Effectiveness of pre-training in relational rule learning

Ongoing. Even young children can learn simple relational rules dependent on color or orientation, but what explains how we reapply rules once we’ve acquired them? This study, supported by the LearnLab and the OAK Lab, assesses how well human adults can learn a simple relation, and how their performance impacts their ability to acquire complex rules dependent on what they have already learned.

“Relational discrimination in non-human primates”

In prep. A plethora of research exists on the understanding of relations between associated concepts or objects, but we as a species lack sufficient explanation for how this ability for abstract thought became possible.  This study, conducted through the CAOs Lab, explores such questions by providing touch-screen games to young children and non-human primates.

“Learning Capacity Across Species and Age to Identify Origins of Human Uniqueness”

Presented at the 2022 Cognitive Developmental Society Conference. While many domain-specific hypotheses address human uniqueness through the lens of specific skills or types of cognition, accounting for general measures of information processing capacity is essential for accurate assessments of ability. This study assesses how the performance of adults, children, and non-human primates compares on a simple matching task.

Project Humans

Ongoing. A tool being developed through the Language and Learning Lab and led by Dr. Jon Willits, this project seeks to make the integration of neural networks into a simulated 3D environment like that facilitated by Unity more approachable to non-programmers.

“Evaluating the contribution of visual feature heterogeneity in conjunction search performance”

Undergraduate Thesis work. In recent years research at Vision Lab has been focused on the parallel processing in visual search and the variability in accumulation of information on non-target distractors to direct attention to the goal stimuli. This work, as a continuation of (Lleras, Wang, Ng, Ballew, Xu, & Buetti, 2020) seeks to investigate the visual search response time in heterogeneous displays when predicted by the distractors’ respective homogeneous search trends.