An official website of the United States government
Collaborative systems design is a human-centered activity dependent on individual decision-making processes. Personality traits have been found to influence individual behaviors and tendencies to compete or cooperate. This paper investigates the effects of Big Five and Locus of Control personality traits on negotiated outcomes of a simplified collaborative engineering design task. Secondary data includes results from short-form personality inventories and outcomes of pair design tasks. The data includes ten sessions of four participants each, where each participant completes a sequence of 12 pair tasks involving design space exploration and negotiation. Regression analysis shows a statistically-significant relationship between Big Five and Locus of Control and total individual value accumulated across the 12 design tasks. Results show the Big Five, aggregating extraversion, agreeableness, conscientiousness, neuroticism, and intellect/imagination to a single factor, negatively affects individual value and internal Locus of Control positively affects individual value. Future work should consider a dedicated experiment to refine understanding of how personality traits influence collaborative systems design and propose interventions to improve collaborative design processes.
more »
« less
Investigating the Role of Locus of Control in Moderating Complex Analytic Workflows
Throughout the last decade, researchers have shown that the effectiveness of a visualization tool depends on the experience, personality, and cognitive abilities of the user. This work has also demonstrated that these individual traits can have significant implications for tools that support reasoning and decision-making with data. However, most studies in this area to date have involved only short-duration tasks performed by lay users. This short paper presents a preliminary analysis of a series of exercises with 22 trained intelligence analysts that seeks to deepen our understanding of how individual differences modulate expert behavior in complex analysis tasks.
more »
« less
- Award ID(s):
- 1755734
- PAR ID:
- 10334147
- Date Published:
- Journal Name:
- EuroVis 2020 - Short Papers
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
State-space models (SSMs), such as Mamba (Gu & Dao, 2023), have been proposed as alternatives to Transformer networks in language modeling, incorporating gating, convolutions, and input-dependent token selection to mitigate the quadratic cost of multi-head attention. Although SSMs exhibit competitive performance, their in-context learning (ICL) capabilities, a remarkable emergent property of modern language models that enables task execution without parameter optimization, remain less explored compared to Transformers. In this study, we evaluate the ICL performance of SSMs, focusing on Mamba, against Transformer models across various tasks. Our results show that SSMs perform comparably to Transformers in standard regression ICL tasks, while outperforming them in tasks like sparse parity learning. However, SSMs fall short in tasks involving non-standard retrieval functionality. To address these limitations, we introduce a hybrid model, MambaFormer, that combines Mamba with attention blocks, surpassing individual models in tasks where they struggle independently. Our findings suggest that hybrid architectures offer promising avenues for enhancing ICL in language models.more » « less
-
We consider the problem of sequential robotic manipulation of deformable objects using tools. Previous works have shown that differentiable physics simulators provide gradients to the environment state and help trajectory optimization to converge orders of magnitude faster than model-free reinforcement learning algorithms for deformable object manipulation. However, such gradient-based trajectory optimization typically requires access to the full simulator states and can only solve short-horizon, single-skill tasks due to local optima. In this work, we propose a novel framework, named DiffSkill, that uses a differentiable physics simulator for skill abstraction to solve long-horizon deformable object manipulation tasks from sensory observations. In particular, we first obtain short-horizon skills using individual tools from a gradient-based optimizer, using the full state information in a differentiable simulator; we then learn a neural skill abstractor from the demonstration trajectories which takes RGBD images as input. Finally, we plan over the skills by finding the intermediate goals and then solve long-horizon tasks. We show the advantages of our method in a new set of sequential deformable object manipulation tasks compared to previous reinforcement learning algorithms and compared to the trajectory optimizer.more » « less
-
The human ability to generalize beyond interpolation, often called extrapolation or symbol-binding, is challenging to recreate with computational models. Biologically plausible models incorporating indirection mechanisms have demonstrated strong performance in this regard. Deep learning approaches such as Long Short-Term Memory (LSTM) and Transformers have shown varying degrees of success, but recent work has suggested that Transformers are capable of extrapolation as well. We evaluate the capabilities of the above approaches on a series of increasingly complex sentence-processing tasks to infer the capacity of each individual architecture to extrapolate sentential roles across novel word fillers. We confirm that the Transformer does possess superior abstraction capabilities compared to LSTM. However, what it does not possess is extrapolation capabilities, as evidenced by clear performance disparities on novel filler tasks as compared to working memory-based indirection models.more » « less
-
Robotic knee-ankle prostheses have often fallen short relative to passive microprocessor prostheses in time-based clinical outcome tests. User ambulation endurance is an alternative clinical outcome metric that may better highlight the benefits of robotic prostheses. However, previous studies were unable to show endurance benefits due to inaccurate high-level classification, discretized mid-level control, and insufficiently difficult ambulation tasks. In this case study, we present a phase-based mid-level prosthesis controller which yields biomimetic joint kinematics and kinetics that adjust to suit a continuum of tasks. We enrolled an individual with an above-knee amputation and challenged him to perform repeated, rapid laps of a circuit comprising activities of daily living with both his passive prosthesis and a robotic prosthesis. The participant demonstrated improved endurance with the robotic prosthesis and our mid-level controller compared to his passive prosthesis, completing over twice as many total laps before fatigue and muscle discomfort required him to stop. We also show that time-based outcome metrics fail to capture this endurance improvement, suggesting that alternative metrics related to endurance and fatigue may better highlight the clinical benefits of robotic prostheses.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.2312/evs.20201050
