A Bayesian Framework for Modeling Confidence in Perceptual Decision Making
Authors: Koosha Khalvati, Rajesh PN Rao
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our model on two experiments on confidence-based decision making involving the well-known random dots motion discrimination task. In both experiments, we show that our model s predictions closely match experimental data. |
| Researcher Affiliation | Academia | Koosha Khalvati, Rajesh P. N. Rao Department of Computer Science and Engineering University of Washington Seattle, WA 98195 {koosha, rao}@cs.washington.edu |
| Pseudocode | No | The paper describes the POMDP model and its components mathematically but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to open-source code for the described methodology or state that the code is available. |
| Open Datasets | No | The paper refers to experimental data from prior studies ([12], [13]) that it models and analyzes, such as 'post-decision wagering in this experiment' and 'reaction-time random dots experiments in [12]', but it does not provide concrete access information (e.g., specific links, DOIs, or repository names) for a publicly available or open dataset used for its analysis. |
| Dataset Splits | No | The paper focuses on modeling and analyzing existing experimental data by fitting POMDP parameters. It describes using 'data from the trials where the sure option is not given' for fitting, and then predicting confidence using these parameters. It does not mention or specify traditional training, validation, or test dataset splits in the context of machine learning model training. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU, memory) used to run the simulations or analyses described. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., specific programming languages, libraries, or solvers) used for its implementation. |
| Experiment Setup | Yes | First, we find the set of parameters for the experimenter s POMDP to reproduce the same accuracy curves as in the experiment for each coherence. ... Fitting the POMDP to the accuracy data yields the mean and variance for each observation function and the cost for sampling. ... if rsure is approximately twothirds the value of the reward for correct direction choice, the POMDP model s prediction matches experimental data (Figure 3b). ... All the free parameters of the POMDP were extracted from this fit. |