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​Possible Intelligences​

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I'm a neuroscientist and AI researcher. My research concerns measuring and modeling the human inner world, from memory and replay to compositional generalization and reasoning, and building and evaluating AI based on this work.

I aim to understand & model varieties of intelligences & agency, especially the biological intelligence of living minds. I'm interested in this at both individual and collective scales. Scientifically, I ask: How do learning, memory, & agency interact to shape natural intelligence? How can this inform building and evaluating computational architectures that generalize & reason in open-ended settings? For two decades, I pursue these questions across human cognition, reinforcement learning (RL), neuroscience, & AI. Philosophically, I am interested in the ontology (or what we take to exist as real) & the epistemology (how do we come to understand) of living & artificial intelligences. My philosophical work examines living & artificial intelligences, genealogies that shaped contemporary notions of mind & AI, & alternatives to optimization-centric views of intelligence.

Currently, I am a Principal Researcher at Microsoft Research NYC, where I broadly focus on building and evaluating generative AI, inspired by my research in cognitive neuroscience, reinforcement learning, and NeuroAI.  I study how humans and AI build models of the world and use them in memory, exploration, & planning. 

My background is in cognitive computational neuroscience (via computer science & philosophy). I've previously worked at Columbia University and Princeton, did my PhD in Berlin's Bernstein Center for Computational Neuroscience, BSc in software engineering (Tehran, Iran), & MSc in Philosophy of Science (Utrecht, Netherlands). 

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I cohost The Learning Salon (with John Krakauer & Melanie Mitchell), & have served as a mentor at the New Museum's New Inc, the incubator for creative science. Recent talk videos include Mathematics of Neuroscience: Memory & planning in Brains & AI and Studying Play: What games reveal about minds & machines. You can hear about my work on Rethinking intelligence, AI frontiers podcast, on neuroscience at BrainInspired + Deep RL & Dopamine + Neuro Deep learning, & on collective cognition Parsing Science. â€‹

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Publications & preprints

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Ida Momennejad (2025) Memory and Planning in Brains and Machines: Multiscale Predictive Representations. Space, Time, and Memory, Edited by Lynn Nadel and Sara Aronovitz, forthcoming book in Oxford University Press.
[ OUP Book link,  arxiv ]

Oliver Eberle, Thomas McGee, Hamza Gaffer, Taylor Webb, Ida Momennejad (2025) Position: We Need An Algorithmic Understanding of Generative AI. International Conference for Machine Learning, ICML 2025, [Spotlight, arXiv ] 

Shuhao Fu, Andrew Jun Lee, Yixin Anna Wang, Ida Momennejad, Trevor Bihl, Hongjing Lu, Taylor Whittington Webb (2025) Evaluating Compositional Scene Understanding in Multimodal Generative Models. TMLR 2025. [arXiv]

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Taylor Webb, Shanka S. Mondal, Ida Momennejad (2025). A brain-inspired agentic architecture to improve planning with LLMs. Nature Communications 16, 8633. [ link ]

Samuel Lippl, Thomas McGee, Kimberly Lopez, Ziwen Pan, Pierce Zhang, Salma Ziadi, Oliver Eberle, Ida Momennejad (2025). Algorithmic Primitives and Compositional Geometry of Reasoning in Language Models. [ arxiv ]

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Ida Momennejad (2026) The Ontological Reversal of Computation and the Brain. Forthcoming in Philosophy and the Mind Sciences. [ Phil papers ]​

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Eleni Nisioti, Sebastian Risi, Ida Momennejad, Pierre-Yves Oudeyer, Clement Moulin-Frier (2024) Collective Innovation in Groups of Large Language Models. ALife 2024. [ arXiv ] 

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Ida Momennejad*, Hosein Hasanbeig*, Felipe Vieira Frujeri*, Hiteshi Sharma, Robert Ness, Nebojsa Jojic, Hamid Palangi, Jonathan Larson (2023)  Evaluating cognitive maps and planning in Large Language Models with CogEval.
[ arXiv ] [ GitHub ] [ conversation logs ] NeurIPS 2023.

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Taylor Webb*, Shanka Subra Mondal*, Yi Wan, Brian Krabach, Ida Momennejad (2023) A prefrontal cortex-inspired architecture for planning in Large Language Models.  

[ arXiv ] [ Github ]

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Safoora Yousefi, Leo Betthauser, Hosein Hasanbeig, Raphael Milliere, Ida Momennejad (2023) Decoding In-Context Learning: Neuroscience-inspired Analysis of Representations in Large Language Models. 

[ arXiv ]

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Hosein Hasanbeig, Hiteshi Sharma, Leo Betthauser, Felipe Vieira Frujeri,
Ida Momennejad (2023) ALLURE: Auditing and improving LLM-based evaluation of text using iterative in-context-learning. 

[ arXiv ]

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Sugandha Sharma, Guy Davidson, Khimya Khetarpal, Anssi Kanervisto, Udit Arora, Katja Hofmann, Ida Momennejad (2024) Toward Human-AI Alignment in Large-Scale Multi-Player Games. 

[ arXiv ]

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Imitating Human Behavior with Diffusion Models (2023)  Pearce, Rashid, Kanervisto, Bigness, Sun, Georgescu, Mac, Zheng Tan, Momennejad, Hofmann, Devlin. ICLR 2023 [ link ]

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Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Harm van Seijen, Sarath Chandar (2023) Replay Buffer With Local Forgetting for Adaptive Deep Model-Based Reinforcement Learning. CoLLAs 2023.

[ arXiv ]

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Stephanie Milani, Arthur Juliani, Ida Momennejad, Raluca Georgescu, Jaroslaw Rzpecki, Alison Shaw, Gavin Costello, Fei Fang, Sam Devlin, Katja Hofmann (2023)  Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games. ACM CHI (Conference on Human Factors in Computing Systems) 2023. [ arXiv ]

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Ida Momennejad (2022)  A Rubric for Human-like Agents and NeuroAI.

Philosophical Transactions of the Royal Society B

[ journal link ]  [ free preprint arXiv ]

Eigen Memory Trees (2022) Mark Rucker, Jordan T. Ash, John Langford, Paul Mineiro, Ida Momennejad

In revision [ arXiv ]

Social Network Structure Shapes Innovation: Experience-sharing in RL with SAPIENS (2022)  Eleni Nisioti, Mateo Mahaut, Pierre-Yves Oudeyer, Ida Momennejad, Clément Moulin-Frier 

In revision [ arXiv ] [ Github Repo ]

Interaction-Grounded Learning with Action-inclusive Feedback
(2022) Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford

NeurIPS 2022 [ arXiv  preprint]

Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods (2022)  Yi Wan, Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Sarath Chandar, Harm van Seijen [ arXiv ]

ICML 2022

NeuroNav: A Library for Neuroscience Inspired Learning (2022) Julliani, Barnett, Davis, Sereno, Momennejad, RLDM 

[ arXiv ] [ Githib Repo ]

PARSR: Priority Adjusted Replay for Successor Representation (2022)

Barnett, Momennejad, RLDM 2022

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Describeland: One-Shot Learning from a Demonstration with Hierarchical Latent Language (2022) Nathaniel Weir, Xingdi Yuan, Marc-Alexandre Cote, Matthew Hausknecht,  Romain Laroche, Ida Momennejad, Harm Van Seijen, Benjamin Van Durme.  AAMAS.

How Humans Perceive Human-like Behavior in Video Game Navigation (2022) Zuniga*, Milani*, Leroy*, Rzepecki, Georgescu, Momennejad, Bignell, Sun, Shaw, Costello, Jacob, Devlin, Hofmann. 
CHI Late Breaking Work, 2022.    [ PDF ] 

https://github.com/microsoft/NTT

Momennejad I (2021) Collective Minds: Social Network Topology Shapes Collective Cognition. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 377 (1843): 20200315.
[ PhilTrans , PDF ]

Brunec I,  Momennejad I (2021) Predictive Representations in Hippocampal and Prefrontal Hierarchies. Journal of Neuroscience 19 November 2021, JN-RM-1327-21.
[ JNeuro Link , free PDF ] 

Pudhiyidath A, Morton NW, Viveros Duran R, Schapiro AC,  Momennejad I, Hinojosa-Rowland DM, Molitor RJ, Preston AR (2022-accepted) Representations of temporal community structure in hippocampus and precuneus predict inductive reasoning decisions.

The Journal of Cognitive Neuroscience    [ bioRxiv ] 

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Momennejad I*, Krakauer J*, Sun C, Yezeretz E, Rajan K, Vogelstein J, Wyble B (2021) The Learning Salon: Toward a new participatory science. Neuron. 
[ Neuron link, PDF ]

Vlasceanu M, Dudik M, Momennejad I (2021) Network Structure, Gender Diversity, and Interdisciplinarity Predict the Centrality of AI Organizations.
[ psyRxiv PDF ]

Russek EM, Momennejad I, Botvinick M, Gershman S, Daw N (2021) Neural evidence for the successor representation in choice evaluation.
[ bioRxiv PDF ] 

Xie T, Lanford J, Mineiro P, Momennejad I (2021) Interaction Grounded Learning. ICML 2021. [ arXiv PDF ]

Devlin S*, Georgescu R*, Momennejad I*, Rzepecki J*, Zuniga E*, Costello G, Leroy G, Shaw A, Hofmann K (2021) Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation. ICML 2021. [ arXiv PDF ] 

https://github.com/microsoft/NTT​

Momennejad I (2020) Learning Structures: Predictive Representations, Replay, and Generalization. Current Opinions in Behavioral sciences.

[ journal link ]

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Brunec I, Momennejad I (2019) Predictive Representations in Hippocampal and Prefrontal Hierarchies.
[ bioRxiv preprint ]

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Sievers B, Momennejad I (2019) SAMPL: Spreading Activation and Memory Plasticity Model.
[ bioRxiv preprint , Model Code on GitHub ]

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Momennejad I, Sinclair S, Cikara M (2019) Computational justice: Simulating structural bias and interventions.
[ bioRxiv preprint , 04/21/2019: python workshop ]

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Zorowitz S, Momennejad I, Daw N (2020) Anxiety, avoidance, and sequential evaluation. Computational Psychiatry, 0 0:0, February, 1–17.
[ Link to published paper , see 2019 bioRxiv preprint ]

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Momennejad I, Duker A, Coman A (2019) Bridge ties bind collective memories. Nature Communications. 
[ Open access paper , Podcast ]

https://pubmed.ncbi.nlm.nih.gov/33307099/

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Momennejad I, Norman KA, Cohen JD, Singh S, Lewis RL (2020). Rational use of Episodic and Working Memory: A Normative Account of Prospective Memory. Neuropsychologia. [ link ] doi:10.1016/j.neuropsychologia.2020.107657. Epub 2020 Dec 8. 

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Momennejad I, Howard M (2018) Predicting the future with multi-scale successor representations.
[ bioRxiv preprint, status: in revision ]

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Momennejad I, Otto RA, Daw N, Norman KA (2018) Offline replay supports planning in human reinforcement learning. eLife 2018;7:e32548. 

[ eLife link, Talk at CCN 2017 , PDF,  open access dataset]

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Russek E*, Momennejad I*,  Botvinick MM, Gershman SJ, Daw N (2017) Predictive representations can link model-based reinforcement learning to model-free mechanisms. Plos Comp Biol.
[ Journal link,  PDF, bioRxiv preprint,  CODE ]

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Momennejad I*, Russek E*, Cheong JH, Botvinick MM, Daw N, Gershman SJ (2017) The successor representation in human reinforcement learning: evidence from retrospective revaluation. Nature Human Behaviour, 1. [ Nat Hum Beh paper , PDF, free preprint , my python tutorials comparing SR & SR-Dyna,  blogpost from Deepmind ] 

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Coman A, Momennejad I, Drach R, Geana A (2016) Mnemonic convergence in social networks: The emergent properties of cognition at a collective level. PNAS. doi: 10.1073/pnas.1525569113.
[ Journal link , Podcast ]

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Wisniewsky D, Reverberi C, Momennejad I, Kahnt T, Haynes J-D (2016) The role of parietal cortex in the representation of task-reward-association. The Journal of Neuroscience. [ Journal link ] 

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Haynes J-D, Wisniewsky D, Gorgen K, Momennejad I, Reverberi C (2015) fMRI decoding of intentions: compositionality, hierarchy, and prospective memory. Brain-Computer Interface (BCI), 3rd International Winter Conference. doi: 10.1109/IWW-BCI.2015.7073031. [ IEEE link ]

Momennejad I, Haynes J-D (2013) Encoding of prospective tasks in the human prefrontal cortex under varying task load. The Journal of Neuroscience 33(44):17342-17349. [ Journal link + PDF file ]

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Momennejad I, Haynes J-D (2012) Human anterior prefrontal cortex encodes the 'what' and 'when' of future intentions. Neuroimage 61(1):139-48.

[ Journal link + PDF file ]

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Recent Teaching

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Sample Talks and Workshops (April-September 2019)

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April 21, 1-5 PM: Computational Justice Workshop (hands-on agent-based simulation in Python), Heart of the Machine series, Pioneerworks, Brooklyn, NY

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May 13-14: Context and Episodic Memory (CEMS), Philadelphia, PA

 

May 16-18: Control Processes 2019, Brown University, Providence, RI

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May 28-29: Talk and visit, Computational Neuroscience Center, University of Washington, Seatle, WA

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July 10, 1-5PM: Structure for Efficient Reinforcement Learning (SERL) Workshop
4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), Montreal, Canada

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August 8-17: "Cognitive Maps" at MIND19 summer school for computational methods in neuroscience at Dartmouth​

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September 10-13: What is biological Computation? Santa Fe Institute

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Book chapters 

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  • Momennejad I, Allahyari M (2017) Refiguring: in Conversation. Futureproof, Haverford College, Haverford, PA, pp 16-31.

  • Momennejad I (2011) Thinking in movements, rehearsing freedom. In: Wagner L and Gomes-Carrillo de Castro Eds., Sounds, Space, Body, a process. Ernst Schering Foundation and Association of Neuroesthetics: Berlin, pp:115-125.

  • Momennejad I (2011) Seeing with eyes closed: the neuro-epistemology of perceptual reality. In: Agudio E and Franke I Eds., Seeing with eyes closed. Association of Neuroesthetics: Berlin, pp:15-21.

  • Momennejad I and Franke I (2011) Two practices of seeing with eyes closed: contemporary art and science in dialogue. In: Agudio E and Franke I Eds., Seeing with eyes closed. Association of Neuroesthetics: Berlin, pp:9-13.

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Media: Talks, outreach, press

 

idamo [at] microsoft.com
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