Interactive Critiquing as a Form of Decision Support: An Empirical Evaluation

Guerlain, Stephanie; Smith, Philip J.; Obradovich, Jodi Heintz; Rudmann, Sally V.; Strohm, Patricia L.; Smith, Jack W.; Svirbely, John R.; Sachs, Larry

doi:10.1518/001872099779577363

Cited by 75 publications

(47 citation statements)

References 27 publications

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“…Mutual control is very often suggested to avoid complacency e!ects (Smith & Crabtree, 1975;Clark & Smyth, 1993;Layton et al, 1994;Denecker & Hoc, 1997;Glover, Prawitt & Spilker, 1997;Roth, Malin & Schreckenghost, 1997;Smith et al, 1997;Dijkstra, 1999;Guerlain et al, 1999;Skitka, Mosier & Burdick, 1999). This research trend usually compares situations, where the machine does the job and the human uses the result, to situations where the machine &&looks over the operator's shoulder'' and detects discrepancies from the usual procedure, in a mutual control way.…”

Section: Mutual Control Interferencementioning

confidence: 99%

Towards a cognitive approach to human–machine cooperation in dynamic situations

Hoc

2001

International Journal of Human-Computer Studies

272

101

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Section: Mutual Control Interferencementioning

confidence: 99%

Towards a cognitive approach to human–machine cooperation in dynamic situations

Hoc

2001

International Journal of Human-Computer Studies

272

101

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“…Approaches such as filters, summarizers, and automated search term selectors (e.g., Maes, 1998, Marx and Schmandt, 1996, Brann, Thurman, and Mitchell, 1996 are strongly committed to the machine processing being correct. Methods that are more weakly committed to machine pre-processing include using automation to index, cluster, organize, highlight, sort, and prioritize elements in a data field, (e.g., Oakes andTaylor, 1998, Letsche andBerry, 1997) and "cooperative machine agents" that notify, remind, or critique a human partner (e.g., Gruen et al, 1999, Guerlain et al, 1999, Fischer and Reeves, 1992.…”

Section: Workload Bottleneckmentioning

confidence: 99%

Can We Ever Escape from Data Overload? A Cognitive Systems Diagnosis

Woods

Patterson

Roth³

2002

Cognition, Technology & Work

166

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Data overload is a generic and tremendously difficult problem that has only grown with each new wave of technological capabilities. As a generic and persistent problem, three observations are in need of explanation: Why is data overload so difficult to address? Why has each wave of technology exacerbated, rather than resolved, data overload? How are people, as adaptive responsible agents in context, able to cope with the challenge of data overload?In this paper, first we examine three different characterizations that have been offered to capture the nature of the data overload problem and how they lead to different proposed solutions. As a result, we propose that (a) data overload is difficult because of the context sensitivity problem -meaning lies, not in data, but in relationships of data to interests and expectations and (b) new waves of technology exacerbate data overload when they ignore or try to finesse context sensitivity. The paper then summarizes the mechanisms of human perception and cognition that enable people to focus on the relevant subset of the available data despite the fact that what is interesting depends on context.By focusing attention on the root issues that make data overload a difficult problem and on people's fundamental competence, we have identified a set of constraints that all potential solutions must meet. Notably among these constraints is the idea that organization precedes selectivity. These constraints point toward regions of the solution space that have been little explored. In order to place data in context, designers need to display data in a conceptual space that depicts the relationships, events, and contrasts that are informative in a field of practice.KEYWORDS: agent, alarm, context, data overload, information visualization, workload Cognition, Technology and Work, in press 3 DATA OVERLOAD IS A GENERIC, DIFFICULT PROBLEM Information is not a scarce resource. Attention is.Herbert Simon 1Each round of technical advances, whether in artificial intelligence, computer graphics, or electronic connectivity promises to help people better understand and manage a whole host of activities from financial analysis to monitoring data from space missions to controlling the national air space. Certainly, this ubiquitous computerization of the modern world has tremendously advanced our ability to collect, transmit and transform data producing unprecedented levels of access to data.However, our ability to interpret this avalanche of data, i.e., to extract meaning from artificial fields of data, has expanded much more slowly, if at all. In studies across multiple settings, we find that practitioners are bombarded with computer processed data, especially when anomalies occur. We find users lost in massive networks of computer based displays, options and modes. For example, one can find a version of the following statement in most accident investigation reports: "although all of the necessary data was physically available, it was not operationally effective. No one could assemble...

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“…The manual mode gives users the freedom to choose a desired course, while still providing efficient and fast path manipulation, as well as confirmation that the path is safe from hazardous areas and poor weather regions (as determined by the thresholds set by the depth and visibility sliders (E and F diamonds)). In this manual mode, automation is leveraged as a "critic" (Guerlain et al, 1999), so that it can prevent user error, but only for path-related issues (including depth and visibility violations). The critiquing automation in the Manual mode does not take into account any information about the contacts.…”

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confidence: 99%