Subjects: Psychology >> Social Psychology submitted time 2021-11-16
Abstract： Algorithm aversion refers to the phenomenon of people preferring human decisions but being reluctant to use superior algorithm decisions. The three-dimensional motivational model of algorithm aversion summarizes the three main reasons: the doubt of algorithm agents, the lack of moral standing, and the annihilation of human uniqueness, corresponding to the three psychological motivations, i.e., trust, responsibility, and control, respectively. Given these motivations of algorithm aversion, increasing human trust in algorithms, strengthening algorithm agents' responsibility, and exploring personalized algorithms to salient human control over algorithms should be feasible options to weaken algorithm aversion. Future research could further explore the boundary conditions and other possible motivations of algorithm aversion from a more social perspective. " "
Subjects: Psychology >> Applied Psychology Subjects: Psychology >> Clinical and Counseling Psychology submitted time 2021-03-26
Abstract： "
Peer Review Status:
Awaiting Review
Subjects: Psychology >> Statistics in Psychology submitted time 2018-05-08
Abstract： Statistical inference plays a critical role in modern scientific research, however, the dominant method for statistical inference in science, null hypothesis significance testing (NHST), is often misunderstood and misused, which leads to unreproducible findings. To address this issue, researchers propose to adopt the Bayes factor as an alternative to NHST. The Bayes factor is a principled Bayesian tool for model selection and hypothesis testing, and can be interpreted as the strength for both the null hypothesis H0 and the alternative hypothesis H1 based on the current data. Compared to NHST, the Bayes factor has the following advantages: it quantifies the evidence that the data provide for both the H0 and the H1, it is not “violently biased” against H0, it allows one to monitor the evidence as the data accumulate, and it does not depend on sampling plans. Importantly, the recently developed open software JASP makes the calculation of Bayes factor accessible for most researchers in psychology, as we demonstrated for the t-test. Given these advantages, adopting the Bayes factor will improve psychological researchers’ statistical inferences. Nevertheless, to make the analysis more reproducible, researchers should keep their data analysis transparent and open.
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
Hits
Hits
Downloads
Comment