An official website of the United States government
Age-related differences in working memory (WM) can be large, but the exact sources are unclear. We hypothesized that young adults outperform older adults on WM tasks because they use controlled attention processes to prioritize the maintenance of relevant information in WM in a proactive mode, whereas older adults tend to rely on the strength of familiarity signals to make memory decisions in a reactive mode. We used a WM task that cued participants to prioritize one item over others and presented repeated lure probes that cause errors when one is engaged in a reactive mode. Results showed that, relative to young adults with full attention available to use proactive control during the delays, older adults with full attention (and young adults with divided attention) during the delays had exaggerated error rates to repeated lure probes compared to control probes. When the amount of proactive interference was increased (by repeating stimuli across trials), older adults were able to engage in proactive control, and this eliminated their exaggerated error rate (while young adults with divided attention could not). These results provide evidence for a dual mechanisms of control account of age differences in WM.
more »
« less
Eliciting Proactive and Reactive Control During Use of an Interactive Learning Environment
The dual mechanisms of control framework describes two modes of goal-directed behavior: proactive control (goal maintenance) and reactive control (goal activation on task demands). Although these mechanisms are relevant to learner behaviors during interaction with intelligent tutoring systems (ITS), their relation to ITSs is under-researched. We propose a manipulation to induce proactive or reactive control during interaction with an online tutoring system. We present two experiments where students solved problems using either proactive or reactive control. Study 1 validates the manipulation by investigating behavioral measures that reflect usage of the intended strategy and assesses whether either mode impacted learning. Study 2 investigates if alternating between control modes during problem solving affects student performance.
more »
« less
- Award ID(s):
- 1912474
- PAR ID:
- 10462532
- Editor(s):
- Mendez, G.; Matsuda, N.; Santos, O. C.; Dimitrova, V.
- Date Published:
- Journal Name:
- Artificial Intelligence in Education (AIED 2023)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
We developed a novel Proactive Reactive and Attentional Dynamics (PRAD) computational model designed to dissect the latent mechanisms of inhibitory control in human cognition. Leveraging data from over 7,500 participants in the NIH Adolescent Brain Cognitive Development study, we demonstrate that PRAD surpasses traditional models by integrating proactive, reactive, and attentional components of inhibitory control. Employing a hierarchical Bayesian framework, PRAD offers a granular view of the dynamics underpinning action execution and inhibition, provides debiased estimates of stop-signal reaction times, and elucidates individual and temporal variability in cognitive control processes. Our findings reveal significant intra-individual variability, challenging conventional assumptions of random variability across trials. By addressing nonergodicity and systematically accounting for the multi-componential nature of cognitive control, PRAD advances our understanding of the cognitive mechanisms driving individual differences in cognitive control and provides a sophisticated computational framework for dissecting dynamic cognitive processes across diverse populations.more » « less
-
The aim of this study was to investigate to what extent PD affects the ability to walk, respond to balance perturbations in a single training session, and produce acute short-term effects to improve compensatory reactions and control of unperturbed walking stability. Understanding the mechanism of compensation and neuroplasticity to unexpected step perturbation training during walking and static stance can inform treatment of PD by helping to design effective training regimens that remediate fall risk. Current rehabilitation therapies are inadequate at reducing falls in people with Parkinson’s disease (PD). While pharmacologic and surgical treatments have proved largely ineffective in treating postural instability and gait dysfunction in people with PD, studies have demonstrated that therapy specifically focusing on posture, gait, and balance may significantly improve these factors and reduce falls. The primary goal of this study was to assess the effectiveness of a novel and promising intervention therapy (protective step training – i.e., PST) to improve balance and reduce falls in people with PD. A secondary goal was to understand the effects of PST on proactive and reactive feedback responses during stance and gait tasks. Multiple-baseline, repeated measures analyses were performed on the multitude of proactive and reactive performance measures to assess the effects of PST on gait and postural stability parameters. In general, the results indicate that participants with PD were able to use experiences with perturbation training to integrate and adapt feedforward and feedback behaviors to reduce falls. The ability of the participants with PD to adapt to changes in task demands suggests that individuals with PD could benefit from the protective step training to facilitate balance control during rehabilitation.more » « less
-
Recent studies on quadruped robots have focused on either locomotion or mobile manipulation using a robotic arm. However, legged robots can manipulate large objects using non-prehensile manipulation primitives, such as planar pushing, to drive the object to the desired location. This paper presents a novel hierarchical model predictive control (MPC) for contact optimization of the manipulation task. Using two cascading MPCs, we split the loco-manipulation problem into two parts: the first to optimize both contact force and contact location between the robot and the object, and the second to regulate the desired interaction force through the robot locomotion. Our method is successfully validated in both simulation and hardware experiments. While the baseline locomotion MPC fails to follow the desired trajectory of the object, our proposed approach can effectively control both object's position and orientation with minimal tracking error. This capability also allows us to perform obstacle avoidance for both the robot and the object during the loco-manipulation task.more » « less
-
Cognitive control and rule learning are two important mechanisms that explain how goals influence behavior and how knowledge is acquired. These mechanisms are studied heavily in cognitive science literature within highly controlled tasks to understand human cognition. Although they are closely linked to the student behaviors that are often studied within intelligent tutoring systems (ITS), their direct effects on learning have not been explored. Understanding these underlying cognitive mechanisms of beneficial and harmful student behaviors can provide deeper insight into detecting such behaviors and improve predictive models of student learning. In this paper, we present a thinkaloud study where we asked students to narrate their thought processes while solving probability problems in ASSISTments. Students are randomly assigned to one of two conditions that are designed to induce the two modes of cognitive control based on the Dual Mechanisms of Control framework. We also observe how the students go through the phases of rule learning as defined in a rule learning paradigm. We discuss the effects of these different mechanisms on learning, and how the information they provide can be used in student modeling.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
