Our ability to briefly retain information is often limited. Proactive Interference (PI) might contribute to these limitations (e.g., when it is hard to reject items in a recognition test that have appeared recently). In visual Working Memory (WM), spatial information might protect WM against PI, especially if encoding items together with their spatial locations makes item-location combinations less confusable than simple items without a spatial component. Here, I ask (1) if PI is observed for spatially distributed items, (2) if it arises among simple items or among item-location combinations, and (3) if spatial information affects PI at all. I show that PI is reliably observed for spatially distributed items except when it is weak. PI mostly reflects items that appear recently or frequently as memory items, while occurrences as test items play a smaller role, presumably because their temporal context is easier to encode. Through mathematical modeling, I then show that interference occurs among simple items rather than item-location combinations. Finally, to understand the effects of spatial information, I separate the effects of (a) the presence and (b) the predictiveness of spatial information on memory and its susceptibility to PI. Memory is impaired when items are spatially distributed, but, depending on the analysis, unaffected by the predictiveness of spatial information. In contrast, the susceptibility to PI is unaffected by either manipulation. Visual memory is thus impaired by PI for spatially distributed items due to interference from recent memory items (rather than test items or item-location combinations).
Preexisting conditions affect disease susceptibility. Here, I show that socio-cultural values are population-level risk factors for disease. Using data from the World Values Survey, I show that, between 2 weeks and 6 months after the first COVID-19-related death in a country, COVID-19-related mortality is increased in countries endorsing values related to political participation, but decreased in countries with more trust in institutions and materialistic orientations. After controlling for income, age, urbanicity, smoking, overweight, private health expenditure and lockdown delay, these socio-cultural values were consistent across country-sets, reduced prediction errors by up to 52% and explained up to 68% of inter-country variability. They were relatively specific to COVID-19-related mortality. I could not identify values predicting general health outcomes, and values predicting increased COVID-19-related mortality predicted decreased mortality due to other causes like environmental-related mortality, explaining up to 90% of inter-country variability. Socio-cultural values might be specific predictors of health outcomes.
As simpler scientific theories are preferable to more convoluted ones, it is plausible to assume (and widely assumed, especially in recent Bayesian models of cognition) that biological learners are also guided by simplicity considerations when acquiring mental representations, and that formal measures of complexity might indicate which learning problems are harder and which ones are easier. However, the history of science suggests that simpler scientific theories are not necessarily more useful if more convoluted ones make calculations easier. Here, I suggest that a similar conclusion applies to mental representations. Using case studies from perception, associative learning and rule learning, I show that formal measures of complexity critically depend on assumptions about the underlying representational and processing primitives and are generally unrelated to what is actually easy to learn and process in humans. An empirically viable notion of complexity thus need to take into consideration the representational and processing primitives that are available to actual learners even if this leads to formally complex explanations.
Preexisting conditions affect disease susceptibility. Here, I show that socio-cultural values are population-level risk factors for disease. Using data from the World Values Survey, I show that, between 2 weeks and 6 months after the first COVID-19-related death in a country, COVID-19-related mortality is increased in countries endorsing values related to political participation, but decreased in countries with more trust in institutions and materialistic orientations. After controlling for income, age, urbanicity, smoking, overweight, private health expenditure and lockdown delay, these socio-cultural values were consistent across country-sets, reduced prediction errors by up to 52% and explained up to 68% of inter-country variability. They were relatively specific to COVID-19-related mortality. I could not identify values predicting general health outcomes, and values predicting increased COVID-19-related mortality predicted decreased mortality due to other causes like environmental-related mortality, explaining up to 90% of inter-country variability. Socio-cultural values might be specific predictors of health outcomes.
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