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Daniel Navarro
Lab Director
My research interests are in the fields of computational cognitive science, mathematical psychology and statistics. Among other things, I’m interested in how people acquire new concepts, how knowledge is structured, and how people make use of information to make inductive inferences. Typical research topics include similarity, categorisation and decision-making. Less frequently, my research also covers psychological topics related to language acquisition and visual perception. Other elements to my work are closer to applied statistics than to cognitive science, covering topics related to Bayesian statistics and information theory in particular.
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Amy Perfors
Lab Director
I’m interested many different questions in language acquisition and higher-order cognition. My interests in language acquisition centre on questions of learnability and domain specificity: what biases must children have in order to acquire knowledge in different domains? To what extent are these biases domain-general? What drives the difference in language acquisition abilities between adults and children? Why does language have the structure it does? My interests in other aspects of cognition focus on categorisation and concept learning, especially questions of representation and how people make sensible inductions given sparse or noisy data.
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Natalie May
Lab Manager
I will be completing my Psychology Honours Degree in 2012. My particular interests lie in the area of language, children, and language acquisition. My honours thesis is on children’s tendency to over-regularise inconsistent input. As a lab manager, my duties include organising weekly lab meetings, recruiting participants, administering experiments, and collating data.
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Simon De Deyne
Postdoc
Like many cognitive scientists, I am interested in how the mind structures the world around us. In my case I am particularly interested in how concepts and the meaning of words are represented. Given that most words are acquired from the linguistic context in which they are used, one approach is to see what kind of structure and information is available in the linguistic environment. This often involves the use of computational models that encode the contingencies between words from text which we can access using large-scale corpora.
Apart from figuring out what kind of information is useful for people to understand the meaning of a word or sentence, it’s also important to consider what kind of knowledge representation might be most suitable. This involves questions about how we can efficiently navigate and retrieve this information, but also how we can make sensible inferences about word meaning even if we have encountered a word only a few times. One approach I’ve been exploring the last couple of years is the representation of word meaning in lexico-semantic networks using a large data-set of word associations. This approach might also learn us something about the way our mental lexicon evolves. One way this question is currently addressed is by looking how semantic networks grow and perish over time by comparing networks of very young people to those of aged individuals. In the future, I also hope to investigate cross-cultural differences in the way people represent the meaning of words and how semantic and non-linguistically represented knowledge can be combined
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Rachel Stephens
Graduate Student
My primary research interest is in understanding the bases of inductive inference and feature generalisation. I’m interested in the factors that influence the use of different information to guide inferences, such as perceptual similarity, causal relations, and category labels or knowledge of concepts. Other research interests include applied decision-making and judgements, such as within legal negotiations or eyewitness reports.
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Anastasia Ejova
Graduate Student
My PhD thesis is on the well-known illusion of control phenomenon (Langer, 1975). Following experimental work and some instinct-driven reading on tentatively related subjects such as the evolution of religious beliefs, I have come to the conclusion that the illusion of control is best explored in gambling settings, where a wide range of control-oriented behaviours can emerge. Some of those behaviours might be based on considerations about the ‘physics’ or ‘design’ of the task at hand. Others might be more superstitious in nature, deriving from beliefs about non-physical forces, such as luck. In coming to this conclusion and assessing its implications for research on the illusion of control, I have developed a broad interest in how the structure of background knowledge influences interpretations of the experimental task. In illusion of control studies and real-world gambling settings, the structural feature of interest is the distinction between physical and non-physical forces. Literatures I hope to one day contribute to in investigating this distinction and other aspects of background knowledge have to do with Relevance Theory, hierarchical Bayesian modelling, Bayesian modelling of causal inference, and the philosophical notion of physical, design and intentional stances. Outside of work and a fascination with all manner of superstitions, my interests include tennis, reading and the search for the perfect cafe.
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Belinda Bruza
Graduate Student
I’m interested in the elicitation of uncertainty: procedures that translate beliefs into a probability distribution. Human beings are not rational information processors, thus different elicitation methods can produce very different estimates – even though they actually ask about the same outcome! My research aims to offer better insight into the cognitive processes underlying both the formation and the conversion of knowledge into a probability distribution; with the ultimate goal of uncovering elicitation methods that help mitigate the impact of harmful decision-making biases (e.g., overconfidence).
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Dinis Gökaydin
Graduate Student
My interests are very diverse, and they can be summarised as: I want to know how the brain works. I believe that, despite the enormous diversity of behaviours and faculties that humans exhibit, there are common mechanisms underlying all, or almost all, cognitive, motor and perceptual functions. I am particularly interested in how information is coded in the brain, and how humans detect patterns in their environment. I use both modelling and experimental approaches in my research, but I am mostly driven by the former. I am also interested in pattern formation per se, biological evolution and diversity and epidemiology, among other topics.
My current research focuses on sequential behaviour and, in particular, sequential effects. Sequential effects are defined as a dependency of the response to the current stimulus on the past history of stimuli. For example: when seeing a sequence ABAB, it is natural to expect an A as the next stimuli, because it forms a local pattern. This expectancy can be measured as a faster reaction time to an A, and slower to a B. I use a variety of modelling techniques in order to explain patterns of sequential effects across different tasks, including n-gram models, Bayesian Markov Models and more recently dynamical systems. I have recently become interested in oscillatory mechanisms in the brain and how they could play a role in sequential effects as well as in the coding of information in the brain.
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