{ "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/ilabcode/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, "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, "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, "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, "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, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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time_stepstimeobservation_input_0x_1_meanx_1_precisionx_1_expected_meanx_1_expected_precisionx_2_meanx_2_precisionx_2_expected_meanx_2_expected_precisionobservation_input_0_surpriseobservation_input_0_expected_precisionx_1_surprisex_2_surprisetotal_surprise
01.01.01.01.0inf0.6224590.2350040.50692354.5366780.50000054.3016740.474077inf1.659764-1.0770381.056803
11.02.01.01.0inf0.6240850.2346030.52058327.5183010.50692327.2836970.471469inf1.660445-0.7316601.400254
21.03.01.01.0inf0.6272840.2337990.54069718.5303550.52058318.2965560.466356inf1.661825-0.5307171.597464
31.04.01.01.0inf0.6319750.2325830.56685914.0674500.54069713.8348670.458906inf1.663944-0.3899231.732927
41.05.00.00.0inf0.6380380.2309460.51097111.4164100.56685911.1854651.016216inf1.698733-0.2709002.444049
\n", "
" ], "text/plain": [ " time_steps time observation_input_0 x_1_mean x_1_precision \\\n", "0 1.0 1.0 1.0 1.0 inf \n", "1 1.0 2.0 1.0 1.0 inf \n", "2 1.0 3.0 1.0 1.0 inf \n", "3 1.0 4.0 1.0 1.0 inf \n", "4 1.0 5.0 0.0 0.0 inf \n", "\n", " x_1_expected_mean x_1_expected_precision x_2_mean x_2_precision \\\n", "0 0.622459 0.235004 0.506923 54.536678 \n", "1 0.624085 0.234603 0.520583 27.518301 \n", "2 0.627284 0.233799 0.540697 18.530355 \n", "3 0.631975 0.232583 0.566859 14.067450 \n", "4 0.638038 0.230946 0.510971 11.416410 \n", "\n", " x_2_expected_mean x_2_expected_precision observation_input_0_surprise \\\n", "0 0.500000 54.301674 0.474077 \n", "1 0.506923 27.283697 0.471469 \n", "2 0.520583 18.296556 0.466356 \n", "3 0.540697 13.834867 0.458906 \n", "4 0.566859 11.185465 1.016216 \n", "\n", " observation_input_0_expected_precision x_1_surprise x_2_surprise \\\n", "0 inf 1.659764 -1.077038 \n", "1 inf 1.660445 -0.731660 \n", "2 inf 1.661825 -0.530717 \n", "3 inf 1.663944 -0.389923 \n", "4 inf 1.698733 -0.270900 \n", "\n", " total_surprise \n", "0 1.056803 \n", "1 1.400254 \n", "2 1.597464 \n", "3 1.732927 \n", "4 2.444049 " ] }, "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, "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[1][\"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, "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[1][\"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/ilabcode/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/ilabcode/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/ilabcode/pyhgf), you probably noted that the toolbox is split into a **perceptual** package [HierarchicalGaussianFiltering.jl](https://github.com/ilabcode/HierarchicalGaussianFiltering.jl) and a **response** package [ActionModels.jl](https://github.com/ilabcode/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/ilabcode/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, "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[1][\"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/ilabcode/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, "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": {}, "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": {}, "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": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Sequential sampling (4 chains in 1 job)\n", "NUTS: [tonic_volatility_2]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "33164d74e619495683e8e1d4dc92af7f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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\n", "
" ], "text/plain": [ " mean sd hdi_3% hdi_97% mcse_mean mcse_sd \\\n", "tonic_volatility_2 -3.966 0.492 -4.862 -3.039 0.012 0.009 \n", "\n", " ess_bulk ess_tail r_hat \n", "tonic_volatility_2 1739.0 2173.0 1.0 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "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/ilabcode/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/ilabcode/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": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Last updated: Thu Jun 27 2024\n", "\n", "Python implementation: CPython\n", "Python version : 3.12.3\n", "IPython version : 8.23.0\n", "\n", "pyhgf : 0.1.2\n", "jax : 0.4.30\n", "jaxlib: 0.4.30\n", "\n", "matplotlib: 3.8.4\n", "arviz : 0.18.0\n", "numpy : 1.26.0\n", "pymc : 5.16.1\n", "jax : 0.4.30\n", "sys : 3.12.3 | packaged by conda-forge | (main, Apr 15 2024, 18:38:13) [GCC 12.3.0]\n", "\n", "Watermark: 2.4.3\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": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }