Exploring the effects of COVID-19 restrictions on wellbeing across different styles of lockdown.

Countries have instigated different restrictions in response to the COVID-19 pandemic. For instance, nationwide, strict "lockdown" in Scotland was enacted with breaches punishable by law, whereas restrictions in Japan allowed for travel and interaction, with citizens requested rather than required to conform. We explored the impact of these differential strategies on health behaviours and wellbeing. In February 2021, 138 Scottish and 139 Japanese participants reported their demographic information, pandemic-induced health behaviour-change (alcohol consumption, diet, perceived sleep quality, physical activity), negative mood, and perceived social isolation. Scottish participants' health behaviours were characterised by greater change (typically negative), most likely due to greater lifestyle disruption, whereas Japanese participants' behaviours were more-stable. Negative changes to health behaviours were typically associated with poorer mental wellbeing and isolation. Interestingly though, Japanese participants reported greater negative mood but not isolation despite the less-restrictive lockdown. Taken together, different lockdown styles led to different changes in health behaviours.

The processing of good-fit semantic anomalies: an ERP investigation.

The failure-to-detect good-fit semantic anomalies is taken as evidence for shallow semantic processing, however the cognitive mechanisms involved are not well understood. To investigate this we recorded event-related potentials (ERPs) to sentences that contained good and poor-fit semantic anomalies and non-anomalous controls. Detected good-fit anomalies elicited an N400 effect when detection accuracy was stressed, indicating the registration of the anomaly. ERP analyses further ruled out that anomaly non-/detection is due to differences in initial word encoding or in processing prior contextual information. In addition, starting in the P2 interval, the ERP waveform was less positive for non-detected than detected anomalies and non-anomalous controls, presumably reflecting a language-driven modulation of visual input processing. And finally, detection of good-fit anomalies may also depend on the integration of sentential information into the discourse model at the end of the critical sentence. Overall, present findings support the shallow processing account of anomaly detection failure.