and Mathematical Organization Theory, 25(1), 4–23.
 
Rice, P. J., & Stocco, A. (2019). The role of dorsal premotor cortex in resolving abstract motor rules: Converging evidence from TMS and cognitive modeling. Topics in Cognitive Science, 11(1), 240-260.
 
Stocco, A. (2019). The Neurocomputations of Neuroemergentism: Long-Term Memory + Reinforcement Learning = Language? Journal of Neurolinguistics, 49C, 248-251.
 
Steine-Hanson, Z. K., Koh, N., & Stocco, A. (2018). Refining the Common Model of Cognition through large neuroscience data. Procedia Computer Science, 145, 813-820.
 
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.
 
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.
 
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.
 
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.
 
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.
 
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.
 
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.
 
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, doi:10.1371/journal.pone.0137303
 
Stocco, A. (2014). Coordinate-based meta-analysis of neuroimaging data with R. The R Journal, 6(2), 5-15.
 
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. doi:10.1371/ journal.pone.0111332
 
Stocco, A., & Prat, C. (2014). Bilingualism trains specific brain circuits involved in flexible rule selection and application. Brain and Language, 137(10), 50-61.
 
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.
 
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.
 
Stocco, A. (2013) The co-emergence of language and rules: Indirection, not recursion, is the key. Comment on “The Bilingual Brain: Flexibility and Control in the Human Cortex” by Buchweitz and Prat. Physics of Life Reviews, 10, 448-449.
 
Cole, M. W., Laurent, P., & Stocco, A. (2013). Rapid instructed task learning: An emerging framework for investigating the neural basis of flexible cognitive control. Cognitive, Affective, & Behavioral Neuroscience, 13(1), 1-22.
 
Stocco, A., Lebiere, C., O’Reilly, R. C., & Anderson, J. R. (2012). Distinct contributions of the caudate nucleus, rostral prefrontal cortex, and parietal cortex to the execution of instructed tasks. Cognitive, Affective, & Behavioral Neuroscience, 12(4), 611-628.
 
Prat, C. S., & Stocco, A. (2012). Information routing in the basal ganglia: Highways to abnormal connectivity in autism? Comment on “Disrupted cortical connectivity theory as an explanatory model for autism spectrum disorders” by Kana et al. Physics of Life Reviews, 9(1), 1-2.
 
Stocco, A. (2012). Acetylcholine-based entropy in response selection: A model of how striatal interneurons modulate exploration, exploitation, and response variability in decision making. Frontiers in Neuroscience, 6, 18.
 
Stocco, A., Lebiere, C., O'Reilly, R. C., & Anderson, J. R. (2010). The role of the anterior prefrontal-basal ganglia circuit as a biological instruction interpreter. Frontiers in Artificial Intelligence and Applications, 221, 153-162.
 
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. doi:10.1371/journal.pone.0012966
 
Stocco, A., Lebiere, C., & Samsonovich, A. (2010). The B-I-C-A of biologically inspired cognitive architectures. International Journal of Machine Consciousness, 2(2), 1-22.
 
Stocco, A., Lebiere, C., & Anderson, J. R. (2010). Conditional routing of information to the cortex: A model of the basal ganglia’s role in cognitive coordination. Psychological Review, 117(2), 540-574.
 
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.
 
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.
 
Anderson, J. R., Fincham, J. M., Qin, Y., & Stocco, A. (2008). A central circuit of the mind. Trends in Cognitive Sciences, 14(4), 136-143.
 
Stocco, A., & Fum, D. (2008). Implicit emotional biases in decision making: The case of the Gambling Task. Brain & Cognition, 66(3), 253-259.
 
Fum, D., Del Missier, F., & Stocco, A. (2007). The cognitive modeling of human behavior: Why a model is (sometimes) better than 10,000 words. Cognitive Systems Research, 8, 135-142.
 
Stocco, A. & Crescentini, C. (2005). Syntactic comprehension in agrammatism: A computational model. Brain and Language, 95, 127-128.
 
Book Chapters
 
Salvucci, D. D., Laird, J. E., Chang, F., Forbus, K. D., Kordjamshidi, P., Mitchell, T., Mohan, S., Spranger, M., Stevenson, S., Stocco, A., & Trafton, J. G. (2019). Learning Task Knowledge. In K. Gluck & J. E. Laird, Interactive Task Learning: Humans, Robots, and Agents Acquiring New Task Through Natural Interaction. Cambridge, MA: MIT Press, pp. 237-257.
 
Peer-Reviewed Papers in Conference Proceedings
 
Smith, B. M., Chiu, M., Yang, Y., Sibert, C., & Stocco, A. (in press) Modeling the effects of post-traumatic stress on hippocampal volume. Proceedings of the 18th International Conference on Cognitive Modeling.
 
Haile, T., Prat, C. S., & Stocco, A. (in press) 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.
 
Yang, Y., Morrison, D., Stocco, A., Orr, M., & Lebiere, C. (submitted) An Expanded Set of Declarative Memory Functionalities in PyACTUp, a Python Implementation of ACT-UP’s Accountable Modeling.
 
Xu, Y. & Stocco, A. (in press). Reliable idiographic parameters from noisy behavioral data: The case of individual differences in a reinforcement learning task. Proceedings of the 42nd Annual Meeting of the Cognitive Science Society.
 
Ketola, M., Thompson, S., Madhyastha, T., Grabowski, T., & Stocco, A. (submitted). Applying the Common Model of Cognition to resting-state fMRI leads to the identification of abnormal functional connectivity in Parkinson’s Disease. Proceedings of the 42nd Annual Meeting of the Cognitive Science Society.
 
Yang, Y. C., & Stocco, A. (2019). Syntactic priming depends on procedural, reward-based computations: Evidence from experimental data and a computational model. Proceedings of the 17th International Conference on Cognitive Modeling
 
Ketola, M., Jiang, L. P., & Stocco, A. (2019). Comparing alternative computational models of the Stroop task using effective connectivity analysis of fMRI Data. In A. Goel., C. Freisert, & C. Freska (Eds.) Proceedings of the 41st Annual Meeting of the Cognitive Science Society, pp. 553–559.
 
Ceballos, J. M., Stocco, A., & Prat, C. S. (2019). The role of basal ganglia reinforcement learning in lexical priming and automatic semantic ambiguity resolution. In A. Goel., C. Freisert, & C. Freska (Eds.) Proceedings of the 41st Annual Meeting of the Cognitive Science Society, pp. 205–211. [Winner of the Marr prize for best student paper]
 
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 [Selected as one of Best Papers at ICCM paper]
 
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. [Best paper at SBP-BRIMS]
 
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. In M. van Vugt, A. Banks, and W. Kennedy (Eds.) Proceedings of the 15h International Conference on Cognitive Modeling, Warwick, UK: University of Warwick, pp. 67-72.
 
McDonald, M. P., & Stocco, A. (2016). The Minimalist Interference Model of the Implicit Association Test predicts working memory confounds. Proceedings of the 14th International Conference on Cognitive Modeling.
 
Lebiere, C., Stocco, A., Reitter, D., & Juvina, I. (2010). Scaling up high-fidelity cognitive modeling to real-world applications. In Proceedings of NATO Workshop on Human Modeling for Military Application. Amsterdam, NL, October 18-20, 2010.
 
Reitter, D., Juvina, I., Stocco, A., & Lebiere, C. (2010) Resistance is futile: Winning lemonade market share through metacognitive reasoning in a three-agent cooperative game. In Proceedings of the 19th Conference on Behavioral Representation in Modeling and Simulation (BRIMS). Charleston, S.C.
 
Stocco, A., Lebiere, C., & Anderson, J. R. (2009). Dopamine, learning, and production rules: The basal ganglia and the flexible control of information transfer in the brain. In A. Samsonovich (Ed.) Biologically Inspired Cognitive Architectures 2009. AAAI Press, pp. 169-175.
 
Borst, J. P., Taatgen, N.A., van Rijn, H., Stocco, A., & Fincham, J. M. (2009). Testing fMRI Predictions of a Dual-Task Interference Model. In A. Howes, D. Peebles, & R. Cooper (Eds), Proceedings of the 9th International Conference on Cognitive Modeling.
 
Fum, D., Napoli, A., & Stocco, A. (2008). Somatic markers and frequency effects: Does emotion really play a role on decision making in the gambling task? In V. Sloutsky, B. Love & K. McRae (Eds.) Proceedings of the 30th Annual Conference of the Cognitive Science Society, pp. 1203-1208.
 
Stocco, A., & Fum, D., (2006). Memory and emotion in the Gambling Task: The case for independent processes. In R. Sun & N. Miyake (Eds.) Proceedings of the 28th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates, pp. 2192-2197.
 
Stocco, A., Fum, D., & Zalla, T. (2005). Revising the role of somatic markers in the Gambling Task: A computational account for neuropsychological impairments. In B. Bara, L. W. Barsalou & M. Bucciarelli (Eds.) Proceedings of the 27th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates, pp. 2074-2079.
 
Fum, D., & Stocco, A. (2004). Memory, Emotion, and Rationality: An ACT-R interpretation for Gambling Task results. In C. D. Schunn, M. C. Lovett, C. Lebiere & P. Munro (Eds.) Proceedings of the 6th International Conference on Cognitive Modeling. Mahwah, New Jersey: Lawrence Erlbaum Associates, pp. 106-111.
 
Stocco, A., Fum, D., & Drioli, S. (2004). High-Level cognitive processes in causal judgments: An integrated model. In K. Forbus, D. Gentner & T. Regier (Eds.) Proceedings of the 26th Annual Meeting of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates, pp. 1267-1272.
 
Fum, D., & Stocco, A. (2003). The role of compound cues in causal judgment: Associative and probabilistic effects. In F. Schmalhofer, R. M. Young & G. Katz (Eds.) Proceedings of the EuroCogSci 03. Mahwah, NJ: Lawrence Erlbaum Associates, pp. 127-132.
 
Fum, D., & Stocco, A. (2003). Outcome evaluation and procedural knowledge in implicit learning. In R. Alterman & D. Kirsh (Eds.) Proceedings of the 25th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates, pp. 426-431.
 
Fum, D., & Stocco, A. (2003). Instance vs. rule-based learning in controlling a dynamic system. In F. Detje, D. Dörner & H. Schaub (Eds.) Proceedings of the 5th International Conference on Cognitive Modelling. Bamberg, Germany: Universitäts-Verlag Bamberg, pp. 105-110.
 
Popular Science Articles
 
Stocco, A. (2019). «Je suis la première personne au monde à avoir reçu un ode d’un autre cerveau et à avoir obéi sans savoir que je le faisais ni quand je le faisais». Philosophie, 131, 38-39.
 
Rao, R. N. P. & Stocco, A. (2014). When two brains connect. Scientific American Mind, 25(6), 36-39.
 

About the Lab

Human thought is characterized by its flexible, dynamic nature. The Cognition and Cortical Dynamics Laboratory (CCDL) consists of a group of researchers interested in better understanding how the brain changes, or adapts, to deal with the ever present fluctuations in information processing demands.  Our research on these issues addresses a set of unifying questions, such as:

What are the biological bases of individual differences in cognitive capabilities?  What are the neural mechanisms underpinning cognitive flexibility?  The CCDL utilizes multiple methods and approaches including functional magnetic resonance imaging (fMRI), biologically constrained computational modeling, transcranial magnetic stimulation (TMS), and individual differences research to collect converging evidence about the biological nature of human thought.

 

Contact

Phone Number: 
(206) 685-8610