Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Power-law efficient neural codes provide general link between perceptual bias and discriminability
Authors: Michael Morais, Jonathan W. Pillow
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In simulations, we explore a range of SNRs and power-law efficient codes to verify these results, and examine a variety of decoders including posterior mode, median, and mean estimators (Section 5), demonstrating the universality of the bias-discriminability relationship across a broad space of models. We used simulated data to test our derived nonlinear and linear relationships between bias and discriminability (eqs. 12 & 13). |
| Researcher Affiliation | Academia | Michael J. Morais & Jonathan W. Pillow Princeton Neuroscience Institute & Department of Psychology Princeton University mjmorais, EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about releasing source code for the methodology described. |
| Open Datasets | No | The paper describes generating 'random smooth priors' for simulations ('As such, we draw random priors as exponentiated draws from Gaussian processes on [−π, π], according to Z exp(f), where f ∼ GP(0, K)') rather than using a pre-existing publicly available dataset, and does not provide access information for this generated data. |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset split information. It discusses using 'simulated data' and 'random priors' for its analysis without specifying formal data partitions for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In all simulations, we propagate each stimulus x ∼ p(x) on a finely tiled grid through a Bayesian observer model numerically, computing a posterior p(x|y) / p(x)N(y; x, kp(x) q) for a power-law efficient code under many powers q and SNRs k, and for each computed the Bayesian estimators associated with various loss functions of interest. [...] As such, we draw random priors as exponentiated draws from Gaussian processes on [−π, π], according to Z exp(f), where f ∼ GP(0, K) for Z as a normalizing constant, and K the radial basis function kernel wherein K(xi, xj) = 2 exp(− 1 2σ2kxixjk2) with magnitude = 1 and lengthscale = 0.75, selected such that a typical prior was roughly bimodal. |