Speakers

Research Focus:

1. Development of general mathematical theories and methodologies for human perception and cognition:

   • Stochastic processes, probability theory, and mathematical statistics.

   • Differential geometry and topology.

2. Cognitive psychology and psychophysics, including:

   • Visual pattern perception and signal detection.

   • Face perception.

   • Configurality and gestalt perception vs. separable and independent dimensions and features.

   • Human decision and planning behavior.

   • Short-term memory search.

3. Clinical science, development, and application of mathematical models in clinical research phenomena:

   • Schizophrenia.

   • Autism.

   • General methodological approaches to clinical science.

Scholarly Overview:

Professor James Townsend has worked in the field of broad-based information processing methods for many years. Within this domain, he has conducted theoretical and experimental research on issues related to parallel versus serial processing and letter and elementary pattern perception.

His work on pattern recognition has laid a solid empirical foundation for certain identification and classification tasks that constitute the basis of current recognition and classification work. It also contributes to understanding how feature perception occurs. Subsequent research includes collaborative efforts with F. Gregory Ashby to develop a generalized identification theory. Recent efforts have focused on establishing a rigorous framework for studying perception dependency.

His early research on parallel versus serial processing suggested a wide area of model simulation. Later efforts identified some promising experimental strategies, supported by mathematical results, to experimentally distinguish these two processing modes. Similar projects involve related topics such as self-terminating versus exhaustive processing and limited versus unlimited versus super capacity.

This research area also involves collaboration with Richard Schweickert to study more complex psychological network theories. They are committed to developing experimental techniques that can experimentally separate normative subclasses within quite complex frameworks. Another line of research involves collaboration with Jerome Busemeyer to study dynamic decision-making theories. The theory attempts to provide quantitative settings for explanation and prediction, contrasting with traditional approaches based on utility theory, which are intrinsic, dynamic, and stochastic.

Professor James Townsend has also published papers on general theoretical methods, measurement, the history of mathematical psychology, and recently on dynamics and chaos theory. He is currently building a laboratory for studying face perception using state-of-the-art computer facilities. He hopes to enrich the theoretical progress in this field with differential and algebraic topology and more standard mathematical psychology tools.

Recent Publications:

Severo, D., Townsend, J., Khisti, A., Makhzani, A., & Ullrich, K. (2023). Compressing multisets with large alphabets. IEEE Journal on Selected Areas in Information Theory.

Ak, A., Wenger, M., Townsend, J., & Newbolds, S. (2023). The Sequential categorization identification paradigm: A New paradigm for combined inferences. Journal of Vision, 23(9), 5434-5434.

Liu, Y., Townsend, J. T., & Wenger, M. J. (2023). Don’t be a Square: The processing mechanisms characterising the elemental dimensions of width and height. Quarterly Journal of Experimental Psychology, 76(4), 792-826. 

Research Focus:

Cognitive Psychology, Cognitive Modeling, Cognitive Workload Capacity, Configuration Processes

Scholarly Overview:

Ami Eidels earned a PhD in Cognitive Psychology from Tel Aviv University, followed by post-doctoral training in Mathematical Psychology at Indiana University. In 2008, a faculty position was accepted at the School of Psychology, University of Newcastle, Australia. 

Their research revolves around information-processing models, examining how diverse information sources are processed and integrated. Collaborating with students, they investigate the impact of cognitive workload on processing efficiency and cognitive system capacity, both in controlled laboratory settings and practical applications. 

Moreover, they upscale cognitive modeling and machine learning methodologies to analyze the performance of human-human and human-bot teams. Collaborative efforts extend to partnerships with defense and industry entities including Newcastle City Council, Keolis Downer, Royal Australian Air Force, Spearpoint Technology, Hensoldt Avionics, DIN, HPRnet, and Airbus Defence and Space.

Recent Publications:

Rendell, A., Adam, M. T., Eidels, A., & Teubner, T. (2022). Nature imagery in user interface design: The influence on user perceptions of trust and aesthetics. Behaviour & Information Technology, 41(13), 2762-2778.

Gronau, Q. F., Bennett, M. S., Brown, S. D., Hawkins, G. E., & Eidels, A. (2023). Do choice tasks and rating scales elicit the same judgments?. Journal of choice modelling, 49, 100437.

Love, J., Gronau, Q., Brown, S., & Eidels, A. (2023). Trust in Human-bot Teaming: Applications of the Judge Advisor System. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 45, No. 45). 

Research Focus:

Cognitive Science, Mathematical Analysis, Bayesian Modeling, Mathematical Psychology, Experimental Psychology, Cognitive Psychology, Memory

Scholarly Overview:

Joseph W. Houpt's research primarily focuses on mathematical cognitive models, particularly configuration perception models, as a framework for understanding basic processes and measuring human performance. Much of this research utilizes System Factorial Technology (SFT), a method for assessing fundamental features of cognitive processing, to which he has made several contributions.

Despite being grounded in rigorous mathematical models, SFT lacks mechanisms for quantitative hypothesis testing. To address this issue, Joseph W. Houpt developed nonparametric null hypothesis significance testing and nonparametric Bayesian testing for each SFT metric. He also expanded the range of cognitive processing models that can be tested within this framework.

Joseph W. Houpt employs the current form of SFT to explore the possibilities of human perception and performance, but much of the existing work has focused on relatively simple environments and tasks. He is working to expand the applicability of SFT to more complex tasks and environments through the extension of theoretical components and the development of new experimental methods.

Recent Publications:

Howard, Z. L., Fox, E. L., Evans, N. J., Loft, S., & Houpt, J. (2023). An extension of the shifted Wald model of human response times: Capturing the time dynamic properties of human cognition. Psychonomic Bulletin & Review, 1-21.

Kneeland, C. M., Houpt, J. W., & Juvina, I. (2023). How Do People Process Information from Automated Decision Aids: an Application of Systems Factorial Technology. Computational Brain & Behavior, 1-23.

Zhang, H., Garrett, P. M., Houpt, J. W., Lin, P. Y., & Yang, C. T. (2023). Chinese holistic processing: Evidence from cognitive mental architecture using Systems Factorial Technology. Heliyon.

Research Focus:

Cognitive Psychology, Decision Making, Mathematical Modeling

Scholarly Overview:

Professor Fifić's research has primarily focused on refining a highly diagnostic and advanced approach for unraveling mental architecture, termed Systems Factorial Technology (SFT). While serving as a research scientist at the Center for Adaptive Behavior and Cognition within the Max Planck Institute for Human Development, their efforts have aimed to apply process-tracing techniques to complex decision-making domains. This work encompasses validation, theoretical enhancement, expansion, and the broader application of SFT.

In the summer of 2020, a paper co-authored by Mario and frequent collaborator Cheng-Ta Yang received the R. Duncan Luce Outstanding Paper Award for its exceptional contribution to the field of Mathematical Psychology, as published in the Journal of Mathematical Psychology. 

Recent Publications:

Fific, M., Little, D. R., & Yang, C. T. (2023). Modular Serial-Parallel Network for Hierarchical Facial Representations.

Yang, C. T., Zhu, P. F., Zhang, H., Hsieh, C. J., & Fifić, M. (2021, July). Task difficulty and task rule affect the group decision efficiency. In 2021 Meeting of the Society for Mathematical Psychology, Virtual.

Hsieh, C. J., Fifić, M., & Yang, C. T. (2020). A new measure of group decision-making efficiency. Cognitive research: principles and implications, 5, 1-23.