The effect of task structure on diffusion dynamics: Implications for diffusion curve and network-based analyses
The effect of task structure on diffusion dynamics: Implications for diffusion curve and network-based analyses
复制标题
DOI：
10.3758/lb.38.3.243
复制
发表时间：
2010-08-01
影响因子：
1.8
通讯作者：
Laland, Kevin N.
中科院分区：
文献类型：
--
作者：
Hoppitt, Will;Kandler, Anne;Laland, Kevin N.
在广泛的社会学习研究领域内的理论分析得出了不同的结论，扩散曲线的形状是否可以用来推断，通过社会或非社会学习的学习特性增加。在这里，我们探讨了任务结构等因素（例如，多步任务）、任务放弃、子目标学习和新恐惧症影响了社交学习行为和社交学习行为的扩散曲线的形状。我们证明，而社会学习的可能性增加的S形曲线，S形模式可以产生完全的社会过程，不能可靠地解释为社会学习的指标。我们的研究结果加强了这样一种观点，即扩散曲线分析不是检测社会传播的可靠方法。我们还提请注意的事实，任务结构同样可以混淆基于网络的扩散分析的解释，并建议解决这个问题。本文的补充材料可以从http://lb.psychonomic-journals.org/content/supplemental下载。
Theoretical analyses within the broad field of social learning research give mixed conclusions on whether the shape of a diffusion curve can be used to infer that a learned trait increases through social or asocial learning. Here we explore how factors such as task structure (e.g., multiple-step tasks), task abandonment, subgoal learning, and neophobia affect the shape of the diffusion curve for both asocially learned and socially learned behavior. We demonstrate that, whereas social learning increases the likelihood of S-shaped curves, sigmoidal patterns can be generated by entirely asocial processes, and cannot be reliably interpreted as indicators of social learning. Our findings reinforce the view that diffusion curve analysis is not a reliable way of detecting social transmission. We also draw attention to the fact that task structure can similarly confound interpretation of network-based diffusion analyses, and suggest resolutions to this problem. Supplemental materials for this article may be downloaded from http://lb.psychonomic-journals.org/content/supplemental.