Proceedings of the Annual Meeting of the Cognitive Science Society

Learning environments are rich with structure but learning that structure can take considerable effort. Given that thesequence with which knowledge is accumulated is important for development (Smith & Slone, 2017), we consider whetheroptimizing the sequence of training examples can accelerate learning, as evaluated by out of sample generalization. Toexamine this issue we used established connectionist networks that map an orthographic input to a phonological output(Cox, Cooper Borkenhagen, & Seidenberg, 2018; Plaut et al., 1996). Utilizing machine teaching (Sen et al., 2018; Zhu,2015) to optimize word selection for a 10,000 word sequence, we observe an 8% average gain (over 100 sequences) ongeneralization accuracy (from 51% to 59%) compared to matched random sequences. These findings have implicationsfor learning domains where generalization is critical, like reading development where the child needs to gain as muchknowledge as possible from limited experience.