6 research outputs found
How does rumination impact cognition? A first mechanistic model.
Rumination is a process of uncontrolled, narrowly-foused neg- ative thinking that is often self-referential, and that is a hall- mark of depression. Despite its importance, little is known about its cognitive mechanisms. Rumination can be thought of as a specific, constrained form of mind-wandering. Here, we introduce a cognitive model of rumination that we devel- oped on the basis of our existing model of mind-wandering. The rumination model implements the hypothesis that rumina- tion is caused by maladaptive habits of thought. These habits of thought are modelled by adjusting the number of memory chunks and their associative structure, which changes the se- quence of memories that are retrieved during mind-wandering, such that during rumination the same set of negative memo- ries is retrieved repeatedly. The implementation of habits of thought was guided by empirical data from an experience sam- pling study in healthy and depressed participants. On the ba- sis of this empirically-derived memory structure, our model naturally predicts the declines in cognitive task performance that are typically observed in depressed patients. This study demonstrates how we can use cognitive models to better un- derstand the cognitive mechanisms underlying rumination and depression
A computational model of focused attention meditation and its transfer to a sustained attention task
A computational model of focused attention meditation and its transfer to a sustained attention task
Shape classification: towards a mathematical description of the face
Recent advances in biostereometric techniques have led to the quick and easy
acquisition of 3D data for facial and other biological surfaces. This has led facial
surgeons to express dissatisfaction with landmark-based methods for analysing the
shape of the face which use only a small part of the data available, and to seek a method
for analysing the face which maximizes the use of this extensive data set. Scientists
working in the field of computer vision have developed a variety of methods for the
analysis and description of 2D and 3D shape. These methods are reviewed and an
approach, based on differential geometry, is selected for the description of facial shape.
For each data point, the Gaussian and mean curvatures of the surface are calculated.
The performance of three algorithms for computing these curvatures are evaluated for
mathematically generated standard 3D objects and for 3D data obtained from an optical
surface scanner. Using the signs of these curvatures, the face is classified into eight
'fundamental surface types' - each of which has an intuitive perceptual meaning. The
robustness of the resulting surface type description to errors in the data is determined
together with its repeatability.
Three methods for comparing two surface type descriptions are presented and illustrated
for average male and average female faces. Thus a quantitative description of facial
change, or differences between individual's faces, is achieved. The possible application
of artificial intelligence techniques to automate this comparison is discussed. The
sensitivity of the description to global and local changes to the data, made by
mathematical functions, is investigated.
Examples are given of the application of this method for describing facial changes
made by facial reconstructive surgery and implications for defining a basis for facial
aesthetics using shape are discussed. It is also applied to investigate the role played by
the shape of the surface in facial recognition