ReviewJournal of vision2017
Linking normative models of natural tasks to descriptive models of neural response.
Review in Journal of vision, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Optimal Estimation of Local Motion-in-Depth with Naturalistic Stimuli.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Feature-specific divisive normalization improves natural image encoding for depth perception.bioRxiv : the preprint server for biology · 2024Article
- Continuous psychophysics shows millisecond-scale visual processing delays are faithfully preserved in movement dynamics.Journal of vision · 2024Article
- Shape, perspective, and what is and is not perceived: Comment on Morales, Bax, and Firestone (2020).Psychological review · 2023Article
- Equivalent noise characterization of human lightness constancy.Journal of vision · 2022Article
- Stereo slant discrimination of planar 3D surfaces: Frontoparallel versus planar matching.Journal of vision · 2022Article
- Unraveling Functional Diversity of Cortical Synaptic Architecture Through the Lens of Population Coding.Frontiers in synaptic neuroscience · 2022Article
- Contact lenses, the reverse Pulfrich effect, and anti-Pulfrich monovision corrections.Scientific reports · 2020Article
- Natural statistics of depth edges modulate perceptual stability.Journal of vision · 2020Article
- Natural scene statistics predict how humans pool information across space in surface tilt estimation.PLoS computational biology · 2020Article
- Predicting the Partition of Behavioral Variability in Speed Perception with Naturalistic Stimuli.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
- The statistics of how natural images drive the responses of neurons.Journal of vision · 2019Article
- Computational luminance constancy from naturalistic images.Journal of vision · 2018Article
- Depth variation and stereo processing tasks in natural scenes.Journal of vision · 2018Article
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Authors and funding
2 authors.
Funding
Abstract
Understanding how nervous systems exploit task-relevant properties of sensory stimuli to perform natural tasks is fundamental to the study of perceptual systems. However, there are few formal methods for determining which stimulus properties are most useful for a given natural task. As a consequence, it is difficult to develop principled models for how to compute task-relevant latent variables from natural signals, and it is difficult to evaluate descriptive models fit to neural response. Accuracy maximization analysis (AMA) is a recently developed Bayesian method for finding the optimal task-specific filters (receptive fields). Here, we introduce AMA-Gauss, a new faster form of AMA that incorporates the assumption that the class-conditional filter responses are Gaussian distributed. Then, we use AMA-Gauss to show that its assumptions are justified for two fundamental visual tasks: retinal speed estimation and binocular disparity estimation. Next, we show that AMA-Gauss has striking formal similarities to popular quadratic models of neural response: the energy model and the generalized quadratic model (GQM). Together, these developments deepen our understanding of why the energy model of neural response have proven useful, improve our ability to evaluate results from subunit model fits to neural data, and should help accelerate psychophysics and neuroscience research with natural stimuli.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.