{ "cells": [ { "cell_type": "markdown", "id": "3aebaca2-f14e-4b6f-9d68-8ff7ea3c7b71", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "(custom_response_functions)=\n", "# Using custom response models" ] }, { "cell_type": "markdown", "id": "1a9a9835-a027-4c14-9649-e7b51545b83d", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ComputationalPsychiatry/pyhgf/blob/master/docs/source/notebooks/2-Using_custom_response_functions.ipynb)" ] }, { "cell_type": "code", "execution_count": 1, "id": "6522fb9d-5799-4fe4-848e-a711bd3c2e15", "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-01-10T13:55:43.255334Z", "iopub.status.busy": "2025-01-10T13:55:43.255080Z", "iopub.status.idle": "2025-01-10T13:55:43.259946Z", "shell.execute_reply": "2025-01-10T13:55:43.259092Z" }, "slideshow": { "slide_type": "" }, "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "import sys\n", "from IPython.utils import io\n", "if 'google.colab' in sys.modules:\n", "\n", " with io.capture_output() as captured:\n", " ! pip install pyhgf watermark" ] }, { "cell_type": "code", "execution_count": 2, "id": "96efb544-a8f7-44af-8345-ab6af53aaaf5", "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-01-10T13:55:43.262960Z", "iopub.status.busy": "2025-01-10T13:55:43.261800Z", "iopub.status.idle": "2025-01-10T13:55:46.486247Z", "shell.execute_reply": "2025-01-10T13:55:46.484936Z" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" ] } ], "source": [ "import arviz as az\n", "import jax.numpy as jnp\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pymc as pm\n", "\n", "from pyhgf import load_data\n", "from pyhgf.distribution import HGFDistribution\n", "from pyhgf.model import HGF\n", "\n", "plt.rcParams[\"figure.constrained_layout.use\"] = True" ] }, { "cell_type": "markdown", "id": "d55938bd-cc60-4c6f-8f69-959b340042d7", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "The probabilistic networks we have been creating so far with the continuous and the binary Hierarchical Gaussian Filter (HGF) are often referred to as {term}`Perceptual model`. This branch of the model acts as a [Bayesian filter](https://en.wikipedia.org/wiki/Recursive_Bayesian_estimation) as it tries to predict the next observation(s) given current and past observations (often noted $u$). In that sense, the HGF is sometimes described as a generalization of Bayesian filters (e.g. a one-level continuous HGF is equivalent to a Kalman filter). Complex probabilistic networks can handle more complex environments for example with nested volatility, with multiple inputs, or with time-varying inputs that dynamically change how beliefs propagate through the structure (see {ref}`probabilistic_networks` for a tutorial on probabilistic networks). \n", "\n", "But no matter how complex those networks are, they are only the *perceptual* side of the model. If we want our agent to be able to act according to the beliefs he/she is having about the environment and evaluate its performances from these actions, we need what is traditionally called a {term}`Response model` (also sometimes referred to as *decision model* or *observation model*). In a nutshell, a {term}`Response model` describe how we think the agent decide to perform some action at time $t$, and how we can measure the \"goodness of fit\" of our perceptual model given the observed actions. It critically incorporates a {term}`Decision rule`, which is the function that, given the sufficient statistics of the network's beliefs sample an action from the possible actions at hand.\n", "\n", "Being able to write and modify such {term}`Response model` is critical for practical applications of the HGF as it allows users to adapt the models to their specific experimental designs. In this notebook, we will demonstrate how it is possible to write custom response models for a given probabilistic network.\n", "\n", "```{figure} ../images/response_models.png\n", "---\n", "name: response-models-fig\n", "---\n", "**The {term}`Perceptual model` and the {term}`Response model` of a Hierarchical Gaussian Filter (HGF)**. The left panel represents the {term}`Perceptual model`. The beliefs that the agent holds on state of the world are updated in the probabilistic network (blue circles) as the agent makes new observations (often noted $u$, e.g. the association between a stimulus and an outcome at time $t$). Using these beliefs, the {term}`Response model` (right panel) selects which decision/action $y$ to perform. Critically here, the {term}`Response model` only operates one-way (i.e. taking beliefs to generate action), but the actions are not modulating the way beliefs are updated (the model does not perform active inference - this point is, however, an active line of researches and new iterations of the model will try to *fusion* the two branch using active inference principles).\n", "```" ] }, { "cell_type": "markdown", "id": "fedbe1d8-3f35-47ce-9cf6-0b40054f8cc7", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "## Creating a new response function: the binary surprise\n", "To illustrate the creation of new response functions, we are going to use the same binary input vector from the decision task described in {cite:p}`Iglesias2021`." ] }, { "cell_type": "code", "execution_count": 3, "id": "2fe26152-d746-4bda-872d-9a3e55ffc586", "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-01-10T13:55:46.488867Z", "iopub.status.busy": "2025-01-10T13:55:46.488261Z", "iopub.status.idle": "2025-01-10T13:55:46.496833Z", "shell.execute_reply": "2025-01-10T13:55:46.496085Z" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "u, _ = load_data(\"binary\")" ] }, { "cell_type": "markdown", "id": "8d0e3852-b46e-48a9-8b7c-349382785cad", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "For the sake of example here, we will consider that the participant underwent a *one-armed bandit task*, which consisted of the presentations of two stimuli ($S_0$ and $S_1$) that could be associated with two types of outcomes ($O_{+}$ and $O_{-}$). On each trial, the participant was presented with the two stimuli, chose one of them and get an outcome. The underlying contingencies of stimuli associated with positive outcomes are changing over time, in a way that can be more or less volatile. The {term}`Perceptual model` tries to track these contingencies by observing the previous associations. We have already been using this {term}`Perceptual model` model before in the tutorial on the binary HGF ({ref}`binary_hgf`). Here the input data $u$ is the observed association between e.g. $S_0$ and $O_{+}$. In order to incorporate a {term}`Response model` on top of that, we need first to define:\n", "1. a range of possible actions $y$. In this case, the participant has only two alternatives so $y$ can be either `0` or `1`.\n", "2. a {term}`Decision rule` stating how the agent selects between the two actions given the beliefs of the probabilistic network at time $t$. \n", "In this situation, it is trivial to write such a decision function and generate actions as new inputs are coming in, for simulation purposes for example. We start by setting a {term}`Perceptual model` (i.e. a network of probabilistic nodes updated by observations). Here this is a standard two-level binary HGF, and the inputs are the binary observations (the association between one of the stimuli and the positive reward)." ] }, { "cell_type": "code", "execution_count": 4, "id": "f543569f-234c-42a0-b23e-18b6379413ee", "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-01-10T13:55:46.499614Z", "iopub.status.busy": "2025-01-10T13:55:46.499074Z", "iopub.status.idle": "2025-01-10T13:55:46.694129Z", "shell.execute_reply": "2025-01-10T13:55:46.693166Z" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "agent = HGF(\n", " n_levels=2,\n", " model_type=\"binary\",\n", " initial_mean={\"1\": .0, \"2\": .5},\n", " initial_precision={\"1\": .0, \"2\": 1e4},\n", " tonic_volatility={\"2\": -4.0},\n", ").input_data(input_data=u)" ] }, { "cell_type": "markdown", "id": "4a8f9ff3-e127-4bd2-b4f2-4c986129bc61", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "This perceptual model has observed the input sequence, meaning that we now have beliefs trajectories for all nodes in the network. The beliefs are stored in the variable `node_trajectories` in the model class, but can also be exported to Pandas using the `to_pandas` method like:" ] }, { "cell_type": "code", "execution_count": 5, "id": "a579cfa3-d00c-4cf3-b1e7-278d732aee37", "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-01-10T13:55:46.696811Z", "iopub.status.busy": "2025-01-10T13:55:46.696568Z", "iopub.status.idle": "2025-01-10T13:55:46.889601Z", "shell.execute_reply": "2025-01-10T13:55:46.888497Z" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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time_stepstimex_0_expected_meanx_0_expected_precisionx_0_meanx_0_precisionx_1_expected_meanx_1_expected_precisionx_1_meanx_1_precisionx_0_surprisex_1_surprisetotal_surprise
01.01.00.6224590.2350041.00.2350040.50000054.3016740.50692354.5366780.474077-1.077038-0.602961
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" ], "text/plain": [ " time_steps time x_0_expected_mean x_0_expected_precision x_0_mean \\\n", "0 1.0 1.0 0.622459 0.235004 1.0 \n", "1 1.0 2.0 0.624085 0.234603 1.0 \n", "2 1.0 3.0 0.627284 0.233799 1.0 \n", "3 1.0 4.0 0.631975 0.232583 1.0 \n", "4 1.0 5.0 0.638038 0.230946 0.0 \n", "\n", " x_0_precision x_1_expected_mean x_1_expected_precision x_1_mean \\\n", "0 0.235004 0.500000 54.301674 0.506923 \n", "1 0.234603 0.506923 27.283697 0.520583 \n", "2 0.233799 0.520583 18.296556 0.540697 \n", "3 0.232583 0.540697 13.834867 0.566859 \n", "4 0.230946 0.566859 11.185465 0.510971 \n", "\n", " x_1_precision x_0_surprise x_1_surprise total_surprise \n", "0 54.536678 0.474077 -1.077038 -0.602961 \n", "1 27.518301 0.471469 -0.731660 -0.260191 \n", "2 18.530355 0.466356 -0.530717 -0.064361 \n", "3 14.067450 0.458906 -0.389923 0.068983 \n", "4 11.416410 1.016216 -0.270900 0.745316 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "agent.to_pandas().head()" ] }, { "cell_type": "markdown", "id": "7f4cc804-fb04-44d5-bfbf-b62b4e2c2ceb", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "### Creating the decision rule" ] }, { "cell_type": "markdown", "id": "6530129d-af70-4e3d-98e6-8e0ec06a410e", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "The next step is to use these beliefs to generate the corresponding decisions at each time point. We can work on the simplest {term}`Decision rule`, which is probably to use the expected value of the first level a time $t$ to sample from a [binomial distribution](https://en.wikipedia.org/wiki/Binomial_distribution) and generate a binary decision. Intuitively, this just means that the agent is more likely to select a given stimulus when the beliefs that is is associated with a positive outcome are close to `1.0`." ] }, { "cell_type": "code", "execution_count": 6, "id": "2558d4fe-448b-4d9c-8944-93087f01feda", "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-01-10T13:55:46.891732Z", "iopub.status.busy": "2025-01-10T13:55:46.891488Z", "iopub.status.idle": "2025-01-10T13:55:46.895389Z", "shell.execute_reply": "2025-01-10T13:55:46.894669Z" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "# a simple decision rule using the first level of the HGF\n", "np.random.seed(1)\n", "responses = np.random.binomial(p=agent.node_trajectories[0][\"expected_mean\"], n=1)" ] }, { "cell_type": "markdown", "id": "214a4f52-ff3f-4d7c-8c43-f0fd9b9d6782", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "This gives us a binary vector of responses $y$ that can be related to observations and underlying beliefs." ] }, { "cell_type": "code", "execution_count": 7, "id": "d4391036-ddb1-42a0-ba66-6e9db3054656", "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-01-10T13:55:46.898195Z", "iopub.status.busy": "2025-01-10T13:55:46.897816Z", "iopub.status.idle": "2025-01-10T13:55:47.102852Z", "shell.execute_reply": "2025-01-10T13:55:47.102096Z" }, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'Trials')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12, 3))\n", "jitter = responses * .1 + (1-responses) * -.1\n", "plt.scatter(np.arange(len(u)), u, label=\"Observations\", color=\"#4c72b0\", edgecolor=\"k\", alpha=.2)\n", "plt.scatter(np.arange(len(responses)), responses + jitter, label=\"Responses\", color=\"#c44e52\", alpha=.2, edgecolor=\"k\")\n", "plt.plot(agent.node_trajectories[0][\"expected_mean\"], label=\"Beliefs\", linestyle=\"--\")\n", "plt.legend()\n", "plt.xlabel(\"Trials\")" ] }, { "cell_type": "markdown", "id": "558452b8-3d7c-484e-a59a-933f0fcf52f5", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "We now have the two ingredients required to create a response model:\n", "- the vector `observations` ($u$) that encode the current association between the stimuli and the outcomes.\n", "- the vector `responses` ($y$) that encode the inferred association by the participant, using the expected value at time $t$ at the first level.\n", "\n", "```{note}\n", "We started by simulation the responses from an agent for the sake of demonstration and parameter recovery, but in the context of an experiment, the user already has access to the vectors $y$ and $u$ and could start from her.\n", "```" ] }, { "cell_type": "markdown", "id": "6055eef2-8e7b-4a13-991e-394895f66391", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "### Creating a new response function\n", "\n", "Let's now consider that the two vectors of observations $u$ and responses $y$ were obtained from a real participant undergoing a real experiment. In this situation, we assume that this participant internally used a {term}`Perceptual model` and a {term}`Decision rule` that might resemble what we defined previously, and we want to infer what are the most likely values for critical parameters in the model (e.g. the evolution rate $\\omega_2$). To do so, we are going to use our dataset (both $u$ and $y$), and try many models. We are going to fix the values of all HGF parameters to reasonable estimates (here, using the exact same values as in the simulation), except for $\\omega_2$. For this last parameter, we will assume a prior set at $\\mathcal{N}(-2.0, 2.0)$. The idea is that we want to sample many $\\omega_2$ values from this distribution and see if the model is performing better with some values.\n", "\n", "But here, we need a clear definition of what this means *to perform better* for a given model. And this is exactly what a {term}`Response model` does, it is a way for us to evaluate how likely the behaviours $y$ for a given {term}`Perceptual model`, and assuming that the participants use this specific {term}`Decision rule`. In [pyhgf](https://github.com/ComputationalPsychiatry/pyhgf), this step is performed by creating the corresponding {term}`Response function`, which is the Python function that will return the surprise $S$ of getting these behaviours from the participant under this decision rule.\n", "\n", "````{hint} What is a *response function*?\n", "Most of the work around HGFs consists in creating and adapting {term}`Response model` to work with a given experimental design. There is no limit in terms of what can be used as a {term}`Response model`, provided that the {term}`Perceptual model` and the {term}`Decision rule` are clearly defined.\n", "\n", "In [pyhgf](https://github.com/ComputationalPsychiatry/pyhgf), the {term}`Perceptual model` is the probabilistic network created with the main {py:class}`pyhgf.model.HGF` and {py:class}`pyhgf.distribution.HGFDistribution` classes. The {term}`Response model` is something that is implicitly defined when we create the {term}`Response function`, a Python function that computes the negative of the log-likelihood of the actions given the perceptual model. This {term}`Response function` can be passed as an argument to the main classes using the keywords arguments `response_function`, `response_function_inputs` and `response_function_parameters`. The `response_function` can be any callable that returns the surprise $S$ of observing action $y$ given this model, and the {term}`Decision rule`. The `response_function_inputs` are the additional data to the response function (optional) while `response_function_parameters` are the additional parameters (optional). The `response_function_inputs` is where the actions $y$ should be provided.\n", "\n", "```{important}\n", "A *response function* should not return the actions given perceptual inputs $y | u$ (this is what the {term}`Decision rule` does), but the [surprise](https://en.wikipedia.org/wiki/Information_content) $S$ associated with the observation of actions given the perceptual inputs $S(y | u)$, which is defined by:\n", "\n", "$$\n", "\\begin{align}\n", "S(y | u) = -\\log[Pr(y | u)]\n", "\\end{align}\n", "$$\n", "```\n", "\n", "If you are already familiar with using HGFs in the Julia equivalent of [pyhgf](https://github.com/ComputationalPsychiatry/pyhgf), you probably noted that the toolbox is split into a **perceptual** package [HierarchicalGaussianFiltering.jl](https://github.com/ComputationalPsychiatry/HierarchicalGaussianFiltering.jl) and a **response** package [ActionModels.jl](https://github.com/ComputationalPsychiatry/ActionModels.jl). This was made to make the difference between the two parts of the HGF clear and be explicit that you can use a perceptual model without any action model. In [pyhgf](https://github.com/ComputationalPsychiatry/pyhgf) however, everything happens in the same package, the response function is merely an optional, additional argument that can be passed to describe how surprise is computed.\n", "````" ] }, { "cell_type": "markdown", "id": "22f83493-de32-4b95-b79d-e93b116712e4", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "Therefore, we want a {term}`Response function` that returns the surprise for observing the response $y$, which is:\n", "\n", "$$\n", "\\begin{align}\n", "surprise | y & = \\sum_{t=1}^{t} - \\log(p(y^t | \\hat{\\mu}_1)) \\\\\n", "& = \\sum_{t=1}^{t} - \\log(\\hat{\\mu}_1^y(1 - \\hat{\\mu}_1)^{1-y}) \\\\\n", "\\end{align}\n", "$$\n", "We can write such a response function in Python as:" ] }, { "cell_type": "code", "execution_count": 8, "id": "9ffe8ee2-1b36-400b-8f71-09e9f097ba2a", "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-01-10T13:55:47.104865Z", "iopub.status.busy": "2025-01-10T13:55:47.104669Z", "iopub.status.idle": "2025-01-10T13:55:47.108838Z", "shell.execute_reply": "2025-01-10T13:55:47.108121Z" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "def response_function(hgf, response_function_inputs, response_function_parameters=None):\n", " \"\"\"A simple response function returning the binary surprise.\"\"\"\n", "\n", " # response_function_parameters can be used to parametrize the response function (e.g. inverse temperature)\n", " # ...<\n", "\n", " # the expected values at the first level of the HGF\n", " beliefs = hgf.node_trajectories[0][\"expected_mean\"]\n", "\n", " # get the decision from the inputs to the response function\n", " return jnp.sum(jnp.where(response_function_inputs, -jnp.log(beliefs), -jnp.log(1.0 - beliefs)))" ] }, { "cell_type": "markdown", "id": "a9be33b8-8fcc-4acb-b151-7007f516044d", "metadata": {}, "source": [ "This function takes the expected probability from the binary node and uses it to predict the participant's decision. The surprise is computed using the binary surprise (see {py:func}`pyhgf.update.binary.binary_surprise`). This corresponds to the standard binary softmax response function that is also accessible from the {py:func}`pyhgf.response.binary_softmax` function." ] }, { "cell_type": "markdown", "id": "6a564169-3fe2-4f7f-8fbe-7ac7b9bf8a9b", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "```{note}\n", "Note here that our {term}`Response function` has a structure that is the standard way to write response functions in [pyhgf](https://github.com/ComputationalPsychiatry/pyhgf), that is with two input arguments:\n", "- the HGF model on which the response function applies (i.e. the {term}`Perceptual model`)\n", "- the additional parameters provided to the response function. This can include additional parameters that can be part of the equation of the model, or the input data used by this model. We then provide the `response` vector ($y$) here.\n", "\n", "Note that the operation inside the function should be compatible with [JAX's core transformations](https://github.com/google/jax#transformations).\n", "```" ] }, { "cell_type": "code", "execution_count": 9, "id": "8abe91c5-7748-4445-826c-e52dff35094c", "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-01-10T13:55:47.111148Z", "iopub.status.busy": "2025-01-10T13:55:47.110945Z", "iopub.status.idle": "2025-01-10T13:55:47.144236Z", "shell.execute_reply": "2025-01-10T13:55:47.143405Z" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Array(205.87854, dtype=float32)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# return the overall surprise\n", "response_function(hgf=agent, response_function_inputs=responses)" ] }, { "cell_type": "markdown", "id": "369ac58b-1a9a-4799-8d2d-a928f1154739", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "We now have a response function that returns the surprise associated with the observation of the agent's action. Conveniently, this is by definition the negative of the log-likelihood of our model, which means that we can easily interface this with other Python packages used for optimisation and Bayesian inference like [PyMC](https://www.pymc.io/projects/docs/en/stable/learn.html) or [BlackJAX](https://blackjax-devs.github.io/blackjax/). We use the surprise as a default output, however, as this metric is more commonly used in computational psychiatry and is more easily connected to psychological functioning." ] }, { "cell_type": "markdown", "id": "5d76e61b-ea8b-452d-a7c9-b2f50fd33f2d", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "## Recovering HGF parameters from the observed behaviors" ] }, { "cell_type": "markdown", "id": "1e1b18b6-dad1-4d95-8835-e994c53259c1", "metadata": {}, "source": [ "Now that we have created our {term}`Response function`, and that we made sure it complies with the standard ways of writing responses functions (see above), we can use it to perform inference over the most likely values of some parameters. We know that the agent used to simulate behaviour had an *evolution rate* set at `-4.0`. In the code below, we create a new HGF distribution using the same values, but setting the $\\omega_2$ parameter free so we can estimate the most likely value, given the observed behaviours." ] }, { "cell_type": "code", "execution_count": 10, "id": "edb96ba7-6824-4d60-9dcf-8511066f2853", "metadata": { "execution": { "iopub.execute_input": "2025-01-10T13:55:47.146501Z", "iopub.status.busy": "2025-01-10T13:55:47.146287Z", "iopub.status.idle": "2025-01-10T13:55:47.151022Z", "shell.execute_reply": "2025-01-10T13:55:47.150191Z" } }, "outputs": [], "source": [ "hgf_logp_op = HGFDistribution(\n", " n_levels=2,\n", " model_type=\"binary\",\n", " input_data=u[jnp.newaxis, :],\n", " response_function=response_function,\n", " response_function_inputs=responses[jnp.newaxis, :]\n", ")" ] }, { "cell_type": "markdown", "id": "1d03b46e-bf4d-4934-964f-eb683fcc1f4a", "metadata": {}, "source": [ "```{note}\n", "The response function that we created above is passed as an argument directly to the HGF distribution, together with the additional parameters. The additional parameters should be a list of tuples that has the same length as the number of models created.\n", "```" ] }, { "cell_type": "code", "execution_count": 11, "id": "c27b15f5-0b79-448f-8b27-024e417fb462", "metadata": { "execution": { "iopub.execute_input": "2025-01-10T13:55:47.152940Z", "iopub.status.busy": "2025-01-10T13:55:47.152743Z", "iopub.status.idle": "2025-01-10T13:55:47.165135Z", "shell.execute_reply": "2025-01-10T13:55:47.164246Z" } }, "outputs": [], "source": [ "with pm.Model() as sigmoid_hgf:\n", "\n", " # prior over the evolution rate at the second level\n", " tonic_volatility_2 = pm.Normal(\"tonic_volatility_2\", -2.0, 2.0)\n", "\n", " # the main HGF distribution\n", " pm.Potential(\"hgf_loglike\", hgf_logp_op(tonic_volatility_2=tonic_volatility_2))" ] }, { "cell_type": "code", "execution_count": 12, "id": "e80e247a-7450-42f3-b4e7-352f965f63f4", "metadata": { "execution": { "iopub.execute_input": "2025-01-10T13:55:47.167939Z", "iopub.status.busy": "2025-01-10T13:55:47.167698Z", "iopub.status.idle": "2025-01-10T13:55:52.097985Z", "shell.execute_reply": "2025-01-10T13:55:52.097031Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Initializing NUTS using jitter+adapt_diag...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Sequential sampling (2 chains in 1 job)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "NUTS: [tonic_volatility_2]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4288a0fef6134218b5ee2e7aeb6f2c73", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "841899aa01434ab7a17464177b21c4eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 3 seconds.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n"
     ]
    }
   ],
   "source": [
    "with sigmoid_hgf:\n",
    "    sigmoid_hgf_idata = pm.sample(chains=2, cores=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4fe53229-92ef-473e-b3c1-4f9c147c3a32",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-10T13:55:52.156152Z",
     "iopub.status.busy": "2025-01-10T13:55:52.156006Z",
     "iopub.status.idle": "2025-01-10T13:55:52.385698Z",
     "shell.execute_reply": "2025-01-10T13:55:52.385025Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "
meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
tonic_volatility_2-3.9430.501-4.849-2.9810.0170.012884.01270.01.0
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       "
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      ],
      "text/plain": [
       "                     mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
       "tonic_volatility_2 -3.943  0.501  -4.849   -2.981      0.017    0.012   \n",
       "\n",
       "                    ess_bulk  ess_tail  r_hat  \n",
       "tonic_volatility_2     884.0    1270.0    1.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_trace(sigmoid_hgf_idata, var_names=[\"tonic_volatility_2\"]);\n",
    "az.summary(sigmoid_hgf_idata, var_names=[\"tonic_volatility_2\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc1052c7-6278-4e98-9532-768be3c5edfe",
   "metadata": {},
   "source": [
    "The results above indicate that given the responses provided by the participant, the most likely values for the parameter $\\omega_2$ are between -4.9 and -3.1, with a mean at -3.9 (you can find slightly different values if you sample different actions from the decisions function). We can consider this as an excellent estimate given the sparsity of the data, and the complexity of the model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fb6a2e6-e5a5-42c0-a3bb-33569d390836",
   "metadata": {},
   "source": [
    "## Glossary\n",
    "\n",
    "```{glossary}\n",
    "\n",
    "Perceptual model\n",
    "    The perceptual model of a Hierarchical Gaussian Filter traditionally refers to the branch receiving observations $u$ about states of the world and that performs the updating of beliefs about these states. By generalisation, the perceptual model is any probabilistic network that can be created in [pyhgf](https://github.com/ComputationalPsychiatry/pyhgf), receiving an arbitrary number of inputs. An HGF that only consists of a perceptual model will act as a Bayesian filter.\n",
    "\n",
    "Response model\n",
    "    The response model of a Hierarchical Gaussian filter refers to the branch that uses the beliefs about the state of the world to generate actions using the {term}`Decision rule`. This branch is also sometimes referred to as the **decision model** or the **observation model**, depending on the fields. Critically, this part of the model can return the surprise ($-\\log[Pr(x)]$) associated with the observations (here, the observations include the inputs $u$ of the probabilistic network, but will also include the responses of the participant $y$ if there are some).\n",
    "\n",
    "Decision rule\n",
    "    The decision rule is a function stating how the agent selects among all possible actions, given the state of the beliefs in the perceptual model, and optionally additional parameters. Programmatically, this is a Python function taking a perceptual model as input (i.e. an instance of the HGF class), and returning a sequence of actions. This can be used for simulation. The decision rule should be clearly defined in order to write the {term}`Response function`.\n",
    "\n",
    "Response function\n",
    "    The response function is a term that we use specifically for this package ([pyhgf](https://github.com/ComputationalPsychiatry/pyhgf)). It refers to the Python function that, using a given HGF model and optional parameter, returns the surprise associated with the observed actions.\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68510623-fdbd-468f-b67a-57faa51a439d",
   "metadata": {},
   "source": [
    "## System configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9fd93ecb-3ba2-4148-bb80-096027161404",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-10T13:55:52.387923Z",
     "iopub.status.busy": "2025-01-10T13:55:52.387725Z",
     "iopub.status.idle": "2025-01-10T13:55:52.401530Z",
     "shell.execute_reply": "2025-01-10T13:55:52.400699Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Fri Jan 10 2025\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.12.3\n",
      "IPython version      : 8.31.0\n",
      "\n",
      "pyhgf : 0.2.1.post4.dev0+d49aafe9\n",
      "jax   : 0.4.31\n",
      "jaxlib: 0.4.31\n",
      "\n",
      "numpy     : 1.26.0\n",
      "pyhgf     : 0.2.1.post4.dev0+d49aafe9\n",
      "IPython   : 8.31.0\n",
      "arviz     : 0.20.0\n",
      "jax       : 0.4.31\n",
      "matplotlib: 3.10.0\n",
      "sys       : 3.12.3 | packaged by conda-forge | (main, Apr 15 2024, 18:38:13) [GCC 12.3.0]\n",
      "pymc      : 5.20.0\n",
      "\n",
      "Watermark: 2.5.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p pyhgf,jax,jaxlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5aa8dbc2",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    }
   },
   "outputs": [],
   "source": []
  }
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