In this article we present a new experimental paradigm: comparative visual search. Each half of a display contains simple geometrical objects of three different colors and forms. The two display halves are identical except for one object mismatched in either color or form. The subject's task is to find this mismatch. We illustrate the potential of this paradigm for investigating the underlying complex processes of perception and cognition by means of an eye‐tracking study. Three possible search strategies are outlined, discussed, (...) and reexamined on the basis of experimental results. Each strategy is characterized by the way it partitions the field of objects into “chunks.” These strategies are: (i) Stimulus‐wise scanning with minimization of total scan path length (a “traveling salesman” strategy), (ii) scanning of the objects in fixed‐size areas (a “searchlight” strategy), and (iii) scanning of object sets based on variably sized clusters defined by object density and heterogeneity (a “clustering” strategy). To elucidate the processes underlying comparative visual search, we introduce besides object density a new entropy‐based measure for object heterogeneity. The effects of local density and entropy on several basic and derived eye‐movement variables clearly rule out the traveling salesman strategy, but are most compatible with the clustering strategy. (shrink)

In a collaborative work situation at a distance, the use of avatars to represent collaborators reduces collaborative effort. Also, animated avatars can help distant users to ground their relationship and facilitate their interaction because they materialise visual clues for the distant collaborators and their current activity. To check the validity of these hypotheses we set up an experiment based on the use of a collaborative virtual environment (CVE) synchronised for collective medical decision-making. Several teams of practitioners from different disciplines will (...) be required to deal with liver tumours displayed in Argonaute 3D. In this paper, we provide some evidence from existing studies for the hypotheses and select several measures to estimate the grounding process in CVEs. Next, we briefly describe the setup used to collect observable data with Argonaute 3D and we summarise first observations acquired with practitioners. (shrink)

Domain experts regularly teach novice students how to perform a task. This often requires them to adjust their behavior to the less knowledgeable audience and, hence, to behave in a more didactic manner. Eye movement modeling examples (EMMEs) are a contemporary educational tool for displaying experts’ (natural or didactic) problem‐solving behavior as well as their eye movements to learners. While research on expert‐novice communication mainly focused on experts’ changes in explicit, verbal communication behavior, it is as yet unclear whether and (...) how exactly experts adjust their nonverbal behavior. This study first investigated whether and how experts change their eye movements and mouse clicks (that are displayed in EMMEs) when they perform a task naturally versus teach a task didactically. Programming experts and novices initially debugged short computer codes in a natural manner. We first characterized experts’ natural problem‐solving behavior by contrasting it with that of novices. Then, we explored the changes in experts’ behavior when being subsequently instructed to model their task solution didactically. Experts became more similar to novices on measures associated with experts’ automatized processes (i.e., shorter fixation durations, fewer transitions between code and output per click on the run button when behaving didactically). This adaptation might make it easier for novices to follow or imitate the expert behavior. In contrast, experts became less similar to novices for measures associated with more strategic behavior (i.e., code reading linearity, clicks on run button) when behaving didactically. (shrink)

Domain experts regularly teach novice students how to perform a task. This often requires them to adjust their behavior to the less knowledgeable audience and, hence, to behave in a more didactic manner. Eye movement modeling examples (EMMEs) are a contemporary educational tool for displaying experts’ (natural or didactic) problem‐solving behavior as well as their eye movements to learners. While research on expert‐novice communication mainly focused on experts’ changes in explicit, verbal communication behavior, it is as yet unclear whether and (...) how exactly experts adjust their nonverbal behavior. This study first investigated whether and how experts change their eye movements and mouse clicks (that are displayed in EMMEs) when they perform a task naturally versus teach a task didactically. Programming experts and novices initially debugged short computer codes in a natural manner. We first characterized experts’ natural problem‐solving behavior by contrasting it with that of novices. Then, we explored the changes in experts’ behavior when being subsequently instructed to model their task solution didactically. Experts became more similar to novices on measures associated with experts’ automatized processes (i.e., shorter fixation durations, fewer transitions between code and output per click on the run button when behaving didactically). This adaptation might make it easier for novices to follow or imitate the expert behavior. In contrast, experts became less similar to novices for measures associated with more strategic behavior (i.e., code reading linearity, clicks on run button) when behaving didactically. (shrink)