The complexity of behaviour in relation to health, safety and sustainability: A psychological network approach

Understanding behaviour in relation to health, safety and sustainability is a complex challenge that benefits from a broad and encompassing approach. An approach that allows for adopting a complexity perspective in empirical research is that of psychological networks. This dissertation evaluates how adopting such an approach in empirical research improves the understanding of behaviour in relation to issues in health, safety and sustainability. It does so through six empirical case studies that cover these domains via the topics of plastic medical devices, the COVID-19 pandemic and risk perceptions of the process industries. The empirical chapters show that different issues show different network structures in which different determinants are relatively important for the behaviour of interest. This result suggests that a psychological network approach provides in-depth insight into the interplay and mutual dependence of behavioural determinants specific to the issue. Such tailored insights are important as they can improve the effectiveness of behavioural interventions in the context of specific issues. Moreover, this dissertation provides recommendations for researchers who want to employ a psychological network approach in the format of a practical guideline. Adopting a psychological network approach in accordance with the guideline in this dissertation will likely further advance research into behaviour in relation to issues in health, safety and sustainability and enables researchers to integrate these domains. To conclude, this dissertation contributes to the scientific debate on the added value of a psychological network approach by providing an applied perspective based on empirical social psychological research

The effect of embodying the elderly on time perception

The present study investigated the perception of stimulus durations represented by elderly faces or by young faces. In a temporal bisection task, participants classified intermediate durations as more similar to a short or a long reference duration. The results showed that the durations represented by elderly faces were less often classified as "long" than the durations represented by young faces. According to internal clock models of time perception, this shortening effect is due to a slowing down of the speed of the internal clock during the perception of elderly faces. Analyses also revealed an interaction between sex of face and sex of participant such that this shortening effect occurred only when the participants share the same sex than the stimulus faces. As discussed, this finding is quite consistent with embodied cognition approaches to information processing, but alternatives accounts are also considered

A Psychological Network Approach to Attitudes and Preventive Behaviors During Pandemics: A COVID-19 Study in the United Kingdom and the Netherlands

Preventive behaviors are crucial to prevent the spread of the coronavirus causing COVID-19. We adopted a complex psychological systems approach to obtain a descriptive account of the network of attitudes and behaviors related to COVID-19. A survey study (N = 1,022) was conducted with subsamples from the United Kingdom (n = 502) and the Netherlands (n = 520). The results highlight the importance of people’s support for, and perceived efficacy of, the measures and preventive behaviors. This also applies to the perceived norm of family and friends adopting these behaviors. The networks in both countries were largely similar but also showed notable differences. The interplay of psychological factors in the networks is also highlighted, resulting in our appeal to policy makers to take complexity and mutual dependence of psychological factors into account. Future research should study the effects of interventions aimed at these factors, including effects on the network, to make causal inferences

Characterization of Multiple Groups of Data

In this paper we propose a new approach for computing characterizations of sets of data by means of partially defined Boolean functions. The main objective is to provide minimal sets of characters that allows the user to discriminate groups of Boolean data representing individuals described by means of presence or absence of characters. Compared to previous approaches, our algorithms are more efficient and are able to compute complete sets of solutions, which may be useful according to our underlying application domain in plant biology

Information about action outcomes differentially affects learning from self-determined versus imposed choices

The valence of new information influences learning rates in humans: good news tends to receive more weight than bad news. We investigated this learning bias in four experiments, by systematically manipulating the source of required action (free versus forced choices), outcome contingencies (low versus high reward) and motor requirements (go versus no-go choices). Analysis of model-estimated learning rates showed that the confirmation bias in learning rates was specific to free choices, but was independent of outcome contingencies. The bias was also unaffected by the motor requirements, thus suggesting that it operates in the representational space of decisions, rather than motoric actions. Finally, model simulations revealed that learning rates estimated from the choice-confirmation model had the effect of maximizing performance across low- and high-reward environments. We therefore suggest that choice-confirmation bias may be adaptive for efficient learning of action–outcome contingencies, above and beyond fostering person-level dispositions such as self-esteem