Foundations of human spatial problem solving

Zarr, Noah; Brown, Joshua W.

doi:10.1038/s41598-023-28834-3

Cited by 6 publications

(8 citation statements)

References 87 publications

(97 reference statements)

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“…The model, following the architecture depicted in Figure 8A, was also trained on the same task. Unlike previous models [54], for scalability and universality, we directly represented all information in text for the model (Figure 9D), similar to the sentences displayed on the screen to the human subjects. These texts were encoded by the OpenAI embedding model “text-embedding-ada-002” and then passed to the goal-reducer and the local policy π .…”

Section: Resultsmentioning

confidence: 99%

“…This framework also aligns with the algorithm proposed in this study, where an agent executes actions by simplifying or reducing ultimate goals into smaller, more achievable goals through a simple policy. Therefore, we compared the model's behavior with that of human subjects in a Treasure Hunt task [54], which necessitates flexible goal representation changes.…”

Section: The Goal-reducer In the Brainmentioning

confidence: 99%

“…We validate this in discrete spatial navigation and continuous robot arm manipulation tasks. Notably, when combined with a local policy not requiring iterative training, the framework functions akin to the GOLSA model [54]. We demonstrate that such combinations surpass traditional accelerated RL algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…This universality suggests a potential model for human problem-solving. Recently, a cognitive neural model, GOLSA (Goal-Oriented Learning and Selection of Action) by [17], demonstrated its potential in mimicking human behavior in goal-directed settings [54]. Like the A* algorithm, GOLSA employs a goal reduction mechanism to identify locally solvable intermediate goals leading to the ultimate objective.…”

Section: Introductionmentioning

confidence: 99%

“…Like the A* algorithm, GOLSA employs a goal reduction mechanism to identify locally solvable intermediate goals leading to the ultimate objective. Importantly, GOLSA’s biologically plausible construction suggests its feasibility in human brains, with fMRI results mapping its mechanisms to various brain regions [54].…”

Section: Introductionmentioning

confidence: 99%

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Goal Reduction with Loop-Removal Accelerates RL and Models Human Brain Activity in Goal-Directed Learning

Cheng,

Brown

2024

Preprint

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Goal-directed planning presents a challenge for classical Reinforcement Learning (RL) algorithms due to the vastness of combinatorial state and goal spaces. Humans and animals adapt to complex environments especially with diverse, non-stationary objectives, often employing intermediate goals for long-horizon tasks. Here we propose a novel method for effectively deriving subgoals from arbitrary and distant original goals, called the deep Goal Oriented Learning and Selection of Action, or deepGOLSA model. Using a loop-removal technique, the method distills high-quality subgoals from a replay buffer, all without the need of prior environmental knowledge. This generalizable and scalable solution applies across different domains. Simulations show that the model can be integrated into existing RL frameworks like Deep Q Networks and Soft Actor-Critic models. DeepGOLSA accelerates performance in both discrete and continuous tasks, such as grid world navigation and robotic arm manipulation, relative to existing RL models. Moreover, the subgoal reduction mechanism, even without iterative training, outperforms its integrated deep RL counterparts when solving a navigation task.The goal reduction mechanism also models human problem-solving. Comparing the model’s performance and activation with human behavior and fMRI data in a treasure hunting task, we found matching representational patterns between specific deepGOLSA model components and corresponding human brain areas, particularly the vmPFC and basal ganglia. The results suggest a new computational framework for examining goal-directed behaviors in humans.

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Section: Resultsmentioning

confidence: 99%

Section: The Goal-reducer In the Brainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Goal Reduction with Loop-Removal Accelerates RL and Models Human Brain Activity in Goal-Directed Learning

Cheng,

Brown

2024

Preprint

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Performant web-based interactive visualization tool for spatially-resolved transcriptomics experiments

Sriworarat¹,

Nguyen²,

Eagles³

et al. 2023

Biol. Imaging

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High-resolution and multiplexed imaging techniques are giving us an increasingly detailed observation of a biological system. However, sharing, exploring, and customizing the visualization of large multidimensional images can be a challenge. Here, we introduce Samui, a performant and interactive image visualization tool that runs completely in the web browser. Samui is specifically designed for fast image visualization and annotation and enables users to browse through large images and their selected features within seconds of receiving a link. We demonstrate the broad utility of Samui with images generated with two platforms: Vizgen MERFISH and 10x Genomics Visium Spatial Gene Expression. Samui along with example datasets is available at https://samuibrowser.com. Impact StatementHigh-resolution and multiplexed imaging techniques are giving us an increasingly detailed observation of a biological system. However, sharing, exploring, and customizing the visualization of large multidimensional images without a graphical user interface, is a major challenge. Here, we introduce Samui Browser, a performant and interactive image visualization tool that runs completely in a web browser. To the best of our knowledge, we are the first to propose a web-based solution to share, explore, and annotate large multidimensional images. This is a significant advance as there are no other web-based visualization tools that can share large images. This statement spans commercial and noncommercial platforms. Examples of how someone might use the Samui Browser are to share the images with collaborators who want to visualize the spatial location of gene expression or proteins from multiplex images. Samui Browser can also be used to generate publicly available data resources for publication to make datasets easily accessible to the broader scientific community. BackgroundRecent technological advances have led to the generation of increasingly large and multi-dimensional images that can be used for spatially resolved transcriptomics (SRT) (1) . This enables researchers to map transcriptome-wide gene expression data to spatial coordinates within intact tissue at near-or subcellular

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Debates on the dorsomedial prefrontal/dorsal anterior cingulate cortex: insights for future research

Clairis,

Lopez-Persem

2023

Brain

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The dorsomedial prefrontal cortex/dorsal anterior cingulate cortex (dmPFC/dACC) is a brain area subject to many theories and debates over its function(s). Even its precise anatomical borders are subject to much controversy. In the past decades, the dmPFC/dACC has been associated with more than fifteen different cognitive processes, which sometimes appear quite unrelated (e.g., body perception, cognitive conflict). As a result, understanding what the dmPFC/dACC does has become a real challenge for many neuroscientists. Several theories of this brain area's function(s) have been developed, leading to successive and competitive publications bearing different models, which sometimes contradict each other. During the last two decades, the lively scientific exchanges around the dmPFC/dACC have promoted fruitful research in cognitive neuroscience. In this review, we provide an overview of the anatomy of the dmPFC/dACC, summarize the state of the art of functions that have been associated with this brain area, and present the main theories aiming at explaining the dmPFC/dACC function(s). We explore the commonalities and the arguments between the different theories. Finally, we explain what can be learned from these debates for future investigations of the dmPFC/dACC and other brain regions’ functions.

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Foundations of human spatial problem solving

Cited by 6 publications

References 87 publications

Goal Reduction with Loop-Removal Accelerates RL and Models Human Brain Activity in Goal-Directed Learning

Goal Reduction with Loop-Removal Accelerates RL and Models Human Brain Activity in Goal-Directed Learning

Performant web-based interactive visualization tool for spatially-resolved transcriptomics experiments

Debates on the dorsomedial prefrontal/dorsal anterior cingulate cortex: insights for future research

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