Different from the traditional learning-based method, SmartTracing does not require human input of training exemplars and can self-adapt to different types of neuroimage data.
In Experiment 1, participants judged a variety of different exemplars to be worse category members if they were less fluent (because they were presented in a smaller typeface).
Coded data were tabulated, with exemplar quotations, to identify different dimensions associated with each category which are summarized in Table 1.
In order to balance the internal variability of the two categories, we chose exemplars belonging to four different subcategories.
They used these exemplars to learn patterns in the testimony.
Plotted genes were chosen to provide extremal exemplars with different performances: the best and worst genes across the whole time scale and two other genes which showed most variation in recent compared with ancient times and vice versa.
Interleaving exemplars from different categories can lead to enhanced memory for the items and superior category learning (Kornell & Bjork, 2008; Rawson, Thomas, & Jacoby, 2015).
The benefits of interleaving exemplars from different categories is thought to arise because learners can better discriminate among categories when they are intermixed (Birnbaum, Kornell, Bjork, & Bjork, 2013; Kang & Pashler, 2012) to the degree that the categories are similar to each other (Carvalho & Goldstone, 2014).
All bird types were morphed with one another to create different exemplars of each category.
In Experiments 1 and 2, results of the study from Xu, Carey, and Quint (2004, Experiment 4) were partially replicated, showing that infants were able to individuate two natural-looking exemplars from different categories, but not two exemplars from the same category.
