Mr Patrick Craston

Research Student

Photo of P Craston
  • Tel:     +44 (0)1227 82-
  • Fax:     +44 (0)1227 762811
  • Email: pc52@kent.ac.uk
  • Room -
    School of Computing
    University of Kent, CT2 7NF

Publications

My publications are available from the Computer Science department publications repository.

PhD Project Summary

I am interested in how humans allocate attention over time. Although we are constantly processing and at least temporarily storing all information that is transferred from sensory inputs, how are we able to separate the relevant from the irrelevant?

During my Phd, my main focus was on the Attentional Blink phenomenon [Raymond et al., 1992], which seems to reflect some of the underlying principles of how humans distribute attentional resources over time.
This paradigm is typically structured as follows: A person is exposed to a stream of digits, that are presented in the same spatial location and at a very rapid rate (typically 10-20 items/second). There are two target letters in this stream, which are to be reported at the end of the stream. Performance in reporting a second target (T2) is strongly impaired if it follows an identified and reported first target (T1) within a time window of 200-600 ms. Nevertheless, if T2 immediately follows T1 (within 100ms - at lag 1), T2 detection is almost unaffected, which has been called lag 1 sparing. However, in this case T1 detection performance suffers. Therefore, at short lags, there is an accuracy trade-off between detection of the targets, rather than sparing, as usually suggested.

For a simplified demo of a typical Attentional Blink experiment, click here.

The ST2 Model of temporal attentional and working memory, developed by Howard Bowman and Brad Wyble, reproduces the empirical results mentioned above. It is based on the Dual Stage Model by Chun and Potter (1995) and the Types-Tokens Account of Working Memory proposed by Nancy Kanwisher (1987). During my PhD, I tested predictions derived from the ST2 Model using EEG techniques, in particular by means of Event-Related Potential (ERP) and time-frequency analysis. Furthermore, we generated virtual ERPs using the neural network implementation of the ST2 Model and compared these to results from EEG studies.

I contributed to the EPSRC Salience Project.

Thesis

Download my PhD thesis as pdf (12MB)

Recent publications

Miscellaneous

Research Interests

I am a member of the following research groups:

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