Finding a face in a crowd is a real-world analog to visual search, but extending the visual search method to such complex social stimuli is rife with potential pitfalls. We need look no further than the well-cited notion that angry faces "pop out" of crowds to find evidence that stimulus confounds can lead to incorrect inferences. Indeed, long before the recent replication crisis in social psychology, stimulus confounds led to repeated demonstrations of spurious effects that were misattributed to adaptive cognitive design. We will first discuss how researchers refuted these errors with systematic "face in the crowd" experiments. We will then contend that these more careful studies revealed something that may actually be adaptive, but at the level of the signal: Happy facial expressions seem designed to be detected efficiently. We will close by suggesting that participant-level manipulations can be leveraged to reveal strategic shifts in performance in the visual search for complex stimuli such as faces. Because stimuluslevel effects are held constant across such manipulations, the technique affords strong inferences about the psychological underpinnings of searching for a face in the crowd.

Integrated with computerized education platforms, the mouse-tracking technique could provide an inexpensive and less intrusive tool for assessing cognitive load. The present study examined whether mouse-tracking can quantify changes in cognitive load. Participants performed a dual-task, which required them to perform primary tasks of moving a computer mouse cursor along the vertical and the horizontal axes to a target, and secondary arithmetic tasks designed to impose different levels of cognitive load. Analyses of the mouse-tracking data indicated that slower mean response time and less trajectory deviation were observed when participants were given secondary tasks imposing a greater cognitive load, whereas slower mean response time and greater trajectory deviation were observed when participants moved a cursor toward a smaller-sized target. The cause behind the quantitative difference between the cognitive load effect, and the motor task difficulty (target size) is discussed, as are implications of these results for computerized education platforms.

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Advances in automated driving systems (ADSs) have shifted the primary responsibility of controlling a vehicle from human drivers to automation. Framing driving a highly automated vehicle as teamwork can reveal practical requirements and design considerations to support the dynamic driver–ADS relationship. However, human–automation teaming is a relatively new concept in ADS research and requires further exploration. We conducted two literature reviews to identify concepts related to teaming and to define the driver–ADS relationship, requirements, and design considerations. The first literature review identified coordination, cooperation, and collaboration (3Cs) as core concepts to define driver–ADS teaming. Based on these findings, we propose the panarchy framework of 3Cs to understand drivers’ roles and relationships with automation in driver–ADS teaming. The second literature review identified main challenges for designing driver–ADS teams. The challenges include supporting mutual communication, enhancing observability and directability, developing a responsive ADS, and identifying and supporting the interdependent relationship between the driver and ADS. This study suggests that the teaming concept can promote a better understanding of the driver–ADS team where the driver and automation require interplay. Eventually, the driver–ADS teaming frame will lead to adequate expectations and mental models of partially automated vehicles.

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