Helie, S. (2024). The role of posterior parietal cortex in detecting changes in feedback contingency. Brain Structure & Function, 229, 775-787. [pdf]
Naik, A., Iqbal, R., Helie, S., & Ambike, S. (2024). Human movement strategies in uncertain environments: A synergy–based approach to the stability–agility tradeoff. Human Movement Science, 97, 103259. [Publisher's Website]
Onuchukwu, I., Esmaeili, B., & Helie, S. (2024). Application of automaticity theory in construction. Journal of Management in Engineering, 40, 04024018. [Publisher's Website]
Randez, A. & Helie, S. (2024). The roles of intrinsic motivation and capability–related factors in cognitive effort–based decision–making. Frontiers in Psychology, 15, 1303262. [Open Access]
Vishton, P. M. & Helie, S. (2024). Editorial: Emerging talents in human neuroscience: Cognitive neuroscience 2023. Frontiers in Human Neuroscience, 18, 1488829. [Publisher's Website]
Yao, S. & Helie, S. (2024). The effect of ostracism on prospective memory in problem solving. Heliyon, 10, e24895. [Open Access]
Helie, S. (2023). Editorial: Emerging talents in human neuroscience: Cognitive neuroscience 2022. Frontiers in Human Neuroscience, 17, 1171860. [Open Access]
Helie, S., Lim, L. X., Adkins, M. J., & Redick, T. S. (2023). A computational model of prefrontal and striatal interactions in perceptual category learning. Brain and Cognition, 168, 105970. [pdf] [Experiment Material]
Kumar, S., & Helie, S. (Eds.) (2023). Emerging Talents in Human Neuroscience: Cognitive Neuroscience 2022. Frontiers in Human Neuroscience. [Open Access]
Lim, L. X., Fansher, M., & Helie, S. (2023). Do cognitive and physical effort costs affect choice behavior similarly? Journal of Mathematical Psychology, 112, 102727. [pdf]
Monni, A., Scandola, M., Helie, S., & Scalas, L. F. (2023). Cognitive flexibility assessment with a new reversal learning task paradigm compared with the Wisconsin Card Sorting Test exploring the moderating effect of gender and stress. Psychological Research, 87, 1439-1453. [Publisher's Website]
Calic, G., Mosakowski, E., Bontis, N., & Helie, S. (2022). Is maximizing creativity good? The importance of elaboration and internal confidence in producing creative ideas. Knowledge Management Research & Practice, 20, 776-791. [Publisher's Website]
Fansher, M., Shah, P., & Helie, S. (2022). The effect of mode of presentation on Tower of Hanoi problem solving. Cognition, 224, 105041. [pdf]
Helie, S. & Pizlo, Z. (2022). When is psychology research useful in artificial intelligence? A case for reducingcomputational complexity in problem solving. Topics in Cognitive Science, 14, 687-701. [pdf]
Helie, S., Shamloo, F., Zhang, H., & Ell, S. W. (2021). The
impact of training methodology and representation on rule-based
categorization: An fMRI study. Cognitive,
Affective, & Behavioral Neuroscience,
21, 717-735. [pdf]
Kovacs, P., Helie, S., Tran, A. N., & Ashby, F. G. (2021). A
neurocomputational theory of how rule–guided behaviors become
automatic. Psychological Review,
128, 488-508. [pdf]
2015 - 2020
Edmondson, D. A., Yeh, C. L., Helie, S., & Dydak, U. (2020). Whole–brain R1 predicts manganese exposure and biological effects in welder. Archives of Toxicology, 94, 3409-3420. [Publisher's Website]
Ell, S. W., Smith, D. B., Deng, R., & Helie, S. (2020).
Learning and generalization of within-category representations in a
rule-based category structure. Attention,
Perception, & Psychophysics, 82, 2448-2462. [pdf]
Helie, S., Shamloo, F., & Ell, S. W. (2020). The impact of training methodology and category structure on the formation of new categories from existing knowledge. Psychological Research, 84, 990-1005. [pdf]
Mishra, P. & Helie, S. (2020). 3D shape estimation in a
constraint optimization network. Vision
Research, 177,
118-129. [Article] [Supplementary
material]
Calic, G., Helie, S., Bontis, N., & Mosakowski, E. (2019). Creativity from paradoxical experience: A theory of how individuals achieve creativity while adopting paradoxical frames. Journal of Knowledge Management, 23, 397-418. [Publisher's Website]
Helie, S. & Sajedinia, Z. (2019). Computational models of
Parkinson’s disease. In V. Cutsuridis (Ed.). Multi–Scale
Models of Brain Disorders (pp. 105–112). Springer.
Lim, L. X. & Helie, S. (2019). Exploration and exploitation
reflect system-switching in learning. In G. Ashok, C. Seifert, &
C. Freksa (Eds.). Proceedings of
the 41st Annual Meeting of the Cognitive Science Society
(pp. 2154–2160). Austin, TX: Cognitive Science Society. [pdf]
Sajedinia, Z., Pizlo, Z., & Helie, S. (2019). Investigating the role of the visual system in solving the traveling salesperson problem. In G. Ashok, C. Seifert, & C. Freksa (Eds.). Proceedings of the 41st Annual Meeting of the Cognitive Science Society (pp. 2702–2707). Austin, TX: Cognitive Science Society. [pdf]
Calic, G. & Helie, S. (2018). Creative sparks or paralysis traps? The effects of contradictions on creative processing and creative products. Frontiers in Psychology, 9, 1489. [Open Access]Crossley, M., Roeder, J., Helie, S., & Ashby, F. (2018). Trial–by–trial switching between procedural and declarative categorization systems. Psychological Research, 82, 371-384. [pdf]
Fleischer, P., Helie, S., & Pizlo, Z. (2018). The role of problem representation in producing near–optimal TSP tours. Journal of Problem Solving, 11, 2. [Open Access]
Helie, S. & Fansher, M. (2018). Categorization system–switching deficits in typical aging and Parkinson’s disease. Neuropsychology, 32, 724-734. [pdf]
Helie, S., Turner, B., & Cousineau, D. (2018). Can categorical knowledge be used in visual search? Acta Psychologica, 191, 52-62. [pdf]
Sajedinia, Z., & Helie, S. (2018). A new computational model for astrocytes and their role in biologically-realistic neural networks. Computational Intelligence and Neuroscience, 2018, 3689487. [Open Access]
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. [Open Access]
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. [Open Access]
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]
Ross, M., Chartier, S., & Helie, S. (2017). The neurodynamics of categorization: Critical challenges and proposed solutions. In H. Cohen & C. Lefebvre (Eds.). Handbook of Categorization in Cognitive Science. 2nd Edition (pp. 1053–1076). Oxford: Elsevier.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. & 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. & Fleischer, P. (2016). Simulating the effect of
reinforcement learning on neuronal synchrony and periodicity in the
striatum. Frontiers in
Computational Neuroscience, 10, 40. [Open Access]
Shamloo, F. & Helie, S. (2016). Changes in default mode network
as automaticity develops in a categorization task. Behavioural
Brain Research, 313,
324-333. [pdf]
2010 - 2015
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., 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]
Helie, S., & Paul, E. J. (2015). Computational models of
Parkinson's disease cognitive deficits. Scholarpedia,
10, 32137. [Open
Access]
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]
Helie, 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., & Helie, 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., Helie, 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. & Cousineau, D. (2014). The cognitive neuroscience of automaticity: Behavioral and brain signatures. In M.-K. Sun (Ed.). Advances in Cognitive and Behavioral Sciences (pp. 141–159). Nova Science Publishers. [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. [Open Access]
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., 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. [Open Access]
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] [Simulation Code]
Helie, S., Waldschmidt, J. G., & Ashby, F. G. (2010). Automaticity in rule-based and information-integration categorization. Attention, Perception, & Psychophysics, 72, 1013-1031. [pdf]
Pre 2010
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]