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  • 时间的多维度空间表征:分离的起源与激活机制

    Subjects: Psychology >> Developmental Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》

    Abstract: A growing body of evidence suggested that time can be represented separately either on the lateral, sagittal or vertical axis. However, how these mental time lines originate and be activated remains controversial. Recent evidence supports that the lateral mental time line mainly originates from sensorimotor experience associated with reading/writing habits, and the activation may be a low-level sensorimotor mechanism. In contrast, the sagittal/vertical mental time line mainly originates from spatial metaphor in language, and the activation may be a high-level semantic mechanism. The future study should focus more on the diversity of origins, the gene basis, the neural mechanism and the analogy between mental time lines and mental number lines.

  • 时间的多维度空间表征:分离的起源与激活机制

    Subjects: Psychology >> Cognitive Psychology submitted time 2020-01-12

    Abstract: 近期研究发现时间可以分别在三个空间维度(左右、前后和上下)进行表征,但不同维度心理时间线的起源和激活机制尚存争议。最新证据表明,左右维度时间线主要起源于阅读/书写习惯所伴随的感觉运动经验,其激活可能是一种低水平的感觉运动机制;前后和上下维度时间线主要起源于语言中的空间隐喻,其激活可能是一种高水平的语义机制。未来研究应更关注心理时间线的起源多样性、先天基础、神经机制以及时间线和数字线的类比。

  • Multinomial Processing Tree Models and Their Application in Social Psychology

    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.

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