About

Chantal Stern is a Professor and Department Chair in the Department of Psychological & Brain Sciences at Boston University. She holds a DPhil from Oxford University, England. Her laboratory focuses on mapping the human brain using functional magnetic resonance imaging (fMRI). The primary goal of her research is to study how the normal brain encodes, stores, and recognizes visual, spatial, and verbal information. In addition to investigating normal short-term and long-term memory processes, her work extends to studying normal aging, Alzheimer’s disease, and HIV-related dementia through behavioral testing and fMRI. Graduate students in her Cognitive Neuroimaging Laboratory conduct research within the psychology department at Boston University and at the Massachusetts General Hospital NMR center.

Research topics

• Computer Science
• Psychology
• Neuroscience
• Artificial Intelligence
• Cognitive psychology
• Medicine
• Developmental psychology
• Engineering

Selected publications

• A Feature-Based Generalizable Prediction Model for Both Perceptual and Abstract Reasoning

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• A feature-based generalizable prediction model for both perceptual and abstract reasoning

  Cognitive Neuroscience · 2025-12-15

  article

  A hallmark of human intelligence is the ability to infer abstract rules from limited experience and apply these rules to unfamiliar situations. This capacity is widely studied in the visual domain using the Raven's Progressive Matrices. Recent advances in deep learning have led to multiple artificial neural network models matching or even surpassing human performance. However, while humans can identify and express the rule underlying these tasks with little to no exposure, contemporary neural networks often rely on massive pattern-based training and cannot express or extrapolate the rule inferred from the task. Furthermore, most Raven's Progressive Matrices or Raven-like tasks used to train neural networks consist only of symbolic challenges, whereas humans can flexibly solve both symbolic and perceptual challenges. In this work, we present an algorithmic approach to rule detection and application using feature detection, affine transformation estimation and search. We applied our model to a simplified Raven's Progressives Matrices task, previously designed for behavioral testing and neuroimaging in humans. The model exhibited one-shot inference and achieved near human-level performance in the symbolic reasoning condition of the simplified task. Furthermore, the model can express the relationships discovered and generate multi-step predictions in accordance with the underlying rule. Finally, the model can handle perceptual challenges containing continuous patterns. We discuss our results and their relevance to studying abstract reasoning in humans, as well as their implications for improving intelligent machines.

  PublisherDOI

• (Don't) look where you are going: Evidence for a travel direction signal in humans that is independent of head direction.

  Journal of Experimental Psychology General · 2024-04-01

  articleOpen access

  We often assume that travel direction is redundant with head direction, but from first principles, these two factors provide differing spatial information. Although head direction has been found to be a fundamental component of human navigation, it is unclear how self-motion signals for travel direction contribute to forming a travel trajectory. Employing a novel motion adaptation paradigm from visual neuroscience designed to preclude a contribution of head direction, we found high-level aftereffects of perceived travel direction, indicating that travel direction is a fundamental component of human navigation. Interestingly, we discovered a higher frequency of reporting perceived travel toward the adapted direction compared to a no-adapt control-an aftereffect that runs contrary to low-level motion aftereffects. This travel aftereffect was maintained after controlling for possible response biases and approaching effects, and it scaled with adaptation duration. These findings demonstrate the first evidence of how a pure travel direction signal might be represented in humans, independent of head direction. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

  PublisherOA PDFDOI

• A Feature-based Generalizable Prediction Model for Both Perceptual and Abstract Reasoning

  arXiv (Cornell University) · 2024-03-08

  preprintOpen access

  A hallmark of human intelligence is the ability to infer abstract rules from limited experience and apply these rules to unfamiliar situations. This capacity is widely studied in the visual domain using the Raven's Progressive Matrices. Recent advances in deep learning have led to multiple artificial neural network models matching or even surpassing human performance. However, while humans can identify and express the rule underlying these tasks with little to no exposure, contemporary neural networks often rely on massive pattern-based training and cannot express or extrapolate the rule inferred from the task. Furthermore, most Raven's Progressive Matrices or Raven-like tasks used for neural network training used symbolic representations, whereas humans can flexibly switch between symbolic and continuous perceptual representations. In this work, we present an algorithmic approach to rule detection and application using feature detection, affine transformation estimation and search. We applied our model to a simplified Raven's Progressive Matrices task, previously designed for behavioral testing and neuroimaging in humans. The model exhibited one-shot learning and achieved near human-level performance in the symbolic reasoning condition of the simplified task. Furthermore, the model can express the relationships discovered and generate multi-step predictions in accordance with the underlying rule. Finally, the model can reason using continuous patterns. We discuss our results and their relevance to studying abstract reasoning in humans, as well as their implications for improving intelligent machines.

  PublisherOA PDFDOI

• Functional network reconfiguration supporting memory-guided attention

  Cerebral Cortex · 2023-03-28 · 13 citations

  articleOpen accessSenior authorCorresponding

  Studies have identified several brain regions whose activations facilitate attentional deployment via long-term memories. We analyzed task-based functional connectivity at the network and node-specific level to characterize large-scale communication between brain regions underlying long-term memory guided attention. We predicted default mode, cognitive control, and dorsal attention subnetworks would contribute differentially to long-term memory guided attention, such that network-level connectivity would shift based on attentional demands, requiring contribution of memory-specific nodes within default mode and cognitive control subnetworks. We expected that these nodes would increase connectivity with one another and with dorsal attention subnetworks during long-term memory guided attention. Additionally, we hypothesized connectivity between cognitive control and dorsal attention subnetworks facilitating external attentional demands. Our results identified both network-based and node-specific interactions that facilitate different components of LTM-guided attention, suggesting a crucial role across the posterior precuneus and restrosplenial cortex, acting independently from the divisions of default mode and cognitive control subnetworks. We found a gradient of precuneus connectivity, with dorsal precuneus connecting to cognitive control and dorsal attention regions, and ventral precuneus connecting across all subnetworks. Additionally, retrosplenial cortex showed increased connectivity across subnetworks. We suggest that connectivity from dorsal posterior midline regions is critical for the integration of external information with internal memory that facilitates long-term memory guided attention.

  PublisherOA PDFDOI

• Combining imitation and deep reinforcement learning to human-level performance on a virtual foraging task

  Adaptive Behavior · 2023-09-15 · 3 citations

  article

  We develop a framework to learn bio-inspired foraging policies using human data. We conduct an experiment where humans are virtually immersed in an open field foraging environment and are trained to collect the highest amount of rewards. A Markov Decision Process (MDP) framework is introduced to model the human decision dynamics. Then, Imitation Learning (IL) based on maximum likelihood estimation is used to train Neural Networks (NN) that map human decisions to observed states. The results show that passive imitation substantially underperforms humans. We further refine the human-inspired policies via Reinforcement Learning (RL) using the on-policy Proximal Policy Optimization (PPO) algorithm which shows better stability than other algorithms and can steadily improve the policies pre-trained with IL. We show that the combination of IL and RL match human performance and that the artificial agents trained with our approach can quickly adapt to reward distribution shift. We finally show that good performance and robustness to reward distribution shift strongly depend on combining allocentric information with an egocentric representation of the environment.

  PublisherDOI

• Navigational systems in the human brain dynamically code for past, present, and future trajectories

  bioRxiv (Cold Spring Harbor Laboratory) · 2023-08-23

  preprintOpen access

  Abstract Navigational trajectory planning requires the interaction of systems that include spatial orientation and memory. Here, we used a complex navigation task paired with fMRI pattern classification to examine head and travel direction tuning throughout the human brain. Rather than a single, static network, we report multiple simultaneous subnetworks that 1) have strong connections with both allocentric (world-centered) and egocentric (viewer-centered) movement trajectories, 2) change during the course of exploration, 3) code for past and future movements as well as the present direction, and 4) are strongest for individuals who convert their trajectories into egocentric movements once they have learned the environment. These findings shift our understanding of the neural processes underlying navigation from static structure-function relationships to a dynamic understanding of the multiple brain networks that support active navigation. The insights into the nature of individual navigation abilities uncovered here challenge the dominant framework of largely allocentric coding for successful navigation in complex environments, and replace this with a new framework that relies on multiple co-existing dynamic computations.

  PublisherOA PDFDOI

• Gated transformations from egocentric to allocentric reference frames involving retrosplenial cortex, entorhinal cortex, and hippocampus

  Hippocampus · 2023 · 65 citations

  • Computer Science
  • Neuroscience
  • Psychology

  This paper reviews the recent experimental finding that neurons in behaving rodents show egocentric coding of the environment in a number of structures associated with the hippocampus. Many animals generating behavior on the basis of sensory input must deal with the transformation of coordinates from the egocentric position of sensory input relative to the animal, into an allocentric framework concerning the position of multiple goals and objects relative to each other in the environment. Neurons in retrosplenial cortex show egocentric coding of the position of boundaries in relation to an animal. These neuronal responses are discussed in relation to existing models of the transformation from egocentric to allocentric coordinates using gain fields and a new model proposing transformations of phase coding that differ from current models. The same type of transformations could allow hierarchical representations of complex scenes. The responses in rodents are also discussed in comparison to work on coordinate transformations in humans and non-human primates.

  PublisherOA PDFDOI

• Evidence for a travel direction signal in humans that is independent of head direction

  bioRxiv (Cold Spring Harbor Laboratory) · 2022-08-22

  preprint

  Abstract We often assume that travel direction is redundant with head direction, but from first principles these two factors provide differing spatial information. Although head direction has been found to be a fundamental component of human navigation, it is unclear how self-motion signals for travel direction contribute to forming a travel trajectory. Employing a novel motion adaptation paradigm from visual neuroscience designed to preclude a contribution of head direction, we found high-level aftereffects of perceived travel direction, indicating that travel direction is a fundamental component of human navigation. Interestingly, we discovered a higher frequency of reporting perceived travel toward the adapted direction compared to a no-adapt control – an aftereffect that runs contrary to low-level motion aftereffects. This travel aftereffect was maintained after controlling for possible response biases and approaching effects, and it scaled with adaptation duration. These findings demonstrate the first evidence of how a pure travel direction signal might be represented in humans, independent of head direction.

  PublisherDOI

• Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging task

  arXiv (Cornell University) · 2022-03-11

  preprintOpen access

  We develop a simple framework to learn bio-inspired foraging policies using human data. We conduct an experiment where humans are virtually immersed in an open field foraging environment and are trained to collect the highest amount of rewards. A Markov Decision Process (MDP) framework is introduced to model the human decision dynamics. Then, Imitation Learning (IL) based on maximum likelihood estimation is used to train Neural Networks (NN) that map human decisions to observed states. The results show that passive imitation substantially underperforms humans. We further refine the human-inspired policies via Reinforcement Learning (RL) using the on-policy Proximal Policy Optimization (PPO) algorithm which shows better stability than other algorithms and can steadily improve the policies pretrained with IL. We show that the combination of IL and RL can match human results and that good performance strongly depends on combining the allocentric information with an egocentric representation of the environment.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R01NS040239

  NIH · $1.5M · 2005

• NIH Grant P50MH071702

  NIH · $17.6M · 2011

• NIH Grant P50MH094263

  NIH · $17.3M · 2017

• NIH Grant R01NS041636

  NIH · $1.4M · 2007

• Functional Organization of Navigational Coding in the Human Brain

  NSF · $670k · 2018–2023

Frequent coauthors

• Robert S. Ross

  National Institute for Health Research

  119 shared

• Michael E. Hasselmo

  81 shared

• Karin Schön

  Center for Systems Biology

  72 shared

• Kim Celone

  Boston University

  63 shared

• Thackery I. Brown

  60 shared

• Yakeel T. Quiroz

  Harvard University

  60 shared

• Francisco Lopera

  57 shared

• Elizabeth R. Chrastil

  University of California, Irvine

  57 shared

Labs

• Cognitive Neuroimaging LaboratoryPI

Education

• Other

  Oxford University