Our courts were not designed to consider the increasingly complex scientific and technical evidence needed to resolve contemporary legal disputes. Moreover, when conflicting evidence requires an understanding and interpretation of scientific or technical issues, allowing the parties to control the presentation of evidence places great strain on the judge and jury. This essay describes and evaluates three prototypical procedures that allow courts to appoint scientists and other experts independent of the parties to assist the court: 1) The appointment of an expert to advise the court and the parties regarding a disputed scientific issue by testifying in open court and being cross-examined by the parties; 2) The appointment of a “technical advisor” who assists the judge regarding scientific issues in much the same way that a law clerk assists regarding legal issues; and 3) The appointment of a special master who takes responsibility for the resolution of a portion of the case and prepares a written report for consideration by the court.

We introduce and implement a cognitively plausible model for learning from generic language, statements that express generalizations about members of a category and are an important aspect of concept development in language acquisition (Carlson & Pelletier, 1995; Gelman, 2009). We extend a computational framework designed to model grounded language acquisition by introducing the concept network. This new layer of abstraction enables the system to encode knowledge learned from generic statements and represent the associations between concepts learned by the system. Through three tasks that utilize the concept network, we demonstrate that our extensions to ADAM can acquire generic information and provide an example of how ADAM can be used to model language acquisition.

Word learning models are typically evaluated as the problem of observing words together with sets of atomic objects and learn-ing an alignment between them. We use ADAM, a Python software platform for modeling grounded language acquisition, to evaluate a particular word learning model, Pursuit (Stevens, Gleitman, Trueswell, & Yang, 2017),under more realistic learning conditions (see e.g. Gleitman and Trueswell (2020) for review). In particular, we manipulate the degree of referential ambiguity and the salience of attentional cues available to the learner, and we present extensions to Pursuit which address the challenges of non-atomic meanings and exploiting attentional cues.