We would like to congratulate some newly graduated PhD and MS students from the ACSHF program as of this summer:
Isaac Flint, PhD
Nishat Alam, MS
Anne Linja, PhD
Well done all!
Titles and abstracts for each can be found below:
Title:The Impact of Cognitive Ability and Age on Movement Corrections: An Exploration of the Neurocognitive and Physiological Contributors to Optimal Feedback Control
Abstract: Making successful movement corrections is an important part of navigating dynamic environments where unexpected obstructions can occur. Failure to engage in successful movement corrections can result in injury and, in some cases, death. One theory used to explain people’s ability to make movement corrections is the optimal feedback control theory, which follows the minimal intervention principle. Experiment 1 shows older adults are as likely as young adults to choose hand paths that require the least effort following a visual perturbation; however, they also commit more collisions and have slower movement speeds. Regression analyses show that success rates and movement times on the obstacle avoidance task are related to individuals’ measures of executive control and processing speed. P3b components, often associated with executive control, were elicited following medium and large cursor jumps. These ERP responses were different between the two conditions for young adults; however, they were not different for older adults. Experiment 2 shows young adults’ performance on obstacle avoidance tasks aligned with what would be predicted according to the minimal intervention principle, regardless of if responding to haptic/proprioceptive or visual feedback. The modality of the perturbation did have an impact on performance when the optimal path was ambiguous. The P3bs observed in Experiment 2 were also affected by the difference in the modality of feedback. When these findings are evaluated with the experiment’s other findings showing 1) greater P3b related activity for collision trials than non-collision trials, 2) very little differences between P3bs from trials with inward and outward movement corrections, and 3) EMG indicators of movement correction onset occur before the P3b peak, it suggests that the neural activity observed in this study has more to do with monitoring the movement corrections than path selection. The regression models from Experiment 2 also show the top-down processing of visual feedback is associated with a greater number of cognitive variables; yet some level of executive control is still associated with participants; tendency to make optimal reaching movements following physical perturbations.
Title: Types of Questions Teachers Ask to Engage Students in Making Sense of a Student Contribution
Abstract: In the student-centered classroom, a teacher’s interpretation and response to student mathematical contributions plays an important role to shape and direct students’ opportunities for sense-making. This research used a scenario-based survey questionnaire to examine what types
of questions middle and high school mathematics teachers indicate they would ask to engage
students in making sense of a high-leverage student mathematical contribution and their
reasoning about why particular questions are or are not productive. From the results, it could be
concluded that teachers asked more productive questions after seeing a set of possible questions.
Their beliefs about the productivity of the questions related to a variety of factors, including the
specificity of the question, student participation, student ability and whether incorrect solutions
should be discussed. The results could inform future work with teachers to productively use
student thinking in their teaching.
EXPLICIT RULE LEARNING : A COGNITIVE TUTORIAL METHOD TO TRAIN
USERS OF ARTIFICIAL INTELLIGENCE/MACHINE LEARNING SYSTEMS
Today’s intelligent software systems, such as Artificial Intelligence/Machine Learning systems, are sophisticated, complicated, sometimes complex systems. In order to effectively interact with these systems, novice users need to have a certain level of understanding. An awareness of a system’s underlying principles, rationale, logic, and goals can enhance the synergistic human-machine interaction. It also benefits the user to know when they can trust the systems’ output, and to discern boundary conditions that might change the output. The purpose of this research is to empirically test the viability of a Cognitive Tutorial approach, called Explicit Rule Learning. Several approaches have been used to train humans in intelligent software systems; one of them is exemplar-based training. Although there has been some success, depending on the structure of the system, there are limitations to exemplars, which oftentimes are post hoc and case-based. Explicit Rule Learning is a global and rule-based training method that incorporates exemplars, but goes beyond specific cases. It provides learners with rich, robust mental models and the ability to transfer the learned skills to novel, previously unencountered situations. Learners are given verbalizable, probabilistic if…then statements, supplemented with exemplars. This is followed up with a series of practice problems, to which learners respond and receive immediate feedback on their correctness. The expectation is that this method will result in a refined representation of the system’s underlying principles, and a richer and more robust mental model that will enable the learner to simulate future states. Preliminary research helped to evaluate and refine Explicit Rule Learning. The final study in this research applied Explicit Rule Learning to a more real-world system, autonomous driving. The mixed-method within-subject study used a more naturalistic environment. Participants were given training material using the Explicit Rule Learning method and were subsequently tested on their ability to predict the autonomous vehicle’s actions. The results indicate that the participants trained with the Explicit Rule Learning method were more proficient at predicting the autonomous vehicle’s actions. These results, together with the results of preceding studies indicate that Explicit Rule Learning is an effective method to accelerate the proficiency of learners of intelligent software systems. Explicit Rule Learning is a low-cost training intervention that can be adapted to many intelligent software systems, including the many types of AI/ML systems in today’s world.