Linking normative models for natural tasks and subunit models of neural response
Bibliographic record
Abstract
Understanding how the nervous system exploits task relevant properties of sensory stimuli to perform natural tasks is central to the study of perceptual systems. Recently, a Bayesian ideal observer method was developed for task-specific dimensionality reduction called Accuracy Maximization Analysis. AMA returns the encoding filters (receptive fields) that extract the most useful stimulus features for specific estimation and categorization tasks. Unfortunately, in its original form, AMA's compute time is quadratic in the number of stimuli in the training set, rendering it impractical for large scale problems without specialized computing resources. Here, we develop AMA-Gauss, a new more practical form of AMA that reduces compute time from quadratic to linear in the number of stimuli by incorporating the assumption that the conditional filter responses are Gaussian distributed. First, we verify the expected compute time decreases with two fundamental tasks in early vision: binocular disparity estimation and retinal speed estimation. Second, we demonstrate that the task-specific receptive fields returned by AMA-Gauss closely approximate the properties of receptive fields in cortex. Third, we show that the Gaussian assumption is justified for all three tasks with natural stimuli and biologically realistic contrast normalization. Fourth, we show that quadratic computations are required to compute the likelihood function and posterior probability distribution over the latent variable. Fifth, we make explicit the formal similarities between AMA-Gauss and the Generalized Quadratic Model (GQM), a recently developed method for neural systems identification. Together, these results provide a normative explanation for why energy-model-like (i.e. quadratic) computations account well for the response properties of neurons involved in these tasks. These developments should help accelerate research with natural stimuli, deepen our understanding of why classic descriptive models have proved successful, and improve our ability to evaluate results from subunit model fits to neural data. Meeting abstract presented at VSS 2017
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".