LLerror.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "J-4TjGGtFT6R"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.optimize as opt\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\n",
    "from tqdm.notebook import tqdm\n",
    "import os\n",
    "import seaborn as sns\n",
    "import random\n",
    "import math\n",
    "import scipy as sp\n",
    "import itertools\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E53BwHW1Gk9l"
   },
   "source": [
    "Load and check data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "RAxDu0zYFedU"
   },
   "outputs": [],
   "source": [
    "alldata = pd.read_csv(\"data/error_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "Dgj051G4GVUO",
    "outputId": "66797014-2896-4c3b-cd11-02bbcdd8d959"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>participant</th>\n",
       "      <th>phase</th>\n",
       "      <th>condition</th>\n",
       "      <th>cue</th>\n",
       "      <th>target</th>\n",
       "      <th>response</th>\n",
       "      <th>rt</th>\n",
       "      <th>correct</th>\n",
       "      <th>Column1</th>\n",
       "      <th>Column2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001.0</td>\n",
       "      <td>learn</td>\n",
       "      <td>1.0</td>\n",
       "      <td>EMPIRE</td>\n",
       "      <td>STATE</td>\n",
       "      <td>KING</td>\n",
       "      <td>3457.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001.0</td>\n",
       "      <td>learn</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CHUNK</td>\n",
       "      <td>BLOCK</td>\n",
       "      <td>HUNK</td>\n",
       "      <td>3361.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001.0</td>\n",
       "      <td>learn</td>\n",
       "      <td>1.0</td>\n",
       "      <td>GLIDE</td>\n",
       "      <td>HANG</td>\n",
       "      <td>FLY</td>\n",
       "      <td>2177.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001.0</td>\n",
       "      <td>learn</td>\n",
       "      <td>1.0</td>\n",
       "      <td>SAIL</td>\n",
       "      <td>OCEAN</td>\n",
       "      <td>BOAT</td>\n",
       "      <td>2341.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001.0</td>\n",
       "      <td>learn</td>\n",
       "      <td>1.0</td>\n",
       "      <td>ELEVATOR</td>\n",
       "      <td>BUILDING</td>\n",
       "      <td>UP</td>\n",
       "      <td>3464.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   participant  phase  condition       cue    target response      rt  \\\n",
       "0       1001.0  learn        1.0    EMPIRE     STATE     KING  3457.0   \n",
       "1       1001.0  learn        1.0     CHUNK     BLOCK     HUNK  3361.0   \n",
       "2       1001.0  learn        1.0     GLIDE      HANG      FLY  2177.0   \n",
       "3       1001.0  learn        1.0      SAIL     OCEAN     BOAT  2341.0   \n",
       "4       1001.0  learn        1.0  ELEVATOR  BUILDING       UP  3464.0   \n",
       "\n",
       "   correct  Column1 Column2  \n",
       "0      2.0      NaN     NaN  \n",
       "1      2.0      NaN     NaN  \n",
       "2      2.0      NaN     NaN  \n",
       "3      2.0      NaN     NaN  \n",
       "4      2.0      NaN     NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hV8XwZcBGg_G",
    "outputId": "b570f05e-02e5-4d81-8a5f-c0dd54dacb34"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5670, 10)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5490, 10)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove rows with all nan\n",
    "alldata = alldata.dropna(how='all')\n",
    "alldata.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uLjxKMVoGub_"
   },
   "source": [
    "# Log likelihood"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UMdZ1ukRG4Y-"
   },
   "source": [
    "Implement ACT-R code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zOnwozhTGttJ",
    "outputId": "3eb02b94-426d-443b-ade5-efef296196f1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5949230018850301"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def activation(traces, time, decay):\n",
    "    \"\"\"Computes the activation of a memory given its history of retrievals\"\"\"\n",
    "    ftraces = [x for x in traces if x < time]\n",
    "    decay = max(0, decay)  # Allows no positive decay rates in equation \n",
    "    decay - min(decay, 5)\n",
    "    times = time - np.array(ftraces)\n",
    "    odds = times ** -decay\n",
    "    return np.log(np.sum(odds))\n",
    "\n",
    "activation([5, 10, 15], 40, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "id": "9O1NxeppG71j",
    "outputId": "fee85f55-f305-451d-9132-40078e459259"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = np.linspace(0.1, 100, 100)\n",
    "traces = [0.001, 5, 25]\n",
    "y = []\n",
    "for t in time:\n",
    "    act = activation(traces, t, 0.5)\n",
    "    y += [act]\n",
    "    \n",
    "plt.plot(time, y)\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Activation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F5O-Ma2bHHCe"
   },
   "source": [
    "Create loglikelihood function for the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "4nYd7AWEHC6a"
   },
   "outputs": [],
   "source": [
    "def boltzmann(options, values, temperature):\n",
    "    \"\"\"Returns a Boltzmann distribution of the probabilities of each option\"\"\"\n",
    "    temperature = max(temperature, 0.01) \n",
    "    vals = np.array(values)/temperature\n",
    "    #bvals = np.exp(vals)/np.sum(np.exp(vals))\n",
    "    bvals = np.exp(vals - np.max(vals)) / np.exp(vals - np.max(vals)).sum()\n",
    "    return dict(zip(options, bvals))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "infLxnLdIU7o",
    "outputId": "a927ebd0-ca38-461a-f4fb-b47c1390d9a4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2: 0.574442516811659, 3: 0.425557483188341}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boltzmann([2,3], [1, 0.7], 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NaK3W3zCI63e"
   },
   "source": [
    "Create a loglikelihood function for the elaborative model, each error item is given additional spreading activation given the max association (mas) parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0hWua1X6GL_h"
   },
   "source": [
    "### Complete *elaborative* model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "KMLIBhGdIteL"
   },
   "outputs": [],
   "source": [
    "def LLelab(alldata, ppt, decay, temp, mas=1.6):\n",
    "    \"\"\"For each trial, calculate the probability of that response, sum the log likelihoods, and update the values\"\"\"\n",
    "    data = alldata[alldata.participant == ppt]\n",
    "\n",
    "    # create a list of error items\n",
    "    errors = data[data.condition == 1].cue.tolist()\n",
    "    # create a list of study items\n",
    "    study = data[data.condition == 2].cue.tolist()\n",
    "    # since we don't have data for when study items were presented in each participant's learning phase\n",
    "    # we will use the presentation of the error items and interleaven the study items in between\n",
    "    pos = 1\n",
    "    present = study[:]\n",
    "    for word in errors:\n",
    "        present.insert(pos, word)\n",
    "        pos += 2\n",
    "\n",
    "    # Create dict with word pairs\n",
    "    pairs = {}\n",
    "    for cue, target in zip(data[data.phase == \"test\"].cue,data[data.phase == \"test\"].target):\n",
    "      pairs[cue] = target\n",
    "    # also create a dict with errors\n",
    "    errorResp = dict()\n",
    "    for cue,response in zip(data[data.phase == \"learn\"].cue,data[data.phase == \"learn\"].response):\n",
    "      errorResp[cue] = response\n",
    "\n",
    "    # model learning phase, encode a single trace for each item: (idk what to set activation at)\n",
    "    DM = dict()\n",
    "    # for DM can we make a dictionary of dictionaries where big keys are cues, values are dictionary of target/\n",
    "    # possible responses and their activation\n",
    "    time = 1\n",
    "    for cue in present:\n",
    "      littleDM = {}\n",
    "      # make a set of all reponses given to a certain cue to be \"vocab for that cue\"\n",
    "      for response in set(alldata[alldata.cue == cue].response):\n",
    "        littleDM[response] = [0.001]\n",
    "      # add retrieval of error for error items\n",
    "      if cue in errorResp.keys():\n",
    "        error = errorResp[cue]\n",
    "        time +=5\n",
    "        littleDM[error] = [0.001, time]\n",
    "      # overwrite smaller activ of correct target to show task learning\n",
    "        time +=5\n",
    "        littleDM[pairs[cue]] = [0.001, time]\n",
    "      else:\n",
    "        time += 10\n",
    "        littleDM[pairs[cue]] = [0.001, time]\n",
    "      DM[cue] = littleDM\n",
    "    time += 300 # time for distractor phase\n",
    "\n",
    "\n",
    "    # model testing phase\n",
    "    LL = 0\n",
    "    for condition, cue, response, rt, feedback in zip(data[data.phase == \"test\"].condition,\n",
    "                                   data[data.phase == \"test\"].cue, \n",
    "                                   data[data.phase == \"test\"].response, \n",
    "                                   data[data.phase == \"test\"].rt, \n",
    "                                   data[data.phase == \"test\"].correct):\n",
    "      # Calculate log likelihood of response- possible options are 19 random integers\n",
    "      # or correct associate\n",
    "      options = DM[cue].keys()\n",
    "      # create spreading activation additional error component given size of cue's dec mem\n",
    "      cueMem = len(DM[cue])\n",
    "      add = (mas - np.log((cueMem + 1)/2)) - (mas - np.log((cueMem + 1)/1))\n",
    "      # if error condition, add spreading activation\n",
    "      values = [activation(DM[cue][opt], time, decay) if condition == 1 else \n",
    "       (activation(DM[cue][opt], time, decay) + add) for opt in options]\n",
    "      prob = boltzmann(options, values, temp)[response]\n",
    "    \n",
    "      # Sum up the LLs\n",
    "      LL += np.log(max(prob, 10e-10))\n",
    "\n",
    "       # add time taken to responde\n",
    "      time += rt/1000\n",
    "    \n",
    "    return LL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yahI4_8z7t5o",
    "outputId": "0db50f1c-5eb6-4b7e-e78e-411638a39f76"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-172.73898573437492"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LLelab(alldata, 1001, 0.5, 1, 1.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "X9bwBrAI8JLq",
    "outputId": "1a837c88-4964-47b8-ad3f-a758c6c72376"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-172.73898573437492"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LLelab(alldata, 1001, 0.5, 1, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bft75J6OTfGn"
   },
   "source": [
    "Create a loglikelihood function of the mediator model, where there are two routes to make a decision: To remember something that worked and to remember something that did not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "6oDf4YA7T3To"
   },
   "outputs": [],
   "source": [
    "def LLmed(data, decay, temp):\n",
    "    \"\"\"For each trial, calculate the probability of that response, sum the log likelihoods, and update the values\"\"\"\n",
    "    # create a list of error items\n",
    "    errors = data[data.phase == \"learn\"].cue.tolist()\n",
    "    # create a list of study items\n",
    "    study = data[data.condition == 2].cue.tolist()\n",
    "    pos = 1\n",
    "    present = errors[:]\n",
    "    for i in range(len(errors)):\n",
    "      word = study[i]\n",
    "      present.insert(pos, word)\n",
    "      pos += 2\n",
    "\n",
    "    # Create dict with word pairs\n",
    "    pairs = {}\n",
    "    for cue, target in zip(data[data.phase == \"test\"].cue,data[data.phase == \"test\"].target):\n",
    "      pairs[cue] = target\n",
    "    # also create a dict with errors\n",
    "    errorResp = dict()\n",
    "    for cue,response in zip(data[data.phase == \"learn\"].cue,data[data.phase == \"learn\"].response):\n",
    "      errorResp[cue] = response\n",
    "\n",
    "    # model learning phase, encode a single trace for each item: (idk what to set activation at)\n",
    "    DM = dict()\n",
    "    # for DM can we make a dictionary of dictionaries where big keys are cues, values are dictionary of target/\n",
    "    # possible responses and their activation\n",
    "    time = 1\n",
    "    for cue in present:\n",
    "      littleDM = {}\n",
    "      # make a set of all reponses given to a certain cue to be \"vocab for that cue\"\n",
    "      for response in set(alldata[alldata.cue == cue].response):\n",
    "        littleDM[response] = [0.001]\n",
    "      # add retrieval of error for error items\n",
    "      if cue in errorResp.keys():\n",
    "        error = errorResp[cue]\n",
    "        time +=5\n",
    "        littleDM[error] = [0.001, time]\n",
    "      # overwrite smaller activ of correct target to show task learning\n",
    "        time +=5\n",
    "        littleDM[pairs[cue]] = [0.001, time]\n",
    "      else:\n",
    "        time += 10\n",
    "        littleDM[pairs[cue]] = [0.001, time]\n",
    "      DM[cue] = littleDM\n",
    "    time += 300 # time for distractor phase\n",
    "\n",
    "    # model testing phase\n",
    "    LL = 0\n",
    "    \n",
    "    for condition, cue, response, rt, feedback in zip(data[data.phase == \"test\"].condition,\n",
    "                                   data[data.phase == \"test\"].cue, \n",
    "                                   data[data.phase == \"test\"].response, \n",
    "                                   data[data.phase == \"test\"].rt, \n",
    "                                   data[data.phase == \"test\"].correct):\n",
    "        # Calculate log likelihood of response- possible options are 19 random integers\n",
    "        # or correct associate\n",
    "        options = DM[cue].keys()\n",
    "        # create spreading activation additional error component given size of cue's dec mem\n",
    "        cueMem = len(DM[cue])\n",
    "        # if error condition, add spreading activation\n",
    "        values = [activation(DM[cue][opt], time, decay) for opt in options]\n",
    "        prob1 = boltzmann(options, values, temp)[response]\n",
    "        \n",
    "        # probability of retrieving error memory\n",
    "        if condition == 1:\n",
    "          error = errorResp[cue]\n",
    "          prob2 = boltzmann(options, values, temp)[error]\n",
    "        else:\n",
    "          prob2 = 0\n",
    "        \n",
    "        # Sum up the LLs\n",
    "        LL += np.log(max(prob1 + prob2, 10e-10))\n",
    "        \n",
    "         # add time taken to responde\n",
    "        time += rt/1000\n",
    "        \n",
    "    return LL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mmmyXeOdETlj",
    "outputId": "20d22e56-5c0f-43ad-b5bf-26d13cdfd41c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-148.37730418310719"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LLmed(alldata[alldata.participant == 1001], 0.5, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RGuZGMVvExA5",
    "outputId": "10d6b9b4-d0ec-4a81-b06f-1574bcd40bb8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-150.8797807573661"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LLmed(alldata[alldata.participant == 1002], 0.5, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AMZivWprDOAT"
   },
   "source": [
    "# Create RT models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "AickrN7GDlaO"
   },
   "outputs": [],
   "source": [
    "def responsetime(activation, ter, F=1, f=1):\n",
    "    return ter + F * np.exp(-f * activation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "id": "_A-v7qV7DmXr",
    "outputId": "f8826f75-85d8-453a-c050-7fa304448f72"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "noise = np.linspace(-2, 2)\n",
    "dist = sp.stats.logistic(0, 0.2)\n",
    "plt.plot(noise, dist.pdf(noise))\n",
    "plt.xlabel(\"Noise $s$\")\n",
    "plt.ylabel(\"Probability $P$\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 480
    },
    "id": "7LaZcjs-D-hL",
    "outputId": "26f7a220-a246-49fd-caf6-d5bb33c39f15"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rts = [responsetime((0.5 - x), 1) for x in noise]\n",
    "plt.plot(rts, dist.pdf(noise))\n",
    "plt.xlabel(\"Response Time (s)\")\n",
    "plt.ylabel(\"Probability $P$\")\n",
    "plt.title(\"RT distributions when $s$ = 0.2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 480
    },
    "id": "nTAIDWYZEC5J",
    "outputId": "8fdb9fc1-fe7d-4e77-c0e1-225139798c2b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for s in np.linspace(0.2, 0.8, 7):\n",
    "    dist = sp.stats.logistic(0, (math.sqrt((math.pi**2)*s)/3))\n",
    "    rts = [responsetime((0.5 - x), 1) for x in noise]\n",
    "    plt.plot(rts, dist.pdf(noise))\n",
    "plt.legend([\"$s$ = %.1f\" % (x,) for x in np.linspace(0.2, 0.8, 7)])\n",
    "plt.xlabel(\"Response Time (s)\")\n",
    "plt.ylabel(\"Probability $P$\")\n",
    "plt.title(\"RT distributions for different levels of noise $s$\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sZv106szPBcS"
   },
   "source": [
    "## Updated noise SD added here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "s0BKpqZQEGJ_"
   },
   "outputs": [],
   "source": [
    "from urllib.request import proxy_bypass\n",
    "def rtProb(activation, s, ter, rt=None):\n",
    "  \"\"\"Takes one parameter for noise, s, and outputs a probability distribution for response times\"\"\"\n",
    "  noise = np.linspace(-2, 2)\n",
    "  dist = sp.stats.logistic(0, (math.sqrt((math.pi**2)*s)/3))\n",
    "  rts = [responsetime((activation - x), ter) for x in noise]\n",
    "  prob = dist.pdf(noise)\n",
    "  rtprob = {rts[i]:prob[i]for i in range(len(noise))}\n",
    "  if rt:\n",
    "    val = min(rtprob.keys(), key=lambda x: abs(x - (rt/1000)))\n",
    "    return rtprob[val]\n",
    "  else:\n",
    "    return rtprob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JKKNDiaFjwfb",
    "outputId": "ce930c89-ddf9-43be-8b4e-71d8a8b154ce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23864176792866348"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rtProb(3, 1, 1, rt = 1050)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dBBYG2u8tGz3"
   },
   "source": [
    "## Elaborative Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "DAmc6sa3DMuC"
   },
   "outputs": [],
   "source": [
    "def LLelabRT(alldata, decay, temp, ter, ppt='sim', mas = 1.6):\n",
    "    \"\"\"For each trial, calculate the probability of that response, sum the log likelihoods, and update the values\"\"\"\n",
    "    data = alldata[alldata.participant == ppt]\n",
    "\n",
    "    # create a list of error items\n",
    "    errors = data[data.condition == 1].cue.tolist()\n",
    "    # create a list of study items\n",
    "    study = data[data.condition == 2].cue.tolist()\n",
    "    pos = 1\n",
    "    present = errors[:]\n",
    "    present = study[:]\n",
    "\n",
    "    for word in errors:\n",
    "        present.insert(pos, word)\n",
    "        pos += 2\n",
    "\n",
    "    # Create dict with word pairs\n",
    "    pairs = {}\n",
    "    for cue, target in zip(data[data.phase == \"test\"].cue,data[data.phase == \"test\"].target):\n",
    "      pairs[cue] = target\n",
    "    # also create a dict with errors\n",
    "    errorResp = dict()\n",
    "    for cue,response in zip(data[data.phase == \"learn\"].cue,data[data.phase == \"learn\"].response):\n",
    "      errorResp[cue] = response\n",
    "\n",
    "    # model learning phase, encode a single trace for each item: (idk what to set activation at)\n",
    "    DM = dict()\n",
    "    # for DM can we make a dictionary of dictionaries where big keys are cues, values are dictionary of target/\n",
    "    # possible responses and their activation\n",
    "    time = 1\n",
    "    for cue in present:\n",
    "      littleDM = {}\n",
    "      # make a set of all reponses given to a certain cue to be \"vocab for that cue\"\n",
    "      for response in set(alldata[alldata.cue == cue].response):\n",
    "        littleDM[response] = [0.001]\n",
    "      # add retrieval of error for error items\n",
    "      if cue in errorResp.keys():\n",
    "        error = errorResp[cue]\n",
    "        time +=5\n",
    "        littleDM[error] = [0.001, time]\n",
    "      # overwrite smaller activ of correct target to show task learning\n",
    "        time +=5\n",
    "        littleDM[pairs[cue]] = [0.001, time]\n",
    "      else:\n",
    "        time += 10\n",
    "        littleDM[pairs[cue]] = [0.001, time]\n",
    "      DM[cue] = littleDM\n",
    "    time += 300 # time for distractor phase\n",
    "\n",
    "\n",
    "    # model testing phase\n",
    "    LL = 0\n",
    "    for condition, cue, response, rt, feedback in zip(data[data.phase == \"test\"].condition,\n",
    "                                   data[data.phase == \"test\"].cue, \n",
    "                                   data[data.phase == \"test\"].response, \n",
    "                                   data[data.phase == \"test\"].rt, \n",
    "                                   data[data.phase == \"test\"].correct):\n",
    "      # Calculate log likelihood of response- possible options are 19 random integers\n",
    "      # or correct associate\n",
    "      options = DM[cue].keys()\n",
    "      # create spreading activation additional error component given size of cue's dec mem\n",
    "      cueMem = len(DM[cue])\n",
    "      add = (mas - np.log((cueMem + 1)/2)) - (mas - np.log((cueMem + 1)/1))\n",
    "      # if error condition, add spreading activation\n",
    "      values = [(activation(DM[cue][opt], time, decay) + add) if condition == 1 else \n",
    "       activation(DM[cue][opt], time, decay) for opt in options]\n",
    "      prob = boltzmann(options, values, temp)[response]\n",
    "    \n",
    "      # now calculate response times:\n",
    "      if condition == 1:\n",
    "        resp_activation = activation(DM[cue][response], time, decay) + add\n",
    "      else: \n",
    "        resp_activation = activation(DM[cue][response], time, decay)\n",
    "      \n",
    "      prob_rt = rtProb(resp_activation, temp, ter, rt=rt)\n",
    "\n",
    "      # Sum up the LLs\n",
    "      LL += (np.log(max(prob, 10e-10)) + np.log(max(prob_rt, 10e-10)))\n",
    "\n",
    "       # add time taken to responde\n",
    "      time += rt/1000\n",
    "    \n",
    "    return LL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "od62A0A0siuO",
    "outputId": "82fe234d-237d-4052-b373-0a1f4a08b03b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-285.98920769327106"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LLelabRT(alldata, 0.5, 1, 1, ppt=1001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c26HJ-h9E6AJ",
    "outputId": "49b9dfb8-3cff-4833-bac5-896f1ff8ee8c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-298.55672775778953"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LLelabRT(alldata, 0.5, 1, 1, ppt=1002)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k94iDUxOtKy_"
   },
   "source": [
    "## Mediator Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZF0EEFvUPNWT"
   },
   "source": [
    "## Updated noise SD *also* added here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "cappHS-GwJ2C"
   },
   "outputs": [],
   "source": [
    "from urllib.request import proxy_bypass\n",
    "def rtProb2(resp_activation, error_activation, condition, s, ter, rt=None):\n",
    "  \"\"\"Takes one parameter for noise, s, and outputs a probability distribution for response times\"\"\"\n",
    "  noise = np.linspace(-2, 2)\n",
    "  dist = sp.stats.logistic(0, (math.sqrt((math.pi**2)*s)/3))\n",
    "  if condition == 1:\n",
    "    rts = [(responsetime((resp_activation - x), ter) + responsetime((error_activation - x), ter)) for x in noise]\n",
    "  else: \n",
    "    rts = [responsetime((resp_activation - x), ter) for x in noise]\n",
    "  prob = dist.pdf(noise)\n",
    "  rtprob = {rts[i]:prob[i]for i in range(len(noise))}\n",
    "  if rt:\n",
    "    val = min(rtprob.keys(), key=lambda x: abs(x - (rt/1000)))\n",
    "    return rtprob[val]\n",
    "  else:\n",
    "    return rtprob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7BDYgqgnPFCz",
    "outputId": "dcb3a8ba-27c1-4c0b-c93b-2fba9847cb40"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.029021222079091556"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rtProb2(3, 2, 1, 0.2, 1, rt=6100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "06orMyzjtFR4"
   },
   "outputs": [],
   "source": [
    "def LLmedRT(alldata, decay, temp, ter, ppt='sim'):\n",
    "    \"\"\"For each trial, calculate the probability of that response, sum the log likelihoods, and update the values\"\"\"\n",
    "    data = alldata[alldata.participant == ppt]\n",
    "    \n",
    "    # create a list of error items\n",
    "    errors = data[data.condition == 1].cue.tolist()\n",
    "    # create a list of study items\n",
    "    study = data[data.condition == 2].cue.tolist()\n",
    "    pos = 1\n",
    "    present = study[:]\n",
    "\n",
    "    for word in errors:\n",
    "        present.insert(pos, word)\n",
    "        pos += 2\n",
    "\n",
    "    # Create dict with word pairs\n",
    "    pairs = {}\n",
    "    for cue, target in zip(data[data.phase == \"test\"].cue,data[data.phase == \"test\"].target):\n",
    "      pairs[cue] = target\n",
    "    # also create a dict with errors\n",
    "    errorResp = dict()\n",
    "    for cue,response in zip(data[data.phase == \"learn\"].cue,data[data.phase == \"learn\"].response):\n",
    "      errorResp[cue] = response\n",
    "\n",
    "    # model learning phase, encode a single trace for each item: (idk what to set activation at)\n",
    "    DM = dict()\n",
    "    # for DM can we make a dictionary of dictionaries where big keys are cues, values are dictionary of target/\n",
    "    # possible responses and their activation\n",
    "    time = 1\n",
    "    for cue in present:\n",
    "      littleDM = {}\n",
    "      # make a set of all reponses given to a certain cue to be \"vocab for that cue\"\n",
    "      for response in set(alldata[alldata.cue == cue].response):\n",
    "        littleDM[response] = [0.001]\n",
    "      # add retrieval of error for error items\n",
    "      if cue in errorResp.keys():\n",
    "        error = errorResp[cue]\n",
    "        time +=5\n",
    "        littleDM[error] = [0.001, time]\n",
    "      # overwrite smaller activ of correct target to show task learning\n",
    "        time +=5\n",
    "        littleDM[pairs[cue]] = [0.001, time]\n",
    "      else:\n",
    "        time += 10\n",
    "        littleDM[pairs[cue]] = [0.001, time]\n",
    "      DM[cue] = littleDM\n",
    "    time += 300 # time for distractor phase\n",
    "\n",
    "    # model testing phase\n",
    "    LL = 0\n",
    "    \n",
    "    for condition, cue, response, rt, feedback in zip(data[data.phase == \"test\"].condition,\n",
    "                                   data[data.phase == \"test\"].cue, \n",
    "                                   data[data.phase == \"test\"].response, \n",
    "                                   data[data.phase == \"test\"].rt, \n",
    "                                   data[data.phase == \"test\"].correct):\n",
    "        # Calculate log likelihood of response- possible options are 19 random integers\n",
    "        # or correct associate\n",
    "        options = DM[cue].keys()\n",
    "\n",
    "        # calculate probability of retrieving given response\n",
    "        values = [activation(DM[cue][opt], time, decay) for opt in options]\n",
    "        prob1 = boltzmann(options, values, temp)[response]\n",
    "        \n",
    "        # probability of retrieving error memory\n",
    "        if condition == 1:\n",
    "          error = errorResp[cue]\n",
    "          prob2 = boltzmann(options, values, temp)[error]\n",
    "        else:\n",
    "          prob2 = 0\n",
    "        \n",
    "        # add response times calculations\n",
    "        # probability of given response time with\n",
    "        respAct = activation(DM[cue][response], time, decay)\n",
    "        if condition == 1:\n",
    "          error = errorResp[cue]\n",
    "          errorAct = activation(DM[cue][error], time, decay)\n",
    "          prob_rt = rtProb2(rt, respAct, errorAct, condition, temp, ter)\n",
    "        else:\n",
    "          errorAct = 0\n",
    "          prob_rt = rtProb2(rt, respAct, errorAct, condition, temp, ter)\n",
    "\n",
    "        # Sum up the LLs\n",
    "        LL += (np.log(max(prob1 + prob2, 10e-10)) + np.log(max(prob_rt, 10e-10)))\n",
    "        \n",
    "         # add time taken to responde\n",
    "        time += rt/1000\n",
    "        \n",
    "    return LL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PU1kSzEG0FJD",
    "outputId": "ac3e70c9-4dc3-48be-ebc5-917b291a9440"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-279.07359732291604"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LLmedRT(alldata, 0.5, 1, 1, ppt=1001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "N8P4j9Lyknay",
    "outputId": "3d3a16a5-6f9a-41ac-81fc-ccd06226d548"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-226.34704529241907"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LLmedRT(alldata, 0.7, 0.25, 0.5, ppt=1002)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nbZ8QgPmB9_j"
   },
   "source": [
    "# Optimizing the Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_xG-LzMACBMZ"
   },
   "source": [
    "## Elaborative Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "1r5gcKhxB9LO"
   },
   "outputs": [],
   "source": [
    "def vLLelab(array):\n",
    "    \"\"\"Vector function of procedural log-likelihood\"\"\"\n",
    "    global data\n",
    "    decay, temp, ter = array\n",
    "    return -1 * LLelabRT(data, decay, temp, ter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Js8XUZXPRItm"
   },
   "source": [
    "## Mediator Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "Z1ld48DEhDEM"
   },
   "outputs": [],
   "source": [
    "def vLLmed(array):\n",
    "    \"\"\"Vector function of procedural log-likelihood\"\"\"\n",
    "    global data\n",
    "    decay, temp, ter = array\n",
    "    return -1 * LLmedRT(data, decay, temp, ter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6lyASKh7oHWm"
   },
   "source": [
    "# Calculate LL for each ppt and assign best model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r7BwnKfMImrY"
   },
   "source": [
    "## compute the best parameters and high log-liklihoods of each model for each participant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-efVYwmgI8QT",
    "outputId": "408c27f5-9ecc-4df7-ec79-7bbf1fea1e22"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1001., 1002., 1003., 1004., 1005., 1006., 1007., 1008., 1009.,\n",
       "       1010., 1011., 1012., 1013., 1014., 1015., 1016., 1017., 1018.,\n",
       "       1019., 1020., 1021., 1022., 1023., 1024., 1025., 1026., 1028.,\n",
       "       1029., 1031., 1032., 1033., 1035., 1036., 1037., 1038., 1039.,\n",
       "       1040., 1041., 1042., 1043., 1044., 1045., 1046., 1047., 1049.,\n",
       "       1050., 1051., 1053., 1054., 1055., 1056., 1057., 1058., 1059.,\n",
       "       1061., 1063., 1064., 1065., 1067., 1068., 1069.])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata.participant.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RSWCFpcnIp_V",
    "outputId": "f111a5d9-7251-42b4-d8ee-2d04a17c0da4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1001.0 : -178, -157\n",
      "1002.0 : -190, -160\n",
      "1003.0 : -185, -156\n",
      "1004.0 : -198, -167\n",
      "1005.0 : -171, -146\n",
      "1006.0 : -181, -155\n",
      "1007.0 : -175, -150\n",
      "1008.0 : -164, -150\n",
      "1009.0 : -163, -143\n",
      "1010.0 : -163, -142\n",
      "1011.0 : -186, -158\n",
      "1012.0 : -183, -161\n",
      "1013.0 : -181, -158\n",
      "1014.0 : -183, -159\n",
      "1015.0 : -222, -183\n",
      "1016.0 : -170, -150\n",
      "1017.0 : -183, -158\n",
      "1018.0 : -172, -148\n",
      "1019.0 : -195, -163\n",
      "1020.0 : -162, -142\n",
      "1021.0 : -186, -162\n",
      "1022.0 : -182, -160\n",
      "1023.0 : -182, -157\n",
      "1024.0 : -176, -151\n",
      "1025.0 : -171, -145\n",
      "1026.0 : -168, -146\n",
      "1028.0 : -165, -143\n",
      "1029.0 : -193, -165\n",
      "1031.0 : -164, -145\n",
      "1032.0 : -219, -182\n",
      "1033.0 : -184, -157\n",
      "1035.0 : -165, -145\n",
      "1036.0 : -171, -149\n",
      "1037.0 : -162, -140\n",
      "1038.0 : -165, -143\n",
      "1039.0 : -177, -153\n",
      "1040.0 : -192, -162\n",
      "1041.0 : -181, -157\n",
      "1042.0 : -173, -150\n",
      "1043.0 : -166, -148\n",
      "1044.0 : -161, -139\n",
      "1045.0 : -169, -147\n",
      "1046.0 : -173, -150\n",
      "1047.0 : -160, -138\n",
      "1049.0 : -172, -150\n",
      "1050.0 : -182, -160\n",
      "1051.0 : -170, -155\n",
      "1053.0 : -179, -159\n",
      "1054.0 : -175, -152\n",
      "1055.0 : -192, -166\n",
      "1056.0 : -169, -150\n",
      "1057.0 : -172, -153\n",
      "1058.0 : -183, -162\n",
      "1059.0 : -163, -140\n",
      "1061.0 : -193, -169\n",
      "1063.0 : -214, -182\n",
      "1064.0 : -160, -141\n",
      "1065.0 : -170, -150\n",
      "1067.0 : -215, -182\n",
      "1068.0 : -159, -141\n",
      "1069.0 : -165, -144\n"
     ]
    }
   ],
   "source": [
    "DO_ALL = False\n",
    "dataframe = []\n",
    "\n",
    "#from pybads import BADS\n",
    "\n",
    "\n",
    "if DO_ALL:\n",
    "    for ppt in alldata.participant.unique(): \n",
    "        data = alldata[alldata.participant == ppt]\n",
    "        eres = opt.minimize(vLLelab, x0 = [0.5, 1, 1], method = \"Powell\", bounds=[[0.4, 0.6], [0, 1], [0.1, 4]])\n",
    "        edecay, etemp, eter = eres.x\n",
    "        llelab = LLelabRT(data, edecay, etemp, eter, ppt=ppt)\n",
    "            \n",
    "        mres = opt.minimize(vLLmed, x0 = [0.5, 1, 1], method = \"Powell\", bounds=[[0.4, 0.6], [0, 1], [0.1, 4]])\n",
    "        mdecay, mtemp, mter = mres.x\n",
    "        llmed = LLmedRT(data, mdecay, mtemp, mter, ppt=ppt)\n",
    "\n",
    "        print(\"%s : %d, %d\" % (ppt, llelab, llmed))\n",
    "        best = \"Mediator\"\n",
    "        if llelab > llmed:\n",
    "            best = \"Elaborative\"\n",
    "            \n",
    "        diff = llmed - llelab\n",
    "            \n",
    "        row = [ppt, edecay, etemp, eter, llelab, mdecay, mtemp, mter, llmed, best, diff]\n",
    "            \n",
    "        dataframe += [row]\n",
    "\n",
    "    result = pd.DataFrame(dataframe, columns=[\"Participant\", \"elab.decay\", \"elab.temp\", \"elab.ter\", \"elab.LL\", \"med.decay\", \"med.temp\", \"med.ter\", \"med.LL\", \"best.model\", \"diff.LL\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-156.53896797422578"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LLelabRT(data, 0.1, 0.5, 0, ppt=1069)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1001., 1002., 1003., 1004., 1005., 1006., 1007., 1008., 1009.,\n",
       "       1010., 1011., 1012., 1013., 1014., 1015., 1016., 1017., 1018.,\n",
       "       1019., 1020., 1021., 1022., 1023., 1024., 1025., 1026., 1028.,\n",
       "       1029., 1031., 1032., 1033., 1035., 1036., 1037., 1038., 1039.,\n",
       "       1040., 1041., 1042., 1043., 1044., 1045., 1046., 1047., 1049.,\n",
       "       1050., 1051., 1053., 1054., 1055., 1056., 1057., 1058., 1059.,\n",
       "       1061., 1063., 1064., 1065., 1067., 1068., 1069.])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata.participant.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "id": "1orKyYBilItT",
    "outputId": "66e3cea0-da5c-476f-bfb8-53bb94d27ceb"
   },
   "outputs": [],
   "source": [
    "if DO_ALL:\n",
    "    result.to_csv(\"LL_model1.csv\")\n",
    "else:\n",
    "    result = pd.read_csv(\"LL_model1.csv\")\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "-Ixin0NltTny",
    "outputId": "dbe1e25e-6930-4d34-ac01-c79e77874ff7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Participant</th>\n",
       "      <th>elab.decay</th>\n",
       "      <th>elab.temp</th>\n",
       "      <th>elab.ter</th>\n",
       "      <th>elab.LL</th>\n",
       "      <th>med.decay</th>\n",
       "      <th>med.temp</th>\n",
       "      <th>med.ter</th>\n",
       "      <th>med.LL</th>\n",
       "      <th>best.model</th>\n",
       "      <th>diff.LL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-178.324259</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-157.240344</td>\n",
       "      <td>Mediator</td>\n",
       "      <td>21.083915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002.0</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-190.043829</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-160.804285</td>\n",
       "      <td>Mediator</td>\n",
       "      <td>29.239544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003.0</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-185.253134</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-156.991858</td>\n",
       "      <td>Mediator</td>\n",
       "      <td>28.261276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004.0</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-198.593962</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-167.204123</td>\n",
       "      <td>Mediator</td>\n",
       "      <td>31.389838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005.0</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-171.181455</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-146.153654</td>\n",
       "      <td>Mediator</td>\n",
       "      <td>25.027801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1064.0</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-160.130752</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-141.623875</td>\n",
       "      <td>Mediator</td>\n",
       "      <td>18.506877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1065.0</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-170.498836</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-150.107481</td>\n",
       "      <td>Mediator</td>\n",
       "      <td>20.391355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1067.0</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-215.916152</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-182.803758</td>\n",
       "      <td>Mediator</td>\n",
       "      <td>33.112394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1068.0</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-159.231240</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-141.282590</td>\n",
       "      <td>Mediator</td>\n",
       "      <td>17.948650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1069.0</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-165.668224</td>\n",
       "      <td>0.599944</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>-144.244392</td>\n",
       "      <td>Mediator</td>\n",
       "      <td>21.423832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Participant  elab.decay  elab.temp  elab.ter     elab.LL  med.decay  \\\n",
       "0        1001.0    0.599944   0.999934  3.999939 -178.324259   0.599944   \n",
       "1        1002.0    0.599944   0.999934  3.999939 -190.043829   0.599944   \n",
       "2        1003.0    0.599944   0.999934  3.999939 -185.253134   0.599944   \n",
       "3        1004.0    0.599944   0.999934  3.999939 -198.593962   0.599944   \n",
       "4        1005.0    0.599944   0.999934  3.999939 -171.181455   0.599944   \n",
       "..          ...         ...        ...       ...         ...        ...   \n",
       "56       1064.0    0.599944   0.999934  3.999939 -160.130752   0.599944   \n",
       "57       1065.0    0.599944   0.999934  3.999939 -170.498836   0.599944   \n",
       "58       1067.0    0.599944   0.999934  3.999939 -215.916152   0.599944   \n",
       "59       1068.0    0.599944   0.999934  3.999939 -159.231240   0.599944   \n",
       "60       1069.0    0.599944   0.999934  3.999939 -165.668224   0.599944   \n",
       "\n",
       "    med.temp   med.ter      med.LL best.model    diff.LL  \n",
       "0   0.999934  3.999939 -157.240344   Mediator  21.083915  \n",
       "1   0.999934  3.999939 -160.804285   Mediator  29.239544  \n",
       "2   0.999934  3.999939 -156.991858   Mediator  28.261276  \n",
       "3   0.999934  3.999939 -167.204123   Mediator  31.389838  \n",
       "4   0.999934  3.999939 -146.153654   Mediator  25.027801  \n",
       "..       ...       ...         ...        ...        ...  \n",
       "56  0.999934  3.999939 -141.623875   Mediator  18.506877  \n",
       "57  0.999934  3.999939 -150.107481   Mediator  20.391355  \n",
       "58  0.999934  3.999939 -182.803758   Mediator  33.112394  \n",
       "59  0.999934  3.999939 -141.282590   Mediator  17.948650  \n",
       "60  0.999934  3.999939 -144.244392   Mediator  21.423832  \n",
       "\n",
       "[61 rows x 11 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "w-Rv_Bijr_VN"
   },
   "outputs": [],
   "source": [
    "elab_LL = result[\"elab.LL\"].values\n",
    "med_LL = result[\"med.LL\"].values\n",
    "\n",
    "BEST = result[\"best.model\"].values\n",
    "BEST_ = np.unique(BEST)\n",
    "\n",
    "COLORS = ['#c9a818', '#674b94']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 727
    },
    "id": "PG9R8GZWypwl",
    "outputId": "ea8a1dbe-30bc-4284-ba89-e4d2df2890ee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.AxLine at 0x176aaf590>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "ax.plot([-320, -320], [-100, -100], \"--\")\n",
    "for best, color in zip(BEST_, COLORS):\n",
    "    idxs = np.where(best == BEST)\n",
    "    # No legend will be generated if we don't pass label=species\n",
    "    ax.scatter(\n",
    "        elab_LL[idxs], med_LL[idxs], label=best,\n",
    "        s=50, color=color, alpha=0.7\n",
    "    )\n",
    "    \n",
    "_ = plt.xlabel(\"Elaborative Log-likelihood\")\n",
    "_ = plt.ylabel(\"Mediator Log-likelihood\")\n",
    "_ = plt.title(\"Log-likelihood of Model Fit by Participant\")\n",
    "_ = plt.xlim([-250, -120])\n",
    "_ = plt.ylim([-250, -120])\n",
    "_ = plt.show\n",
    "ax.legend(loc = \"upper left\");\n",
    "ax.axline((0,0), slope=1, linestyle=\"--\", color='lightgrey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "SbKdLhyqP06Y",
    "outputId": "8b1cb4fe-d86b-414b-ed6c-7030aacb88fd"
   },
   "outputs": [],
   "source": [
    "res = pd.read_csv(\"LL_model1.csv\")\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 462
    },
    "id": "KgglweONP46n",
    "outputId": "e6dc5780-e8bc-42b7-ca8f-5a51dfaec74b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(result, x=\"diff.LL\", hue=\"best.model\", binwidth=4, binrange=[-20, 60], stat=\"count\", legend=\"False\")\n",
    "plt.legend(loc=\"upper left\", title=\"Best-fitting Model\", labels=['Elaborative', 'Mediator'])\n",
    "plt.xlabel(\"$\\log(x|M_{Mediator}) - \\log(x|M_{Elaborative})$\") \n",
    "plt.ylabel(\"Number of Participants\") \n",
    "\n",
    "plt.axvline(x=0, linestyle=\"dotted\", color=\"red\")\n",
    "#plt.axvline(x=np.mean(res['diff.LL']), linestyle=\"dashed\", color=\"blue\", linewidth=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Recovery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def policy(bprobs):\n",
    "    \"\"\"Selects an action based on a distribution of probabilities\"\"\"\n",
    "    options = list(bprobs.keys())\n",
    "    probs = [bprobs[opt] for opt in options]\n",
    "    return np.random.choice(options, p=probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_memory(alldata):\n",
    "    unique_cues = alldata.cue.unique()\n",
    "    DM = {}\n",
    "    for cue in unique_cues:\n",
    "        littleDM = {}\n",
    "        # make a set of all reponses given to a certain cue to be \"vocab for that cue\"\n",
    "        for response in set(alldata[alldata.cue == cue].response):\n",
    "            littleDM[response] = [0.01]\n",
    "        DM[cue] = littleDM\n",
    "    return DM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def learning_phase(alldata, pairs, errors, study, decay, temp, ter):\n",
    "    # dataframe to store task results\n",
    "    dataframe = []\n",
    "\n",
    "    # create memory\n",
    "    DM = create_memory(alldata)\n",
    "    # learning phase\n",
    "    time = 1\n",
    "    errorResp = {}\n",
    "    \n",
    "    for cue in pairs.keys():\n",
    "       if cue in errors:\n",
    "          # Make a 'guess'\n",
    "          options = DM[cue].keys()\n",
    "          values = [activation(DM[cue][opt], time, decay) for opt in options]\n",
    "          prob = boltzmann(options, values, temp)\n",
    "          response = policy(prob)\n",
    "          # if response is nan try again (response is np string)\n",
    "          while response == \"NaN\" or response == \"nan\" or response == \"None\" or response == \"\":\n",
    "            response = policy(prob)\n",
    "\n",
    "          # Save the response\n",
    "          errorResp[cue] = response\n",
    "\n",
    "          # calculate response time\n",
    "          response_time = responsetime(activation(DM[cue][response], time, decay), ter)\n",
    "          response_time = round(response_time*1000, 0)\n",
    "\n",
    "          # update dataframe\n",
    "          dataframe += [['sim', 'learn', 1, cue, pairs[cue], response, response_time, pairs[cue] == response.upper()]]\n",
    "\n",
    "          time +=5\n",
    "\n",
    "          # Update the memory\n",
    "          DM[cue][response] = [0.01, time]\n",
    "\n",
    "          # Overwrite smaller activation of correct target to show task learning\n",
    "          time +=5\n",
    "          DM[cue][pairs[cue]] = [0.01, time]\n",
    "       elif cue in study:\n",
    "          time += 10\n",
    "          DM[cue][pairs[cue]] = [0.01, time]\n",
    "    time += 300 # time for distractor phase\n",
    "    return DM, dataframe, errorResp, time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating Elaborative Retrieval "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate_data_elab(alldata, decay=0.5, temp=1, ter=1, mas = 1.6):\n",
    "    # create a list of error items\n",
    "    errors = alldata[alldata.condition == 1].cue.unique().tolist()\n",
    "    # create a list of study items\n",
    "    study = alldata[alldata.condition == 2].cue.unique().tolist()\n",
    "\n",
    "    # Create dict with word pairs\n",
    "    pairs = {}\n",
    "    for cue, target in zip(alldata.cue.unique(),alldata.target.unique()):\n",
    "      pairs[cue] = target\n",
    "\n",
    "    DM, dataframe, errorResp, time = learning_phase(alldata, pairs, errors, study, decay, temp, ter)\n",
    " \n",
    "    # shuffle cue words\n",
    "    cue_shuffled = list(pairs.keys())\n",
    "    random.shuffle(cue_shuffled)\n",
    "  \n",
    "    # testing phase\n",
    "    for cue in cue_shuffled:\n",
    "      condition = 1 if cue in errors else 2\n",
    "      options = DM[cue].keys()\n",
    "\n",
    "      # create spreading activation additional error component given size of cue's dec mem\n",
    "      cueMem = len(DM[cue])\n",
    "      add = (mas - np.log((cueMem + 1)/2)) - (mas - np.log((cueMem + 1)/1))\n",
    "\n",
    "      values = [activation(DM[cue][opt], time, decay) if opt != pairs[cue] else activation(DM[cue][pairs[cue]], time, decay) + add for opt in options]\n",
    "      prob = boltzmann(options, values, temp)\n",
    "      response = policy(prob)\n",
    "\n",
    "      # if response is nan try again (response is np string)\n",
    "      while response == \"NaN\" or response == \"nan\" or response == \"None\" or response == \"\":\n",
    "        response = policy(prob)\n",
    "\n",
    "      if condition == 1:\n",
    "        resp_activation = activation(DM[cue][response], time, decay) + add\n",
    "      else: \n",
    "        resp_activation = activation(DM[cue][response], time, decay)\n",
    "      \n",
    "      response_time = responsetime(resp_activation, ter=ter)\n",
    "      response_time = round(response_time*1000, 0)\n",
    "\n",
    "      dataframe += [['sim', 'test', condition, cue, pairs[cue], response, response_time, pairs[cue]==response.upper()]] \n",
    "\n",
    "      # addtime taken to respond\n",
    "      time += response_time/1000\n",
    "\n",
    "    return pd.DataFrame(dataframe, columns=['participant', 'phase', 'condition', 'cue', 'target', 'response', 'rt',\n",
    "       'correct'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>participant</th>\n",
       "      <th>phase</th>\n",
       "      <th>condition</th>\n",
       "      <th>cue</th>\n",
       "      <th>target</th>\n",
       "      <th>response</th>\n",
       "      <th>rt</th>\n",
       "      <th>correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sim</td>\n",
       "      <td>learn</td>\n",
       "      <td>1</td>\n",
       "      <td>EMPIRE</td>\n",
       "      <td>STATE</td>\n",
       "      <td>KINGDOM</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sim</td>\n",
       "      <td>learn</td>\n",
       "      <td>1</td>\n",
       "      <td>CHUNK</td>\n",
       "      <td>BLOCK</td>\n",
       "      <td>HUNK</td>\n",
       "      <td>4315.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sim</td>\n",
       "      <td>learn</td>\n",
       "      <td>1</td>\n",
       "      <td>GLIDE</td>\n",
       "      <td>HANG</td>\n",
       "      <td>SAIL</td>\n",
       "      <td>5581.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sim</td>\n",
       "      <td>learn</td>\n",
       "      <td>1</td>\n",
       "      <td>SAIL</td>\n",
       "      <td>OCEAN</td>\n",
       "      <td>SHIP</td>\n",
       "      <td>6567.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sim</td>\n",
       "      <td>learn</td>\n",
       "      <td>1</td>\n",
       "      <td>ELEVATOR</td>\n",
       "      <td>BUILDING</td>\n",
       "      <td>MUSIC</td>\n",
       "      <td>7402.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>sim</td>\n",
       "      <td>test</td>\n",
       "      <td>2</td>\n",
       "      <td>SOUTH</td>\n",
       "      <td>WEST</td>\n",
       "      <td>EAST</td>\n",
       "      <td>50251.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>sim</td>\n",
       "      <td>test</td>\n",
       "      <td>2</td>\n",
       "      <td>SINK</td>\n",
       "      <td>HOLE</td>\n",
       "      <td>FOUNTAIN</td>\n",
       "      <td>50759.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>sim</td>\n",
       "      <td>test</td>\n",
       "      <td>2</td>\n",
       "      <td>NATURE</td>\n",
       "      <td>FOREST</td>\n",
       "      <td>F</td>\n",
       "      <td>51266.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>sim</td>\n",
       "      <td>test</td>\n",
       "      <td>1</td>\n",
       "      <td>PORTRAY</td>\n",
       "      <td>SHOW</td>\n",
       "      <td>PLAY</td>\n",
       "      <td>26387.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>sim</td>\n",
       "      <td>test</td>\n",
       "      <td>1</td>\n",
       "      <td>INTRODUCE</td>\n",
       "      <td>PRESENT</td>\n",
       "      <td>NAME</td>\n",
       "      <td>13638.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   participant  phase  condition        cue    target  response       rt  \\\n",
       "0          sim  learn          1     EMPIRE     STATE   KINGDOM   1995.0   \n",
       "1          sim  learn          1      CHUNK     BLOCK      HUNK   4315.0   \n",
       "2          sim  learn          1      GLIDE      HANG      SAIL   5581.0   \n",
       "3          sim  learn          1       SAIL     OCEAN      SHIP   6567.0   \n",
       "4          sim  learn          1   ELEVATOR  BUILDING     MUSIC   7402.0   \n",
       "..         ...    ...        ...        ...       ...       ...      ...   \n",
       "85         sim   test          2      SOUTH      WEST      EAST  50251.0   \n",
       "86         sim   test          2       SINK      HOLE  FOUNTAIN  50759.0   \n",
       "87         sim   test          2     NATURE    FOREST         F  51266.0   \n",
       "88         sim   test          1    PORTRAY      SHOW      PLAY  26387.0   \n",
       "89         sim   test          1  INTRODUCE   PRESENT      NAME  13638.0   \n",
       "\n",
       "    correct  \n",
       "0     False  \n",
       "1     False  \n",
       "2     False  \n",
       "3     False  \n",
       "4     False  \n",
       "..      ...  \n",
       "85    False  \n",
       "86    False  \n",
       "87    False  \n",
       "88    False  \n",
       "89    False  \n",
       "\n",
       "[90 rows x 8 columns]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test\n",
    "data = alldata\n",
    "data = simulate_data_elab(data)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " message: Optimization terminated successfully.\n",
       " success: True\n",
       "  status: 0\n",
       "     fun: 153.21749988306487\n",
       "       x: [ 4.796e-01  8.784e-01  1.926e+00]\n",
       "     nit: 3\n",
       "   direc: [[ 1.000e+00  0.000e+00  0.000e+00]\n",
       "           [ 0.000e+00  1.000e+00  0.000e+00]\n",
       "           [-4.196e-05 -5.324e-06  3.395e-03]]\n",
       "    nfev: 175"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt.minimize(vLLelab, x0 = [0.5, 1, 1], method = \"Powell\", bounds=[[0.01, 2], [0, 2], [0.1, 2]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating Mediator Retrievals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_f(alldata):\n",
    "    \"\"\"Calculate mean proportion of correct responses for error trials in the data\"\"\"\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    alldata = alldata[(alldata.condition == 1) & (alldata.phase == \"test\")]\n",
    "    for target, response in zip(alldata.target, alldata.response):\n",
    "        if target == response:\n",
    "            correct += 1\n",
    "        total += 1\n",
    "    return correct/total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5918032786885246"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = calc_f(alldata)\n",
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate_data_med(alldata, decay=0.5, temp=1, ter=1):\n",
    "    # start by calculating f\n",
    "    f = calc_f(alldata)\n",
    "\n",
    "    # create a list of error items\n",
    "    errors = alldata[alldata.condition == 1].cue.unique().tolist()\n",
    "    # create a list of study items\n",
    "    study = alldata[alldata.condition == 2].cue.unique().tolist()\n",
    "\n",
    "    # Create dict with word pairs\n",
    "    pairs = {}\n",
    "    for cue, target in zip(alldata.cue.unique(), alldata.target.unique()):\n",
    "        pairs[cue] = target\n",
    "\n",
    "    DM, dataframe, errorResp, time = learning_phase(alldata, pairs, errors, study, decay, temp, ter)\n",
    "    # shuffle cue words\n",
    "    cue_shuffled = list(pairs.keys())\n",
    "    random.shuffle(cue_shuffled)\n",
    "\n",
    "    # testing phase\n",
    "    for cue in cue_shuffled:\n",
    "        condition = 1 if cue in errors else 2\n",
    "        options = DM[cue].keys()\n",
    "\n",
    "        # calculate probability of retrieving given response\n",
    "        values = [activation(DM[cue][opt], time, decay) for opt in options]\n",
    "        prob = boltzmann(options, values, temp)\n",
    "        response = policy(prob)\n",
    "\n",
    "        # if response is nan try again (response is np string)\n",
    "        while response == \"NaN\" or response == \"nan\" or response == \"None\" or response == \"\":\n",
    "            response = policy(prob)\n",
    "\n",
    "        # if retrieved error -- correct f proportion of the time\n",
    "        if condition == 1:\n",
    "            error = errorResp[cue]\n",
    "            if (response == error) & (np.random.rand() < f):\n",
    "                response = pairs[cue]\n",
    "                # calculate response time\n",
    "                errorAct = activation(DM[cue][error], time, decay)\n",
    "                error_rt = responsetime(errorAct, ter=0)\n",
    "                time += error_rt\n",
    "                respAct = activation(DM[cue][response], time, decay)\n",
    "                resp_rt = responsetime(respAct, ter=ter)\n",
    "                time += resp_rt\n",
    "                response_time = resp_rt + error_rt\n",
    "                response_time = round(response_time*1000, 0)\n",
    "            else:\n",
    "                respAct = activation(DM[cue][response], time, decay)\n",
    "                response_time = responsetime(respAct, ter=ter)\n",
    "                time += response_time\n",
    "                response_time = round(response_time*1000, 0)\n",
    "        else:\n",
    "                respAct = activation(DM[cue][response], time, decay)\n",
    "                response_time = responsetime(respAct, ter=ter)\n",
    "                time += response_time\n",
    "                response_time = round(response_time*1000, 0)\n",
    "\n",
    "        dataframe += [['sim', 'test', condition, cue, pairs[cue], response, response_time, pairs[cue]==response.upper()]]\n",
    "\n",
    "        \n",
    "    \n",
    "    return pd.DataFrame(dataframe, columns=['participant', 'phase', 'condition', 'cue', 'target', 'response', 'rt',\n",
    "       'correct'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>participant</th>\n",
       "      <th>phase</th>\n",
       "      <th>condition</th>\n",
       "      <th>cue</th>\n",
       "      <th>target</th>\n",
       "      <th>response</th>\n",
       "      <th>rt</th>\n",
       "      <th>correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sim</td>\n",
       "      <td>learn</td>\n",
       "      <td>1</td>\n",
       "      <td>EMPIRE</td>\n",
       "      <td>STATE</td>\n",
       "      <td>KINGDOM</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sim</td>\n",
       "      <td>learn</td>\n",
       "      <td>1</td>\n",
       "      <td>CHUNK</td>\n",
       "      <td>BLOCK</td>\n",
       "      <td>MASS</td>\n",
       "      <td>4315.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sim</td>\n",
       "      <td>learn</td>\n",
       "      <td>1</td>\n",
       "      <td>GLIDE</td>\n",
       "      <td>HANG</td>\n",
       "      <td>LINE</td>\n",
       "      <td>5581.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sim</td>\n",
       "      <td>learn</td>\n",
       "      <td>1</td>\n",
       "      <td>SAIL</td>\n",
       "      <td>OCEAN</td>\n",
       "      <td>W</td>\n",
       "      <td>6567.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sim</td>\n",
       "      <td>learn</td>\n",
       "      <td>1</td>\n",
       "      <td>ELEVATOR</td>\n",
       "      <td>BUILDING</td>\n",
       "      <td>LEVELS</td>\n",
       "      <td>7402.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>sim</td>\n",
       "      <td>test</td>\n",
       "      <td>1</td>\n",
       "      <td>CRUNCH</td>\n",
       "      <td>NOISE</td>\n",
       "      <td>BITE</td>\n",
       "      <td>58704.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>sim</td>\n",
       "      <td>test</td>\n",
       "      <td>1</td>\n",
       "      <td>DIAMETER</td>\n",
       "      <td>LENGTH</td>\n",
       "      <td>DISTAN</td>\n",
       "      <td>59210.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>sim</td>\n",
       "      <td>test</td>\n",
       "      <td>1</td>\n",
       "      <td>CHIMNEY</td>\n",
       "      <td>BRICK</td>\n",
       "      <td>enemy</td>\n",
       "      <td>59717.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>sim</td>\n",
       "      <td>test</td>\n",
       "      <td>2</td>\n",
       "      <td>ANCESTOR</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>29737.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>sim</td>\n",
       "      <td>test</td>\n",
       "      <td>1</td>\n",
       "      <td>WELL</td>\n",
       "      <td>DONE</td>\n",
       "      <td>NES</td>\n",
       "      <td>60473.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   participant  phase  condition       cue    target response       rt  \\\n",
       "0          sim  learn          1    EMPIRE     STATE  KINGDOM   1995.0   \n",
       "1          sim  learn          1     CHUNK     BLOCK     MASS   4315.0   \n",
       "2          sim  learn          1     GLIDE      HANG     LINE   5581.0   \n",
       "3          sim  learn          1      SAIL     OCEAN        W   6567.0   \n",
       "4          sim  learn          1  ELEVATOR  BUILDING   LEVELS   7402.0   \n",
       "..         ...    ...        ...       ...       ...      ...      ...   \n",
       "85         sim   test          1    CRUNCH     NOISE     BITE  58704.0   \n",
       "86         sim   test          1  DIAMETER    LENGTH   DISTAN  59210.0   \n",
       "87         sim   test          1   CHIMNEY     BRICK    enemy  59717.0   \n",
       "88         sim   test          2  ANCESTOR    FAMILY   FAMILY  29737.0   \n",
       "89         sim   test          1      WELL      DONE      NES  60473.0   \n",
       "\n",
       "    correct  \n",
       "0     False  \n",
       "1     False  \n",
       "2     False  \n",
       "3     False  \n",
       "4     False  \n",
       "..      ...  \n",
       "85    False  \n",
       "86    False  \n",
       "87    False  \n",
       "88     True  \n",
       "89    False  \n",
       "\n",
       "[90 rows x 8 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = alldata\n",
    "data = simulate_data_med(data)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " message: Optimization terminated successfully.\n",
       " success: True\n",
       "  status: 0\n",
       "     fun: 168.8804080706096\n",
       "       x: [ 1.003e-02  2.000e+00  2.000e+00]\n",
       "     nit: 2\n",
       "   direc: [[ 1.000e+00  0.000e+00  0.000e+00]\n",
       "           [ 0.000e+00  0.000e+00  1.000e+00]\n",
       "           [ 7.710e-06 -1.573e-05 -1.573e-05]]\n",
       "    nfev: 179"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt.minimize(vLLmed, x0 = [0.5, 1, 1], method = \"Powell\", bounds=[[0.01, 2], [0, 2], [0.1, 2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_recovery(sim_data):\n",
    "    alpha, temp, ter = opt.minimize(vLLelab, x0 = [0.5, 0.5, 1], method = \"Powell\", bounds=[[0.25, 0.75], [0, 1], [0.1, 4]]).x\n",
    "    elab = LLelabRT(sim_data, alpha, temp, ter)\n",
    "\n",
    "    alpha, temp, ter = opt.minimize(vLLmed, x0 = [0.5, 0.5, 1], method = \"Powell\", bounds=[[0.25, 0.75], [0, 1], [0.1, 4]]).x\n",
    "    med = LLmedRT(sim_data, alpha, temp, ter)\n",
    "\n",
    "    best = \"Mediator\"\n",
    "    if elab > med:\n",
    "        best = \"Elaborative\"\n",
    "\n",
    "    diff = med - elab\n",
    "\n",
    "    return [alpha, temp, ter, elab, med, best, diff]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running model recovery\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9e1d7d7e35d94ef9b7c38c881f6d23e9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "97a47ef2bb864b36858ba0a983d1632c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# turn off warnings for this cell\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "DO_RECOVERY = True\n",
    "recovery_df = []\n",
    "\n",
    "# number of runs\n",
    "n_runs = 10\n",
    "\n",
    "if DO_RECOVERY:\n",
    "    print(\"Running model recovery\")\n",
    "    global data\n",
    "    for model in [\"Elaborative\", \"Mediator\"]:\n",
    "        # data = alldata\n",
    "        decay = np.nan\n",
    "        temp = np.nan\n",
    "        ter = np.nan\n",
    "        results = []\n",
    "        for run in tqdm(range(n_runs)):\n",
    "            #print(run)\n",
    "            if model == \"Elaborative\":\n",
    "                #randomizing parameters can break the code so we'll loop for success\n",
    "                success = False\n",
    "                while success == False:\n",
    "                    try:\n",
    "                        decay = np.random.uniform(0.25, 0.75)\n",
    "                        temp = np.random.uniform(0, 1)\n",
    "                        ter = np.random.uniform(0.1, 4)\n",
    "\n",
    "                        # print(decay, temp, ter)\n",
    "                        sim_data = simulate_data_elab(alldata, decay=decay, temp=temp, ter=ter)\n",
    "                        data = sim_data\n",
    "                        results = model_recovery(sim_data)\n",
    "\n",
    "                        success = True\n",
    "                    except:\n",
    "                        success = False\n",
    "            else:\n",
    "                success = False\n",
    "                while success == False:\n",
    "                    try:\n",
    "                        decay = np.random.uniform(0.25, 0.75)\n",
    "                        temp = np.random.uniform(0, 1)\n",
    "                        ter = np.random.uniform(0.1, 4)\n",
    "\n",
    "                        # print(decay, temp, ter)\n",
    "                        sim_data = simulate_data_med(alldata, decay=decay, temp=temp, ter=ter)\n",
    "                        data = sim_data\n",
    "                        results = model_recovery(sim_data)\n",
    "\n",
    "                        success = True\n",
    "                    except:\n",
    "                        success = False\n",
    "            \n",
    "            row = [model, decay, temp, ter] + results\n",
    "            recovery_df += [row]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orig.model</th>\n",
       "      <th>orig.decay</th>\n",
       "      <th>orig.temp</th>\n",
       "      <th>orig.ter</th>\n",
       "      <th>recov.decay</th>\n",
       "      <th>recov.temp</th>\n",
       "      <th>recov.ter</th>\n",
       "      <th>elab.LL</th>\n",
       "      <th>med.LL</th>\n",
       "      <th>best.model</th>\n",
       "      <th>diff.LL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Elaborative</td>\n",
       "      <td>0.492039</td>\n",
       "      <td>0.830359</td>\n",
       "      <td>1.383316</td>\n",
       "      <td>0.250048</td>\n",
       "      <td>0.999933</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-150.467556</td>\n",
       "      <td>-165.697876</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-15.230320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Elaborative</td>\n",
       "      <td>0.385358</td>\n",
       "      <td>0.305381</td>\n",
       "      <td>3.947635</td>\n",
       "      <td>0.250048</td>\n",
       "      <td>0.999940</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-112.953267</td>\n",
       "      <td>-144.424297</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-31.471030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Elaborative</td>\n",
       "      <td>0.722109</td>\n",
       "      <td>0.738990</td>\n",
       "      <td>2.120232</td>\n",
       "      <td>0.250048</td>\n",
       "      <td>0.999931</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-159.995217</td>\n",
       "      <td>-175.726164</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-15.730947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elaborative</td>\n",
       "      <td>0.472153</td>\n",
       "      <td>0.550953</td>\n",
       "      <td>1.940992</td>\n",
       "      <td>0.250048</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-149.757675</td>\n",
       "      <td>-164.593994</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-14.836319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Elaborative</td>\n",
       "      <td>0.366608</td>\n",
       "      <td>0.016041</td>\n",
       "      <td>1.449597</td>\n",
       "      <td>0.749947</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>30.530296</td>\n",
       "      <td>-133.085500</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-163.615796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Elaborative</td>\n",
       "      <td>0.502101</td>\n",
       "      <td>0.161594</td>\n",
       "      <td>0.479467</td>\n",
       "      <td>0.250053</td>\n",
       "      <td>0.999947</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>35.370364</td>\n",
       "      <td>-132.392896</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-167.763260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Elaborative</td>\n",
       "      <td>0.630148</td>\n",
       "      <td>0.721707</td>\n",
       "      <td>3.385794</td>\n",
       "      <td>0.250048</td>\n",
       "      <td>0.999932</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-152.589741</td>\n",
       "      <td>-168.157816</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-15.568075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Elaborative</td>\n",
       "      <td>0.685418</td>\n",
       "      <td>0.744066</td>\n",
       "      <td>3.662630</td>\n",
       "      <td>0.250048</td>\n",
       "      <td>0.999932</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-150.766745</td>\n",
       "      <td>-167.077536</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-16.310791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Elaborative</td>\n",
       "      <td>0.295390</td>\n",
       "      <td>0.708434</td>\n",
       "      <td>1.067135</td>\n",
       "      <td>0.250048</td>\n",
       "      <td>0.999933</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-152.965858</td>\n",
       "      <td>-167.540907</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-14.575049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Elaborative</td>\n",
       "      <td>0.599226</td>\n",
       "      <td>0.022397</td>\n",
       "      <td>1.358789</td>\n",
       "      <td>0.749947</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>3.999939</td>\n",
       "      <td>22.077647</td>\n",
       "      <td>-133.085500</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-155.163146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mediator</td>\n",
       "      <td>0.298815</td>\n",
       "      <td>0.719960</td>\n",
       "      <td>1.700768</td>\n",
       "      <td>0.250048</td>\n",
       "      <td>0.999933</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-163.863949</td>\n",
       "      <td>-174.623145</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-10.759196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mediator</td>\n",
       "      <td>0.344949</td>\n",
       "      <td>0.210908</td>\n",
       "      <td>3.997306</td>\n",
       "      <td>0.250048</td>\n",
       "      <td>0.999933</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-133.862169</td>\n",
       "      <td>-153.278300</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-19.416131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Mediator</td>\n",
       "      <td>0.691123</td>\n",
       "      <td>0.127256</td>\n",
       "      <td>3.343078</td>\n",
       "      <td>0.250048</td>\n",
       "      <td>0.999930</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-96.763034</td>\n",
       "      <td>-136.732984</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-39.969950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Mediator</td>\n",
       "      <td>0.492564</td>\n",
       "      <td>0.368564</td>\n",
       "      <td>1.387130</td>\n",
       "      <td>0.250048</td>\n",
       "      <td>0.999932</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-150.905169</td>\n",
       "      <td>-165.141121</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-14.235952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Mediator</td>\n",
       "      <td>0.303718</td>\n",
       "      <td>0.036717</td>\n",
       "      <td>2.545164</td>\n",
       "      <td>0.250056</td>\n",
       "      <td>0.999957</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-70.108376</td>\n",
       "      <td>-133.090795</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-62.982419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Mediator</td>\n",
       "      <td>0.371598</td>\n",
       "      <td>0.809356</td>\n",
       "      <td>1.938372</td>\n",
       "      <td>0.250048</td>\n",
       "      <td>0.999933</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-169.747527</td>\n",
       "      <td>-178.311638</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-8.564112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Mediator</td>\n",
       "      <td>0.280675</td>\n",
       "      <td>0.922092</td>\n",
       "      <td>3.381902</td>\n",
       "      <td>0.250048</td>\n",
       "      <td>0.999933</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-162.264944</td>\n",
       "      <td>-172.970455</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-10.705511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mediator</td>\n",
       "      <td>0.656953</td>\n",
       "      <td>0.498955</td>\n",
       "      <td>0.741497</td>\n",
       "      <td>0.250048</td>\n",
       "      <td>0.999933</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-154.523358</td>\n",
       "      <td>-167.474087</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-12.950729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Mediator</td>\n",
       "      <td>0.619091</td>\n",
       "      <td>0.345446</td>\n",
       "      <td>3.864690</td>\n",
       "      <td>0.250048</td>\n",
       "      <td>0.999932</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-143.476875</td>\n",
       "      <td>-159.518868</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-16.041992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Mediator</td>\n",
       "      <td>0.478682</td>\n",
       "      <td>0.509505</td>\n",
       "      <td>1.363256</td>\n",
       "      <td>0.250048</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-154.886097</td>\n",
       "      <td>-166.626318</td>\n",
       "      <td>Elaborative</td>\n",
       "      <td>-11.740221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     orig.model  orig.decay  orig.temp  orig.ter  recov.decay  recov.temp  \\\n",
       "0   Elaborative    0.492039   0.830359  1.383316     0.250048    0.999933   \n",
       "1   Elaborative    0.385358   0.305381  3.947635     0.250048    0.999940   \n",
       "2   Elaborative    0.722109   0.738990  2.120232     0.250048    0.999931   \n",
       "3   Elaborative    0.472153   0.550953  1.940992     0.250048    0.999934   \n",
       "4   Elaborative    0.366608   0.016041  1.449597     0.749947    0.999934   \n",
       "5   Elaborative    0.502101   0.161594  0.479467     0.250053    0.999947   \n",
       "6   Elaborative    0.630148   0.721707  3.385794     0.250048    0.999932   \n",
       "7   Elaborative    0.685418   0.744066  3.662630     0.250048    0.999932   \n",
       "8   Elaborative    0.295390   0.708434  1.067135     0.250048    0.999933   \n",
       "9   Elaborative    0.599226   0.022397  1.358789     0.749947    0.999934   \n",
       "10     Mediator    0.298815   0.719960  1.700768     0.250048    0.999933   \n",
       "11     Mediator    0.344949   0.210908  3.997306     0.250048    0.999933   \n",
       "12     Mediator    0.691123   0.127256  3.343078     0.250048    0.999930   \n",
       "13     Mediator    0.492564   0.368564  1.387130     0.250048    0.999932   \n",
       "14     Mediator    0.303718   0.036717  2.545164     0.250056    0.999957   \n",
       "15     Mediator    0.371598   0.809356  1.938372     0.250048    0.999933   \n",
       "16     Mediator    0.280675   0.922092  3.381902     0.250048    0.999933   \n",
       "17     Mediator    0.656953   0.498955  0.741497     0.250048    0.999933   \n",
       "18     Mediator    0.619091   0.345446  3.864690     0.250048    0.999932   \n",
       "19     Mediator    0.478682   0.509505  1.363256     0.250048    0.999934   \n",
       "\n",
       "    recov.ter     elab.LL      med.LL   best.model     diff.LL  \n",
       "0    4.000000 -150.467556 -165.697876  Elaborative  -15.230320  \n",
       "1    4.000000 -112.953267 -144.424297  Elaborative  -31.471030  \n",
       "2    4.000000 -159.995217 -175.726164  Elaborative  -15.730947  \n",
       "3    4.000000 -149.757675 -164.593994  Elaborative  -14.836319  \n",
       "4    3.999939   30.530296 -133.085500  Elaborative -163.615796  \n",
       "5    3.999939   35.370364 -132.392896  Elaborative -167.763260  \n",
       "6    4.000000 -152.589741 -168.157816  Elaborative  -15.568075  \n",
       "7    4.000000 -150.766745 -167.077536  Elaborative  -16.310791  \n",
       "8    4.000000 -152.965858 -167.540907  Elaborative  -14.575049  \n",
       "9    3.999939   22.077647 -133.085500  Elaborative -155.163146  \n",
       "10   4.000000 -163.863949 -174.623145  Elaborative  -10.759196  \n",
       "11   4.000000 -133.862169 -153.278300  Elaborative  -19.416131  \n",
       "12   4.000000  -96.763034 -136.732984  Elaborative  -39.969950  \n",
       "13   4.000000 -150.905169 -165.141121  Elaborative  -14.235952  \n",
       "14   4.000000  -70.108376 -133.090795  Elaborative  -62.982419  \n",
       "15   4.000000 -169.747527 -178.311638  Elaborative   -8.564112  \n",
       "16   4.000000 -162.264944 -172.970455  Elaborative  -10.705511  \n",
       "17   4.000000 -154.523358 -167.474087  Elaborative  -12.950729  \n",
       "18   4.000000 -143.476875 -159.518868  Elaborative  -16.041992  \n",
       "19   4.000000 -154.886097 -166.626318  Elaborative  -11.740221  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if DO_RECOVERY:\n",
    "    recovery_df = pd.DataFrame(recovery_df, columns=[\"orig.model\", \"orig.decay\", \"orig.temp\", \"orig.ter\",\n",
    "                                                     \"recov.decay\", \"recov.temp\", \"recov.ter\", \"elab.LL\", \"med.LL\", \"best.model\", \"diff.LL\"])    \n",
    "    # recovery_df.to_csv(\"model_recovery_new1.csv\")\n",
    "else:\n",
    "    recovery_df = pd.read_csv(\"model_recovery_new1.csv\")\n",
    "recovery_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = pd.crosstab(recovery_df['orig.model'], recovery_df['best.model'], rownames=['Actual'], colnames=['Predicted'], margins=False)\n",
    "sns.heatmap(confusion_matrix/(10), annot=True, fmt=\".1%\")\n",
    "plt.title(\"Recovery Probabilities by Model\")\n",
    "plt.savefig(\"model-recovery.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>participant</th>\n",
       "      <th>phase</th>\n",
       "      <th>condition</th>\n",
       "      <th>cue</th>\n",
       "      <th>target</th>\n",
       "      <th>response</th>\n",
       "      <th>rt</th>\n",
       "      <th>correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sim</td>\n",
       "      <td>learn</td>\n",
       "      <td>1</td>\n",
       "      <td>EMPIRE</td>\n",
       "      <td>STATE</td>\n",
       "      <td>STATE</td>\n",
       "      <td>3852.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sim</td>\n",
       "      <td>learn</td>\n",
       "      <td>1</td>\n",
       "      <td>CHUNK</td>\n",
       "      <td>BLOCK</td>\n",
       "      <td>space</td>\n",
       "      <td>5127.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sim</td>\n",
       "      <td>learn</td>\n",
       "      <td>1</td>\n",
       "      <td>GLIDE</td>\n",
       "      <td>HANG</td>\n",
       "      <td>c</td>\n",
       "      <td>5691.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sim</td>\n",
       "      <td>learn</td>\n",
       "      <td>1</td>\n",
       "      <td>SAIL</td>\n",
       "      <td>OCEAN</td>\n",
       "      <td>JUNGLE</td>\n",
       "      <td>6096.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sim</td>\n",
       "      <td>learn</td>\n",
       "      <td>1</td>\n",
       "      <td>ELEVATOR</td>\n",
       "      <td>BUILDING</td>\n",
       "      <td>BUILDING</td>\n",
       "      <td>6422.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>sim</td>\n",
       "      <td>test</td>\n",
       "      <td>1</td>\n",
       "      <td>EYES</td>\n",
       "      <td>COLOR</td>\n",
       "      <td>MOUTH</td>\n",
       "      <td>15503.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>sim</td>\n",
       "      <td>test</td>\n",
       "      <td>1</td>\n",
       "      <td>TEA</td>\n",
       "      <td>LEAVES</td>\n",
       "      <td>STEAM</td>\n",
       "      <td>15544.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>sim</td>\n",
       "      <td>test</td>\n",
       "      <td>1</td>\n",
       "      <td>STERN</td>\n",
       "      <td>BOAT</td>\n",
       "      <td>BAO</td>\n",
       "      <td>15584.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>sim</td>\n",
       "      <td>test</td>\n",
       "      <td>1</td>\n",
       "      <td>SWING</td>\n",
       "      <td>TREE</td>\n",
       "      <td>BALL</td>\n",
       "      <td>15624.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>sim</td>\n",
       "      <td>test</td>\n",
       "      <td>1</td>\n",
       "      <td>PARCEL</td>\n",
       "      <td>BOX</td>\n",
       "      <td>BOX</td>\n",
       "      <td>9144.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   participant  phase  condition       cue    target  response       rt  \\\n",
       "0          sim  learn          1    EMPIRE     STATE     STATE   3852.0   \n",
       "1          sim  learn          1     CHUNK     BLOCK     space   5127.0   \n",
       "2          sim  learn          1     GLIDE      HANG         c   5691.0   \n",
       "3          sim  learn          1      SAIL     OCEAN    JUNGLE   6096.0   \n",
       "4          sim  learn          1  ELEVATOR  BUILDING  BUILDING   6422.0   \n",
       "..         ...    ...        ...       ...       ...       ...      ...   \n",
       "85         sim   test          1      EYES     COLOR     MOUTH  15503.0   \n",
       "86         sim   test          1       TEA    LEAVES     STEAM  15544.0   \n",
       "87         sim   test          1     STERN      BOAT       BAO  15584.0   \n",
       "88         sim   test          1     SWING      TREE      BALL  15624.0   \n",
       "89         sim   test          1    PARCEL       BOX       BOX   9144.0   \n",
       "\n",
       "    correct  \n",
       "0     False  \n",
       "1     False  \n",
       "2     False  \n",
       "3     False  \n",
       "4      True  \n",
       "..      ...  \n",
       "85    False  \n",
       "86    False  \n",
       "87    False  \n",
       "88    False  \n",
       "89     True  \n",
       "\n",
       "[90 rows x 8 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elab: best.model\n",
      "Elaborative    8\n",
      "Mediator       2\n",
      "Name: count, dtype: int64\n",
      "Med: best.model\n",
      "Elaborative    10\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "elab_score = recovery_df[recovery_df[\"orig.model\"] == \"Elaborative\"][\"best.model\"].value_counts()\n",
    "med_score = recovery_df[recovery_df[\"orig.model\"] == \"Mediator\"][\"best.model\"].value_counts()\n",
    "print('Elab:', elab_score)\n",
    "print('Med:', med_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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