The Human and Machine Learning (HaML) Lab is recruiting a PhD student to start in Fall 2024. If you’re interested in human learning, machine learning, and neuroimaging, apply to work with me at UWM! See for details about research and training in the lab.

Interested in cognitive psychology and neuroimaging? The Human and Machine Learning Lab at UWM is hiring a full-time Research Coordinator to facilitate cutting-edge behavioral, imaging, and simulation studies of human learning! See here for details:.

RT @FrickKaryn: Just 2 more days left to apply to do your PhD with me and my fantastic colleagues in the Neuroscience area of the Dept of P….

RT @cdmlab: It's been a while, but our lab has an @SfNtweets presentation! @AtaBK will talk about how he uses behavioral and neural RSA to….

The OSF link above has a typo; remove the "and" from the end.

Code for running all analyses is publicly available: Data are not yet publicly available but are available upon request. Message me if you want to read the paper but don't have access.

In our new paper, we used pattern analysis to track semantic features during memory formation. To facilitate this approach in other projects, we have released code to estimate semantic similarity for well-known stimuli; see

RT @preston_lab: Our new paper at Cerebral Cortex finds that representations of space in hippocampus and ventromedial prefrontal cortex (vm….

I will be starting a new lab at the University of Wisconsin-Milwaukee this Fall! My lab will examine how the human brain uses cognitive maps to represent structure in the world, facilitate memory retrieval, and guide decision making. @UWMPsychology.

RT @preston_lab: Congratulations to @cacoughlin1 who will be starting a faculty position at the University of Illinois Chicago (#UIC) this….

For the time being, I will stop posting on Twitter. My handle on the other social network that I’m not allowed to talk about here is in my profile, at least until that gets censored.

At #SfN2022 today at 1:15 in SDCC 24 I’ll talk about how an optimal interleaved learning schedule may help shape neural representations in medial prefrontal cortex to support efficient generalization of category knowledge. @sharon_noh @preston_lab

RT @mmack: The Mack Lab is at #SfN22!.Saturday PM poster: @PerovicMateja & @em_heffernan UU2.Sunday AM poster: @yongzhen_xie WW37.Monday PM….

RT @preston_lab: Check out our posters and talks at #SfN2022! There's a lot going on and we look forward to seeing you all there! https://t….

The analyses in Psifr are based on measures developed by @PolynSean, @KahanaMichael, myself, and many others. See the user guide for references.

Psifr is written in Python, but the in-development {psifrr} R package makes it possible to call Psifr functions directly from R. Feedback on both Psifr and {psifrr} is welcome!.

Psifr is designed to be extensible, and new analyses are being added over time. The codebase for Psifr has been peer-reviewed and is published in the Journal of Open Source Software. Special thanks to @cMadan and @paxt0n4 for their helpful feedback!.

Each analysis in Psifr is checked for correctness using automated testing. These tests, which are defined based on published test cases when available, are automatically run every time the codebase is changed to ensure correctness and stability.

Each analysis is highly customizable. For example, if there were multiple categories of items on a list, you may want to conditionalize an analysis based on whether each transition was within the same category or across different categories.

Many standard analyses are supported in Psifr, such as recall by serial position and probability of Nth recall. Recall order can be analyzed to test for clustering based on temporal, categorical, or semantic features of the study list.