We are very excited to share Melisa Gumus' paper, "Distinct contributions of hippocampal pathways in learning regularities and exceptions revealed by functional footprints", recently published in PNAS. In this work, Melisa proposes a new method that localizes hippocampal pathways with diffusion weighted imaging to interrogate their distinct functional signatures during category learning. She finds that whereas category regularities engage the monosynaptic pathway (i.e., the direct pathway between entorhinal cortex and hippocampal subfield CA1) early in learning, category exceptions engage the trisynaptic pathway (i.e., the pathway that loops in dentate gyrus and CA3) later in learning. These findings provide unique supporting evidence for the functional distinction for these pathways and offer a multimodal approach to further characterizing information flow in the brain. Fun fact: Melisa collected this dataset as an undergrad in 2018!

Read the paper and check out the press release!

A huge congratulations to Dr. Melisa Gumus for successfully defending her PhD thesis! Melisa's impressive dissertation hit all the corners of the lab's research: characterizing white matter pathways in the hippocampus and their relationship to cognition, linking computational mechanisms of learning to hippocampal processes and representations, and exploring how attention learning is encoded into flexible concept knowledge. Well done, Dr. Gumus!

Melisa was one of the first trainees in the Mack Lab, starting way back in 2016 as an undergraduate research assistant. In the following 9 years, she wore many hats in the lab including RA, lab manager, graduate RA while she completed a master's degree with Carmela Tartaglia, and finally PhD student in Psychology. The Mack Lab is what it is today because of Melisa's persistent motivation for innovative research, her infectious positive spirit, and her gracious support of everyone around her. We will miss her dearly!

Melisa's next step is a postdoctoral position with Mariam Aly at UC Berkeley. Best of luck, Dr. Gumus!!

Congratulations to Dr. Mateja Perovic on a very successful defence of her PhD dissertation! Mateja's dissertation details her impressive and ambitious research on how sex hormones and menstrual cycle impact the core mechanisms of category learning. Spanning behavioural experiments, fMRI, hormone analysis, and genotype testing, Mateja's PhD work is comprehensive and provides key theoretical insights in fields of cognitive psychology, neuroscience, and women's health. So very proud of Dr. Perovic!

Mateja's next stop is a postdoctoral position, supported by a CIHR Postdoctoral Fellowship, in Liisa Galea's lab. Good luck, Dr. Perovic!

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 F47 - Familiarity to everyday scene categories in adults and children.
Sanjivan Loganathan, Sagana Vijayarajah, Margaret Schlichting

​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!

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