Ryan Blything | Research Associate
Office 3D3 | +44 (0) 117 3317895 | ryan.blything [at]
Research Interest | My research focuses on the ability to generalize existing knowledge to new situations. For example, if we learn a new word, we must be able to generalize its use to a range of morphologically-inflected forms, even if we have never heard the new word used in that way before (e.g., we can spontaneously add -ed to a new verb to signal its past-tense form; text→texted). As part of the ERC project, I investigate generalization in a range of domains, including visual word recognition, spoken word recognition, and visual object recognition. The project investigates what representations underlie the human brain’s ability to generalize and the extent to which these representations support generalization. I will relate these findings to predictions made by symbolic and non-symbolic neural networks with a view to examining whether symbolic representations are required for human-like generalization.

Prof. Jeff Bowers | Professor
Office 3D22 | +44 (0) 117 928 8573 | j.bowers [at]
Research Interest | My research addresses a range of issues in language and memory. In one line of work I have attempted to gain insight into how word knowledge is coded in the brain. On one general view, word knowledge (and indeed all forms of knowledge) is coded in a distributed (and non-symbolic) manner, such that a word is coded as a pattern of activation across a set of units (neurons), with no one unit devoted to a single letter or word (typically associated with the PDP approach). On another view, word knowledge is coded in a localist (and symbolic) manner, with each letter and word uniquely coded by an individual unit. I’ve carried out a series of behavioral experiments that provide evidence that letters and words are coded in a localist and symbolic manner (e.g., Davis & Bowers, 2005, 2006), as well as some computer simulations that support this conclusion (Bowers, Damian, & Davis, in press, Psychological Review, Bowers & Davis, 2009). I’ve also argued that localist models are more biological plausible than the distributed representations learned in PDP networks (Bowers, 2009).

Dr Danielle Colenbrander | Postdoctoral Research Associate
Office 1D19 | +44 (0) 117 3310541
d.colenbrander [at] 

Research Interest | I am interested in the development of reading and oral language abilities and how impairments in these areas can be treated. In particular, I am interested in relationships between vocabulary knowledge, reading aloud and reading comprehension. I completed a Masters of Speech Language Pathology at Macquarie University, Australia. After my Masters, I worked as a project coordinator and was responsible for measuring the effectiveness of reading intervention programmes delivered to indigenous children. I then completed my PhD at Macquarie University in the ARC Centre for Excellence in Cognition and its Disorders (CCD). My research explored the role of oral vocabulary in reading comprehension difficulties. I am currently running a randomised controlled trial comparing the effectiveness of a morphology-based intervention programme for reading and spelling difficulties (Structured Word Inquiry) to a programme teaching vocabulary and reading comprehension strategies.

Dr Ella Gale | Research Associate
Office 3D03 | +44 (0) 117 3317892 | ella.gale[at]
Research Interest |  I am interested in the dual questions of how could we build a neuromorphic computer and how does the brain compute. I believe that attempting to copy human intelligence we will learn a lot about the fundamentals of computation. I graduated from Imperial College London with a PhD in computational chemistry where I modelled molecular electronics components for nanotechnology.  I then took a four year post-doc at the University of the West of England working on building unconventional bioinspired computers from memristors, and occasionally, soft matter. After that, I spent a year at Khalifa University modelling spintronic components, before moving to the University of Bath to simulate soft cellulose materials. Currently, I am engaged in building neural networks that can do tasks humans are good at (image recognition, for example) and analyzing how they do it, in order to shed light on how information is represented, passed around and then used for computation in both the mind and our bio-inspired models of it.

Dr Gaurav Malhotra | Research Associate
Office 3D03 | +44 (0) 117 954 6616
Research Interest | My research focuses on understanding the principles underlying cognition. To understand these principles, I develop both normative and mechanistic models of cognition and compare predictions of these models with human behaviour. I have applied this method to investigate how people produce language, how they perceive time, how they make perceptual decisions and how they represent knowledge. As part of the ERC project, I am investigating the similarities and differences between humans and artificial neural networks on their ability to generalise on visual and numerical tasks.

Mr Nick Martin | Postgraduate Student
Office G2, 5 Priory Road | +44 (0) 117 33 10494
Research Interest | I studied for my undergraduate degree in Experimental Psychology at the University of Bristol.  Whilst there I completed a research apprenticeship at Bristol Cognitive Development Centre, working on a project investigating mind-body dualism and afterlife beliefs in children.  I also undertook a vacation scholarship where i developed a social learning task to investigate whether awareness of ‘goal-states’ influences over-imitation in adults.  My thesis focused on orthographic processing, specifically, the role of letter-case in brand name recognition. I am currently completing my PhD at the University of Bristol working on a project that aims to understand and interpret the activity of hidden layer units in neural networks.