UNASSIGNED: We used Bayesian cognitive modelling to identify the underlying causes of apparent @entity51 in the stop-signal paradigm. The analysis was applied to stop-signal data reported by Badcock et al. (Psychological Medicine 32: 87-297, 2002) and Hughes et al. (Biological Psychology 89: 220-231, 2012), where @entity161 @entity1 and control @entity1 made rapid choice responses, but on some trials were signalled to stop their ongoing response. Previous research has assumed an inhibitory deficit in @entity161 , because estimates of the mean time taken to react to the stop signal are longer in @entity1 than controls. We showed that these longer estimates are partly due to failing to react to the stop signal ("trigger failures") and partly due to a slower initiation of inhibition, implicating a failure of attention rather than a deficit in the inhibitory process itself. Correlations between the probability of trigger failures and event-related potentials reported by Hughes et al. are interpreted as supporting the attentional account of @entity51 . Our results, and those of Matzke et al. (2016), who report that controls also display a substantial although lower trigger-failure rate, indicate that attentional factors need to be taken into account when interpreting results from the stop-signal paradigm.

Failures of cognitive control or attention? The case of @entity51 in XXXX .

@entity1 :: ('9606', 'Species') :: ['patients', 'participants'] @entity51 :: ('MESH:D009461', 'Disease') :: ['inhibitory deficits'] @entity161 :: ('MESH:D012559', 'Disease') :: ['schizophrenia']
@entity161 :: (MESH:D012559,Disease) :: ['schizophrenia']
