Anderson_EZ_diffusion_

anderson_ez_diffusion_.ipynb

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        "import numpy as np"
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        "**Given the percent correct and variance in RT, fit the EZ diffusion model** <br>\n",
        "(Britt Anderson book, page 134)\n",
        "\n",
        "parameters: <br>\n",
        "pc : proportion correct <br>\n",
        "vrt : variance in RT\n",
        "\n",
        "return values: <br>\n",
        "v : drift rate <br>\n",
        "a : the separation of our two decision boundaries from the starting point"
      ]
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      "source": [
        "def logit(pc):\n",
        "  return np.log((pc/(1-pc)))"
      ],
      "execution_count": 5,
      "outputs": []
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      "source": [
        "logit(0.5)"
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      "source": [
        "def fit_EZ_parameters(pc, vrt, s=0.1):\n",
        "  # first term\n",
        "  # sign function: https://numpy.org/doc/stable/reference/generated/numpy.sign.html\n",
        "  t1 = np.sign(pc-0.5) * s\n",
        "  # second term\n",
        "  t2_num = logit(pc) * (pc**2 * logit(pc) - pc*logit(pc) + pc - 0.5)\n",
        "  t2 = (t2_num/vrt)**0.25\n",
        "  # calculate the drift rate\n",
        "  v = t1*t2\n",
        "  # using drift rate, calculate separation\n",
        "  a = ((s**2) * logit(pc)) / v\n",
        "  # done. return results\n",
        "  return v,a"
      ],
      "execution_count": 19,
      "outputs": []
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      "source": [
        "v1, a1 = fit_EZ_parameters(0.8, 5.83)\n",
        "v1, a1"
      ],
      "execution_count": 20,
      "outputs": [
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              "(0.03692653509562729, 0.37541956144270117)"
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      "source": [
        "v2, a2 = fit_EZ_parameters(0.6, 10.1)\n",
        "v2, a2"
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      "execution_count": 21,
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