Cognitive support: designing aiding to supplement human knowledge

Explanations from knowledge-based systems and cooperative problem solving: an empirical study

“…The context is that in which cooperative problem solving is required. This term, following De Greef and Neerincx (1995), is used to refer to situations in which software systems support human problem solving, rather than taking over the problem solving. In this context, The current study is signi"cant because no other empirical work can be found that investigates a requirement for cooperative problem solving as a trigger of explanation use.…”

“…Explanatory support improved the quality of an intelligent information-retrieval tool (Brajnik, Mizarro & Tasso, 1996). An explanatory &&aiding function'' helped users of a statistical programme achieve better performance (De Greef & Neerincx, 1995). A review of empirical work on explanations from intelligent systems showed that explanations, when suitably designed, improve performance and learning and result in more positive user perceptions of a system (Gregor & Benbasat, 1999).…”

A natural language generation approach to support understanding and traceability of multi-dimensional preferential sensitivity analysis in multi-criteria decision making

“…While novices use the explanations primarily to understand the obtained results, experts verify the underlying assumptions and resolve anomalies of the involved stakeholders. Therefore, explanatory DSSs have proven beneficial for fostering the understanding of both novices and expert users (Buchanan and Shortliffe, 1984;Greef and Neerincx, 1995;Ye, 1995;Gregor and Benbasat, 1999;Mao and Benbasat, 2000). However, it shall also be noted that the understanding of explanations is not unambiguous (Kahneman et al, 1982) and can also cause behavioural influences on the DMs' judgement (Silver, 1991a).…”

“…To compensate for this, further explanations of decision analysis results promote understanding of the decision situation and thus help to increase trust and acceptability of the system (Greer et al, 1994;Greef and Neerincx, 1995;Dhaliwal and Benbasat, 1996;Gregor and Benbasat, 1999;Parikh et al, 2001;Geldermann, 2010). The use of natural language generation (NLG) techniques to generate such explanations automatically based on the model results has been proposed, for instance, by Papamichail and French (2003), Geldermann et al (2009) or Clark et al (2010).…”

“…Consequently, a lot of cognitive effort is required in order to analyse, interpret and derive adequate implications from the obtained model results which is particularly challenging for novice users (Spiegelhalter and KnillJones, 1984;Henrion and Druzdzel, 1991;Gregor and Benbasat, 1999). DMs consider such models as a 'black box', so they mistrust or even reject them (Brans and Mareschal, 1994;Bell et al, 2003), which leads to a gap between available information on the one hand and processible information on the other hand.To compensate for this, further explanations of decision analysis results promote understanding of the decision situation and thus help to increase trust and acceptability of the 3 system (Greer et al, 1994;Greef and Neerincx, 1995;Dhaliwal and Benbasat, 1996;Gregor and Benbasat, 1999;Parikh et al, 2001;Geldermann, 2010). The use of natural language generation (NLG) techniques to generate such explanations automatically based on the model results has been proposed, for instance, by Papamichail and French (2003), Geldermann et al (2009) or Clark et al (2010.…”