- Agent-based modelling of human social behaviour
- Cognitive Neuroscience with LEGO Mindstorms NXT
- Modelling of EEG-data
- What is wrong with Deep Neural Networks?
- Computational modelling of tool innovation
Agent-based modelling of human social behaviour
An agent-based model (ABM) consists of independent agents interacting with each other and the environment. The behaviour of each agent is defined by a set of rules, only prominent to each agent. This type of model allows the researcher to observe how individual behaviours of agents (microscopic level) result in patterns on the macroscopic level (emergent behaviour). These properties make ABMs an ideal approach to modelling human social behaviour. For instance, the seminal Schelling model showed that social segregation can emerge from a minor preference to live in a neighbourhood populated by a similar social group. In collaborations with my colleagues from the social psychology group I aim to apply ABMs to a broad range of social phenomena, e.g. help seeking of ethnic minorities, social destigmatisation, social housing market, etc.
Dr Dietmar Heinke
EPSRC, ESRC, BBSRC
Cognitive Neuroscience with LEGO Mindstorms NXT
In a recent publication we showed that LEGO Mindstorms NXT can contribute to our understanding of the brain, especially the interactions between visual cognition and motor behaviour. Based on this work numerous projects are possible:
- There is a wealth of data on human reaching and grasping. However, these data have never been modelled in such a framework.
- In recent years, more and more evidence supports the idea that humans utilize feed-forward models (well-known from technical control theory) to guide reaching and grasping. Our Lego robot constitutes an ideal environment to test this theory in a natural set-up.
- Recent progress in developmental psychology shows that toddlers' interactions with the environment foster their learning progression. With the LEGO robot, we can mimic these interactions (developmental robotics) and for instance, explore how the recognition of objects can be learnt in such a scenario.
JAVA, Lego Mindstroms NXT, Image processing.
Modelling of EEG-data
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 little 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 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.
This project will focus on evidence from visual search tasks as test case for this novel approach. Visual search tasks aim to understand how humans analyse complex visual scenes and find behaviourally relevant object in them.
Behavioural experiments, EEG experiments, MatLab, Computational modelling
Dietmar Heinke, Dominic Standage
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. https://doi.org/10.1080/13506285.2017.1352055
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.
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.
What is wrong with Deep Neural Networks?
Deep neural networks (DNNs) have been behind recent headline-grabbing successes for artificial intelligence, such as AlphaGo beating the world's best player in the board game Go. Nevertheless, in many areas DNNs have not lead to technical solutions that match human abilities, especially with regards to object recognition. Perhaps the most obvious reason for such failures stems from the fact that DNNs employ very different processing mechanisms to those found in humans (e.g. Lake et al. 2016).
This project aims to compare DNN’s abilities with human abilities and ascertain how the two differ. Based on this understand we aim to develop novel approaches to automated object recognition.
Lake, B., Ullman, T., Tenenbaum, J., & Gershman, S. (2016). Behavioral and Brain Sciences, 1-101.
Computational modelling of tool innovation
Humans’ ability to use tools has drastically transformed our planet, and is a skill we use in our daily lives. Building autonomous agents that possess this ability is crucial in allowing them to function in the world we live in. Recent developments in machine learning, such as deep reinforcement learning (DRL), has resulted in numerous successes in creating software and robot agents that are able to effectively use man-made tools. However, transferring the ability to innovate, such as inventing new tools or new uses for existing tools, has received relatively little attention so far in artificial intelligence (AI) research.
This project aims to develop an algorithm for inventing tools in the very simple scenario of the LEGO task. The LEGO task uses pairs of novel objects built with LEGO blocks. Participants are asked to determine how one object can transport (either lift or shift) the other object. They had three minutes to complete the task (see https://tinyurl.com/yck2rgfj and https://tinyurl.com/y9ug8snt for two examples of videos. The aim of the project is mimic human behaviour in these tasks.
Osiurak, F. (2014). What neuropsychology tells us about human tool use? The four constraints theory (4CT): mechanics, space, time, and effort. Neuropsychol. Rev., 24 (2), 88-115.
Osiurak, F., & Heinke, D. (2018) Looking for Intoolligence: A unified framework for the cognitive study of human tool use and technology. American Psychologist, 73(2), 169-185. http://dx.doi.org/10.1037/amp0000162.