The use of transcranial magnetic stimulation (TMS) has expanded rapidly since its application became possible in a meaningful way in the experimental psychology/neuroscience environment in the mid-1980s. This reflects this technique allowing the contribution of different brain areas to a range of processes to be investigated by disruption in healthy individuals, rather than requiring a patient with a lesion in an ‘appropriate’ cortical area (either as the result of an accident or surgery) to be available. It is now employed experimentally in a variety of paradigms, including as a ‘virtual-lesion’ technique in cognitive experiments, or to deliver stimulation to investigate the neuro-motor system. It has also been investigated as a potential treatment in neuropsychiatric disorders such as depression and schizophrenia. Despite this, many of the stimulation approaches are not particularly based around knowledge of the effects of TMS on neural systems (but see theta-burst TMS, Huang et al., 2005), meaning the nature of disruption of any stimulated area is unclear. While in many cases this is not important, particularly if the experiment is simply trying to determine whether a brain area is involved in a task (or what the timing of such involvement is), when the nature of the contribution of the area is of interest then interpretation of effects is more speculative. An example of this is in the case of search for a visual target defined by a combination of attributes (conjunction visual search). While several brain areas have been shown to be involved in performing this task, and with different times of involvement, the differences in the nature of their contribution is much less clear. Here it is proposed to investigate TMS effects on task performance in combination with drift diffusion model (DDM) based analysis, which, by analysis of response time and accuracy data, can provide more detailed measures that can be related to neurophysiological data and potentially provide insight into the nature of effects. This project therefore aims to assess whether the effects of TMS on task performance can be more clearly assessed using DDM analysis; to determine if such an approach can inform about any differences in the contribution to different brain areas to performance of tasks where multiple areas have been shown to be involved (such as visual search); to see if this can strengthen (and be strengthened by) combined EEG-TMS investigations; and assess whether such an approach can help with determining whether a TMS-induced effect is cognitive in nature, rather than due to ‘side-effects’, such as discomfort or noise associated with TMS delivery. Should this approach be successful it has the potential to greatly expand the utility of TMS in cognitive investigations without a significant increase in complexity of the experimental design.