<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Tutorial on Wise Lab Wiki</title><link>https://wiki.thewiselab.org/docs/computational_modelling/tutorial/</link><description>Recent content in Tutorial on Wise Lab Wiki</description><generator>Hugo</generator><language>en</language><copyright>Copyright (c) 2020-2024 Hyas</copyright><lastBuildDate>Mon, 01 Jan 0001 00:00:00 +0000</lastBuildDate><atom:link href="https://wiki.thewiselab.org/docs/computational_modelling/tutorial/index.xml" rel="self" type="application/rss+xml"/><item><title>1. Overview</title><link>https://wiki.thewiselab.org/docs/computational_modelling/tutorial/1.-overview/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://wiki.thewiselab.org/docs/computational_modelling/tutorial/1.-overview/</guid><description>This tutorial will take you through the implementation of a commonly-used reinforcement learning model, the Rescorla-Wagner model. We will implement an asymmetric version of this model that allows for differential updating of value estimates in response to positive and negative prediction errors (see here).</description></item><item><title>2. Implementing an update function</title><link>https://wiki.thewiselab.org/docs/computational_modelling/tutorial/2.-implementing-an-update-function/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://wiki.thewiselab.org/docs/computational_modelling/tutorial/2.-implementing-an-update-function/</guid><description>Implementing an update function First, we need to create a function that implements the core update method for our model (i.</description></item><item><title>3. Selecting actions</title><link>https://wiki.thewiselab.org/docs/computational_modelling/tutorial/3.-selecting-actions/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://wiki.thewiselab.org/docs/computational_modelling/tutorial/3.-selecting-actions/</guid><description>Selecting actions In most learning tasks we are asking participants to select different actions or stimuli based on their estimated value.</description></item><item><title>4. Updating value across trials</title><link>https://wiki.thewiselab.org/docs/computational_modelling/tutorial/4.-updating-value-across-trials/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://wiki.thewiselab.org/docs/computational_modelling/tutorial/4.-updating-value-across-trials/</guid><description>Updating value across trials So far, we&amp;rsquo;ve implemented a function that will select an action and update its value estimate based on the reward received for a single trial.</description></item><item><title>5. Running the model for multiple subjects</title><link>https://wiki.thewiselab.org/docs/computational_modelling/tutorial/5.-running-the-model-for-multiple-subjects/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://wiki.thewiselab.org/docs/computational_modelling/tutorial/5.-running-the-model-for-multiple-subjects/</guid><description>Updating value across subjects The model we&amp;rsquo;ve implemented so far works for a single subject, but we will typically want to run it for multiple subjects.</description></item><item><title>6. Model fitting using MCMC</title><link>https://wiki.thewiselab.org/docs/computational_modelling/tutorial/6.-model-fitting-using-mcmc/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://wiki.thewiselab.org/docs/computational_modelling/tutorial/6.-model-fitting-using-mcmc/</guid><description>Model fitting using MCMC Next, we&amp;rsquo;ll try fitting our model to some simulated data.
Imports First, we import necessary packages.</description></item></channel></rss>