Natalia Alzate ’24 and Joemari Pulido ’24
Implicit Statistical Learning in Morphological Structure and Sub-lexical MO-LS Mappings
Natalia Alzate ’24, Biology and Psychology major, Philosophy minor
Joemari Pulido ’24, Computer Science and Psychology major
Faculty Mentor: Dr. Johanna Morris, Psychology
Implicit Statistical Learning (ISL) is the mechanism that enables humans to pick up on patterns in the environment. Is ISL being used to acquire word structure or morphology when reading? Do individual differences in ISL influence the ability to acquire word structure? Reading and writing are essential skills, especially in the world today. Still, learning disorders, such as dyslexia, make it difficult for individuals to acquire these much-needed skills. By understanding the role that ISL plays in the acquisition of word structure and the extent to which individual differences influence this process, we will have a better understanding of how to aid individuals, such as those with dyslexia, to acquire the skills of reading and writing. This study’s overall aim is to measure reader’s lexical representation quality, sensitivity to the internal structure of words, and the ability to notice statistical patterns in visual sequences. These behavioral measures will then be correlated with event-related potentials (ERPs) to identify neural signals of ISL and determine if similar neural signals are used during the decomposition of complex words. The data presented here are electrophysiological data and demographic data which suggest that there are two different types of readers that can be differentiated based on their ability to decompose word structure when reading.
Poster Presentation: Wednesday, April 26, 2 – 4 p.m.
