The Brain Uses Reliability of Stimulus Information when Making Perceptual Decisions
Authors: Sebastian Bitzer, Stefan Kiebel
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using this result, we will analyse the results of a perceptual decision making experiment [11] and will show that only variants DDM and DEPC, which use trial-dependent reliability, are consistent with previous findings about perceptual decision making in the brain. |
| Researcher Affiliation | Academia | 1Department of Psychology, Technische Universit at Dresden, 01062 Dresden, Germany |
| Pseudocode | No | The paper describes mathematical models and relationships but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the methodology described. |
| Open Datasets | Yes | In the following we will analyse the data presented in [11]. This data set has two major advantages for our purposes: 1) Reported accuracies and mean reaction times (Fig. 1d,f) are averages based on 15,937 trials in total. ... The behavioural data of the remaining six coherence levels are presented in Table 1. [11] Anne K Churchland, Roozbeh Kiani, and Michael N Shadlen. Decision-making with multiple alternatives. Nat Neurosci, 11(6):693 702, Jun 2008. |
| Dataset Splits | No | The paper describes fitting models to a given dataset and simulating results, but it does not specify any training, validation, or test splits for its own analysis or model fitting process. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU, GPU models, memory) used for running its analyses or simulations. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers (e.g., Python, MATLAB, specific libraries or solvers) used for its analyses. |
| Experiment Setup | Yes | In accordance with previous approaches we selected values for the respective redundant parameters. Since the redundant parameter value, or its inverse, simply scales the fitted parameter values (cf. Eqs. 9 and 10), the exact value is irrelevant and we fix, in each model variant, the redundant parameter to 1. ... The top row of Fig. 3 shows example drift diffusion trajectories (y in Eq. (1)) simulated at a resolution of 1ms for two coherences. ... We have summarised predicted neural responses to all coherences in the bottom row of Fig. 3 where we show averages of y across 5000 trials either aligned to the start of decision making (left panels) or aligned to the decision time (right panels). |