Return to search

Distinguishing Emergent and Sequential Processes by Learning Emergent Second-Order Features

abstract: Emergent processes can roughly be defined as processes that self-arise from interactions without a centralized control. People have many robust misconceptions in explaining emergent process concepts such as natural selection and diffusion. This is because they lack a proper categorical representation of emergent processes and often misclassify these processes into the sequential processes category that they are more familiar with. The two kinds of processes can be distinguished by their second-order features that describe how one interaction relates to another interaction. This study investigated if teaching emergent second-order features can help people more correctly categorize new processes, it also compared different instructional methods in teaching emergent second-order features. The prediction was that learning emergent features should help more than learning sequential features because what most people lack is the representation of emergent processes. Results confirmed this by showing participants who generated emergent features and got correct features as feedback were better at distinguishing two kinds of processes compared to participants who rewrote second-order sequential features. Another finding was that participants who generated emergent features followed by reading correct features as feedback did better in distinguishing the processes than participants who only attempted to generate the emergent features without feedback. Finally, switching the order of instruction by teaching emergent features and then asking participants to explain the difference between emergent and sequential features resulted in equivalent learning gain as the experimental group that received feedback. These results proved teaching emergent second-order features helps people categorize processes and demonstrated the most efficient way to teach them. / Dissertation/Thesis / Masters Thesis Psychology 2015

Identiferoai:union.ndltd.org:asu.edu/item:34815
Date January 2015
ContributorsXu, Dongchen (Author), Chi, Michelene (Advisor), Homa, Donald (Committee member), Glenberg, Arthur (Committee member), Arizona State University (Publisher)
Source SetsArizona State University
LanguageEnglish
Detected LanguageEnglish
TypeMasters Thesis
Format55 pages
Rightshttp://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved

Page generated in 0.001 seconds