Traditional response inhibition tasks are assumed to capture one’s ability to inhibit a response. This ability, however, requires a reactive process and a proactive process, post-error slowing (PES). Recent evidence shows that Stop-Signal Tasks (SSTs) measure the reactive process, and while the Sustained Attention to Response Task measures overall response inhibition, that measure is confounded by a proactive process. Since the diseases associated with response inhibition deficits often co-occur with symptoms that diminish the capacity for lengthy behavioural testing, and, since it is unknown to which process such decrements can be attributed and where in the brain these processes are generated, rapid and precise measurement of reactive and proactive processes is important. To address these issues, we administered a battery of four response inhibition tasks to healthy young adults (N = 123), two SSTs and two Go/No-Go tasks. In three tasks, we implemented adaptations to allow direct observation of proactive inhibition, reactive inhibition, and overall response inhibition. We introduce a novel cueing procedure to investigate the possibility of a predictive mechanism of proactive inhibition, arguing that slower response times on trials with a higher Stop/No-Go probability indicate predictive proactive inhibition. Based on these findings, we propose a novel demarcation to proactive inhibition: remedial proactive inhibition (PES), and predictive proactive inhibition. Additionally, we provide empirical support for a Bayesian adaptive staircase (Livesey & Livesey, 2016) that allows rapid convergence on estimates of reactive inhibition in SSTs in as few as 20 trials that are robust against potential predictive proactive inhibition confounds.
Social engineering cyber-attacks such as phishing emails pose a serious threat to the safety of many organizations. Given that the effectiveness of these attacks heavily relies on poor human decision making, an improved understanding of the individual characteristics that increase cybersecurity vulnerability could inform more targeted training. The current study aimed to identify whether several factors, including phishing email detection ability, confidence in one’s phishing identification decisions, attitudes toward one’s level of responsibility and efficacy, and employee satisfaction and loyalty to the organization, can predict behavior in a naturalistic phishing simulation in an employment setting. We followed up employees of a large organization who had been recently targeted by a phishing simulation and asked them to complete a survey that included a phishing detection task. The employee’s behavior in the phishing simulation was ranked according to its safety: reporting the suspicious email, neither reporting nor clicking on the embedded link, and clicking on the link. We found that fewer years of employment at the organization and lower employee satisfaction and loyalty predicted increasingly unsafe behavior in the simulation. This suggests that newer and unsatisfied employees are most vulnerable to phishing attempts and might benefit most from targeted cybersecurity training.
The slowing down of a response after committing an error in speeded response tasks has been reliably observed over the last 60 years, but no explanation has yet been articulated to account for it. Post-error slowing (PES) is thought to reflect a proactive mechanism to improve one’s chances of successfully inhibiting a response or selecting the correct response from an array of possibilities. Recently, Dutilh and colleagues (2012a) used computational modelling to compare how well several accounts of PES fit real and simulated data. They concluded that PES is the result of participants widening their response boundaries, which they assumed corresponds to increased caution. This explanation supports a proactive account of PES. We used EEG to test the same four accounts modelled by Dutilh and colleagues to provide direct neural evidence to supplement their simulated data. In a Go/NoGo task administered to N = 100 healthy young adults (24.3 ± 4.8 yrs), we mapped ERP parameters to the theoretical drift parameters established by Dutilh and colleagues. Their hypothesis would predict larger N2 after errors and that the amplitude of the N2 should correlate with magnitude of PES. Our results did not support these predictions (N2 amplitude was smaller after errors, p = .015, and there was no correlation between N2 amplitude and PES, p = .523). Our findings support another common account of PES, a disorienting account, that supposes errors disrupt attentional processing. The post-error anterior N1 was significantly disrupted by errors (p = .020) and was correlated with the magnitude of PES (p = .016). We, therefore, suggest that PES is not completely proactive, but rather is partially the consequence of disruptions to attentional processing that only incidentally improve response inhibition by offsetting the initiation of response execution. Interestingly, the post-error N1 in older adults was diminished (p = .0008), but higher general intelligence rescued such disruptions to attention (p < .0001), indicating a partial compensatory mechanism in ageing that is supported by general intelligence.
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