{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "06b9992d",
   "metadata": {},
   "source": [
    "# Ventriloquist Effect"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef527cb0",
   "metadata": {},
   "source": [
    "In this example we show how we can compare models in an audio-visual spatial disparity task to reproduce the Ventriloquist Effect as shown in:\n",
    "\n",
    ">Paredes, R., Cabral, J. B., & Series, P. (2025). Scikit-NeuroMSI: A Generalized Framework for Modeling Multisensory Integration. bioRxiv, 2025-05. doi: https://doi.org/10.1101/2025.05.26.656124"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c3574cf",
   "metadata": {},
   "source": [
    "## Implementation of models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2591e60d",
   "metadata": {},
   "source": [
    "For this paradigm we call the relevant models that account for spatial multisensory integration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "129198b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skneuromsi.neural import Cuppini2017\n",
    "from skneuromsi.neural import Paredes2025\n",
    "from skneuromsi.mle import AlaisBurr2004\n",
    "from skneuromsi.bayesian import Kording2007\n",
    "\n",
    "model_alaisburr = AlaisBurr2004(position_range=(0, 90), position_res=1)\n",
    "model_kording = Kording2007(position_range=(0, 90), position_res=1)\n",
    "model_cuppini2017 = Cuppini2017(\n",
    "    neurons=90, position_range=(0, 90), position_res=1\n",
    ")\n",
    "model_paredes = Paredes2025(\n",
    "    neurons=90, position_range=(0, 90), position_res=1, time_range=(0, 100)\n",
    ")\n",
    "causal_model_paredes = Paredes2025(\n",
    "    neurons=90, position_range=(0, 90), position_res=1, time_range=(0, 150)\n",
    ")\n",
    "\n",
    "temporal_model_kording = Kording2007(\n",
    "    time_range=(0, 500), time_res=1, position_range=(0, 1), position_res=1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e53e48d2",
   "metadata": {},
   "source": [
    "## Experiment setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc36e3ce",
   "metadata": {},
   "source": [
    "We are interested to explore implicit causal inference (auditory bias) and explicit causal inference (unity judgement) responses for audio-visual stimuli at different spatial disparities."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fab68fe0",
   "metadata": {},
   "source": [
    "To simulate the explicit causal inference responses, we define a customized `ProcessingStrategy`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a6d30f03",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from skneuromsi.sweep import ProcessingStrategyABC\n",
    "\n",
    "\n",
    "class CausesProcessingStrategy(ProcessingStrategyABC):\n",
    "    def map(self, result):\n",
    "        causes = result.causes_\n",
    "        del result._nddata\n",
    "        return causes\n",
    "\n",
    "    def reduce(self, results, **kwargs):\n",
    "        return np.array(results, dtype=np.float16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dbf7f00",
   "metadata": {},
   "source": [
    "Now we setup the experiment simulation for each model using the `ParameterSweep` class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d03169d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skneuromsi.sweep import ParameterSweep\n",
    "\n",
    "dis = np.array([-24, -12, -6, -3, 3, 6, 12, 24])  # spatial disparities\n",
    "\n",
    "sp_alaisburr = ParameterSweep(\n",
    "    model=model_alaisburr,\n",
    "    target=\"visual_position\",\n",
    "    repeat=1,\n",
    "    n_jobs=-2,\n",
    "    range=45 + dis,\n",
    ")\n",
    "\n",
    "sp_kording = ParameterSweep(\n",
    "    model=model_kording,\n",
    "    target=\"visual_position\",\n",
    "    repeat=1,\n",
    "    n_jobs=-2,\n",
    "    range=45 + dis,\n",
    ")\n",
    "\n",
    "sp_cuppini2017 = ParameterSweep(\n",
    "    model=model_cuppini2017,\n",
    "    target=\"visual_position\",\n",
    "    repeat=1,\n",
    "    n_jobs=-2,\n",
    "    range=45 + dis,\n",
    ")\n",
    "sp_paredes = ParameterSweep(\n",
    "    model=model_paredes,\n",
    "    target=\"visual_position\",\n",
    "    repeat=1,\n",
    "    n_jobs=-2,\n",
    "    range=45 + dis,\n",
    ")\n",
    "\n",
    "causal_sp_kording = ParameterSweep(\n",
    "    model=model_kording,\n",
    "    target=\"visual_position\",\n",
    "    repeat=1,\n",
    "    n_jobs=-2,\n",
    "    range=45 + dis,\n",
    "    processing_strategy=CausesProcessingStrategy(),\n",
    ")\n",
    "\n",
    "causal_sp_cuppini2017 = ParameterSweep(\n",
    "    model=model_cuppini2017,\n",
    "    target=\"visual_position\",\n",
    "    repeat=1,\n",
    "    n_jobs=-2,\n",
    "    range=45 + dis,\n",
    "    processing_strategy=CausesProcessingStrategy(),\n",
    ")\n",
    "\n",
    "causal_sp_paredes = ParameterSweep(\n",
    "    model=causal_model_paredes,\n",
    "    target=\"visual_position\",\n",
    "    repeat=1,\n",
    "    n_jobs=-2,\n",
    "    range=45 + dis,\n",
    "    processing_strategy=CausesProcessingStrategy(),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f596418",
   "metadata": {},
   "source": [
    "## Auditory Bias simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64fd79b9",
   "metadata": {},
   "source": [
    "We are now ready to run the experiments and extract auditory bias responses:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2a49bcc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "60dcae871c1b4de5bba17401de356658",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sweeping 'visual_position':   0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "model_id": "dd8bc1c64c99481bb4e58aee6714c220",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Collecting metadata:   0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "7d2300cc3efd4e069045e6ae3ca06956",
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      "text/plain": [
       "Sweeping 'visual_position':   0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
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     "output_type": "display_data"
    },
    {
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       "model_id": "94e19e2ac52d46a29c3799ba6cd68361",
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       "version_minor": 0
      },
      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "6129d34c0e394c03a45e272e9ff5601e",
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       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b07be15c8b9245a9b39f3034451462f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sweeping 'visual_position':   0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "658cd56c64a54160a52a51d412d35b68",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_alaisburr = sp_alaisburr.run(\n",
    "    auditory_position=45,\n",
    "    auditory_sigma=4.695e01,\n",
    "    visual_sigma=4.505e01,\n",
    "    noise=False,\n",
    ")\n",
    "\n",
    "res_kording = sp_kording.run(\n",
    "    auditory_position=45,\n",
    "    auditory_sigma=2.490e01,\n",
    "    visual_sigma=2.171e01,\n",
    "    prior_mu=4.069e01,\n",
    "    prior_sigma=5.277e00,\n",
    "    noise=False,\n",
    ")\n",
    "\n",
    "res_cuppini2017 = sp_cuppini2017.run(\n",
    "    auditory_position=45,\n",
    "    auditory_sigma=4.511e01,\n",
    "    visual_sigma=2.495e01,\n",
    "    auditory_intensity=1.191e01,\n",
    "    visual_intensity=1.387e01,\n",
    "    noise=False,\n",
    ")\n",
    "res_paredes = sp_paredes.run(\n",
    "    auditory_position=45,\n",
    "    auditory_sigma=2.000e01,\n",
    "    visual_sigma=1.854e01,\n",
    "    auditory_intensity=1.113e00,\n",
    "    visual_intensity=7.728e00,\n",
    "    noise=False,\n",
    "    auditory_soa=None,\n",
    "    auditory_stim_n=1,\n",
    "    visual_stim_n=1,\n",
    "    auditory_duration=100,\n",
    "    visual_duration=100,\n",
    "    auditory_onset=0,\n",
    "    visual_onset=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9228d3fb",
   "metadata": {},
   "source": [
    "## Spatial Causal Inference simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f45c255",
   "metadata": {},
   "source": [
    "Next, we run the experiments to extract explicit causal inference responses:\n",
    "\n",
    ">**Note:** For simplicity and computational efficiency, we are asking the models to provide causal inference responses as probability values (instead of counts) with the `causes_kind` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2c937abd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "088a89249d2a4b2dbb4dc19c561c9bda",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sweeping 'visual_position':   0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ce9d00ff75df476d846c82ca2e76858c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sweeping 'visual_position':   0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "893ca86b21ee47fc984d6b1c96ad724c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sweeping 'visual_position':   0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_kording_causes = causal_sp_kording.run(\n",
    "    auditory_position=45,\n",
    "    auditory_sigma=8.042e00,\n",
    "    visual_sigma=8.041e00,\n",
    "    noise=False,\n",
    "    strategy=\"selection\",\n",
    "    prior_mu=6.898e01,\n",
    "    prior_sigma=4.501e01,\n",
    "    causes_kind=\"prob\",\n",
    ")\n",
    "\n",
    "res_cuppini2017_causes = causal_sp_cuppini2017.run(\n",
    "    auditory_position=45,\n",
    "    auditory_sigma=2.803e01,\n",
    "    visual_sigma=8.033e00,\n",
    "    auditory_intensity=1.328e01,\n",
    "    visual_intensity=2.366e01,\n",
    "    noise=False,\n",
    "    causes_kind=\"prob\",\n",
    "    causes_dim=\"space\",\n",
    ")\n",
    "\n",
    "res_paredes_causes = causal_sp_paredes.run(\n",
    "    auditory_position=45,\n",
    "    auditory_sigma=4.215e01,\n",
    "    visual_sigma=1.673e01,\n",
    "    auditory_intensity=2.698e01,\n",
    "    visual_intensity=4.776e00,\n",
    "    noise=False,\n",
    "    causes_kind=\"prob\",\n",
    "    causes_dim=\"space\",\n",
    "    auditory_soa=None,\n",
    "    auditory_stim_n=1,\n",
    "    visual_stim_n=1,\n",
    "    auditory_duration=150,\n",
    "    visual_duration=150,\n",
    "    auditory_onset=0,\n",
    "    visual_onset=0,\n",
    "    causes_peak_threshold=0.15,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "450a272f",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f31e19f",
   "metadata": {},
   "source": [
    "Finally we plot our results to visually compare the performance of the models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec0c2a2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7d0aab3a4c40>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 7200x3000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# initializes figure and plots\n",
    "fig, axs = plt.subplots(\n",
    "    1, 2, figsize=(12, 5), sharex=False, dpi=600, gridspec_kw={\"hspace\": 0.3}\n",
    ")\n",
    "\n",
    "colors = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
    "\n",
    "# Panel A\n",
    "ax1 = plt.subplot(121)\n",
    "res_alaisburr.bias.bias_mean(\n",
    "    influence_parameter=\"auditory_position\", mode=\"multi\"\n",
    ").plot(ax=ax1)\n",
    "res_kording.bias.bias_mean(\n",
    "    influence_parameter=\"auditory_position\", mode=\"auditory\"\n",
    ").plot(ax=ax1)\n",
    "res_cuppini2017.bias.bias_mean(\n",
    "    influence_parameter=\"auditory_position\", mode=\"auditory\"\n",
    ").plot(ax=ax1)\n",
    "res_paredes.bias.bias_mean(\n",
    "    influence_parameter=\"auditory_position\", mode=\"auditory\"\n",
    ").plot(ax=ax1)\n",
    "ax1.set_ylim(0.05, 1.05)\n",
    "ax1.set_xlabel(\"A-V disparity (deg)\", size=12, weight=\"bold\")\n",
    "ax1.set_ylabel(\"Auditory Bias (prop)\", size=12, weight=\"bold\")\n",
    "ax1.tick_params(axis=\"both\", labelsize=10)\n",
    "\n",
    "legend = ax1.legend(\n",
    "    [\n",
    "        \"AlaisBurr 2004\",\n",
    "        \"Kording 2007\",\n",
    "        \"Cuppini 2017\",\n",
    "        \"Paredes 2025\",\n",
    "        \"Noel 2022 data\",\n",
    "    ],\n",
    "    fontsize=12,\n",
    "    loc=\"center left\",\n",
    "    bbox_to_anchor=(1.4, 0.25),\n",
    ")\n",
    "ax1.get_legend().remove()\n",
    "\n",
    "# Panel B\n",
    "ax2 = plt.subplot(122)\n",
    "ax2.set_prop_cycle(color=colors[1:4])\n",
    "ax2.plot(dis, res_kording_causes)\n",
    "ax2.plot(dis, res_cuppini2017_causes)\n",
    "ax2.plot(dis, res_paredes_causes)\n",
    "ax2.set_ylim(0.05, 1.05)\n",
    "ax2.set_xlabel(\"A-V disparity (deg)\", size=12, weight=\"bold\")\n",
    "ax2.set_ylabel(\"C=1 (prop)\", size=12, weight=\"bold\")\n",
    "ax2.tick_params(axis=\"both\", labelsize=10)\n",
    "ax2.add_artist(legend)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "neuromsi",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}