ArticleFrontiers in neuroscience2017
Dual Coding Theory Explains Biphasic Collective Computation in Neural Decision-Making.
Article in Frontiers in neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Integrated Ising model with global inhibition for decision-making.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Continuous Attractor Networks for Laplace Neural Manifolds.Computational brain & behavior · 2025Article
- Revealing non-trivial information structures in aneural biological tissues via functional connectivity.PLoS computational biology · 2025Article
- Integrated Ising Model with global inhibition for decision making.bioRxiv : the preprint server for biology · 2024Article
- AI-enhanced collective intelligence.Patterns (New York, N.Y.) · 2024Review
- Identifying a developmental transition in honey bees using gene expression data.PLoS computational biology · 2023Article
- Neuromodulatory control of complex adaptive dynamics in the brain.Interface focus · 2023Review
- Beyond collective intelligence: Collective adaptation.Journal of the Royal Society, Interface · 2023Review
- Investigation of a Novel Activity-Based Checks (ABC) Functional Pain Scale in the Post-Operative Urologic Surgery Patient.Kansas journal of medicine · 2023Article
- Discovering sparse control strategies in neural activity.PLoS computational biology · 2022Article
- Exploring how healthcare teams balance the neurodynamics of autonomous and collaborative behaviors: a proof of concept.Frontiers in human neuroscience · 2022Article
- Quantifying the impact of network structure on speed and accuracy in collective decision-making.Theory in biosciences = Theorie in den Biowissenschaften · 2021Article
- The population doctrine in cognitive neuroscience.Neuron · 2021Review
- Parsing Neurodynamic Information Streams to Estimate the Frequency, Magnitude and Duration of Team Uncertainty.Frontiers in systems neuroscience · 2021Article
- The wisdom of stalemates: consensus and clustering as filtering mechanisms for improving collective accuracy.Proceedings. Biological sciences · 2020Article
- Collective Computation in Animal Fission-Fusion Dynamics.Frontiers in robotics and AI · 2020Article
- Quantifying Dynamical High-Order Interdependencies From the O-Information: An Application to Neural Spiking Dynamics.Frontiers in physiology · 2020Article
- Highlights from the 28th Annual Meeting of the Society for the Neural Control of Movement.Journal of neurophysiology · 2018Article
- Conflicts of interest improve collective computation of adaptive social structures.Science advances · 2018Article
- Coarse-graining as a downward causation mechanism.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2017Review
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3 authors.
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Abstract
A central question in cognitive neuroscience is how unitary, coherent decisions at the whole organism level can arise from the distributed behavior of a large population of neurons with only partially overlapping information. We address this issue by studying neural spiking behavior recorded from a multielectrode array with 169 channels during a visual motion direction discrimination task. It is well known that in this task there are two distinct phases in neural spiking behavior. Here we show Phase I is a distributed or incompressible phase in which uncertainty about the decision is substantially reduced by pooling information from many cells. Phase II is a redundant or compressible phase in which numerous single cells contain all the information present at the population level in Phase I, such that the firing behavior of a single cell is enough to predict the subject's decision. Using an empirically grounded dynamical modeling framework, we show that in Phase I large cell populations with low redundancy produce a slow timescale of information aggregation through critical slowing down near a symmetry-breaking transition. Our model indicates that increasing collective amplification in Phase II leads naturally to a faster timescale of information pooling and consensus formation. Based on our results and others in the literature, we propose that a general feature of collective computation is a "coding duality" in which there are accumulation and consensus formation processes distinguished by different timescales.
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