About

Ilker Yildirim is a professor of psychology at Yale’s Faculty of Arts and Sciences. His research focuses on understanding memory development, particularly how memories are encoded and retrieved in the brain during early childhood. He is the senior author of a Yale study that provides new insights into infantile amnesia, demonstrating that memories can indeed be encoded in the hippocampus during the first years of life, contrary to previous beliefs that the hippocampus is not developed enough to support memory encoding at that stage. Yildirim’s work involves pioneering methods for conducting functional magnetic resonance imaging (fMRI) with awake infants, which has historically been challenging due to infants’ short attention spans and inability to follow instructions. His research explores the development of different types of memory, including episodic memory and statistical learning, and investigates how these memory systems evolve during infancy and childhood. As part of his ongoing work, Yildirim is examining the durability of hippocampal memories over time and their potential persistence into adulthood.

Research topics

• Computer Science
• Artificial Intelligence
• Psychology
• Cognitive psychology
• Cognitive science
• Information Retrieval
• Natural Language Processing
• Linguistics
• Physics
• Mathematics
• Computer vision
• Epistemology

Selected publications

• Hypothesis-driven identification of neural algorithms with dynamical structure-preserving manifolds

  2026-05-08

  articleOpen accessSenior author
  PublisherOA PDFDOI

• L3GO: Language Agents with Chain-of-3D-Thoughts for Generating Unconventional Objects

  2025-01-01 · 3 citations

  articleOpen access

  Yutaro Yamada, Khyathi Chandu, Bill Yuchen Lin, Jack Hessel, Ilker Yildirim, Yejin Choi. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations). 2025.

  PublisherOA PDFDOI

• Computational models reveal that intuitive physics underlies visual processing of soft objects

  Nature Communications · 2025-07-09 · 3 citations

  articleOpen accessSenior author

  Computational explorations of human cognition have been especially successful when applied to visual perception. Existing models have primarily focused on rigid objects, emphasizing shape-preserving invariance to changes in viewpoint, lighting, object size, and scene context. Yet many objects in our everyday environments, such as cloths, are soft. This poses both quantitatively greater and qualitatively different challenges for models of perception, due to soft objects' dynamic and high-dimensional internal structure, as in the changing folds and wrinkles of a cloth waving in the wind. Soft object perception is also correspondingly rich, involving distinct properties such as stiffness. Here we explore the ability of different kinds of computational models to capture visual perception of the physical properties of cloths (e.g., their degrees of stiffness) undergoing different naturalistic transformations (e.g., falling vs. waving in the wind). Across visual matching tasks, both the successes and failures of human performance are well explained by Woven: a new model that incorporates physics-based simulations to infer probabilistic representations of cloths. Woven outperforms powerful, performance-equated alternatives, including its ablations and a deep neural network, and suggests that humanlike machine vision may also require representations that transcend image statistics, and involve intuitive physics.

  PublisherOA PDFDOI

• A new algorithm of human attention – ERRATUM

  Behavioral and Brain Sciences · 2025-01-01 · 1 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Adaptive computation as a new mechanism of dynamic human attention.

  Psychological Review · 2025-06-26 · 4 citations

  articleSenior author

  -a new computational mechanism of human attention that bridges the momentary application of perceptual computations with their impact on decision outcomes. Adaptive computation is a dynamic algorithm that rations perceptual computations across objects on-the-fly, enabled by a novel and general formulation of task relevance. We evaluate adaptive computation in a case study of multiple object tracking (MOT)-a paradigmatic example of selection as a dynamic process, where observers track a set of target objects moving amid visually identical distractors. Adaptive computation explains the attentional dynamics of object selection with unprecedented depth. It not only recapitulates several classic features of MOT (e.g., trial-level tracking accuracy and localization error of targets), but also captures properties that have not previously been measured or modeled-including both the subsecond patterns of attentional deployment between objects, and the resulting sense of subjective effort. Critically, this approach captures such data within a framework that is in-principle domain-general, and, unlike past models, without using any MOT-specific heuristic components. Beyond this case study, we also look to the future, discussing how adaptive computation may apply more generally, providing a new type of mechanistic model for the dynamic operation of many forms of visual attention. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

  PublisherDOI

• Intuitive physics underlies material perception: Computational, psychophysical, and neural evidence

  Journal of Vision · 2025-07-15

  articleOpen accessSenior author

  From the wrinkles and folds a soft object makes, how do we see, not just these changes in geometry, but also physical material properties, including their mass and stiffness? A common view states that the brain relies on high-level image and motion statistics that differentiate the degrees of these physical properties (e.g., discriminating a soft cloth from a stiff cloth). I’ll counter this view with an alternative framework, in which the brain inverts an internalized, physics-based generative model to arrive at the scene-level causes underlying visual inputs. In this account, material perception is cast as posterior inference of physical properties under a generative model of “soft body dynamics” (a game-engine style description of how non-rigid materials move and react to external forces) and simple graphics to project these scenes to sensory measurements. I’ll present a computational model that implements this framework and evaluate it in psychophysical and neural experiments. First, I’ll show that the physics-based model explains both the successes and failures in material perception across multiple match-to-sample tasks. It outperforms a performant DNN model that solves material perception by acquiring high-level image and motion statistics for discriminating the physical properties. Next, I'll evaluate these models in a new fMRI experiment. I'll present evidence for a double dissociation, where a set of higher-order frontoparietal regions aligns with the physics-based model and an occipitotemporal region aligns with DNN. Together, these findings suggest that visual material perception transcends image statistics to also involve intuitive physics—formalized as probabilistic simulations of soft-body dynamics.

  PublisherDOI

• A new algorithm of human attention

  Behavioral and Brain Sciences · 2025-01-01

  articleSenior authorCorresponding

  Abstract How do our goals continually impact perceptual processing? The answer could arise from a computational specification of perception in terms of visual tasks, or perhaps several mechanisms operating over specific contexts. Here, we suggest an alternative: adaptive computation , a new algorithmic account of attention that rations the general resource of perceptual computations according to their impact on decision making.

  PublisherDOI

• Multiarea processing in body patches of the primate inferotemporal cortex implements inverse graphics

  Proceedings of the National Academy of Sciences · 2025-07-08 · 1 citations

  articleOpen accessSenior author

  Stimulus-driven, multiarea processing in the inferotemporal (IT) cortex is thought to be critical for transforming sensory inputs into useful representations of the world. What are the formats of these neural representations and how are they computed across the nodes of the IT networks? A growing literature in computational neuroscience focuses on the computational-level objective of acquiring high-level image statistics that supports useful distinctions, including between object identities or categories. Here, inspired by classic theories of vision, we suggest an alternative possibility. We show that inferring 3D objects may be a distinct computational-level objective of IT, implemented via an algorithm analogous to graphics-based generative models of how 3D scenes form and project to images, but in the reverse order. Using perception of bodies as a case study, we show that inverse graphics spontaneously emerges in inference networks trained to map images to 3D objects. Remarkably, this correspondence to the reverse of a graphics-based generative model also holds across the body processing network of the macaque IT cortex. Finally, inference networks recapitulate the feedforward progression across the stages of this IT network and do so better than the currently dominant vision models, including both supervised and unsupervised variants, none of which aligns with the reverse of graphics. This work suggests inverse graphics as a multiarea neural algorithm implemented within IT, and points to ways for replicating primate vision capabilities in machines.

  PublisherOA PDFDOI

• From task structures to world models: what do LLMs know?

  Trends in Cognitive Sciences · 2024 · 72 citations

  1st authorCorresponding
  • Computer Science
  • Psychology
  • Cognitive science
  PublisherOA PDFDOI

• Response to Goddu et al.: new ways of characterizing and acquiring knowledge

  Trends in Cognitive Sciences · 2024-09-19 · 1 citations

  review1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Robert A. Jacobs

  17 shared

• Joshua B. Tenenbaum

  Massachusetts Institute of Technology

  16 shared

• Josh Tenenbaum

  Massachusetts Institute of Technology

  8 shared

• Brian J. Scholl

  Yale University

  7 shared

• Goker Erdogan

  7 shared

• Jiajun Wu

  Jianghan University

  7 shared

• Kimberly W. Wong

  Yale University

  6 shared

• Wenyan Bi

  Yale University

  6 shared

Labs

• Yale Brain Imaging LabPI

Awards & honors

• Breakthrough Prize in Fundamental Physics (2026)