Order Effects in the Integration of Verbal Descriptions

Respondents were provided with a series of message ensembles, each of which contained nine descriptions, all based on the same referent-photograph. Their task was to identify the appropriate target-photograph. Descriptions that were presented at the beginning or at the end of a message-set were more influential determinants of choice than those presented in the middle of the series.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/69066/2/10.1177_014616727900500112.pd

Presenting Proofs with Adapted Granularity

When mathematicians present proofs they usually adapt their explanations to their didactic goals and to the (assumed) knowledge of their addressees. Modern automated theorem provers, in contrast, present proofs usually at a fixed level of detail (also called granularity). Often these presentations are neither intended nor suitable for human use. A challenge therefore is to develop user- and goal-adaptive proof presentation techniques that obey common mathematical practice. We present a flexible and adaptive approach to proof presentation based on classification. Expert knowledge for the classification task can be handauthored or extracted from annotated proof examples via machine learning techniques. The obtained models are employed for the automated generation of further proofs at an adapted level of granularity