David Peebles

Publications

A chronological list of journal articles, conference papers, chapters and research reports. Recent stable DOI and open-access links are included where available. See also ORCID and Google Scholar.

2026

  • Journal article. Nami, M., Peebles, D., Thabtah, F., & Kamalov, F. (2026). RDoC-informed explainable AI as a paradigm for multilevel Alzheimer’s disease diagnosis and progression prediction: A systematic review. Brain Informatics, 13, Article 28. [DOI] [PDF]
  • Journal article. Peebles, D., & Street, C. N. H. (2026). Developing a cognitively plausible model of lie-truth judgements: An adaptive lie detector account. Cognitive Systems Research, 96, Article 101434. [DOI] [PDF]
  • Chapter / reference work. Cooper, R. P., Peebles, D., & Frank, M. C. (2026). Levels of Analysis. In M. C. Frank & A. Majid (Eds.), The Open Encyclopedia of Cognitive Science. MIT Press. [DOI]
  • Preprint. Kamalov, F., Thabtah, F., Peebles, D., & Ibrahim, A. (2026). Selectively Augmented Decision Tree for Explainable Dementia Detection. medRxiv. [Preprint]

2025

  • Journal article. Thabtah, F., & Peebles, D. (2025). Cognitive feature evaluation for disease progression in dementia and its precursors using feature selection. Health and Technology, 15(6), 1075–1086. [DOI] [PDF]

2024

  • Journal article. Phillips, C., Peebles, D., & Wilmott, D. (2024). Emotional versus neutral trial language on mock jury recall, moral disengagement and verdict harshness ratings within an acquaintance rape trial. Polish Psychological Bulletin, vol. 55 138–149. [PDF]
  • Conference paper. Peebles, D. (2024). Predicting learning and retention in a complex task. Proceedings of the 22nd Annual Conference on Cognitive Modelling. July 19–22, Tilburg, The Netherlands. [PDF]
  • Conference paper. Peebles, D., & Street, C. N. H. (2024). Combining individuating and context-general cues in lie detection. Proceedings of the 46th Annual Meeting of the Cognitive Science Society. July 24–27, Rotterdam, The Netherlands. [PDF]

2023

  • Journal article. Thabtah, F., & Peebles, D. (2023). Assessment for Alzheimer’s disease advancement using classification models with rules. Applied Sciences. 13(22):12152. [PDF]
  • Conference paper. Peebles, D. (2023). The effective communication of risk: Insights from cognitive science. Proceedings of the United Kingdom Alliance for Disaster Research Annual Conference. December 18–19, University of Huddersfield, UK.
  • Report. Teal, J., Buontempo, M., Kusev, P., Peebles, D., & Ngo, B. (2023). Democratising professional bra-fitting using Artificial Intelligence. Report for the Future Fashion Factory – Digitally Enabled Design & Manufacture of Designer Products for Circular Economies research programme.
  • Preprint. Jazzaa, H., McCluskey, T., & Peebles, D. (2023). Improving task planning knowledge robustness for autonomous robots. SSRN. [Preprint]

2022

  • Journal article. Thabtah, F., Ong, S., & Peebles, D. (2022). Detection of dementia progression from functional activities data using machine learning techniques. Intelligent Decision Technologies, 16(3), pp. 615–630. [PDF]
  • Journal article. Thabtah, F., Ong, S., & Peebles, D. (2022). Examining cognitive factors for Alzheimer’s disease progression using computational intelligence. Healthcare. 10(10), p. 2045. [PDF]
  • Journal article. Thabtah, F., Spencer, R., & Peebles, D. (2022). Common dementia screening procedures: DSM-5 fulfilment and mapping to cognitive domains. International Journal of Behavioural and Healthcare Research (IJBHR), 8, 1/2, pp. 104–120.
  • Conference paper. Jazzaa, H., McCluskey, T., & Peebles, D. (2022). Improving task planning knowledge robustness for autonomous robots. The 10th ICAPS Workshop on Planning and Robotics, PlanRob 2022. [PDF]
  • Conference paper. Teal, J., Kusev, P., Peebles, D., Vukadinova, S., Buontempo, M., Martin, R., Trang Ngo, B., (2022). How perceived privacy risk determines people’s willingness to use online fashion technologies. In Abstracts of the Psychonomic Society, Vol 27. 63rd Annual Meeting of the Psychonomic Society. Boston, USA.
  • Report. Peebles, D. (2022). Testing the Predictive Performance Equation: An Experimental Study. Report for Defence Science and Technology Laboratory, Future Workforce and Human Performance (FWaHP) research programme, Ministry of Defence.

2021

  • Journal article. Mathew, R. K.; Immersive Healthcare Collaboration; Mushtaq, F. (2021). Three principles for the progress of immersive technologies in healthcare training and education. BMJ Simul Technol Enhanc Learn. 25;7(5):459–460.
  • Report. The Immersive Healthcare Collaboration. (2021). Immersive Technology in Healthcare Training & Education: Three Principles for Progress. Report published by the Centre for Immersive Technologies, University of Leeds, UK.

2020

  • Journal article. Abdelhamid, N., Padmavathy, A., Peebles, D., Thabtah, F., & Goulder-Horobin, D. (2020). Data imbalance in autism pre-diagnosis classification systems: An experimental study. Journal of Information & Knowledge Management, 19(1).
  • Journal article. Thabtah, F., Mampusti, E., Peebles, D., Herradura, R., & Varghese, J. (2020). A mobile-based screening system for data analyses of early dementia traits detection. Journal of Medical Systems, 44(1), 24.
  • Journal article. Thabtah, F., Peebles, D., Retzler, J., & Hathurusingha, C. (2020). A review of dementia screening tools based on Mobile application. Health and Technology.
  • Journal article. Thabtah, F., Peebles, D., Retzler, J., & Hathurusingha, C. (2020). Dementia medical screening using mobile applications: A systematic review with a new mapping model. Journal of Biomedical Informatics. 111, 103573.
  • Conference paper. Jazzaa, H., McCluskey, T., & Peebles, D. (2020). Reasoning by anomaly detection to improve planning robustness for autonomous robots in changing environments. The 35th Workshop of the UK Planning & Scheduling Special Interest Group, PlanSIG 2020. [PDF]
  • Report. Peebles, D. (2020). Personalised Training Using the Predictive Performance Equation. Report for Defence Science and Technology Laboratory, Future Workforce and Human Performance (FWaHP) research programme, Ministry of Defence.

2019

  • Journal article. Thabtah, F. & Peebles, D. (2019). A new machine learning model based on induction rules for autism detection. Health Informatics Journal.
  • Journal article. Thabtah, F. & Peebles, D. (2019). Early autism screening: A comprehensive review. International Journal of Environmental Research and Public Health.
  • Journal article. Thabtah, F., Abdelhamid, N., & Peebles, D. (2019). A machine learning autism classification based on logistic regression analysis. Health Information Science and Systems.
  • Conference paper. Peebles, D. (2019). Modelling alternative strategies for mental rotation. In T. C. Stewart (Ed), Proceedings of the 17th International Conference on Cognitive Modelling. Montreal, Canada. [PDF]
  • Conference paper. Peebles, D. (2019). Modelling mental imagery in the ACT-R cognitive architecture. In A. Goel, C. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Meeting of the Cognitive Science Society. Montreal, Canada. [PDF]

2018

  • Journal article. Pulijala, Y., Ma, M., Pears, M., Peebles, D., Ayoub, A. (2018). An innovative virtual reality training tool for orthognathic surgery. International Journal of Oral & Maxillofacial Surgery. [PDF]
  • Conference paper. Ali, N. & Peebles, D. (2018). The effect of graphical format and instruction on the interpretation of three-variable bar and line graphs. In P. Chapman, G. Stapleton, A. Moktefi, S. Perez-Kriz, and F. Bellucci (Eds.), Proceedings of the 10th International Conference on the Theory and Application of Diagrams, June 18th–22nd, 2018.

2017

  • Journal article. Cooper, R. P., & Peebles, D. (2017). On the Relation Between Marr’s Levels: A Response to Blokpoel. Topics in Cognitive Science. 1–5. [PDF]
  • Journal article. Pulijala, Y., Ma, M., Pears, M., Peebles, D., Ayoub, A. (2017). Effectiveness of immersive virtual reality in surgical training—A randomized control trial. Journal of Oral and Maxillofacial Surgery. [PDF]
  • Journal article. Ward, P., Hoffman, R. R., Conway, G. E., Schraagen, J. M., Peebles, D., Hutton, R. J. B., & Petushek, E. J. (2017). Editorial: Macrocognition: The Science and Engineering of Sociotechnical Work Systems. Frontiers in Psychology.
  • Conference paper. Peebles, D., & Cheng, P. C.-H. (2017). Multiple Representations in Cognitive Architectures. AAAI Fall Symposium 2017: “A Standard Model of the Mind”, Washington, USA, November 9–11. [PDF]
  • Report. Peebles, D. & McCluskey, T. L. (2017). Autonomous Agents. Report for Defence Science and Technology Laboratory, Defence and Security Analysis Division, Ministry of Defence.

2016

  • Conference paper. Peebles, D. (2016). Two methods for search and optimising cognitive model parameters. In D. Reitter & F. E. Ritter (Eds.), Proceedings of the 14th International Conference on Cognitive Modeling (pp. 234–235). University Park, PA: Penn State.

2015

  • Journal article. Cooper, R. P., & Peebles, D. (2015). Beyond single-level accounts: The role of cognitive architectures in cognitive scientific explanation. Topics in Cognitive Science, 7, 243-258. [PDF]
  • Journal article. Peebles, D., & Ali, N. (2015). Expert interpretation of bar and line graphs: The role of graphicacy in reducing the effect of graph format. Frontiers in Psychology, 6:1673. DOI: 10.3389/fpsyg.2015.01673. [PDF]
  • Journal article. Peebles, D., & Cooper, R. P. (2015). Thirty years after Marr’s Vision: Levels of analysis in cognitive science. Topics in Cognitive Science, 7, 187-190. [PDF]

2014

  • Conference paper. Peebles, D. & Jones, C. (2014). A model of object location memory. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (pp. 2747–2752). Austin, TX: Cognitive Science Society.
  • Conference paper. Peebles, D., & Ali, N. (2014). A cognitive architecture-based modelling approach to understanding biases in visualisation behaviour. DECISIVe: Dealing with Cognitive Biases in Visualisations workshop. Proceedings of the IEEE Visualisation Conference (VIS). November 9–14, Paris.
  • Report. Peebles, D. & Ramduny-Ellis, D. (2014). Computational modelling of human performance with unmanned autonomous systems using the ACT-R cognitive architecture. Report for Defence Science and Technology Laboratory funded ‘Autonomous Systems Underpinning Research’ (ASUR) programme, Ministry of Defence.

2013

  • Journal article. Ali, N. & Peebles, D. (2013). The effect of Gestalt laws of perceptual organisation on the comprehension of three-variable bar and linegraphs, Human Factors.
  • Journal article. Peebles, D. (2013). Strategy and pattern recognition in expert comprehension of 2 × 2 interaction graphs. Cognitive Systems Research, 24, 43–51.
  • Journal article. van Rijn, H., Rußwinkel, N., & Peebles, D. (2013). Editorial. Cognitive Systems Research. 24. 1.
  • Conference paper. Ali, N. & Peebles, D. (2013). Reactivity effects of concurrent verbalisation during a graph comprehension task. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 1720–1725). Austin, TX: Cognitive Science Society.

2012

  • Conference paper. Bonner, J., Ramduny-Ellis, D., & Peebles, D. (2012). Making audience experiences more meaningful and emotionally engaging through mixed visual and audio media. Electronic Visualisation and the Arts (EVA 2012), London, UK. British Computer Society, London.
  • Conference paper. Peebles, D. (2012). A cognitive architecture-based model of graph comprehension. In N. Rußwinkel, U. Drewitz, J. Dzaack, & H. van Rijn, Proceedings of the 11th International Conference on Cognitive Modeling, Berlin, Germany.

2011

  • Journal article. Peebles, D., Cooper, R. P., & Howes, A. (2011). Editorial. Cognitive Systems Research. 12. 83.
  • Chapter / reference work. Ali, A., Ingleby, M., & Peebles, D. (2011). Anglophone perceptions of Arabic syllable structure. In C. E. Cairns and E. Raimy (Eds.) Handbook of the Syllable, 329–349. Leiden, The Netherlands: Brill.
  • Conference paper. Ali, N. & Peebles, D. (2011). The different effects of thinking aloud and writing on graph comprehension. In L. Carlson, C. Holscher, & T. Shipley (Eds.). Proceedings of the 33rd Annual Conference of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum.
  • Report. Peebles, D. (2011). The effect of graphical format and instruction on the interpretation of three-variable bar and line graphs. Report for the Higher Education Academy Psychology Network.

2010

  • Journal article. Davies, C. & Peebles, D. (2010). Spaces or scenes: Map-based orientation in urban environments. Spatial Cognition and Computation, 10, 135–156.
  • Journal article. Peebles, D. & Banks, A. P. (2010). Modelling dynamic decision making with the ACT-R cognitive architecture. Journal of Artificial General Intelligence, 2(2), 52–68.

2009

  • Conference paper. Howes, A., Peebles, D. & Cooper, R. P. (Eds). (2009). Proceedings of the 9th International Conference on Cognitive Modeling – ICCM2009. Manchester, UK.
  • Conference paper. Peebles, D. & Ali, N. (2009). Differences in comprehensibility between three-variable bar and line graphs. In N. Taatgen, H. van Rijn, J. Nerbonne & L. Schomaker (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum.

2008

  • Journal article. Peebles, D. (2008). The effect of emergent features on judgments of quantity in configural and separable displays. Journal of Experimental Psychology: Applied. 14, 85–100.
  • Chapter / reference work. Cox, A. L. & Peebles, D. (2008). Cognitive Modelling in HCI Research. In P. A. Cairns, & A. L. Cox. Research Methods for Human Computer Interaction. Cambridge. Cambridge University Press.

2007

  • Journal article. Ropar, D., & Peebles, D. (2007). Sorting preference in children with autism: The dominance of concrete features. Journal of Autism and Developmental Disorders, 37, 270–280.
  • Conference paper. Davies, C., & Peebles, D. (2007). Strategies for orientation: The role of 3D landmark salience and map alignment. In D. McNamara & G. Trafton (Eds.), Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum.
  • Conference paper. Peebles, D., Davies, C., & Mora, R. (2007). Effects of geometry, landmarks and orientation strategies in the ‘drop-off’ orientation task. In S. Winter, M. Duckham, L. Kulik, & B. Kuipers (Eds.), Spatial Information Theory. Springer.

2006

  • Chapter / reference work. Peebles, D., & Cox, A, L. (2006). Modelling interactive behaviour with a rational cognitive architecture. In P. Zaphiris, & S. Kurniawan (Eds.), Human Computer Interaction Research in Web Design and Evaluation. London. Idea Group Inc. Reprinted in E. Szewczak. (Ed.), (2008). Selected Readings on the Human Side of Information Technology. IGI Global.
  • Conference paper. Davies, C., Mora, R. & Peebles, D. (2006). Isovists for Orientation: Can space syntax help us predict directional confusion? Proceedings of the ‘Space Syntax and Spatial Cognition’ workshop, Spatial Cognition 2006, Bremen, Germany, 24 September, 2006.

2004

  • Conference paper. Peebles, D. (2004). Distortions of perceptual judgement in diagrammatic representations. In K. Forbus, D. Gentner & T. Regier (Eds.), Proceedings of the 26th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum.
  • Conference paper. Peebles, D., & Bothell, D. (2004). Modelling performance in the Sustained Attention to Response Task. In M. Lovett, C. D. Schunn, C. Lebiere & P. Munro (Eds.), Proceedings of the 6th International Conference on Cognitive Modeling. Mahwah, NJ: Lawrence Erlbaum.

2003

  • Journal article. Peebles, D., & Cheng, P. C.-H. (2003). Modeling the effect of task and graphical representation on response latency in a graph reading task. Human Factors, 45, 28–46.

2002

  • Journal article. Peebles, D., & Cheng, P. C.-H. (2002). Extending task analytic models of graph-based reasoning: A cognitive model of problem solving with Cartesian graphs in ACT-R/PM. Cognitive Systems Research, 3, 77–86.

2001

  • Conference paper. Peebles, D., & Cheng, P. C.-H. (2001). Extending task analytic models of graph-based reasoning: A cognitive model of problem solving with Cartesian graphs in ACT-R/PM. In E. M. Altmann, A. Cleermans, C. D. Schunn & W. D. Gray (Eds.), Proceedings of the Fourth International Conference on Cognitive Modeling.
  • Conference paper. Peebles, D., & Cheng, P. C.-H. (2001). Graph-based reasoning: From task analysis to cognitive explanation. In J. D. Moore & K. Stenning (Eds.), Proceedings of the Twenty Third Annual Conference of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum.

2000

  • Journal article. Peebles, D. (2000). Review of The MIT Encyclopedia of the Cognitive Sciences. Perception, 29 (5), 628–629.

1999

  • Conference paper. Cupit, J., Shadbolt, N., Cheng, P. C.-H., & Peebles, D. (1999). Compiling ontologies into structured views and interviews: The design of a graph drawing tool for knowledge elicitation. Twelfth Workshop on Knowledge Acquisition, Modelling and Management, Banff, Alberta, Canada (KAW’99).
  • Conference paper. Peebles, D. & Lamberts, K. (1999). A connectionist model of categorisation response times. In D. Heinke, G. W. Humphreys, & A. Olson, (Eds.), Connectionist Models in Cognitive Neuroscience. London, Springer.
  • Conference paper. Peebles, D., Cheng, P. C.-H., & Shadbolt, N. R. (1999). Multiple processes in graph-based reasoning. In M. Hahn, & S. C. Stoness (Eds.), Proceedings of the Twenty First Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Lawrence Erlbaum.