Session: LBR Highlights
March 5–8, 2012, Boston, Massachusetts, USA
Beyond “Spatial Ability”: Examining the Impact of Multiple Individual Differences in a Perception by Proxy Framework Thomas Fincannon
Florian Jentsch
Brittany Sellers
Joseph R. Keebler
University of Central Florida 3100 Technology Pkwy Orlando, FL 32826
[email protected]
University of Central Florida 3100 Technology Pkwy Orlando, FL 32826
[email protected]
University of Central Florida 3100 Technology Pkwy Orlando, FL 32826
[email protected]
University of Central Florida 3100 Technology Pkwy Orlando, FL 32826
[email protected]
perceptual task (e.g., detection of unknown stimulus), which may require human supervision. As a result, human performance of these precursors of requests for support should be understood.
ABSTRACT Prior research has proposed the use of a Perception by Proxy framework that relies on human perception to support actions of autonomy. Given the importance of human perception, this framework highlights the need to understand how human cognitive abilities factor into the human-robot dynamic. The following paper uses a military reconnaissance task to examine how cognitive abilities interact with the gradual implementation of autonomy in a Perception by Proxy framework (i.e., autonomy to detect; autonomy to support rerouting) to predict three dimensions of sequential performance (i.e., speeded detection; target identification; rerouting). Results showed that, in addition to effects of autonomy and task setting, different individual abilities predicted unique aspects of performance. This highlights the need to broaden consideration of cognitive abilities in HRI.
Existing research with human operators has focused on concurrent perceptual tasks (e.g., identification & localization) that precede synthesis and decision making [2], but Perception by Proxy is somewhat unique in that it highlights how perceptual tasks are performed sequentially. In a military example, detection of an unknown obstruction that is in the path of an unmanned vehicle (UV) would precede interventions to stop the vehicle, identify the stimulus, and follow the rules of engagement (ROEs) accordingly. This element of speeded detection by an UV operator has not received much attention, and this implies a need to understand human performance for appropriate supervision.
1.2 Cognitive Abilities
Categories and Subject Descriptors
Given the importance of human perception for a Perception by Proxy framework, there is a need to understand human cognition and how individual differences factor into this relationship. Much of the research in this area focuses generally on spatial ability, but our research has have advocated a more construct driven approach [2]. Using Carroll’s [3] model of intelligence, which provides one of the extensive meta analyses on the subject, we found [2] that much of the existing research on spatial ability uses measures that load onto on a single construct (i.e., visualization [or VZ]). Furthermore, we argued for a broader consideration of other constructs that have not received much consideration in existing HRI literature. The study reported here examined the following abilities that:
H.1.2 [Models and Principles]: Human/Machine Systems Human Factors and Human Information Processing– human factors, human information processing.
General Terms Experimentation, Human Factors, Management, Performance.
Keywords Cognitive Abilities, Perception by Proxy.
1. INTRODUCTION In the domain of human-robot interaction (HRI), interest has been directed toward the concept of teaming between humans and robots. Not surprisingly, this trend has led to a variety of models that can be used to discuss various dynamics. Within this body of work, researchers have recently proposed a framework of Perception by Proxy [1]. According to this perspective, the perceptual limitations of a robot create a need for a human counterpart to act as a perceptual proxy. Given that research on “spatial ability” has shown that operators are not equally proficient at performing this task [2], there is a concurrent need to understand how individual differences in cognitive ability will impact performance. This paper intends to explore this dynamic.
Visualization (VZ): the ability to mentally perform complex manipulations to visual objects Closure Speed (CS): the ability to recognize obscured objects that are stored in long term memory Perceptual Speed (P): the speed in finding a known pattern in a visual field
1.3 Purpose The purpose of this paper is to provide an exploratory analysis of how different cognitive abilities interact with a gradual implementation of a Perception by Proxy framework. Considering that the implementation of autonomy in the field is not expected to be fully reliable [1], this should provide an initial understanding of how multiple cognitive abilities support human supervision of a robotic teammate.
1.1 Perception by Proxy As described by Atherton and Goodrich [1], robots can be limited in their ability to perceive elements of a remote environment, and this can hinder performance. It also creates a need to understand the strengths and weaknesses of a robot, such that the autonomy allows for a request for help when appropriate. It is important to note, however, that this requires the robot to first perform a
2. METHOD In the study, 39 students from the University of Central Florida participated for course credit. Using training and methodologies that were consistent with prior work [2], participants were asked to use two simulated UVs to complete four reconnaissance
Copyright is held by the author/owner(s). HRI’12, March 5–8, 2012, Boston, Massachusetts, USA. ACM 978-1-4503-1063-5/12/03.
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Session: LBR Highlights
March 5–8, 2012, Boston, Massachusetts, USA
missions in a scaled Military Operations in Urban Terrain (MOUT) environment. A reengineered commercial-off-the-shelf (COTS) vehicle was used as the unmanned ground vehicle (UGV), and the unmanned air vehicle (UAV) was simulated by an overhead camera attached to an automated pulley system. For the task, participants used the vehicles to follow preplanned routes and comply with the specified ROEs (i.e., stipulations regarding how to reroute) to support operations. Performance was focused on safe operations for the UGV, which involved: (1) stopping the UGV when personnel and vehicles were encountered unexpectedly (i.e., speeded detection); (2) correctly identifying these targets (e.g., a republican guard soldier, a group of civilians, a T-80 tank, etc.) based on the identification booklet provided; and (3) using this information to reroute (i.e., following ROEs).
operators with greater P perceived lower workload. No other variable had a significant impact on this construct.
4. DISCUSSION Summary of Findings. Together, the results indicated that, in addition to effects of autonomy and task setting, different individual abilities predicted unique aspects of performance. First, in the speeded detection task of stopping at an unexpected obstruction, operators with high VZ or CS abilities did best and could do as well as the autonomous system. In contrast, with respect to the more analytical task of following ROEs, only operators with high VZ ability could perform as well as the autonomy. Finally, perceptual speed P was not critical for either of the performance metrics, but it was associated with lower workload estimates, replicating findings of dissociations between workload and performance.
As prescribed by Carroll’s [3] model, the Vandenberg and Kuse test of Mental Rotation was used to assess VZ; the Hidden Word test was used to assess CS; and the Finding A’s test was used to assess P. Performance was assessed based on the three safe operations tasks mentioned above. Subjective workload was assessed with using the NASA TLX.
Interpretation. With respect to the individual differences, each construct demonstrated a unique relationship with different outcomes for performance and workload. The impact of VZ on speeded detection and implementation of reconnaissance to follow ROEs showed that this construct had the most significant impact on performance as a whole. This is consistent with Carroll’s [3] depiction of VZ as broad factor of power, or level of mastery, for mental transformation, which would be expected to form an association with a variety of perceptual tasks.
Semi-autonomous waypoint control was used to reach preplanned objectives, but a manipulation to autonomy that mimicked a Perception by Proxy framework was used to react to unexpected stimuli that were encountered along these routes. There were three levels of autonomy that included manual stop (i.e., when there were unexpected stimuli along the route, the operator had to stop the UGV in order to identify the stimulus and determine the appropriate ROE), autonomous stop (i.e., the UGV stopped automatically, and the operator had to identify the stimulus to determine the appropriate ROE), and collaborative autonomy (i.e., the UGV would autonomously stop the vehicle, and the UAV would use its autonomy to identify information provided by the operator to suggest an appropriate ROE). As in prior work [2], confederates were used in a man-behind-the-curtain methodology.
According to Carroll’s model [3], CS is a speeded dimension of visual intelligence that does not require any mental manipulation of an object. As a result, it was not surprising to see that it accounted for unique variance in the prediction of a speeded detection task. At first glance, results regarding P may seem counter-intuitive. While speed is a component of this ability, it was not associated with speeded detection in our study. Instead, this ability was associated with workload and following ROE as a measure of synthesis, which implies that this ability is more strongly associated with decision making. As reviewed by Carroll [3], P can be viewed as a marker of cognitive speed, which is theorized to be related to working memory, and the results are more consistent with this explanation.
3. RESULTS To examine relevant relationships, the manipulation was dummy coded for autonomous detection (i.e., whether the operator or autonomy stops the UGV) and autonomous decision making (i.e., whether the operator or autonomy uses target identity to suggest ROEs alterations). Multilevel linear modeling was then used examine interactions between autonomy and cognitive ability.
Overall, this study illustrated how a Perception by Proxy framework can be used to highlight sequential measures of perception that exhibit unique relationships with cognitive abilities that are not commonly examined in HRI. Further research will be required to explore relevant theory.
With respect to speeded detection needed for stopping the UGV at an unknown obstruction, positive associations were observed for VZ [F(1, 33) = 13.22, p < .05], CS[F(1, 33) = 20.99, p < .05], and automated detection [F(1, 33) = 101.23, p < .05]. Furthermore, automated detection was found to interact with VZ [F(1, 33) = 4.73, p < .05] and CS[F(1, 33) = 16.39, p < .05], such that spatial ability only improved performance without autonomous support.
5. REFERENCES [1] Atherton, J. and Goodrich, M. 2011. Perception by Proxy: Humans helping robots see in a manipulation task. Proceedings of the 6th International Conference on Human Robot Interaction, 109-110.
In terms of following ROEs, identification [F(1, 150) = 44.28, p < .05], autonomous decision making [F(1, 150) = 50.48, p < .05], and VZ [F(1, 150) = 8.14, p < .05] were all found to have a main effect. Interestingly, P was also found to interact with identification [F(1, 150) = 4.65, p < .05], such that accurate reconnaissance was necessary for P to improve ROE performance.
[2] Fincannon, T., Evans, A.W.III, Jentsch, F., and Keebler, J.R. 2010. Dimensions of spatial ability and their influence on performance with unmanned systems. In: D. H. Andrews, R. P. Herz, & M. B. Wolf, eds. Human factors in defense: Human factors in combat identification. Burlington, VT: Ashgate Publishing, 67-81.
With respect to the NASA TLX, P was found to have a negative association with workload [F(1, 33) = 4.15, p < .05], in that
[3] Carroll, J. 1993. Human cognitive abilities: A survey of factor analytic studies. New York: Cambridge U. Press.
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