Narratives & Naturalistic Contexts

In 2018 we explored narratives and natural contexts focusing on analyses, experiments, and models that capture the rich spatiotemporal structure of the “real world.” There has been renewed interest in moving the study of neural and psychological processes from highly controlled laboratory studies to more naturalistic contexts. However, this requires developing new methods to develop experiments and analyze data. We are excited to incorporate some of the latest theoretical, methodological, and experimental advances that are advancing progress towards this goal. Our program will feature a mix of expertise at a broad range of spatiotemporal scales and domains (e.g. behavioral, cognitive, social; circuit, whole-brain, and social networks; etc.).

Participants

Claire Chang, Vassiki Chauhan, Robert Chavez, Hung-Tu Chen, Andy Chen, Damien Crone, Josh Davis, Halle Dimsdale-Zucker, Thomas Donoghue, Justin Drake Shin, Andy Dykstra, Emily Finn, Linda Geerligs, Ariel Goldstein, Jarrod Hicks, Hongmi Lee, Feilong Ma, David Maisson, Judith Mildner, Nils Nyberg, Temidayo Orederu, Lucy Owen, Prateekshit Pandey, Kumar Pankaj Gupta, Athula Pudhiyidath, Angela Radulescu, Touran Rahimi-Moghaddam, Shawn Rhoads, Megan Speer, Mark Straccia, Emma Templeton, Weizhen Xie, Asieh Zadbood

Faculty

                
Luke ChangDartmouth College

Janice ChenJohns Hopkins University

Michale FeeMassachusetts Institute of Technology

Yaroslav HalchenkoDartmouth College

James HaxbyDartmouth College

Christopher HoneyJohns Hopkins University

Alex HuthUniversity of Texas Austin

Caleb KemereRice University

Jeremy ManningDartmouth College

Ida MomennejadColumbia University

Emily Mower-ProvostUniversity of Michigan

Carolyn ParkinsonUniversity of California Los Angeles

Alireza SoltaniDartmouth College

Mark ThorntonPrinceton University

Matthijs van der MeerDartmouth College

Thalia WheatleyDartmouth College

Tutorials

                
Scientific Computingby: Luke Chang

Containersby: Lucy Owen

Githubby: Eshin Jolly

Jupyter Notebooksby: Eshin Jolly

Science Hacking 101by: Jeremy Manning

Tools from the Center for Open Neuroscienceby: Yarik Halchenko

Modeling fine-scale cortical topographiesby: Jim Haxby

Endogenous variation in emotional experiencesby: Luke Chang

Internally generated activity in rodent hippocampusby: Matt van der Meer

Contextual Dynamicsby: Jeremy Manning

Computational modeling at different levelsby: Alireza Soltani

Encoding models for natural stimuliby: Alex Huth

Speech model tutorialby: Alex Huth

The cortical map during ripplesby: Caleb Kemere

Hippocampal analysis with nelpyby: Caleb Kemere

Hierarchical timescales of perception and memoryby: Chris Honey

Timescales tutorialby: Chris Honey

The songbird as a model system for complex learned behaviorsby: Michaele Fee

Shared Perception & Memoryby: Janice Chen

ROI-Level Pattern Similarity Analyses Tutorialby: Janice Chen

Sculpting cognitive mapsby: Ida Momennejad

Successor Representationsby: Ida Momennejad

Conversationby: Thalia Wheatley

Human centered computing and the complexity of emotionby: Emily Mower Provost

The brain in the social worldby: Carolyn Parkinson

Social network tutorialby: Carolyn Parkinson

Gaussian Process Regression with SuperEEGby: Jeremy Manning

The neural representation of social knowledgeby: Mark Thornton

Representational Similarity Analysisby: Mark Thornton