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Crossley, M., Roeder, J., Helie, S., & Ashby, F. (in press). Trial–by–trial switching between procedural and declarative categorization systems. Psychological Research. [pdf]

Ell, S. W., Smith, D. B., Peralta, G., & Helie, S. (2017). The impact of category structure and training methodology on learning and generalizing within–category representations. Attention, Perception, & Psychophysics, 79, 1777-1794. [pdf]

Helie, S. (2017). Practice and preparation time facilitate system–switching in perceptual categorization. Frontiers in Psychology, 8, 1964. [Website]

Helie, S. (2017). The effect of integration masking on visual processing in perceptual categorization. Brain and Cognition, 116, 63-70. [pdf]

Helie, S., Shamloo, F., & Ell, S. (2017). The effect of training methodology on knowledge representation in categorization. PLOS ONE, 12, e0183904. [Website]

Helie, S., Shamloo, F., Novak, K., & Foti, D. (2017). The roles of valuation and reward processing in cognitive function and psychiatric disorders. Annals of the New York Academy of Sciences, 1395, 33-48. [pdf]

Helie, S., Turner, B. O., Crossley, M. J., Ell, S. W., & Ashby, F. G. (2017). Trial-by-trial identification of categorization strategy using iterative decision bound modeling. Behavior Research Methods, 49, 1146-1162. [pdf]

Berberian, N., Aamir, Z., , Helie, S., and Chartier, S. (2016). Encoding sparse features in a bidirectional associative memory. Proceedings of the International Joint Conference on Neural Networks (pp. 5119-5126), Vancouver, BC. IEEE Press.

Calic, G. and Helie, S. (2016). Big ideas, one small idea at a time: The power of cognitively proximate search to drive action on cognitively distant ideas. 32nd EGOS Colloquium, Naples, IT.

Donaldson, K., Oumeziane, B., Helie, S., & Foti, D. (2016). The temporal dynamics of reversal learning: P3 amplitude predicts valence-specific behavioral adjustment. Physiology & Behavior, 161, 24-32. [pdf]

Helie, S. and Fleischer, P. (2016). Simulating the effect of reinforcement learning on neuronal synchrony and periodicity in the striatum. Frontiers in Computational Neuroscience, 10, 40. [Website]

Shamloo, F. & Helie, S. (2016). Changes in default mode network as automaticity develops in a categorization task. Behavioural Brain Research, 313, 324-333. [pdf]

Calic, G., Helie, S., & Mosakowski, E. (2015). Nonlinear effects of paradoxical frames on creativity. 31st EGOS Colloquium, Athens, Greece.

Helie, S., & Cousineau, D. (2015). Differential effect of visual masking in perceptual categorization. Journal of Experimental Psychology: Human Perception and Performance, 41, 816-825. [pdf]

Helie, S., Ell, S.W., & Ashby, F.G. (2015). Learning robust cortico-frontal associations with the basal ganglia: An integrative review. Cortex, 64, 123-135. [pdf]

Helie, S., Ell, S.W., Filoteo, J.V., & Maddox, W. T. (2015). Criterion learning in rule-based categorization: Simulation of neural mechanism and new data. Brain and Cognition, 95, 19-34. [pdf]

Hélie, S., & Paul, E. J. (2015). Computational models of Parkinson's disease cognitive deficits. Scholarpedia, 10, 32137. [Website]

Helie, S., Roeder, J. L., Vucovich, L., Rünger, D., & Ashby, F. G. (2015). A neurocomputational model of automatic sequence production. Journal of Cognitive Neuroscience, 27, 1412-1426. [pdf]

Hélie, S. & Sun, R. (2015). Cognitive architectures and agents. In J. Kacprzyk & W. Pedrycz (Eds.) Springer Handbook of Computational Intelligence (pp. 683-696). Springer. [pdf]

Sun, R., & Hélie, S.(2015). Accounting for creativity using a psychologically realistic cognitive architecture. In T. R. Besold, M. Schorlemmer, & A. Small (Eds.) Computational Creativity Research: Towards Creative Machines (pp. 151-166). Springer. [pdf]

Barbu, A., Barrett, D., Chen, W., Siddarth, N., Xiong, C., Corso, J. J., Fellbaum, C. D., Hanson, C., Hanson, S. J., Hélie, S., Malaia, E., Pearlmutter, B. A., Siskind, J. M., Talavage, T. M., Wilbur, R. B. (2014). Seeing is worse than believing: Reading people’s minds better than computer-vision methods recognize actions. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.). Computer Vision – ECCV 2014 (pp. 612-627). Springer. [pdf]

Cousineau, D., & Helie, S. (2014). Outstanding data sets: A new category of articles that promotes modelling published in the Quantitative Methods for Psychology. The Quantitative Methods for Psychology, 10, 1-4. [pdf]

Filoteo, J.V., Paul, E.J., Ashby, F.G., Frank, G.K.W. , Helie, S., Rockwell, R., Bischoff-Grethe, A., Wierenga, C., & Kaye, W.H. (2014). Simulating category learning and set shifting deficits in patients weight-restored anorexia nervosa. Neuropsychology, 28, 741-751. [pdf]

Helie, S. & Sun, R. (2014). An integrative account of memory and reasoning phenomena. New Ideas in Psychology, 35, 36-52. [pdf]

Helie, S. & Sun, R. (2014). Autonomous Learning in Psychologically-Oriented Cognitive Architectures: A Survey. New Ideas in Psychology, 34, 37-55. [pdf]

Cousineau, D., & Helie, S. (2013). Improving maximum likelihood estimation using prior probabilities: A tutorial on maximum a posteriori estimation and an examination of the Weibull distribution. Tutorials in Quantitative Methods for Psychology, 9, 61-71. [pdf]

Cousineau, D., Lacroix, G.L., Giguere, G., & Helie, S. (2013). Learning curves as strong evidence for testing models: The EBRW case. Journal of Mathematical Psychology, 57, 107-116. [pdf]

Helie, S. (Ed.). (2013). The Psychology of Problem Solving: An Interdisciplinary Approach. New York: Nova Publishers.

Helie, S. (2013). Towards a unified neurobiological theory of creative problem solving. Proceedings of the International Joint Conference on Neural Networks (pp. 1622-1629). Dallas, TX: IEEE Press. [pdf]

Helie, S., Chakravarthy, S., & Moustafa, A. A. (2013). Exploring the cognitive and motor functions of the basal ganglia: An integrative review of computational cognitive neuroscience models. Frontiers in Computational Neuroscience, 7, 174. [Website]

Soto, F.A., Waldschmidt, J.G., Helie, S. & Ashby, F.G. (2013). Brain activity across the development of automatic categorization: A comparison of categorization tasks using multi-voxel pattern analysis. NeuroImage, 71, 284-297. [pdf]

Sun, R., & Helie, S. (2013). Psychologically realistic cognitive agents: Taking human cognition seriously. Journal of Experimental & Theoretical Artificial Intelligence, 25, 65-92. [pdf]

Helie, S. & Sun, R. (2013). Implicit cognition in problem solving. In. S. Helie (Ed.) The Psychology of Problem Solving: An Interdisciplinary Approach (pp. 45-59). New York: Nova Publishers. [pdf]

Ell, S. W., Helie, S., & Hutchinson, S. (2012). Contributions of the putamen to cognitive function. In A. Costa & E. Villalba (Eds.) Horizon in Neuroscience. Volume 7 (pp. 29-52). Nova Publishers. [pdf]

Helie, S. & Ashby, F. G. (2012). Learning and transfer of category knowledge in an indirect categorization task. Psychological Research, 76, 292-303. [pdf]

Helie, S., Paul, E.J., & Ashby, F.G. (2012). A neurocomputational account of cognitive deficits in Parkinson's disease. Neuropsychologia, 50, 2290-2302. [pdf]

Helie, S., Paul, E.J., & Ashby, F.G. (2012). Simulating the effect of dopamine imbalance on cognition: From positive affect to Parkinson's disease. Neural Networks, 32, 74-85. [pdf]

Sun, R., & Helie, S. (2012). Reasoning with heuristics and induction: An account based on the CLARION cognitive architecture. Proceedings of the International Joint Conference on Neural Networks (pp. 1359-1366). Brisbane, AU: IEEE Press. [pdf]

Ashby, F. G. & Helie, S. (2011). A Tutorial on Computational Cognitive Neuroscience: Modeling the Neurodynamics of Cognition. Journal of Mathematical Psychology, 55, 273-289. [pdf]

Helie, S., & Cousineau, D. (2011). The cognitive neuroscience of automaticity: Behavioral and brain signatures. Cognitive Sciences, 6, 25-43. [pdf]

Helie, S., Paul, E. J., & Ashby, F. G. (2011). Simulating Parkinson's disease patient deficits using a COVIS-based computational model. Proceedings of the International Joint Conference on Neural Networks (pp. 207-214). San Jose, CA: IEEE Press. [pdf]

Helie, S., Proulx, R., & Lefebvre, B. (2011). Bottom-up learning of explicit knowledge using a Bayesian algorithm and a new Hebbian learning rule. Neural Networks, 24, 219-232. [pdf]

Helie, S. & Sun, R. (2011). How the Core Theory of CLARION Captures Human Decision-Making. Proceedings of the International Joint Conference on Neural Networks (pp. 173-180). San Jose, CA: IEEE Press. [pdf]

Helie, S., Roeder, J. L., & Ashby, F. G. (2010). Evidence for cortical automaticity in rule-based categorization. Journal of Neuroscience, 30, 14225-14234. [Website]

Helie, S. & Sun, R. (2010). Creative problem solving: A CLARION theory. Proceedings of the International Joint Conference on Neural Networks (pp. 1460-1466). Barcelona, ES: IEEE Press. [pdf]

Helie, S. & Sun, R. (2010). Incubation, insight, and creative problem solving: A unified theory and a connectionist model. Psychological Review, 117, 994-1024. [pdf] [website]

Helie, S., Waldschmidt, J. G., & Ashby, F. G. (2010). Automaticity in rule-based and information-integration categorization. Attention, Perception, & Psychophysics, 72, 1013-1031. [pdf]

Helie, S. & Ashby, G. F. (2009). A neurocomputational model of automaticity and maintenance of abstract rules. Proceedings of the International Joint Conference on Neural Networks (pp. 1192-1198). Atlanta, GA: IEEE Press. [pdf]

Helie, S. & Sun, R. (2009). Simulating incubation effects using the Explicit - Implicit Interaction with Bayes factor (EII-BF) model. Proceedings of the International Joint Conference on Neural Networks (pp. 1199-1205). Atlanta, GA: IEEE Press. [pdf]

Helie, S. (2008). Energy Minimization in the Nonlinear Dynamic Recurrent Associative Memory. Neural Networks, 21, 1041-1044. [pdf]

Helie, S. & Sun, R. (2008). Knowledge integration in creative problem solving. In B.C. Love, K. McRae, & V.M. Sloutsky (Eds.) Proceedings of the 30th Annual Meeting of the Cognitive Science Society (pp. 1681-1686). Austin, TX: Cognitive Science Society. [pdf]

Helie, S., Sun, R., & Xiong, L. (2008). Mixed effects of distractor tasks on incubation. In B.C. Love, K. McRae, & V.M. Sloutsky (Eds.) Proceedings of the 30th Annual Meeting of the Cognitive Science Society (pp. 1251-1256). Austin, TX: Cognitive Science Society. [pdf]

Helie, S. (2007). Understanding statistical power using noncentral probability distributions: Chi-squared, G-squared, and ANOVA. Tutorials in Quantitative Methods for Psychology, 3, 63-69. [pdf]

Helie, S. (2006). An introduction to model selection: Tools and algorithms. Tutorials in Quantitative Methods for Psychology, 2, 1-10. [pdf]

Helie, S., Chartier, S., & Proulx, R. (2006). Are unsupervised neural networks ignorant? Sizing the effect of environmental distributions on unsupervised learning. Cognitive Systems Research, 7, 357-371. [pdf]

Helie, S., Giguere, G., Cousineau, D., & Proulx, R. (2006). Using knowledge partitioning to investigate the psychological plausibility of mixtures of experts. Artificial Intelligence Review, 25, 119-138. [pdf]

Helie, S., Proulx, R., & Lefebvre, B. (2006). JPEX: A psychologically plausible Joint Probability EXtractor. In R. Sun & N. Miyake (Eds.) Proceedings of the 28th Annual Meeting of the Cognitive Science Society (pp. 1482-1487). Mahwah, NJ: Lawrence Erlbaum Associates. [pdf]

Chartier, S., Helie, S., Boukadoum, M., & Proulx, R. (2005). SCRAM: Statistically Converging Recurrent Associative Memory. Proceedings of the International Joint Conference on Neural Networks (pp. 723-728). Montreal, QC: IEEE Press. [pdf]

Helie, S. & Cousineau, D. (2005). Mixed effects of training on transfer. In B.G. Bara, L. Barsalou, & M. Bucciarelli (Eds.) Proceedings of the 27th Annual Meeting of the Cognitive Science Society (pp. 929-934). Mahwah, NJ: Lawrence Erlbaum Associates. [pdf]

Helie, S., Giguere, G., Cousineau, D., & Proulx, R. (2005). Are mixtures of experts psychologically plausible? In N. Creany (Ed.) AICS'05: Proceedings of the 16th Irish Conference on Artificial Intelligence and Cognitive Science (pp. 61-70). Coleraine, UK: University of Ulster. [pdf]

Proulx, R. & Helie, S. (2005). Adaptive categorization and neural networks. In C. Lefebvre & H. Cohen (Eds.) Handbook of Categorization in Cognitive Science (pp. 793-815). Oxford: Elsevier. [pdf]

Giguere, G., Helie. S., & Cousineau, D. (2004). Manifeste pour le retour des sciences en psychologie. Revue Quebecoise de Psychologie, 25, 117-130. [pdf]

Cousineau, D., Helie, S., & Lefebvre, C. (2003).Testing Curvatures of learning functions on individual trial and block average data. Behavior Research Methods, Instruments, and Computers, 35, 493-503. [pdf]

Cousineau, D., Lacroix, G. L., & Helie, S. (2003). Redefining the rules: Providing race models with a connectionist learning rule. Connection Science, 15, 27-43. [pdf]