It was suggested that children's referent selection may not lay memory traces sufficiently strong to lead to retention of new word-object mappings. If this was the case we expect incorrect selections to be easily rectified through feedback. Previous work suggested this to be the case in toddlers at typical likelihood (TL) but not in those at elevated likelihood (EL) for autism spectrum disorder (ASD) (Bedford et al., 2013). Yet group differences in lexical knowledge may have confounded these findings. Here, TL (N = 29) and EL toddlers (N = 75) chose one of two unfamiliar objects as a referent for a new word. Both groups retained the word-referent mapping above chance when their choices were immediately reinforced but were at chance after corrective feedback. The same pattern of results was obtained when children observed another experimenter make the initial referent choice. Thus, children's referent choices lay memory traces that compete with subsequent correction; these strong word-object associations are not a result of children actively choosing potential referents for new words.

To support formal reasoning in mathematical and software engineering applications, it is desirable to have a generic prover that can be instantiated with a range of logics. This allows the prover to be applied to a wider variety of reasoning tasks than a fixed-logic prover. This paper describes the design principles and the architecture of the latest version of the Ergo proof engine, Ergo 6. Ergo 6 is a generic interactive theorem prover, similar to Isabelle, but with better support for proving schematic theorems with user-defined constraints, and with a different approach to handling variable scoping. A major theme of the paper is that Prolog implementation technology can be generalized to obtain efficient implementations of generic proof engines. This is demonstrated via a Qu-Prolog implementation of Ergo 6.