Skip to main content Skip to navigation

Developing and testing computational models of human cognitive abilities using EEG data: A case study in complex visual scene analysis

Primary Supervisor: Dr Dietmar Heinke, School of Psychology

Secondary supervisor: Professor Howard Bowman

PhD project title: Developing and testing computational models of human cognitive abilities using EEG data: A case study in complex visual scene analysis

University of Registration: University of Birmingham

Project outline:

Contemporary EEG techniques focus on a range of statistical methods, such as fitting general linear models, correlations, time-frequency analysis, dynamic causal models, etc. However, there has been less work on connecting EEG signals directly to computational models of cognitive abilities. Such models are similar to methods in artificial intelligence (AI) which aim at mimicking human cognitive abilities (e.g. playing and winning the ancient board game, GO). The aim of this PhD project is to develop a novel method that tests such computational models by benchmarking them against EEG signals. In other words, this novel method will allow us to establish more directly links between human cognition and EEG signals than contemporary EEG methods. Consequently, we will be able to advance our understanding of neural mechanisms underlying cognitive abilities. Note that even though this project preliminary focuses on humans the novel method can be easily applied to research into animal cognition.

As a test case for the new method the project will use the human visual system; to be more specific, the amazing ability of the human visual system to find and recognize behaviourally relevant objects in our complex visual environment. Even though this capacity has been researched for decades it is still far from being understood and it is still a hot topic in ongoing research. In controlled experimental settings the visual system is examined by means of visual search tasks (e.g. Abadi, et al., 2019; Heinke & Backhaus, 2011; Lin et al., 2015; Narbutas et al., 2017). In a search task, participants are asked to indicate the presence of a certain object among irrelevant objects on a screen by pressing a key. Heinke and Backhaus (2011) have developed a highly successful computational model of this visual search experiment. Similar to AI approaches the model takes a functional approach to modelling visual search. However, in contrast to current AI methods it utilizes mathematical descriptions of biophysical processes in neurons. It is therefore particularly well suited for comparing it to EEG signals. As a starting point to implement this comparison we will use recent advances with evolutionary algorithms and Bayesian model fitting.

The PhD project will be highly interdisciplinary. Not only will it require the postgraduate researcher to implement and test novel computational/mathematical methods but also conduct experiments with human participants, including EEG experiments. Therefore applicants should have a strong background in computer science, artificial intelligence or statistics, but also be prepared to conduct studies with human participants.


  1. Abadi, A. K., Yahya, K., Amini, M., Friston, K., & Heinke, D. (2019). Excitatory versus inhibitory feedback in Bayesian formulations of scene construction. Journal of The Royal Society Interface , 16(154),
  2. Heinke, D. & Backhaus, A. (2011) Modeling visual search with the Selective Attention for Identification model (VS-SAIM): A novel explanation for visual search asymmetries. Cognitive Computation, 3(1), 185-205.
  3. Lin, Y., Heinke, D. & Humphreys, G. W. (2015) Modeling visual search using three-parameter probability functions in a hierarchical Bayesian framework. Attention, Perception, & Psychophysics, 77, 3, 985-1010.
  4. Narbutas, V., Lin, Y.-S., Kristan, M., & Heinke, D. (2017) Serial versus parallel search: A model comparison approach based on reaction time distributions. Visual Cognition, 1-3, 306-325.

BBSRC Strategic Research Priority: Understanding the Rules of Life: Neuroscience and Behaviour

Techniques that will be undertaken during the project:

  • Artificial intelligence
  • Computational modelling
  • Behavioural experiments
  • EEG experiments
  • Bayesian modelling

Contact: Dr Dietmar Heinke, University of Birmingham