Evidence Accumulation Model Selector

Purpose

This skill encodes expert knowledge for selecting among evidence accumulation models (EAMs) when analyzing choice response-time (RT) data. A competent programmer without cognitive science training would typically analyze only mean RT and accuracy separately, missing the critical insight that RT distributions and speed-accuracy tradeoffs carry rich information about latent cognitive processes. Selecting the wrong EAM -- or applying one when the data violate its assumptions -- leads to uninterpretable or misleading parameter estimates.

When to Use

Use this skill when:

• You have choice-time data (both accuracy and full RT distributions, not just means)
• You want to decompose observed performance into latent cognitive processes (evidence quality, response caution, non-decision time)
• You need to distinguish speed-accuracy tradeoff effects from genuine sensitivity changes
• You are deciding which model class (DDM, LBA, EZ-diffusion, race model) is appropriate for your experimental design

Do not use this skill when:

• You only have accuracy data without RTs (use signal detection theory instead)
• RTs are from simple detection (single response option) rather than choice tasks