Implications of Personality on Cognitive Workload, Affect, and Task Performance in Remote Robot Control (2303.04398v2)

Abstract: This paper explores how the personality traits of robot operators can influence their task performance during remote control of robots. It is essential to explore the impact of personal dispositions on information processing, both directly and indirectly, when working with robots on specific tasks. To investigate this relationship, we utilize the open-access multi-modal dataset MOCAS to examine the robot operator's personality traits, affect, cognitive load, and task performance. Our objective is to confirm if personality traits have a total effect, including both direct and indirect effects, that could significantly impact the performance levels of operators. Specifically, we examine the relationship between personality traits such as extroversion, conscientiousness, and agreeableness, and task performance. We conduct a correlation analysis between cognitive load, self-ratings of workload and affect, and quantified individual personality traits along with their experimental scores. The findings show that personality traits do not have a total effect on task performance.

• The paper analyzes how personality traits like extroversion and agreeableness influence cognitive workload, affect, and task performance in remote robot control using the MOCAS dataset.
• Key results indicate extroversion positively affects task scores, agreeableness negatively impacts click success, and while personality affects workload and arousal directly, its direct impact on task performance is limited.
• The study highlights the complex interaction of human factors in HRI, suggesting the need for adaptive robotic systems that accommodate operator personality traits for optimized human-robot interaction.

Analysis of Personality Traits on Remote Robot Control Task Performance

The paper, "Implications of Personality on Cognitive Workload, Affect, and Task Performance in Remote Robot Control," authored by Go-Eum Cha, Wonse Jo, and Byung-Cheol Min, explores the influences of personality traits on various aspects of robot control tasks. Researchers in this paper have meticulously examined how intrinsic factors such as the personality dimensions of extroversion, conscientiousness, and agreeableness correlate with cognitive workload, affective responses, and subsequent task efficiency. They have utilized the open-access multimodal MOCAS dataset to scrutinize the profiles of robot operators in remote control scenarios comprising different task complexity levels.

Main Hypotheses

The paper was conducted with multiple hypotheses aimed at deciphering the role personality traits play in influencing task outcomes:

• Hypothesis 1 predicted a correlation between the Big Five Personality traits and task performance, particularly with extroversion and conscientiousness contributing positively to task completion rates.
• Hypothesis 2 anticipated a direct impact of personality traits on the success rates of operators, specifically indicating a higher impact from conscientiousness and an inverse effect of agreeableness.
• Hypothesis 3 posited that cognitive workload and emotional states (affect) are influenced by personality factors, with extroverted traits being linked to higher workload perceptions.
• Hypothesis 4 explored both direct and indirect effects of personality traits on task performance metrics.

Methodological Approaches

The paper is grounded in empirical analysis using the multimodal MOCAS dataset. The dataset derives from experimental conditions that test cognitive workload fluctuations due to variables like robot speed and camera view numbers. This structured experiment permitted comprehensive data collection including physiological signals, mouse movements, and subjective questionnaires that gauge cognitive and affective responses using established measures like NASA-TLX and SAM.

Results and Analysis

The paper's detailed statistical analysis, encompassing Pearson's correlation, exploratory factor analysis (EFA), and structural equation modeling (SEM), yielded insightful revelations:

• Extroversion was found to have a positive effect on task scores, suggesting some alignment with hypothesis $\mathcal{H}_1$a.
• Emotional stability (PES) was seen to negatively correlate with workload, though this didn't translate significantly to performance impacts.
• Agreeableness (PA) inversely influenced the success rate of operational clicks, affirming hypothesis $\mathcal{H}_2$b partially.
• Conscientiousness (PC) failed to meet anticipated correlations with performance measures consistently, thus rejecting hypotheses $\mathcal{H}_1$b and $\mathcal{H}_2$a.
• Correlational studies also hinted limited, if any, indirect effects (rejecting $\mathcal{H}_4$).

The factor and path analysis showed that the considered personality traits bore no indirect significant impact on performance but were directly associated with variables like self-reported workload.

Implications and Future Directions

The conclusions drawn uniformly suggest that while personality traits do affect individual cognitive workload and arousal, they lack a substantial direct transformation into task performance improvement, particularly in remote control robotics. This insight prompts further investigations into adaptive robotic systems that accommodate operator personality traits for optimized human-robot interaction (HRI).

Future work could involve scaling experiments with a broader range of demographic variability in personality traits and embedding more real-time adaptive features into robotics interfaces. This paper opens avenues for deeper neural network modeling and real-time feedback loops to enhance task optimization based on human psychological profiling in robotic systems.

In summary, the paper underscores the complex interplay of intrinsic human factors in multidisciplinary interactions between humans and robots, offering substantive insights for AI and robotics domain researchers focused on human-centric system design.