Abstract
We used Bayesian cognitive modelling to identify the underlying causes of apparent inhibitory deficits 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 schizophrenia patients and control participants made rapid choice responses, but on some trials were signalled to stop their ongoing response. Previous research has assumed an inhibitory deficit in schizophrenia, because estimates of the mean time taken to react to the stop signal are longer in patients 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 inhibitory deficits. 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.
Highlights
We used Bayesian cognitive modelling to identify the underlying causes of apparent inhibitory deficits in the stop-signal paradigm
Inference about group differences was based on overlap between the posterior distributions using Bayesian p values, the proportion of posterior samples that are lower in the schizophrenia group than in controls; p values close to 0 indicate that the posterior distribution of schizophrenia patients is shifted to higher values, and provide evidence for the presence of a group difference
Σgo and τgo were shifted to higher values for patients relative to controls in both studies, with Bayesian p values ranging between 0.18 and 0.09 in the two data sets. These results provide suggestive evidence that the slowing of mean go reaction time (RT) in schizophrenia is largely attributable to slowing in the tail of the RT distribution, on average by 45 ms in Badcock et al (2002) and 36 ms in Hughes et al (2012)
Summary
We used Bayesian cognitive modelling to identify the underlying causes of apparent inhibitory deficits in the stop-signal paradigm. Previous research has assumed an inhibitory deficit in schizophrenia, because estimates of the mean time taken to react to the stop signal are longer in patients than controls. Correlations between the probability of trigger failures and event-related potentials reported by Hughes et al are interpreted as supporting the attentional account of inhibitory deficits Our results, and those of Matzke et al (2016), who report that controls display a substantial lower trigger-failure rate, Keywords Stop-signal paradigm. A summary measure of inhibitory ability in the form of the mean time for the stop process to reach threshold (i.e., stop-signal RT or SSRT) can be estimated nonparametrically by assuming that go RT distributions for trials with and without stop signal are the same (e.g., Band et al, 2003). Band et al (2003) showed that even appropriately transformed inhibition functions are unable to discriminate between trigger failures and differences in go RT and SSRT variabilty
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