Melisa, Mateja, Dory, and Sagana presented posters on their new findings at Poster Session C on Sunday the April 30! ![]() Poster C49 - Emergence of Attentional Templates in Concept Learning and the Underlying Neural Mechanisms Melisa Gumus, Wen Jia Zhao , Zoey Zhi Yi Lee, Michael L. Mack ![]() Poster C144 - Menstrual cycle effects on rule-plus-exception category learning vary by BDNF genotype Mateja Perovic, Janice Hou, Shreeansha Bhattarai, Cathlin Han, Michael L. Mack ![]() Poster C151 - Hippocampal Representational Shifts Underlie the Learning of Exceptions to Category Knowledge Yongzhen Xie, Michael L. Mack Poster C21 - Children’s but not adults’ CA2,3/DG differentiates samecategory information in memory
Sagana Vijayarajah, Margaret Schlichting We are excited to be presenting new studies and cool findings! Check out Mateja’s poster on Tuesday and Melisa’s talk on Wednesday! Melisa will also be presenting a poster as part of her SfN trainee professional development award! Congratulations!
![]() Congrats to Dr. Emily Heffernan for successfully defending her PhD thesis! Emily's PhD studies have provided valuable insights into the attentional and neural mechanisms underlying the learning of surprising categorical information. Emily is now a postdoctoral fellow in the APPLY lab at the University of Toronto Mississauga, where she is investigating digital readability. We are all very proud of you, Dr. Heffernan!
Melisa gave an excellent data blitz (and was an outstanding Mack Lab representative) at this year's CEMS. She presented her work on the functional footprints of hippocampal pathways in category learning. Well done, Melisa!
Mack Lab poster presentations at VSS this year!
Saturday, May 18, 2024, 8:30 am – 12:30 pm
![]() Dory Xie published her first paper in Psychonomic Bulletin & Review. In this work, Dory used novel computational modelling & behavioural approaches to show how selective pattern differentiation and integration support learning and generalization of category exceptions. One interesting wrinkle in the data is that latent representational spaces of category items based on a computational model (thanks again, SUSTAIN!) differ from participants' similarity ratings. Indeed, whereas the model's latent space shows that category exceptions are differentiated from items that follow category regularities, explicit similarity ratings suggest participants are simply grouping exceptions with their respective category. Maybe similarity ratings don't reveal cognition's latent spaces? Read it here: https://link.springer.com/article/10.3758/s13423-024-02501-8 The Mack Lab will be all over CNS this year!
Saturday
|
AuthorWrite something about yourself. No need to be fancy, just an overview. Archives
March 2025
Categories |