{
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"# Tutorial for analyzing instrumental learning data with the HDDMrl module\n",
"This is a tutorial for using the HDDMrl module to simultaneously estimate reinforcement learning parameters and decision parameters within a fully hierarchical bayesian estimation framework, including steps for sampling, assessing convergence, model fit, parameter recovery, and posterior predictive checks (model validation). The module uses the reinforcement learning drift diffusion model (RLDDM), a reinforcement learning model that replaces the standard “softmax” choice function with a drift diffusion process. The softmax and drift diffusion process is equivalent for capturing choice proportions, but the DDM also takes RT distributions into account; options are provided to also only fit RL parameters without RT. The RLDDM estimates trial-by-trial drift rate as a scaled difference in expected rewards (expected reward for upper bound alternative minus expected reward for lower bound alternative). Expected rewards are updated with a delta learning rule using either a single learning rate or with separate learning rates for positive and negative prediction errors. The model also includes the standard DDM-parameters. The RLDDM is described in detail in [Pedersen, Frank & Biele (2017).](http://ski.clps.brown.edu/papers/PedersenEtAl_RLDDM.pdf) (Note this approach differs from Frank et al (2015) who used HDDM to fit instrumental learning but did not allow for simultaneous estimation of learning parameters). \n",
"\n",
"## OUTLINE \n",
"\n",
"[1. Background](#1.-Background) \n",
"[2. Installing the module](#2.-Installing-the-module) \n",
"[3. How the RLDDM works](#3.-How-the-RLDDM-works) \n",
"[4. Structuring data](#4.-Structuring-data) \n",
"[5. Running basic model](#5.-Running-basic-model) \n",
"[6. Checking results](#6.-Checking-results) \n",
"[7. Posterior predictive checks](#7.-Posterior-predictive-checks) \n",
"[8. Parameter recovery](#8.-Parameter-recovery) \n",
"[9. Separate learning rates for positive and negative prediction errors](#9.-Separate-learning-rates-for-positive-and-negative-prediction-errors) \n",
"[10. depends_on vs. split_by](#10.-depends_on-vs.-split_by) \n",
"[11. Probabilistic binary outcomes vs. normally distributed outcomes](#11.-Probabilistic-binary-outcomes-vs.-normally-distributed-outcomes) \n",
"[12. HDDMrlRegressor](#12.-HDDMrlRegressor) \n",
"[13. Regular RL without RT](#13.-Regular-RL-without-RT)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Background\n",
"Traditional RL models typically assume static decision processes (e.g. softmax), and the DDM typically assumes static decision variables (stimuli are modeled with the same drift rate across trials). The RLDDM combines dynamic decision variables from RL and dynamic choice process from DDM by assuming trial-by-trial drift rate that depends on the difference in expected rewards, which are updated on each trial by a rate of the prediction error dependent on the learning rate. The potential benefit of the RLDDM is thus to gain a better insight into decision processes in instrumental learning by also accounting for speed of decision making."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Installing the module\n",
"The new version of HDDM (version 0.7.1) that includes the RLDDM is currently not uploaded to conda. So to install you would either have to use pip: \n",
"'pip install hddm' \n",
"OR \n",
"docker: 'pull madslupe/hddm', which runs hddm in jupyter notebook "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. How the RLDDM works\n",
"The main idea of the RLDDM is that reward-based choices can be captured by an accumulation-to-bound process where drift rate is proportional to the difference in expected reward between options, and where expected rewards subsequently are updated in a trial-by-trial basis via reinforcement learning.
\n",
"__drift rate on each trial depends on difference in expected rewards for the two alternatives (q_up and q_low):__ \n",
"drift rate = (q_up - q_low) * scaling
\n",
"_where the scaling parameter describes the weight to put on the difference in q-values._
\n",
"__expeceted reward (q) for chosen option is updated according to delta learning rule :__ \n",
"q_chosen = q_chosen + alpha * (feedback-q_chosen)
\n",
"_where alpha weights the rate of learning on each trial._
\n",
"So in principle all you need is the Wiener first passage time likelihood-function. The reason why HDDM is useful (and hence also HDDMrl) is that it automates the process of setting up and running your model, which tends to be very time consuming. So after structuring the data it is simple to run a model with HDDMrl. In particular it separates subjects and conditions (using the split_by-column, see next section) so that the updating process works correctly, which can be especially difficult to do for RL models. "
]
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"## 4. Structuring data\n",
"The HDDMrl module was created to make it easier to model instrumental learning data with the RLDDM. If you are familiar with using HDDM it shouldn't be a big step to start using HDDMrl. Please refresh your memory by starting with the [tutorial for HDDM](http://ski.clps.brown.edu/hddm_docs/index.html) first (especially critical if you have not used HDDM at all). Running HDDMrl does require a few extra steps compared to HDDM, and because the model includes increased potential for parameter colinearity (typically learning rate and the scaling parameter on drift rate) it is even more important to assess model fit, which will be covered below. Here are the most important steps for structuring your dataframe:\n",
"1. Sort trials in ascending order within subject and condition, to ensure proper updating of expected rewards.\n",
"2. Include a column called __'split_by'__ which identifies the different task conditions (__as integers__), to ensure reward updating will work properly for each condition without mixing values learned from one trial type to another. \n",
"3. Code the response column with [stimulus-coding] (http://ski.clps.brown.edu/hddm_docs/howto.html#code-subject-responses). Although stimulus-coding and accuracy-coding often are the same in instrumental learning it is important that the upper and lower boundaries are represented by the same alternative within a condition, because expected rewards are linked to the thresholds/boundaries.\n",
"4. __feedback__-column. This should be the reward received for the chosen option on each trial.\n",
"5. __q_init__. Adjusting initial q-values is something that can improve model fit quite a bit. To allow the user to set their own initial values we therefore require that the dataframe includes a column called q_init. The function will set all initial q-values according to the first value in q_init. So this is not the most elegant method of allowing users to set inital value for expected rewards, but it works for now.\n",
"\n",
"### Required columns in data:\n",
"* __rt__: in seconds, same as in HDDM\n",
"* __response__: 0 or 1. defines chosen stimulus, not accuracy.\n",
"* __split_by__: needs to be an integer. Split_by defines conditions (trial-types) that should have the same variables (e.g. Q values) within subject: the trials need to be split by condition to ensure proper updating of expected rewards that do not bleed over into other trial-types. (e.g. if you have stimulus A and get reward you want that updated value to impact choice only for the next stimulus A trial but not necessarily the immediate trial afterwards, which may be of a different condition) \n",
"* __subj_idx__: same as in HDDM, but even more important here because it is used to split trials\n",
"* __feedback__: feedback on the current trial. can be any value.\n",
"* __q_init__: used to initialize expected rewards. can be any value, but an unbiased initial value should be somewhere between the minimum and and maximum reward values (e.g. 0.5 for tasks with rewards of 0 and 1)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Running basic model\n",
"To illustrate how to run the model we will use example data from the learning phase of the probabilistic selection task (PST). During the learning phase of the PST subjects choose between two stimuli presented as Hiragana-letters (here represented as letters from the latin alphabet). There are three conditions with different probabilities of receiving reward (feedback=1) and non-reward (feedback=0). In the AB condition A is rewarded with 80% probability, B with 20%. In the CD condition C is rewarded with 70% probability and D with 30%, while in the EF condition E is rewarded with a 60% probability and F with 40%. The dataset is included in the data-folder in your installation of HDDM."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/madslundpedersen/anaconda/envs/py36/lib/python3.6/site-packages/IPython/parallel.py:13: ShimWarning: The `IPython.parallel` package has been deprecated since IPython 4.0. You should import from ipyparallel instead.\n",
" \"You should import from ipyparallel instead.\", ShimWarning)\n"
]
}
],
"source": [
"# import\n",
"import pandas as pd\n",
"import numpy as np\n",
"import hddm\n",
"from scipy import stats\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import pymc\n",
"import kabuki\n",
"\n",
"sns.set(style=\"white\")\n",
"%matplotlib inline\n",
"from tqdm import tqdm\n",
"import warnings\n",
"\n",
"warnings.filterwarnings(\"ignore\", category=np.VisibleDeprecationWarning)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
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"metadata": {
"needs_background": "light"
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"output_type": "display_data"
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"source": [
"# plot the posteriors of parameters\n",
"m.plot_posteriors()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Fig__. The mixing of the posterior distribution and autocorrelation looks ok."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Convergence of chains\n",
"The Gelman-Rubin statistic is a test of whether the chains in the model converges. The Gelman-Ruben statistic measures the degree of variation between and within chains. Values close to 1 indicate convergence and that there is small variation between chains, i.e. that they end up as the same distribution across chains. A common heuristic is to assume convergence if all values are below 1.1. To run this you need to run multiple models, combine them and perform the Gelman-Rubin statistic:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 1500 of 1500 complete in 148.3 sec"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/madslundpedersen/anaconda/envs/py36/lib/python3.6/site-packages/kabuki/analyze.py:148: FutureWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
" samples[i,:] = model.nodes_db.ix[name, 'node'].trace()\n",
"/Users/madslundpedersen/anaconda/envs/py36/lib/python3.6/site-packages/pandas/core/indexing.py:961: FutureWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
" return getattr(section, self.name)[new_key]\n"
]
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"{'a': 1.0032460874018214,\n",
" 'a_std': 1.0009841521872085,\n",
" 'a_subj.3': 1.0004396186849591,\n",
" 'a_subj.4': 0.9999534502455131,\n",
" 'a_subj.5': 1.003486683386713,\n",
" 'a_subj.6': 1.0019388857243001,\n",
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" 'v': 1.0205809795803613,\n",
" 'v_std': 1.017843838450935,\n",
" 'v_subj.3': 1.0084966266683277,\n",
" 'v_subj.4': 0.9995268898509083,\n",
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" 'v_subj.75': 1.0011359012034806,\n",
" 'v_subj.80': 1.0071658884593422,\n",
" 't': 1.0005057563118305,\n",
" 't_std': 1.0022540551935895,\n",
" 't_subj.3': 1.000335111817265,\n",
" 't_subj.4': 0.9995572720794353,\n",
" 't_subj.5': 1.0010911542829217,\n",
" 't_subj.6': 1.0005078503952383,\n",
" 't_subj.8': 1.000937902509564,\n",
" 't_subj.12': 1.0002256652751758,\n",
" 't_subj.17': 1.0036774720777926,\n",
" 't_subj.18': 1.0057241781128603,\n",
" 't_subj.19': 1.000598016225987,\n",
" 't_subj.20': 1.0000045946218536,\n",
" 't_subj.22': 0.99983171259197,\n",
" 't_subj.23': 1.0005420765808288,\n",
" 't_subj.24': 1.0016080999110242,\n",
" 't_subj.26': 0.999512841375442,\n",
" 't_subj.33': 0.9998364286694233,\n",
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" 't_subj.36': 1.0042098480709771,\n",
" 't_subj.39': 1.0006515809595558,\n",
" 't_subj.42': 1.000771348791104,\n",
" 't_subj.50': 1.002215744431939,\n",
" 't_subj.52': 1.001052279977618,\n",
" 't_subj.56': 1.0017299690798878,\n",
" 't_subj.59': 1.0046305114183551,\n",
" 't_subj.63': 0.9995008742263429,\n",
" 't_subj.71': 1.001174121937735,\n",
" 't_subj.75': 1.0153800235690629,\n",
" 't_subj.80': 0.9999996479882802,\n",
" 'alpha': 1.0117546794208931,\n",
" 'alpha_std': 1.016548833490214,\n",
" 'alpha_subj.3': 1.0095453823767975,\n",
" 'alpha_subj.4': 0.9999967981599148,\n",
" 'alpha_subj.5': 1.0013067894285956,\n",
" 'alpha_subj.6': 1.0015822997094852,\n",
" 'alpha_subj.8': 1.028833703352052,\n",
" 'alpha_subj.12': 0.999682056083204,\n",
" 'alpha_subj.17': 1.002551456868468,\n",
" 'alpha_subj.18': 1.0128686930345914,\n",
" 'alpha_subj.19': 1.0216644572624185,\n",
" 'alpha_subj.20': 1.003469044949529,\n",
" 'alpha_subj.22': 0.9998485992621597,\n",
" 'alpha_subj.23': 1.0211627966678443,\n",
" 'alpha_subj.24': 1.0029351365812929,\n",
" 'alpha_subj.26': 1.0082171167684313,\n",
" 'alpha_subj.33': 1.0088110869085494,\n",
" 'alpha_subj.34': 1.000806268244852,\n",
" 'alpha_subj.35': 0.9997657478647997,\n",
" 'alpha_subj.36': 1.016349397314595,\n",
" 'alpha_subj.39': 1.0154377853905259,\n",
" 'alpha_subj.42': 1.0380447906771046,\n",
" 'alpha_subj.50': 1.014307146980194,\n",
" 'alpha_subj.52': 1.0421339866800452,\n",
" 'alpha_subj.56': 1.0345554568486672,\n",
" 'alpha_subj.59': 1.0004809998032806,\n",
" 'alpha_subj.63': 1.0167696837574038,\n",
" 'alpha_subj.71': 1.0030045474552403,\n",
" 'alpha_subj.75': 1.000110789496688,\n",
" 'alpha_subj.80': 1.0061715847351584}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# estimate convergence\n",
"from kabuki.analyze import gelman_rubin\n",
"\n",
"models = []\n",
"for i in range(3):\n",
" m = hddm.HDDMrl(data=data)\n",
" m.sample(1500, burn=500, dbname=\"traces.db\", db=\"pickle\")\n",
" models.append(m)\n",
"\n",
"gelman_rubin(models)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0446533107136062"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.max(list(gelman_rubin(models).values()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The model seems to have converged, i.e. the Gelman-Rubin statistic is below 1.1 for all parameters. It is important to always run this test, especially for more complex models ([as with separate learning rates for positive and negative prediction errors](#9.-Separate-learning-rates-for-positive-and-negative-prediction-errors)). So now we can combine these three models to get a better approximation of the posterior distribution."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Combine the models we ran to test for convergence.\n",
"m = kabuki.utils.concat_models(models)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Joint posterior distribution\n",
"Another test of the model is to look at collinearity. If the estimation of parameters is very codependent (correlation is strong) it can indicate that their variance trades off, in particular if there is a negative correlation. The following plot shows there is generally low correlation across all combinations of parameters. It does not seem to be the case for this dataset, but common for RLDDM is a negative correlation between learning rate and the scaling factor, similar to what's usually observed between learning rate and inverse temperature for RL models that uses softmax as the choice rule (e.g. [Daw, 2011](https://www.oxfordscholarship.com/view/10.1093/acprof:oso/9780199600434.001.0001/acprof-9780199600434-chapter-001))."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"alpha, t, a, v = m.nodes_db.node[[\"alpha\", \"t\", \"a\", \"v\"]]\n",
"samples = {\"alpha\": alpha.trace(), \"t\": t.trace(), \"a\": a.trace(), \"v\": v.trace()}\n",
"samp = pd.DataFrame(data=samples)\n",
"\n",
"\n",
"def corrfunc(x, y, **kws):\n",
" r, _ = stats.pearsonr(x, y)\n",
" ax = plt.gca()\n",
" ax.annotate(\"r = {:.2f}\".format(r), xy=(0.1, 0.9), xycoords=ax.transAxes)\n",
"\n",
"\n",
"g = sns.PairGrid(samp, palette=[\"red\"])\n",
"g.map_upper(plt.scatter, s=10)\n",
"g.map_diag(sns.distplot, kde=False)\n",
"g.map_lower(sns.kdeplot, cmap=\"Blues_d\")\n",
"g.map_lower(corrfunc)\n",
"g.savefig(\"matrix_plot.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Posterior predictive checks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An important test of the model is its ability to recreate the observed data. This can be tested with posterior predictive checks, which involves simulating data using estimated parameters and comparing observed and simulated results."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### extract traces\n",
"The first step then is to extract the traces from the estimated model. The function get_traces() gives you the samples (row) from the approaximated posterior distribution for all of the estimated group and subject parameters (column)."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
" a a_std a_subj.3 a_subj.4 a_subj.5 a_subj.6 a_subj.8 \\\n",
"0 1.663258 0.379335 1.999766 1.964781 1.501228 2.558870 2.134354 \n",
"1 1.623170 0.359708 1.912802 1.967012 1.462844 2.466133 2.347265 \n",
"2 1.817655 0.312626 2.013651 1.870517 1.438784 2.332917 2.426746 \n",
"3 1.762559 0.573961 1.852805 1.920585 1.456088 2.437470 2.679242 \n",
"4 1.725824 0.472488 1.907957 1.954045 1.462033 2.394734 2.389626 \n",
"\n",
" a_subj.12 a_subj.17 a_subj.18 ... alpha_subj.39 alpha_subj.42 \\\n",
"0 1.808065 1.428145 1.904139 ... -2.569240 -2.348125 \n",
"1 2.031387 1.476833 1.883079 ... -2.593007 -2.564579 \n",
"2 2.079006 1.264283 1.939135 ... -3.187908 -2.566549 \n",
"3 2.099067 1.311264 1.902507 ... -2.045972 -2.466571 \n",
"4 1.928428 1.334218 1.790217 ... -2.035124 -2.679132 \n",
"\n",
" alpha_subj.50 alpha_subj.52 alpha_subj.56 alpha_subj.59 alpha_subj.63 \\\n",
"0 -2.279293 -3.002542 -2.038603 -2.255088 -0.153830 \n",
"1 -2.787299 -2.817155 0.128265 -2.358720 -0.709526 \n",
"2 -3.341771 -3.206621 -0.724311 -2.446694 -1.133453 \n",
"3 -3.093191 -3.204751 -3.220443 -2.381405 -1.060397 \n",
"4 -3.821553 -3.372584 -1.139438 -2.372234 -0.895417 \n",
"\n",
" alpha_subj.71 alpha_subj.75 alpha_subj.80 \n",
"0 -1.809325 -1.738580 -2.323516 \n",
"1 -1.876886 -1.428454 -3.140597 \n",
"2 -2.153231 -1.589570 -2.702218 \n",
"3 -1.521510 -1.892220 -2.902676 \n",
"4 -1.900813 -2.196233 -3.063793 \n",
"\n",
"[5 rows x 120 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"traces = m.get_traces()\n",
"traces.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### simulating data\n",
"__Now that we have the traces the next step is to simulate data using the estimated parameters. The RLDDM includes a function to simulate data. Here's an example of how to use the simulation-function for RLDDM. This example explains how to generate data with binary outcomes. See [here](#11.-Probabilistic-binary-outcomes-vs.-normally-distributed-outcomes) for an example on simulating data with normally distributed outcomes. Inputs to function: \n",
"a__ = decision threshold \n",
"**t** = non-decision time \n",
"__alpha__ = learning rate \n",
"__pos_alpha__ = defaults to 0. if given it defines the learning rate for positive prediction errors. alpha then becomes the learning rate_ for negative prediction errors. \n",
"__scaler__ = the scaling factor that is multiplied with the difference in q-values to calculate trial-by-trial drift rate \n",
"__p_upper__ = the probability of reward for the option represented by the upper boundary. The current version thus only works for outcomes that are either 1 or 0 \n",
"__p_lower__ = the probability of reward for the option represented by the lower boundary. \n",
"__subjs__ = number of subjects to simulate data for. \n",
"__split_by__ = define the condition which makes it easier to append data from different conditions. \n",
"__size__ = number of trials per subject. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
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"\n",
" trial \n",
"0 1 \n",
"1 2 \n",
"2 3 \n",
"3 4 \n",
"4 5 \n",
"5 6 \n",
"6 7 \n",
"7 8 \n",
"8 9 \n",
"9 10 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hddm.generate.gen_rand_rlddm_data(\n",
" a=1,\n",
" t=0.3,\n",
" alpha=0.2,\n",
" scaler=2,\n",
" p_upper=0.8,\n",
" p_lower=0.2,\n",
" subjs=1,\n",
" split_by=0,\n",
" size=10,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__How to interpret columns in the resulting dataframe__ \n",
"__q_up__ = expected reward for option represented by upper boundary \n",
"__q_low__ = expected reward for option represented by lower boundary \n",
"__sim_drift__ = the drift rate for each trial calculated as: (q_up-q_low)*scaler \n",
"__response__ = simulated choice \n",
"__rt__ = simulated response time \n",
"__feedback__ = observed feedback for chosen option \n",
"__subj_idx__ = subject id (starts at 0) \n",
"__split_by__ = condition as integer \n",
"__trial__ = current trial (starts at 1) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simulate data with estimated parameter values and compare to observed data\n",
"Now that we know how to extract traces and simulate data we can combine this to create a dataset similar to our observed data. This process is currently not automated but the following is an example code using the dataset we analyzed above."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 50/50 [31:09<00:00, 37.39s/it]\n",
"/Users/madslundpedersen/anaconda/envs/py36/lib/python3.6/site-packages/pandas/core/frame.py:7138: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
"of pandas will change to not sort by default.\n",
"\n",
"To accept the future behavior, pass 'sort=False'.\n",
"\n",
"To retain the current behavior and silence the warning, pass 'sort=True'.\n",
"\n",
" sort=sort,\n"
]
}
],
"source": [
"from tqdm import tqdm # progress tracker\n",
"\n",
"# create empty dataframe to store simulated data\n",
"sim_data = pd.DataFrame()\n",
"# create a column samp to be used to identify the simulated data sets\n",
"data[\"samp\"] = 0\n",
"# load traces\n",
"traces = m.get_traces()\n",
"# decide how many times to repeat simulation process. repeating this multiple times is generally recommended,\n",
"# as it better captures the uncertainty in the posterior distribution, but will also take some time\n",
"for i in tqdm(range(1, 51)):\n",
" # randomly select a row in the traces to use for extracting parameter values\n",
" sample = np.random.randint(0, traces.shape[0] - 1)\n",
" # loop through all subjects in observed data\n",
" for s in data.subj_idx.unique():\n",
" # get number of trials for each condition.\n",
" size0 = len(\n",
" data[(data[\"subj_idx\"] == s) & (data[\"split_by\"] == 0)].trial.unique()\n",
" )\n",
" size1 = len(\n",
" data[(data[\"subj_idx\"] == s) & (data[\"split_by\"] == 1)].trial.unique()\n",
" )\n",
" size2 = len(\n",
" data[(data[\"subj_idx\"] == s) & (data[\"split_by\"] == 2)].trial.unique()\n",
" )\n",
" # set parameter values for simulation\n",
" a = traces.loc[sample, \"a_subj.\" + str(s)]\n",
" t = traces.loc[sample, \"t_subj.\" + str(s)]\n",
" scaler = traces.loc[sample, \"v_subj.\" + str(s)]\n",
" alphaInv = traces.loc[sample, \"alpha_subj.\" + str(s)]\n",
" # take inverse logit of estimated alpha\n",
" alpha = np.exp(alphaInv) / (1 + np.exp(alphaInv))\n",
" # simulate data for each condition changing only values of size, p_upper, p_lower and split_by between conditions.\n",
" sim_data0 = hddm.generate.gen_rand_rlddm_data(\n",
" a=a,\n",
" t=t,\n",
" scaler=scaler,\n",
" alpha=alpha,\n",
" size=size0,\n",
" p_upper=0.8,\n",
" p_lower=0.2,\n",
" split_by=0,\n",
" )\n",
" sim_data1 = hddm.generate.gen_rand_rlddm_data(\n",
" a=a,\n",
" t=t,\n",
" scaler=scaler,\n",
" alpha=alpha,\n",
" size=size1,\n",
" p_upper=0.7,\n",
" p_lower=0.3,\n",
" split_by=1,\n",
" )\n",
" sim_data2 = hddm.generate.gen_rand_rlddm_data(\n",
" a=a,\n",
" t=t,\n",
" scaler=scaler,\n",
" alpha=alpha,\n",
" size=size2,\n",
" p_upper=0.6,\n",
" p_lower=0.4,\n",
" split_by=2,\n",
" )\n",
" # append the conditions\n",
" sim_data0 = sim_data0.append([sim_data1, sim_data2], ignore_index=True)\n",
" # assign subj_idx\n",
" sim_data0[\"subj_idx\"] = s\n",
" # identify that these are simulated data\n",
" sim_data0[\"type\"] = \"simulated\"\n",
" # identify the simulated data\n",
" sim_data0[\"samp\"] = i\n",
" # append data from each subject\n",
" sim_data = sim_data.append(sim_data0, ignore_index=True)\n",
"# combine observed and simulated data\n",
"ppc_data = data[\n",
" [\"subj_idx\", \"response\", \"split_by\", \"rt\", \"trial\", \"feedback\", \"samp\"]\n",
"].copy()\n",
"ppc_data[\"type\"] = \"observed\"\n",
"ppc_sdata = sim_data[\n",
" [\"subj_idx\", \"response\", \"split_by\", \"rt\", \"trial\", \"feedback\", \"type\", \"samp\"]\n",
"].copy()\n",
"ppc_data = ppc_data.append(ppc_sdata)\n",
"ppc_data.to_csv(\"ppc_data_tutorial.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plotting\n",
"Now that we have a dataframe with both observed and simulated data we can plot to see whether the simulated data are able to capture observed choice and reaction times. To capture the uncertainty in the simulated data we want to identify how much choice and reaction differs across the simulated data sets. A good measure of this is to calculate the highest posterior density/highest density interval for summary scores of the generated data. Below we calculate highest posterior density with an alpha set to 0.1, which means that we are describing the range of the 90% most likely values."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# for practical reasons we only look at the first 40 trials for each subject in a given condition\n",
"plot_ppc_data = ppc_data[ppc_data.trial < 41].copy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Choice"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# bin trials to for smoother estimate of response proportion across learning\n",
"plot_ppc_data[\"bin_trial\"] = pd.cut(\n",
" plot_ppc_data.trial, 11, labels=np.linspace(0, 10, 11)\n",
").astype(\"int64\")\n",
"# calculate means for each sample\n",
"sums = (\n",
" plot_ppc_data.groupby([\"bin_trial\", \"split_by\", \"samp\", \"type\"])\n",
" .mean()\n",
" .reset_index()\n",
")\n",
"# calculate the overall mean response across samples\n",
"ppc_sim = sums.groupby([\"bin_trial\", \"split_by\", \"type\"]).mean().reset_index()\n",
"# initiate columns that will have the upper and lower bound of the hpd\n",
"ppc_sim[\"upper_hpd\"] = 0\n",
"ppc_sim[\"lower_hpd\"] = 0\n",
"for i in range(0, ppc_sim.shape[0]):\n",
" # calculate the hpd/hdi of the predicted mean responses across bin_trials\n",
" hdi = pymc.utils.hpd(\n",
" sums.response[\n",
" (sums[\"bin_trial\"] == ppc_sim.bin_trial[i])\n",
" & (sums[\"split_by\"] == ppc_sim.split_by[i])\n",
" & (sums[\"type\"] == ppc_sim.type[i])\n",
" ],\n",
" alpha=0.1,\n",
" )\n",
" ppc_sim.loc[i, \"upper_hpd\"] = hdi[1]\n",
" ppc_sim.loc[i, \"lower_hpd\"] = hdi[0]\n",
"# calculate error term as the distance from upper bound to mean\n",
"ppc_sim[\"up_err\"] = ppc_sim[\"upper_hpd\"] - ppc_sim[\"response\"]\n",
"ppc_sim[\"low_err\"] = ppc_sim[\"response\"] - ppc_sim[\"lower_hpd\"]\n",
"ppc_sim[\"model\"] = \"RLDDM_single_learning\"\n",
"ppc_sim.to_csv(\"ppc_choicedata_tutorial.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plotting evolution of choice proportion for best option across learning for observed and simulated data.\n",
"fig, axs = plt.subplots(figsize=(15, 5), nrows=1, ncols=3, sharex=True, sharey=True)\n",
"for i in range(0, 3):\n",
" ax = axs[i]\n",
" d = ppc_sim[(ppc_sim.split_by == i) & (ppc_sim.type == \"simulated\")]\n",
" ax.errorbar(\n",
" d.bin_trial,\n",
" d.response,\n",
" yerr=[d.low_err, d.up_err],\n",
" label=\"simulated\",\n",
" color=\"orange\",\n",
" )\n",
" d = ppc_sim[(ppc_sim.split_by == i) & (ppc_sim.type == \"observed\")]\n",
" ax.plot(d.bin_trial, d.response, linewidth=3, label=\"observed\")\n",
" ax.set_title(\"split_by = %i\" % i, fontsize=20)\n",
" ax.set_ylabel(\"mean response\")\n",
" ax.set_xlabel(\"trial\")\n",
"plt.legend()\n",
"fig.savefig(\"PPCchoice.pdf\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Fig.__ The plots display the rate of choosing the best option (response = 1) across learning and condition. The model generates data (orange) that closely follows the observed behavior (blue), with the exception of overpredicting performance early in the most difficult condition (split_by=2). Uncertainty in the generated data is captured by the 90% highest density interval of the means across simulated datasets."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### RT"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# set reaction time to be negative for lower bound responses (response=0)\n",
"plot_ppc_data[\"reaction time\"] = np.where(\n",
" plot_ppc_data[\"response\"] == 1, plot_ppc_data.rt, 0 - plot_ppc_data.rt\n",
")\n",
"# plotting evolution of choice proportion for best option across learning for observed and simulated data. We use bins of trials because plotting individual trials would be very noisy.\n",
"g = sns.FacetGrid(plot_ppc_data, col=\"split_by\", hue=\"type\")\n",
"g.map(sns.kdeplot, \"reaction time\", bw=0.05).set_ylabels(\"Density\")\n",
"g.add_legend()\n",
"g.savefig(\"PPCrt_dist.pdf\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Fig.__ Density plots of observed and predicted reaction time across conditions. RTs for lower boundary choices (i.e. worst option choices) are set to be negative (0-RT) to be able to separate upper and lower bound responses."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Parameter recovery\n",
"To validate the RLDDM we ran a parameter recovery study to test to which degree the model can recover the parameter values used to simulate data. To do this we generated 81 synthetic datasets with 50 subjects performing 70 trials each. The 81 datasets were simulated using all combinations of three plausible parameter values for decision threshold, non-decision time, learning rate and the scaling parameter onto drift rate."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Estimated values split by simulated vales \n",
"We can plot simulated together with the estimated values to test the models ability to recover parameters, and to see if there are any values that are more difficult to recover than others."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"param_recovery = hddm.load_csv(\"recovery_sim_est_rlddm.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"alpha, t, a, v, pos_alpha = m_dual.nodes_db.node[[\"alpha\", \"t\", \"a\", \"v\", \"pos_alpha\"]]\n",
"samples = {\n",
" \"alpha\": alpha.trace(),\n",
" \"pos_alpha\": pos_alpha.trace(),\n",
" \"t\": t.trace(),\n",
" \"a\": a.trace(),\n",
" \"v\": v.trace(),\n",
"}\n",
"samp = pd.DataFrame(data=samples)\n",
"\n",
"\n",
"def corrfunc(x, y, **kws):\n",
" r, _ = stats.pearsonr(x, y)\n",
" ax = plt.gca()\n",
" ax.annotate(\"r = {:.2f}\".format(r), xy=(0.1, 0.9), xycoords=ax.transAxes)\n",
"\n",
"\n",
"g = sns.PairGrid(samp, palette=[\"red\"])\n",
"g.map_upper(plt.scatter, s=10)\n",
"g.map_diag(sns.distplot, kde=False)\n",
"g.map_lower(sns.kdeplot, cmap=\"Blues_d\")\n",
"g.map_lower(corrfunc)\n",
"g.savefig(\"matrix_plot.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Fig.__ The correlation between parameters is generally low. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Posterior predictive check\n",
"The DIC for this dual learning rate model is better than for the single learning rate model. We can therefore check whether we can detect this improvement in the ability to recreate choice and RT patterns:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 50/50 [31:12<00:00, 37.44s/it]\n",
"/Users/madslundpedersen/anaconda/envs/py36/lib/python3.6/site-packages/pandas/core/frame.py:7138: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
"of pandas will change to not sort by default.\n",
"\n",
"To accept the future behavior, pass 'sort=False'.\n",
"\n",
"To retain the current behavior and silence the warning, pass 'sort=True'.\n",
"\n",
" sort=sort,\n"
]
}
],
"source": [
"# create empty dataframe to store simulated data\n",
"sim_data = pd.DataFrame()\n",
"# create a column samp to be used to identify the simulated data sets\n",
"data[\"samp\"] = 0\n",
"# get traces, note here we extract traces from m_dual\n",
"traces = m_dual.get_traces()\n",
"# decide how many times to repeat simulation process. repeating this multiple times is generally recommended as it better captures the uncertainty in the posterior distribution, but will also take some time\n",
"for i in tqdm(range(1, 51)):\n",
" # randomly select a row in the traces to use for extracting parameter values\n",
" sample = np.random.randint(0, traces.shape[0] - 1)\n",
" # loop through all subjects in observed data\n",
" for s in data.subj_idx.unique():\n",
" # get number of trials for each condition.\n",
" size0 = len(\n",
" data[(data[\"subj_idx\"] == s) & (data[\"split_by\"] == 0)].trial.unique()\n",
" )\n",
" size1 = len(\n",
" data[(data[\"subj_idx\"] == s) & (data[\"split_by\"] == 1)].trial.unique()\n",
" )\n",
" size2 = len(\n",
" data[(data[\"subj_idx\"] == s) & (data[\"split_by\"] == 2)].trial.unique()\n",
" )\n",
" # set parameter values for simulation\n",
" a = traces.loc[sample, \"a_subj.\" + str(s)]\n",
" t = traces.loc[sample, \"t_subj.\" + str(s)]\n",
" scaler = traces.loc[sample, \"v_subj.\" + str(s)]\n",
" # when generating data with two learning rates pos_alpha represents learning rate for positive prediction errors and alpha for negative prediction errors\n",
" alphaInv = traces.loc[sample, \"alpha_subj.\" + str(s)]\n",
" pos_alphaInv = traces.loc[sample, \"pos_alpha_subj.\" + str(s)]\n",
" # take inverse logit of estimated alpha and pos_alpha\n",
" alpha = np.exp(alphaInv) / (1 + np.exp(alphaInv))\n",
" pos_alpha = np.exp(pos_alphaInv) / (1 + np.exp(pos_alphaInv))\n",
" # simulate data for each condition changing only values of size, p_upper, p_lower and split_by between conditions.\n",
" sim_data0 = hddm.generate.gen_rand_rlddm_data(\n",
" a=a,\n",
" t=t,\n",
" scaler=scaler,\n",
" alpha=alpha,\n",
" pos_alpha=pos_alpha,\n",
" size=size0,\n",
" p_upper=0.8,\n",
" p_lower=0.2,\n",
" split_by=0,\n",
" )\n",
" sim_data1 = hddm.generate.gen_rand_rlddm_data(\n",
" a=a,\n",
" t=t,\n",
" scaler=scaler,\n",
" alpha=alpha,\n",
" pos_alpha=pos_alpha,\n",
" size=size1,\n",
" p_upper=0.7,\n",
" p_lower=0.3,\n",
" split_by=1,\n",
" )\n",
" sim_data2 = hddm.generate.gen_rand_rlddm_data(\n",
" a=a,\n",
" t=t,\n",
" scaler=scaler,\n",
" alpha=alpha,\n",
" pos_alpha=pos_alpha,\n",
" size=size2,\n",
" p_upper=0.6,\n",
" p_lower=0.4,\n",
" split_by=2,\n",
" )\n",
" # append the conditions\n",
" sim_data0 = sim_data0.append([sim_data1, sim_data2], ignore_index=True)\n",
" # assign subj_idx\n",
" sim_data0[\"subj_idx\"] = s\n",
" # identify that these are simulated data\n",
" sim_data0[\"type\"] = \"simulated\"\n",
" # identify the simulated data\n",
" sim_data0[\"samp\"] = i\n",
" # append data from each subject\n",
" sim_data = sim_data.append(sim_data0, ignore_index=True)\n",
"# combine observed and simulated data\n",
"ppc_dual_data = data[\n",
" [\"subj_idx\", \"response\", \"split_by\", \"rt\", \"trial\", \"feedback\", \"samp\"]\n",
"].copy()\n",
"ppc_dual_data[\"type\"] = \"observed\"\n",
"ppc_dual_sdata = sim_data[\n",
" [\"subj_idx\", \"response\", \"split_by\", \"rt\", \"trial\", \"feedback\", \"type\", \"samp\"]\n",
"].copy()\n",
"ppc_dual_data = ppc_dual_data.append(ppc_dual_sdata)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"# for practical reasons we only look at the first 40 trials for each subject in a given condition\n",
"plot_ppc_dual_data = ppc_dual_data[ppc_dual_data.trial < 41].copy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Choice"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"# bin trials to for smoother estimate of response proportion across learning\n",
"plot_ppc_dual_data[\"bin_trial\"] = pd.cut(\n",
" plot_ppc_dual_data.trial, 11, labels=np.linspace(0, 10, 11)\n",
").astype(\"int64\")\n",
"# calculate means for each sample\n",
"sums = (\n",
" plot_ppc_dual_data.groupby([\"bin_trial\", \"split_by\", \"samp\", \"type\"])\n",
" .mean()\n",
" .reset_index()\n",
")\n",
"# calculate the overall mean response across samples\n",
"ppc_dual_sim = sums.groupby([\"bin_trial\", \"split_by\", \"type\"]).mean().reset_index()\n",
"# initiate columns that will have the upper and lower bound of the hpd\n",
"ppc_dual_sim[\"upper_hpd\"] = 0\n",
"ppc_dual_sim[\"lower_hpd\"] = 0\n",
"for i in range(0, ppc_dual_sim.shape[0]):\n",
" # calculate the hpd/hdi of the predicted mean responses across bin_trials\n",
" hdi = pymc.utils.hpd(\n",
" sums.response[\n",
" (sums[\"bin_trial\"] == ppc_dual_sim.bin_trial[i])\n",
" & (sums[\"split_by\"] == ppc_dual_sim.split_by[i])\n",
" & (sums[\"type\"] == ppc_dual_sim.type[i])\n",
" ],\n",
" alpha=0.1,\n",
" )\n",
" ppc_dual_sim.loc[i, \"upper_hpd\"] = hdi[1]\n",
" ppc_dual_sim.loc[i, \"lower_hpd\"] = hdi[0]\n",
"# calculate error term as the distance from upper bound to mean\n",
"ppc_dual_sim[\"up_err\"] = ppc_dual_sim[\"upper_hpd\"] - ppc_dual_sim[\"response\"]\n",
"ppc_dual_sim[\"low_err\"] = ppc_dual_sim[\"response\"] - ppc_dual_sim[\"lower_hpd\"]\n",
"ppc_dual_sim[\"model\"] = \"RLDDM_dual_learning\""
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plotting evolution of choice proportion for best option across learning for observed and simulated data.\n",
"fig, axs = plt.subplots(figsize=(15, 5), nrows=1, ncols=3, sharex=True, sharey=True)\n",
"for i in range(0, 3):\n",
" ax = axs[i]\n",
" d = ppc_dual_sim[(ppc_dual_sim.split_by == i) & (ppc_dual_sim.type == \"simulated\")]\n",
" ax.errorbar(\n",
" d.bin_trial,\n",
" d.response,\n",
" yerr=[d.low_err, d.up_err],\n",
" label=\"simulated\",\n",
" color=\"orange\",\n",
" )\n",
" d = ppc_sim[(ppc_dual_sim.split_by == i) & (ppc_dual_sim.type == \"observed\")]\n",
" ax.plot(d.bin_trial, d.response, linewidth=3, label=\"observed\")\n",
" ax.set_title(\"split_by = %i\" % i, fontsize=20)\n",
" ax.set_ylabel(\"mean response\")\n",
" ax.set_xlabel(\"trial\")\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Fig.__ The plots display the rate of choosing the best option (response = 1) across learning and condition. The model generates data (orange) that closely follows the observed behavior (blue), with the exception of performance early in the most difficult condition (split_by=2)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### PPC for single vs. dual learning rate\n",
"To get a better sense of differences in ability to predict data between the single and dual learning rate model we can plot them together:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/madslundpedersen/anaconda/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:7: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plotting evolution of choice proportion for best option across learning for observed and simulated data. Compared for model with single and dual learning rate.\n",
"fig, axs = plt.subplots(figsize=(15, 5), nrows=1, ncols=3, sharex=True, sharey=True)\n",
"for i in range(0, 3):\n",
" ax = axs[i]\n",
" d_single = ppc_sim[(ppc_sim.split_by == i) & (ppc_sim.type == \"simulated\")]\n",
" # slightly move bin_trial to avoid overlap in errorbars\n",
" d_single[\"bin_trial\"] += 0.2\n",
" ax.errorbar(\n",
" d_single.bin_trial,\n",
" d_single.response,\n",
" yerr=[d_single.low_err, d_single.up_err],\n",
" label=\"simulated_single\",\n",
" color=\"orange\",\n",
" )\n",
" d_dual = ppc_dual_sim[\n",
" (ppc_dual_sim.split_by == i) & (ppc_dual_sim.type == \"simulated\")\n",
" ]\n",
" ax.errorbar(\n",
" d_dual.bin_trial,\n",
" d_dual.response,\n",
" yerr=[d_dual.low_err, d_dual.up_err],\n",
" label=\"simulated_dual\",\n",
" color=\"green\",\n",
" )\n",
" d = ppc_sim[(ppc_dual_sim.split_by == i) & (ppc_dual_sim.type == \"observed\")]\n",
" ax.plot(d.bin_trial, d.response, linewidth=3, label=\"observed\")\n",
" ax.set_title(\"split_by = %i\" % i, fontsize=20)\n",
" ax.set_ylabel(\"mean response\")\n",
" ax.set_xlabel(\"trial\")\n",
"plt.xlim(-0.5, 10.5)\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Fig.__ The predictions from the model with two learning rates are not very different from the model with single learning rate, and a similar overprediction of performance early on for the most difficult condition (split_by =2)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### RT"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_ppc_data[\"type_compare\"] = np.where(\n",
" plot_ppc_data[\"type\"] == \"observed\",\n",
" plot_ppc_data[\"type\"],\n",
" \"simulated_single_learning\",\n",
")\n",
"plot_ppc_dual_data[\"type_compare\"] = np.where(\n",
" plot_ppc_dual_data[\"type\"] == \"observed\",\n",
" plot_ppc_dual_data[\"type\"],\n",
" \"simulated_dual_learning\",\n",
")\n",
"dual_vs_single_pcc = plot_ppc_data.append(plot_ppc_dual_data)\n",
"dual_vs_single_pcc[\"reaction time\"] = np.where(\n",
" dual_vs_single_pcc[\"response\"] == 1,\n",
" dual_vs_single_pcc.rt,\n",
" 0 - dual_vs_single_pcc.rt,\n",
")\n",
"# plotting evolution of choice proportion for best option across learning for observed and simulated data. We use bins of trials because plotting individual trials would be very noisy.\n",
"g = sns.FacetGrid(dual_vs_single_pcc, col=\"split_by\", hue=\"type_compare\", height=5)\n",
"g.map(sns.kdeplot, \"reaction time\", bw=0.01).set_ylabels(\"Density\")\n",
"g.add_legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Fig.__ Again there's not a big difference between the two models. Both models slightly overpredict performance for the medium (split_by =1) and hard (split_by = 2) conditions, as identified by lower densities for the negative (worst option choices) in the simulated compared to observed data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transform alpha and pos_alpha\n",
"To interpret the parameter estimates for alpha and pos_alpha you have to transform them with the inverse logit where learning rate for negative prediction error is alpha and learning rate for positive prediction errors is pos_alpha. For this dataset the learning rate is estimated to be higher for positive than negative prediction errors."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plot alpha for positive and negative learning rate\n",
"traces = m_dual.get_traces()\n",
"neg_alpha = np.exp(traces[\"alpha\"]) / (1 + np.exp(traces[\"alpha\"]))\n",
"pos_alpha = np.exp(traces[\"pos_alpha\"]) / (1 + np.exp(traces[\"pos_alpha\"]))\n",
"sns.kdeplot(\n",
" neg_alpha, color=\"r\", label=\"neg_alpha: \" + str(np.round(np.mean(neg_alpha), 3))\n",
")\n",
"sns.kdeplot(\n",
" pos_alpha, color=\"b\", label=\"pos_alpha: \" + str(np.round(np.mean(pos_alpha), 3))\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Fig.__ The positive learning rate is estimated to be stronger than the negative learning rate. Sticky choice, tendencies to repeat choices, could be driving some of this difference. The current model does not allow to test for this, however, but it could be tested in the future if we implement a regression version of RLDDM (similar to HDDMRegressor)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simulate data with learning rates for positive and negative prediction errors\n",
"Here's how you would simulate data with a learning rate for positive and negative predictions of 0.2 and 0.4, respectively:"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
" q_up q_low sim_drift response rt feedback subj_idx split_by \\\n",
"0 0.50000 0.5000 0.00000 1.0 0.583 1.0 0 0 \n",
"1 0.70000 0.5000 0.40000 0.0 0.479 0.0 0 0 \n",
"2 0.70000 0.4000 0.60000 1.0 0.450 1.0 0 0 \n",
"3 0.82000 0.4000 0.84000 1.0 0.403 1.0 0 0 \n",
"4 0.89200 0.4000 0.98400 0.0 0.732 0.0 0 0 \n",
"5 0.89200 0.3200 1.14400 0.0 0.396 0.0 0 0 \n",
"6 0.89200 0.2560 1.27200 1.0 0.964 0.0 0 0 \n",
"7 0.71360 0.2560 0.91520 1.0 0.493 1.0 0 0 \n",
"8 0.82816 0.2560 1.14432 0.0 0.360 0.0 0 0 \n",
"9 0.82816 0.2048 1.24672 1.0 0.483 1.0 0 0 \n",
"\n",
" trial \n",
"0 1 \n",
"1 2 \n",
"2 3 \n",
"3 4 \n",
"4 5 \n",
"5 6 \n",
"6 7 \n",
"7 8 \n",
"8 9 \n",
"9 10 "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hddm.generate.gen_rand_rlddm_data(\n",
" a=1, t=0.3, alpha=0.2, pos_alpha=0.4, scaler=2, p_upper=0.8, p_lower=0.2, size=10\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 10. depends_on vs. split_by\n",
"HDDMrl can be used to estimate separate parameters just as in the standard HDDM. But in RL you typically estimate the same learning rates and inverse temperature across conditions. That's one reason why you have to specify condition in the split_by-column instead of depends_on. (The other is that if you use depends_on the expected rewards will not get updated properly). But depends_on is still useful, for example if you want to estimate the effect of group on parameters. As an example we can simulate a dataset with two groups that have different decision thresholds:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 1500 of 1500 complete in 380.4 sec"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data1 = hddm.generate.gen_rand_rlddm_data(\n",
" a=1, t=0.3, alpha=0.4, scaler=2, p_upper=0.8, p_lower=0.2, subjs=50, size=50\n",
")\n",
"data1[\"group\"] = \"group1\"\n",
"data2 = hddm.generate.gen_rand_rlddm_data(\n",
" a=2, t=0.3, alpha=0.4, scaler=2, p_upper=0.8, p_lower=0.2, subjs=50, size=50\n",
")\n",
"data2[\"group\"] = \"group2\"\n",
"group_data = data1.append(data2)\n",
"group_data[\"q_init\"] = 0.5\n",
"m = hddm.HDDMrl(\n",
" group_data, depends_on={\"v\": \"group\", \"a\": \"group\", \"t\": \"group\", \"alpha\": \"group\"}\n",
")\n",
"m.sample(1500, burn=500, dbname=\"traces.db\", db=\"pickle\")"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Posterior of decision threshold group means')"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# the plot shows that the model was able to recover the different decision threshold across groups.\n",
"a_group1, a_group2 = m.nodes_db.node[[\"a(group1)\", \"a(group2)\"]]\n",
"hddm.analyze.plot_posterior_nodes([a_group1, a_group2])\n",
"plt.xlabel(\"decision threshold\")\n",
"plt.ylabel(\"Posterior probability\")\n",
"plt.xlim(0.7, 2.3)\n",
"plt.title(\"Posterior of decision threshold group means\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 11. Probabilistic binary outcomes vs. normally distributed outcomes\n",
"The examples so far have all been using a task structure where the outcomes are binary and probabilistic. But the model can also be applied to other types of outcomes. Here we show how you can generate and model data with normally distributed outcomes. As you will see you don't have to do any modifications to the model estimation process, but you have to change the input for generating data. Also note that the scaling parameter (v) will scale negatively with the values of the observed outcomes because the combined drift rate needs to be plausible."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
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