Using computational modelling to understand the effects of pharmacological manipulations on cognitive function
Lead Research Organisation:
University College London
Department Name: Institute of Cognitive Neuroscience
Abstract
This collaborative project is a partnership between Prof Jonathan Roiser (UCL Institute of Cognitive Neuroscience) and Cambridge Cognition Ltd. Prof Roiser's group focuses on understanding the cognitive and neural mechanisms underpinning mental health problems, especially depression. Over the past 5 years his work increasingly adopted a computational approach, using generative models to better understand patterns of behaviour (and brain responses) observed when human participants perform cognitive tasks.
Cambridge Cognition is a world leader in developing computerised cognitive testing, and it sells the well-known Cambridge Neuropsychological Test Automated Battery (CANTAB) software. It develops, often in partnership with academics, computerised tests that allow the measurement of specific domains of cognition, for example executive function, memory, attention and decision making. The tests it develops are based on a wealth of neuroscientific data from humans and animals.
The analysis of data derived from computerised cognitive tests is an active area of development, and over the past decade it has become increasingly common to use a computational approach to create parameters that summarise specific cognitive processes. This approach involves specifying, in mathematical form, precisely how the experimenter believes that the participant is completing the task. This is known as a "generative model" (because, given the same task as the human would perform, the model can generate behavioural responses). The behaviour of the model is governed by specific parameters, which can be interpreted in a cognitive framework, and by fitting the model to data collected in human participants it is possible to estimate parameters. These parameters then serve as summary statistics, in contrast to the traditional descriptive approach to data analysis, which usually involves simply calculating means or differences between conditions. The major advantage of the computational approach is that it can capitalise on the richness inherent in the data, which is usually overlooked in the traditional approach (for example, processes that evolve gradually over time which are difficult to capture using a purely descriptive approach).
A major aim of this project is to develop computational models for CANTAB tests, which are currently analysed using traditional descriptive approaches. Cambridge Cognition owns a wealth of data collected in the general population (both face-to-face and using its online platform) that can be used for this purpose. Following the development of the models, experiments will be conducted to understand how the model parameters are influenced by symptoms of mental health problems (especially depression), and by pharmacological interventions, particularly with cognitive enhancing drugs.
Cambridge Cognition is a world leader in developing computerised cognitive testing, and it sells the well-known Cambridge Neuropsychological Test Automated Battery (CANTAB) software. It develops, often in partnership with academics, computerised tests that allow the measurement of specific domains of cognition, for example executive function, memory, attention and decision making. The tests it develops are based on a wealth of neuroscientific data from humans and animals.
The analysis of data derived from computerised cognitive tests is an active area of development, and over the past decade it has become increasingly common to use a computational approach to create parameters that summarise specific cognitive processes. This approach involves specifying, in mathematical form, precisely how the experimenter believes that the participant is completing the task. This is known as a "generative model" (because, given the same task as the human would perform, the model can generate behavioural responses). The behaviour of the model is governed by specific parameters, which can be interpreted in a cognitive framework, and by fitting the model to data collected in human participants it is possible to estimate parameters. These parameters then serve as summary statistics, in contrast to the traditional descriptive approach to data analysis, which usually involves simply calculating means or differences between conditions. The major advantage of the computational approach is that it can capitalise on the richness inherent in the data, which is usually overlooked in the traditional approach (for example, processes that evolve gradually over time which are difficult to capture using a purely descriptive approach).
A major aim of this project is to develop computational models for CANTAB tests, which are currently analysed using traditional descriptive approaches. Cambridge Cognition owns a wealth of data collected in the general population (both face-to-face and using its online platform) that can be used for this purpose. Following the development of the models, experiments will be conducted to understand how the model parameters are influenced by symptoms of mental health problems (especially depression), and by pharmacological interventions, particularly with cognitive enhancing drugs.