Subjects: Psychology >> Developmental Psychology submitted time 2020-11-22
Abstract： Age-related positivity effect refers to the phenomenon that older adults display a preference for positive rather than negative information in cognitive processing. Recent researches in working memory (WM) have found the effect of the interaction between emotional valance and task-relevance on positivity effect. Positivity effect has been observed in WM studies with emotional valence acting as a kind of task-relevant information. For instance, older people have enhanced performance in WM tasks with positive emotional stimuli, and decreased performance on negative emotional stimuli. In contrast, less attention has focused on the area of emotional valence as task-irrelevant information in WM and conflicting findings also have been reported. These remind that both emotional valence and task relevance are critical components in the processing of positivity effect in WM. Preliminary neuroimaging studies have revealed that the associations between age-related functional changes in the dorsal executive system and ventral affective system and the age effect in emotional process of WM. The socioemotional selectivity theory and the dual-competition model have been found to mainly account for age-related positivity effect in WM. But there is a lack of empirical evidence to support the dynamic integration theory. Overall, future studies are warranted in exploring the characteristics of emotional processing in different stages of WM in older adults, clarifying the potential influences of internal encoding processes of emotional materials on the mechanism of positivity effect, uncovering the important neural circuits related to the impact of task-relevance of emotion on positivity effect, as well as revealing the underlying mechanisms and potential benefits of emotional WM training on the improvement of cognitive functions and emotional experience in the elderly.
Peer Review Status:
Awaiting Review
Subjects: Psychology >> Social Psychology submitted time 2018-01-17
Abstract： Understanding the psychological processes and mechanisms behind social behaviors is one of the most important goals of social psychology. Psychologists have proposed many theoretical models to explain people’s social behaviors. It is still, however, difficult to quantify the contribution of hypothesized psychological processes to a specific behaviour. Recently, social psychologist introduced multinomial processing tree (MPT) models to dissociate different processes and quantify the contributions of each hypothesized process to behaviors. MPT, which combined knowledge from cognitive psychology, statistics, and other related disciplines, is a simple and effective way to model behavioural data. In these models, different hypothesized psychological processes take the external stimuli as input and determine the behavioural outcomes in a tree-like manner. More specifically, each stimulus was first processed by a hypothetical psychological process (i.e., a branch with certain probability), which results in a binary outcome (i.e., a point): either a behavioural response (i.e., a resulting behavior), or an intermediate outcome that will be determined further by a downstream psychological process (i.e., another branch, with a different probability) until behavioural outputs were produced. In this way, each behavioural output can be viewed as the combination of the processes before it, while the sum of all the behavioural output to a specific stimulus sum up to one. By fitting the behavioral data to multiple nominal formulas, the probability of each psychological process can be estimated. Given that the psychological processes in MPT models need to be specified, researchers should construct the model structure before using the model. After the model structure is specified, researchers also need to fit the model with behavioral data and test the goodness-of-fit. Researchers need further validate the model and its parameters based on theory, only after validation, the model can be regarded as an acceptable model for such question. Then, the validated models can be used to generate and test new hypotheses. Although the logic behind the MPT model is easy to understand, the estimation of parameter-estimation and goodness-of-fit test often require massive computation that could hardly be finished by hand. Therefore, several computer programs (e.g. multitree, treeBugs) were developed, to simplify the calculating procedure. These user-friendly programs make the MPT models more accessible to social psychologists. By now, MPT models have been applied in many areas of social psychology, such as attitude, stereotype acquisition etc. Recently, MPT models were applied to moral decision-making. For instance, Gawronski et al. (2017) built the CNI (consequence, norm, inaction preference) model based on MPT model. The CNI model can dissociate the contributions of consequences, norm, and inaction preference, therefore, extended previous studies on moral decision making by considering the possibility that moral decision-making can be motivated by both utilitarian and deontological motivations simultaneously, or neither of both. Using CNI model, Gawronski et al. (2017) tested the effect of gender, cognitive load, framework effect and psychopathy on moral decision-making. It becomes increasingly clear that MPT models can serve as a tool for dissociating and quantifying the psychological processes underlying human behaviors. However, it is noteworthy that MPT models require clear assumptions about psychological processes and corresponding outcomes, this pre-request should be carefully checked before use. In addition, although MPT models fit well with many behavioral results, the neural correlates of the assumed psychological processes in MPT models are largely unknown, further studies are needed to explore and validate the neural basis of these models. Finally, MPT models might increase the research flexibilities, which might cause false positive results. Thus, researchers should keep transparent of their analysis and decision process when applying MPT to their own research questions.
Peer Review Status:Awaiting Review
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