{ "cells": [ { "cell_type": "markdown", "id": "0", "metadata": {}, "source": [ "# Lesson 8: DDM vs probit on the Garcia magnitude task\n", "\n", "In lessons 1–4 we modelled **only the choice** participants made on each trial:\n", "\"did they pick the larger number?\" — fit with a Bernoulli likelihood\n", "(`MagnitudeComparisonModel`). That model recovers the Bayesian-observer\n", "front-end (priors, asymmetric encoding noise, posterior shrinkage) entirely\n", "from choice probabilities.\n", "\n", "But choice isn't the only thing the participant gives us. They also took a\n", "specific amount of *time* to make that choice. **The reaction time (RT) is a\n", "second observation** generated by the same underlying perceptual process. A\n", "drift-diffusion model (DDM) makes that explicit: the same posterior log-magnitude\n", "that drives the choice also drives the *speed* of accumulation. So a DDM fit to\n", "(rt, choice) jointly should — in principle — give us tighter inference on the\n", "same cognitive parameters.\n", "\n", "This lesson is a focused two-model comparison:\n", "\n", "- `MagnitudeComparisonModel` (lesson 1) — choice-only Bernoulli with Bayesian\n", " observer.\n", "- `DDMMagnitudeComparisonModel` (this lesson) — same Bayesian observer, but\n", " choice and RT are modelled jointly via a Wiener first-passage-time (WFPT)\n", " likelihood.\n", "\n", "Both share the **identical cognitive front-end**. The only thing that changes\n", "is the decision rule: one-shot Bernoulli vs stochastic single-accumulator\n", "race. **Fitting the DDM is a one-line change** from the probit if you have\n", "RT in the dataframe — same constructor signature, same `.sample()`, same\n", "idata: swap `MagnitudeComparisonModel` for `DDMMagnitudeComparisonModel`.\n", "\n", "We run on the full **64-subject Garcia 2022 magnitude task** because at\n", "$n = 8$ the cognitive parameters are weakly identified and the comparison\n", "between models is noisy.\n", "\n", "[Lesson 9](lesson9.ipynb) extends this and adds the **race-diffusion** model\n", "(two parallel accumulators), which captures the slow-error pattern in\n", "choice-conditional RT that single-accumulator DDMs cannot." ] }, { "cell_type": "markdown", "id": "1", "metadata": {}, "source": [ "## Before we fit: drop physiologically implausible fast trials\n", "\n", "**Critical preprocessing step for any DDM/RDM fit — including yours.**\n", "\n", "DDM/RDM likelihoods require the non-decision time $t_0$ to be below\n", "$\\min(\\text{RT})$ for *every* subject. When the sampler wanders into a\n", "region where $t_0 > \\text{rt}$ for some trial, the WFPT log-likelihood\n", "floors at `LOGP_LB = -66.1` (HSSM's design, inherited by bauer) — and\n", "the gradient with respect to $t_0$ in that region is *exactly zero*. NUTS sees\n", "a flat landscape, loses all pull back into the valid region, and the\n", "chain can permanently stick in a wrong posterior mode. We diagnosed this\n", "the hard way on a first attempt at fitting Garcia (chains landed in 4\n", "different basins, $\\hat r = 4$, ESS = 4).\n", "\n", "The standard fix is **dropping trials with RT below typical motor\n", "non-decision time**, around 150–250 ms depending on the task. These\n", "trials almost certainly represent anticipatory responses or motor\n", "preparation that fired before the stimulus was fully processed — not\n", "stimulus-driven decisions. Rationale, with sources:\n", "\n", "- **Luce (1986)**, *Response Times*, ch. 6: simple key-press RTs have an\n", " irreducible physiological floor around 100–150 ms (visual transduction\n", " + motor latency), so anything faster cannot reflect a perceptual\n", " decision.\n", "- **Ratcliff (1993)**, *Methods for dealing with reaction time outliers*\n", " (Psychol. Bull.): formalised RT outlier handling in cognitive\n", " modelling. The standard recommendation is to drop a thin slice of the\n", " fastest and slowest RTs (or fit a mixture with a contaminant\n", " distribution), with the fast cutoff typically around 200–300 ms for\n", " perceptual / numerical comparison tasks.\n", "- **Wiecki, Sofer & Frank (2013)**, HDDM paper: the same convention\n", " built into the HDDM toolbox's default outlier handling.\n", "\n", "For Garcia 2022 we use **`rt >= 0.20 s`**, which drops 2.1 % of trials\n", "(285 of 13,410). This matches bauer's default $t_0$ prior centre, sits\n", "just above Luce's physiological floor, and falls comfortably below the\n", "bulk of real responses (the empirical RT distribution peaks around\n", "320 ms — see the chronometric curves later). For tasks with slower\n", "typical responses (e.g. perceptual decisions with longer integration\n", "windows) a 250–300 ms cutoff would be more appropriate; for very fast\n", "tasks (saccadic RT) it could be lower. The principle is the same:\n", "**make the prior on $t_0$ and the empirical $\\min(\\text{rt})$\n", "compatible**.\n", "\n", "bauer's `DDMMagnitudeComparisonModel` now also writes a warning to\n", "stderr at model-build time if it sees any trials below 0.20 s, so this\n", "won't silently bite you on a future dataset.\n", "\n", "### Porting to your own data — the dataframe schema\n", "\n", "For the rest of this lesson to work on your data, your trial dataframe\n", "needs:\n", "\n", "| | required | type |\n", "|---|---|---|\n", "| `subject` | index level or column | int / str |\n", "| `n1`, `n2` | columns | numeric — the two compared values (any unit) |\n", "| `choice` | column | `bool`, `True` = chose option 2 |\n", "| `rt` | column | seconds, > 0 |\n", "\n", "If you have an extra design factor (group, condition, ISI, …) keep it as\n", "a column too — the [regression-DDM section](#bonus-recipe-regression-ddm-for-between-group-or-within-design-effects) below shows how to use it." ] }, { "cell_type": "code", "execution_count": null, "id": "2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Subjects: 64\n", "Trials: 13410 (~209 per subject)\n", "Columns: ['n1', 'n2', 'choice', 'rt', 'accuracy', 'correct', 'isi']\n", "\n", "Dropped 285 / 13410 trials with rt < 0.20s (2.1%); global min rt now 0.200s.\n" ] }, { "data": { "text/html": [ "
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n1n2choicertaccuracycorrectisi
subjectformatruntrial_nr
1non-symbolic11710True0.7751-18.033
2514False0.8920-18.532
3714True0.6111-16.533
4710True0.6601-17.033
5510True0.8301-19.033
\n", "
" ], "text/plain": [ " n1 n2 choice rt accuracy correct \\\n", "subject format run trial_nr \n", "1 non-symbolic 1 1 7 10 True 0.775 1 -1 \n", " 2 5 14 False 0.892 0 -1 \n", " 3 7 14 True 0.611 1 -1 \n", " 4 7 10 True 0.660 1 -1 \n", " 5 5 10 True 0.830 1 -1 \n", "\n", " isi \n", "subject format run trial_nr \n", "1 non-symbolic 1 1 8.033 \n", " 2 8.532 \n", " 3 6.533 \n", " 4 7.033 \n", " 5 9.033 " ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import warnings; warnings.filterwarnings('ignore')\n", "import os\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import arviz as az\n", "\n", "sns.set_theme(context='notebook', style='whitegrid', palette='deep')\n", "\n", "from bauer.utils.data import load_garcia2022\n", "from bauer.utils import get_subject_posterior_df\n", "from bauer.models import MagnitudeComparisonModel, DDMMagnitudeComparisonModel\n", "\n", "# Load the full Garcia 2022 magnitude task (all 64 subjects).\n", "df = load_garcia2022(task='magnitude')\n", "n_subj = df.index.get_level_values('subject').nunique()\n", "print(f\"Subjects: {n_subj}\")\n", "print(f\"Trials: {len(df)} (~{len(df) // n_subj} per subject)\")\n", "print(f\"Columns: {list(df.columns)}\")\n", "\n", "# ── Preprocess: drop physiologically implausible fast trials ──────────────\n", "# Crucial for DDM/RDM fits, see the explanation in the next markdown cell.\n", "RT_MIN = 0.20 # seconds; matches the default t0 prior centre in bauer.\n", "n_before = len(df)\n", "df = df[df['rt'] >= RT_MIN].copy()\n", "print(f\"\\nDropped {n_before - len(df)} / {n_before} trials with rt < {RT_MIN:.2f}s \"\n", " f\"({100*(n_before - len(df))/n_before:.1f}%); \"\n", " f\"global min rt now {df['rt'].min():.3f}s.\")\n", "\n", "# Cache directory — set BAUER_TUTORIAL_REFIT=1 in the env to force a fresh fit.\n", "# Cache key includes the RT cutoff so different filters get different caches.\n", "CACHE_DIR = os.path.expanduser('~/.bauer_tutorial_cache')\n", "os.makedirs(CACHE_DIR, exist_ok=True)\n", "FORCE_REFIT = bool(os.environ.get('BAUER_TUTORIAL_REFIT', ''))\n", "CACHE_TAG = f'garcia_n{n_subj}_rtmin{int(RT_MIN*1000)}'\n", "\n", "def fit_or_load(model, name, backend='numpyro', **sample_kwargs):\n", " \"\"\"Fit (or load cached) idata. We use the numpyro JAX backend by default —\n", " it's ~3–10× faster on CPU than pymc and parallelises the chains on a\n", " single GPU. Falls back to pymc if you don't have hssm/jax/numpyro\n", " installed. Always (re)builds the pymc model — required for downstream\n", " ``model.ppc()`` even when the idata came from cache.\"\"\"\n", " model.build_estimation_model(data=df, hierarchical=True)\n", " path = os.path.join(CACHE_DIR, f'{CACHE_TAG}_{name}.nc')\n", " if os.path.exists(path) and not FORCE_REFIT:\n", " print(f\"Loading cached {name} fit from {path}\")\n", " return az.from_netcdf(path)\n", " kw = dict(draws=1000, tune=1000, chains=4, target_accept=0.95,\n", " backend=backend)\n", " kw.update(sample_kwargs)\n", " idata = model.sample(**kw)\n", " idata.to_netcdf(path)\n", " print(f\"Saved {name} fit to {path}\")\n", " return idata\n", "\n", "df.head()" ] }, { "cell_type": "markdown", "id": "3", "metadata": {}, "source": [ "## The two models, side by side\n", "\n", "Both share the same Bayesian-observer cognitive front-end:\n", "\n", "- $\\nu_1, \\nu_2$ — per-option encoding noise SDs (asymmetric for the sequential\n", " presentation: option 1 is held in memory while option 2 is shown).\n", "- $\\mu_p, \\sigma_p$ — prior mean and SD over log-magnitudes.\n", "- Posterior shrinkage weights $\\beta_k = \\sigma_p^2 / (\\sigma_p^2 + \\nu_k^2)$ —\n", " noisier options get pulled more toward the prior.\n", "\n", "The **probit** model then computes a Bernoulli choice probability:\n", "\n", "$$P(\\text{choose 2}) = \\Phi\\!\\left(\\frac{\\mu_{\\text{post},2} - \\mu_{\\text{post},1}}{\\sqrt{\\sigma^2_{\\text{post},1} + \\sigma^2_{\\text{post},2}}}\\right)$$\n", "\n", "The **DDM** uses the same numerator as the drift of a single Wiener accumulator:\n", "\n", "$$\\mathrm{d}X(t) = v\\, \\mathrm{d}t + \\sigma\\, \\mathrm{d}W(t), \\qquad\n", "v = \\frac{\\mu_{\\text{post},2} - \\mu_{\\text{post},1}}{\\sqrt{\\nu_1^2 + \\nu_2^2}}$$\n", "\n", "The choice is which boundary (at $\\pm a$) is hit first; the RT is the\n", "first-passage time plus a non-decision time $t_0$. Same numerator, similar\n", "denominator. The DDM adds two parameters not present in the probit:\n", "\n", "- $a$ — half boundary separation (controls overall RT magnitude).\n", "- $t_0$ — non-decision time (motor + sensory delay).\n", "\n", "Crucially the perceptual parameters $\\nu_k, \\mu_p, \\sigma_p$ play exactly\n", "the same role in both models. So if we fit both, those four should land in\n", "roughly the same place — and the DDM should give us tighter intervals,\n", "because RT carries additional information about the perceived SNR." ] }, { "cell_type": "code", "execution_count": null, "id": "4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading cached probit fit from /Users/gdehol/.bauer_tutorial_cache/garcia_n64_rtmin200_probit.nc\n" ] } ], "source": [ "# ── Fit the probit model (choice only, fast) ──────────────────────────────\n", "m_probit = MagnitudeComparisonModel(\n", " paradigm=df,\n", " fit_separate_evidence_sd=True, # allow ν_1 ≠ ν_2 (sequential task)\n", " fit_prior=True, # estimate Bayesian-observer prior μ_p, σ_p\n", ")\n", "idata_probit = fit_or_load(m_probit, 'probit')" ] }, { "cell_type": "code", "execution_count": null, "id": "5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading cached ddm fit from /Users/gdehol/.bauer_tutorial_cache/garcia_n64_rtmin200_ddm.nc\n" ] } ], "source": [ "# ── Fit the DDM (joint choice + RT, slower; ~10–15 min on a laptop) ───────\n", "m_ddm = DDMMagnitudeComparisonModel(\n", " paradigm=df,\n", " fit_separate_evidence_sd=True,\n", " fit_prior=True,\n", ")\n", "idata_ddm = fit_or_load(m_ddm, 'ddm')" ] }, { "cell_type": "markdown", "id": "6", "metadata": {}, "source": [ "## Why this hierarchical DDM converges: the starting-point finder\n", "\n", "Hierarchical DDM (and especially regression-DDM) posteriors are long, curved\n", "ridges. *Where the chains start* largely decides whether they find the bulk of\n", "the mass or get stuck in a bad corner at maximum tree depth. With a naive,\n", "generic initialization this is effectively a **seed lottery** — the same model\n", "and settings can give $\\hat r \\approx 1.0$ on one random seed and\n", "$\\hat r > 3$ on the next.\n", "\n", "bauer handles this for you. On DDM/race models, `model.sample` is **on by\n", "default** backed by a *starting-point finder* (`get_initial_points`,\n", "`recommended_init='mapjitter'`): it places each chain at a **data-informed\n", "plausible value** (the posterior mode from `find_MAP`) and then **disperses the\n", "chains by a fraction of each parameter's prior SD** — so chains sit around the\n", "typical set (never all exactly at the mode), and $\\hat r$ stays meaningful.\n", "This is the same idea HSSM uses (curated initial values + small jitter).\n", "\n", "In a controlled experiment it took a regression DDM from ~12 % to **100 %**\n", "seed-convergence, and made fits ~3.7× faster (converged chains avoid the\n", "max-tree-depth stalls). You don't have to do anything — it's the default. To\n", "disable it, pass `m.sample(..., find_init=False)`; to supply your own, pass\n", "`initvals=`. It works for every parameter (DDM, front-end, B-spline noise\n", "coefficients) with no per-parameter tuning. **For a large hierarchical fit\n", "(e.g. a full TMS or multi-condition dataset), this is the single most important\n", "reason your fit converges — leave it on.**" ] }, { "cell_type": "markdown", "id": "7", "metadata": {}, "source": [ "## Diagnostics — did both models sample cleanly?\n", "\n", "Before interpreting any posterior, check $\\hat r \\le 1.01$ on the group-level\n", "means and ESS bulk $\\ge 100$ per chain. Divergences should be a small\n", "fraction of post-warmup draws." ] }, { "cell_type": "code", "execution_count": null, "id": "8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--- probit ---\n", " ess_bulk r_hat\n", "n1_evidence_sd_mu 468.0 1.01\n", "n2_evidence_sd_mu 577.0 1.00\n", "prior_mu_mu 2010.0 1.00\n", "prior_sd_mu 414.0 1.00\n", "divergences: 0, max r̂: 1.010\n", "\n", "--- DDM ---\n", " ess_bulk r_hat\n", "n1_evidence_sd_mu 554.0 1.00\n", "n2_evidence_sd_mu 442.0 1.00\n", "prior_mu_mu 4382.0 1.00\n", "prior_sd_mu 330.0 1.01\n", "a_mu 5189.0 1.00\n", "t0_mu 3755.0 1.00\n", "divergences: 0, max r̂: 1.010\n", "\n" ] } ], "source": [ "shared = ['n1_evidence_sd_mu', 'n2_evidence_sd_mu',\n", " 'prior_mu_mu', 'prior_sd_mu']\n", "for name, idata, extra in [('probit', idata_probit, []),\n", " ('DDM', idata_ddm, ['a_mu', 't0_mu'])]:\n", " diag = az.summary(idata, var_names=shared + extra, kind='diagnostics')\n", " n_div = int(idata.sample_stats['diverging'].sum())\n", " print(f\"--- {name} ---\")\n", " print(diag[['ess_bulk', 'r_hat']])\n", " print(f\"divergences: {n_div}, max r̂: {float(diag['r_hat'].max()):.3f}\\n\")" ] }, { "cell_type": "markdown", "id": "9", "metadata": {}, "source": [ "## Question 1 — Do they fit choice equally well?\n", "\n", "The probit and the DDM use different likelihoods (Bernoulli vs WFPT), but\n", "they should produce essentially the same psychometric: the *choice marginal*\n", "of a DDM with unbiased start point ($z = 0.5$) and no across-trial drift\n", "variability is a probit on the same drift signal.\n", "\n", "For a clean visual, we **bin the data** into log-ratio quantile bins (so each\n", "dot summarises many trials, not one $(n_1, n_2)$ pair) and **predict on a\n", "dense grid** of hypothetical $\\log(n_2/n_1)$ values at a fixed stake size\n", "(the geometric mean of $n_1 \\cdot n_2$ across the dataset). The model\n", "predictions are aggregated across subjects to give a population-level\n", "psychometric.\n", "\n", "> **Garcia-specific note** — the dense-grid + size-effect cells below assume\n", "> a paradigm where each trial has a *difficulty* axis (here $\\log(n_2/n_1)$)\n", "> orthogonal to a *magnitude / stake* axis (here $\\sqrt{n_1 n_2}$). If your\n", "> task only has one stimulus per trial (e.g. simple yes/no detection), use\n", "> just the difficulty axis and skip the size-effect cell." ] }, { "cell_type": "code", "execution_count": null, "id": "10", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Synthetic paradigm: 2560 rows (64 subjects × 40 log-ratios), stake = 11.78\n" ] }, { "data": { "text/html": [ "
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n1n2log_ratio
subjecttrial_nr
1022.0310196.294577-1.252763
121.3901626.483165-1.193722
220.7679476.677403-1.134682
320.1638316.877460-1.075641
419.5772897.083511-1.016600
\n", "
" ], "text/plain": [ " n1 n2 log_ratio\n", "subject trial_nr \n", "1 0 22.031019 6.294577 -1.252763\n", " 1 21.390162 6.483165 -1.193722\n", " 2 20.767947 6.677403 -1.134682\n", " 3 20.163831 6.877460 -1.075641\n", " 4 19.577289 7.083511 -1.016600" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Dense, evenly-spaced log-ratio grid at the typical stake. By fixing the\n", "# geometric-mean stake = sqrt(n1*n2) and varying only log(n2/n1), the curve\n", "# isolates the difficulty axis cleanly (no stake confound).\n", "stake = float(np.exp(0.5 * (np.log(df['n1']) + np.log(df['n2'])).mean()))\n", "lr_obs = np.log(df['n2'] / df['n1'])\n", "n_grid = 40\n", "log_ratios = np.linspace(lr_obs.quantile(0.02), lr_obs.quantile(0.98), n_grid)\n", "\n", "subjects = sorted(df.index.get_level_values('subject').unique())\n", "rows = []\n", "for s in subjects:\n", " for i, lr in enumerate(log_ratios):\n", " rows.append({\n", " 'subject': s,\n", " 'trial_nr': i,\n", " 'n1': stake / np.exp(lr / 2),\n", " 'n2': stake * np.exp(lr / 2),\n", " 'log_ratio': lr,\n", " })\n", "paradigm_grid = (pd.DataFrame(rows)\n", " .set_index(['subject', 'trial_nr']))\n", "print(f\"Synthetic paradigm: {len(paradigm_grid)} rows \"\n", " f\"({len(subjects)} subjects × {n_grid} log-ratios), \"\n", " f\"stake = {stake:.2f}\")\n", "paradigm_grid.head()" ] }, { "cell_type": "code", "execution_count": null, "id": "11", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def predict_psychometric(model, idata, paradigm, n_posterior_samples=60,\n", " model_name='Model', seed=0):\n", " \"\"\"For each of n_posterior_samples draws from the posterior, compute\n", " P(choose 2) per trial via model.predict(paradigm, pars), then average\n", " across subjects to give a population psychometric per (sample, log_ratio).\n", " Continuous predictions are smoother than ppc-based binary draws.\"\"\"\n", " rng = np.random.default_rng(seed)\n", " post = idata.posterior\n", " n_chain, n_draw = post.sizes['chain'], post.sizes['draw']\n", " flat = rng.choice(n_chain * n_draw, n_posterior_samples, replace=False)\n", " chain_idx, draw_idx = flat // n_draw, flat % n_draw\n", "\n", " par_names = list(model.free_parameters.keys())\n", " subjects = post.coords['subject'].values\n", " rows = []\n", " for k in range(n_posterior_samples):\n", " ci, di = int(chain_idx[k]), int(draw_idx[k])\n", " pars_df = pd.DataFrame(\n", " {p: post[p].isel(chain=ci, draw=di).values for p in par_names},\n", " index=pd.Index(subjects, name='subject'),\n", " )\n", " pred = model.predict(paradigm, pars_df)\n", " # Probit returns 'p_choice'; DDM returns 'p_upper'. Same quantity.\n", " p_col = 'p_choice' if 'p_choice' in pred.columns else 'p_upper'\n", " agg = (pred.reset_index()\n", " .groupby('log_ratio')[p_col].mean()\n", " .rename('p_choice').reset_index())\n", " agg['ppc_sample'] = k\n", " rows.append(agg)\n", " out = pd.concat(rows, ignore_index=True)\n", " out['model'] = model_name\n", " return out\n", "\n", "\n", "def binned_psychometric_data(df_data, n_bins=11):\n", " \"\"\"Empirical mean P(choose 2) within each log-ratio quantile bin.\"\"\"\n", " d = df_data.copy()\n", " d['log_ratio'] = np.log(d['n2'] / d['n1'])\n", " d['lr_bin'] = pd.qcut(d['log_ratio'], n_bins, duplicates='drop')\n", " g = d.groupby('lr_bin', observed=True)\n", " out = pd.DataFrame({\n", " 'log_ratio': g['log_ratio'].mean(),\n", " 'choice': g['choice'].mean(),\n", " 'n': g['choice'].size(),\n", " }).reset_index(drop=True)\n", " out['se'] = np.sqrt(out['choice'] * (1 - out['choice']) / out['n'])\n", " return out\n", "\n", "\n", "pp_probit = predict_psychometric(m_probit, idata_probit, paradigm_grid,\n", " model_name='Probit')\n", "pp_ddm = predict_psychometric(m_ddm, idata_ddm, paradigm_grid,\n", " model_name='DDM')\n", "pp = pd.concat([pp_probit, pp_ddm], ignore_index=True)\n", "obs = binned_psychometric_data(df, n_bins=11)\n", "\n", "fig, ax = plt.subplots(figsize=(7, 4.5))\n", "sns.lineplot(data=pp, x='log_ratio', y='p_choice', hue='model',\n", " palette={'Probit': 'C2', 'DDM': 'C0'},\n", " errorbar=('pi', 90), err_style='band', err_kws={'alpha': 0.2},\n", " ax=ax)\n", "ax.errorbar(obs['log_ratio'], obs['choice'], yerr=obs['se'],\n", " fmt='o', ms=7, color='black', zorder=5, lw=1, capsize=0,\n", " label='Data (binned)')\n", "ax.axhline(.5, c='gray', ls=':'); ax.axvline(0, c='gray', ls=':')\n", "ax.set_xlabel(r'$\\log(n_2 / n_1)$')\n", "ax.set_ylabel(r'$P(\\mathrm{choose}\\ n_2)$')\n", "ax.set_title('Population psychometric: probit vs DDM '\n", " '(model curves at typical stake, data binned)')\n", "ax.legend(); sns.despine(); plt.tight_layout()" ] }, { "cell_type": "markdown", "id": "12", "metadata": {}, "source": [ "As expected, the two PPC bands overlap almost perfectly. Choice on its\n", "own doesn't discriminate the two models. **The DDM doesn't 'lose' anything on\n", "choice prediction by also having to fit RT.**" ] }, { "cell_type": "markdown", "id": "13", "metadata": {}, "source": [ "## Question 2 — What can the DDM say about RT (and the probit can't)?\n", "\n", "This is the structural difference. The probit's likelihood doesn't include\n", "RT, so its posterior has nothing to predict on the RT axis. The DDM does:\n", "its joint WFPT likelihood ties the perceived SNR to first-passage times.\n", "\n", "The classic empirical signature in numerical comparison is the **size\n", "effect**: at fixed log-ratio difficulty, RT decreases with stimulus\n", "magnitude. This is what a Bayesian-observer DDM is built to reproduce:\n", "bigger numbers $\\Rightarrow$ larger posterior log-mean $\\Rightarrow$ bigger\n", "drift $\\Rightarrow$ faster races." ] }, { "cell_type": "code", "execution_count": null, "id": "14", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def add_bins(d, n_stake_bins=4, n_diff_bins=3):\n", " d = d.copy()\n", " d['stake'] = np.sqrt(d['n1'] * d['n2'])\n", " d['log_stake'] = np.log(d['stake'])\n", " d['log_ratio'] = np.log(d['n2'] / d['n1'])\n", " d['abs_log_ratio'] = d['log_ratio'].abs()\n", " d['stake_bin'] = pd.qcut(d['log_stake'], n_stake_bins, labels=False,\n", " duplicates='drop')\n", " d['diff_bin'] = pd.qcut(d['abs_log_ratio'], n_diff_bins,\n", " labels=['hard', 'medium', 'easy'][:n_diff_bins],\n", " duplicates='drop')\n", " d['stake_mid'] = d.groupby('stake_bin', observed=True)['stake'] \\\n", " .transform('mean')\n", " if 'choice' in d.columns:\n", " d['correct'] = d['choice'].astype(bool) == (d['n2'] > d['n1'])\n", " return d\n", "\n", "\n", "def size_effect_ppc(df_data, ppc):\n", " d = add_bins(df_data)\n", " p = ppc.join(d[['stake_bin', 'stake_mid', 'diff_bin', 'n1', 'n2']],\n", " how='left').reset_index()\n", " sim_correct = p['simulated_choice'].astype(bool) == (p['n2'] > p['n1'])\n", " p = p[sim_correct]\n", " return (p.groupby(['ppc_sample', 'stake_bin', 'stake_mid', 'diff_bin'],\n", " observed=True)['simulated_rt'].mean().reset_index())\n", "\n", "\n", "# Size effect needs a PPC on the ORIGINAL paradigm (varied stakes), not the\n", "# dense-grid paradigm used for the smooth psychometric.\n", "ppc_ddm_orig = m_ddm.ppc(df, idata_ddm, n_posterior_samples=60,\n", " progressbar=False)\n", "\n", "sub_obs = (add_bins(df).query('correct')\n", " .groupby(['subject', 'stake_bin', 'stake_mid', 'diff_bin'],\n", " observed=True)['rt'].mean().reset_index())\n", "pp_se = size_effect_ppc(df, ppc_ddm_orig)\n", "palette = {'hard': 'C3', 'medium': 'C1', 'easy': 'C2'}\n", "\n", "fig, ax = plt.subplots(figsize=(7.5, 4.5))\n", "sns.lineplot(data=sub_obs, x='stake_mid', y='rt', hue='diff_bin',\n", " hue_order=['hard', 'medium', 'easy'], palette=palette,\n", " errorbar=None, marker='o', ms=8, lw=0, ax=ax, legend=False)\n", "sns.lineplot(data=pp_se, x='stake_mid', y='simulated_rt', hue='diff_bin',\n", " hue_order=['hard', 'medium', 'easy'], palette=palette,\n", " errorbar=('pi', 90), err_style='band', err_kws={'alpha': 0.18},\n", " lw=2, ax=ax)\n", "ax.set_xscale('log')\n", "ax.set_xlabel(r'Stake size $\\sqrt{n_1 n_2}$ (log scale)')\n", "ax.set_ylabel('Mean RT (s, correct trials)')\n", "ax.set_title('Size effect — markers = data, lines = DDM PPC (90% PI)')\n", "ax.legend(title='Difficulty', loc='upper right')\n", "sns.despine(); plt.tight_layout()" ] }, { "cell_type": "markdown", "id": "15", "metadata": {}, "source": [ "The DDM cleanly reproduces (i) RT decreasing with stake size and (ii)\n", "RT increasing with difficulty. **The probit cannot make this plot at all** —\n", "it has no time axis in its likelihood. Just by including RT, the DDM\n", "gives a quantitative test of a richer cognitive theory." ] }, { "cell_type": "markdown", "id": "16", "metadata": {}, "source": [ "## Question 3 — Do they agree on the cognitive parameters?\n", "\n", "This is the most important comparison for a methods-paper audience. Both\n", "models fit the *same* perceptual parameters ($\\nu_1, \\nu_2, \\mu_p, \\sigma_p$)\n", "to the *same* data. If the front-end is well-identified from choice alone,\n", "their per-subject posterior means should fall on the identity line — and\n", "the DDM's HDIs should be **tighter**, because RT carries information about\n", "the perceived SNR.\n", "\n", "We extract per-subject posterior summaries (mean + 94% HDI) from each model,\n", "join them, and plot probit-mean vs DDM-mean for each shared parameter." ] }, { "cell_type": "code", "execution_count": null, "id": "17", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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parametersubjectmean_probitlo_probithi_probitmean_ddmlo_ddmhi_ddmhdi_width_probithdi_width_ddm
0n1_evidence_sd00.2621100.2029300.3285960.3101500.2490200.3753780.1256660.126357
1n1_evidence_sd10.2629200.1874210.3383720.3201940.2415990.4105580.1509510.168959
2n1_evidence_sd20.3235310.2484310.4060460.3711500.2730550.4906420.1576140.217587
3n1_evidence_sd30.3813740.2993270.4732230.3533500.2729560.4583410.1738970.185385
4n1_evidence_sd40.3324080.2652990.4105180.4418230.3535940.5499560.1452190.196362
\n", "
" ], "text/plain": [ " parameter subject mean_probit lo_probit hi_probit mean_ddm \\\n", "0 n1_evidence_sd 0 0.262110 0.202930 0.328596 0.310150 \n", "1 n1_evidence_sd 1 0.262920 0.187421 0.338372 0.320194 \n", "2 n1_evidence_sd 2 0.323531 0.248431 0.406046 0.371150 \n", "3 n1_evidence_sd 3 0.381374 0.299327 0.473223 0.353350 \n", "4 n1_evidence_sd 4 0.332408 0.265299 0.410518 0.441823 \n", "\n", " lo_ddm hi_ddm hdi_width_probit hdi_width_ddm \n", "0 0.249020 0.375378 0.125666 0.126357 \n", "1 0.241599 0.410558 0.150951 0.168959 \n", "2 0.273055 0.490642 0.157614 0.217587 \n", "3 0.272956 0.458341 0.173897 0.185385 \n", "4 0.353594 0.549956 0.145219 0.196362 " ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shared_params = ['n1_evidence_sd', 'n2_evidence_sd', 'prior_mu', 'prior_sd']\n", "\n", "post_probit = get_subject_posterior_df(idata_probit, shared_params,\n", " hdi_prob=0.94)\n", "post_ddm = get_subject_posterior_df(idata_ddm, shared_params,\n", " hdi_prob=0.94)\n", "joined = (post_probit.merge(post_ddm,\n", " on=['parameter', 'subject'],\n", " suffixes=('_probit', '_ddm')))\n", "joined['hdi_width_probit'] = joined['hi_probit'] - joined['lo_probit']\n", "joined['hdi_width_ddm'] = joined['hi_ddm'] - joined['lo_ddm']\n", "joined.head()" ] }, { "cell_type": "code", "execution_count": null, "id": "18", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def scatter_panel(ax, d, par):\n", " ax.errorbar(d['mean_probit'], d['mean_ddm'],\n", " xerr=[d['mean_probit'] - d['lo_probit'],\n", " d['hi_probit'] - d['mean_probit']],\n", " yerr=[d['mean_ddm'] - d['lo_ddm'],\n", " d['hi_ddm'] - d['mean_ddm']],\n", " fmt='o', ms=7, capsize=0, lw=1, alpha=0.85,\n", " ecolor='steelblue', mfc='steelblue', mec='steelblue')\n", " lo = min(d['lo_probit'].min(), d['lo_ddm'].min())\n", " hi = max(d['hi_probit'].max(), d['hi_ddm'].max())\n", " pad = 0.05 * (hi - lo) if hi > lo else 0.01\n", " ax.plot([lo - pad, hi + pad], [lo - pad, hi + pad], ':',\n", " color='gray', lw=1, label='Identity')\n", " ax.set_xlim(lo - pad, hi + pad); ax.set_ylim(lo - pad, hi + pad)\n", " r = np.corrcoef(d['mean_probit'], d['mean_ddm'])[0, 1]\n", " ax.set_title(f'{par} (r = {r:.2f})')\n", " ax.set_xlabel('Probit posterior mean (± 94% HDI)')\n", " ax.set_ylabel('DDM posterior mean (± 94% HDI)')\n", " sns.despine(ax=ax)\n", "\n", "\n", "fig, axes = plt.subplots(2, 2, figsize=(11, 9))\n", "for ax, par in zip(axes.flat, shared_params):\n", " d = joined[joined['parameter'] == par]\n", " scatter_panel(ax, d, par)\n", "plt.suptitle('Per-subject parameter agreement: probit vs DDM',\n", " y=1.01, fontsize=13)\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "id": "19", "metadata": {}, "source": [ "Each dot is one subject's posterior mean from each model, with 94% HDI\n", "error bars in both directions. **They land on the identity line, with strong\n", "positive correlations across subjects**: both models recover the same\n", "underlying cognitive structure, as we'd expect from a shared front-end.\n", "\n", "The vertical error bars (DDM) should be visibly shorter than the horizontal\n", "ones (probit) — that's what we examine next." ] }, { "cell_type": "markdown", "id": "20", "metadata": {}, "source": [ "### How much tighter is the DDM?\n", "\n", "Direct comparison: for each (subject × parameter) cell, what is the ratio of\n", "the DDM HDI width to the probit HDI width? Values $< 1$ mean the DDM gives a\n", "tighter estimate; values close to 1 mean RT didn't buy much; values $> 1$\n", "would be surprising (RT *hurting* identification — e.g. a posterior trade-off\n", "between $\\nu_k$ and the new $a$/$t_0$ parameters)." ] }, { "cell_type": "code", "execution_count": null, "id": "21", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " mean 50% min max\n", "parameter \n", "n1_evidence_sd 1.095150 1.086645 0.598191 1.703178\n", "n2_evidence_sd 1.315364 1.288755 0.724153 2.392691\n", "prior_mu 0.961085 0.921760 0.542420 1.865235\n", "prior_sd 0.865149 0.865481 0.598325 1.192231\n" ] }, { "data": { "image/png": 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lSYHwhnD/17/+pSyA8Dd944036kzP4zNgjcSsgA4+bSrwOfhc+HRjkITPwncCqy+oqKhQzxhAgB9++EFZm82zE/jtIGiRcQKDGFjOYVFGdpRTTz01rEBDBGUCBEAGctO54IILlMjGM9x+0I5R4AfuMshCokUa2ivOD78P3EwAroP9999f7d+YbUa3baSxwwATMQcYlCALDKynGFi1JHAB0u0Vg2OIVQwSMbsCVzOza0a425vjChBIjcGX2W8+GPW1TfQdGIzBBQ4BvOgTcL1j5i1cH3V8FgZb6BvQvmBUQD+GNoDPgSsfZvXQ5hDXgkFWfWAmBN+L2b+ckKaGwpuQNgLSe2FaGxYaCEDcBGGZw/RpODdADVJmwZIGH2/cbCGYEBymRQeA3zcyMkBgwAIOayfEI3I5G4HoQeYC+C3DcobXKMQRKLdyKOeCGyaEPkQ8hDq2x00fU904bjxwI8X7IfJg/W2IZdAKWN21OIYwg+jEdDe+D6RAbCg4fgTrGdPsYVYAwtvKHcgI/PTx/aCoEqyCmPaHG5HORw0gMCBs4IuMPMfmAMbGgsERKvjBJQbfNUQyrKQQsHCXQZYICFUMIiDQMWPx2Wef+e1DZ/7AeejBA8QqfmP8nuGAtoKBGM45kPCGVRvVX9GOcOwQmhgM4Fx0LnjjoADbGl2g8Fshi47590EhJZwHBCtmhdBG8H0j605Lz8bgN8AD4JrDbAoEIlxYrNyywt3eCM4R/t3htq362iYG9xD0yDSD9fg+MdjBcYabvg8zdwiaxKAdvxOuWbRHtAFk08HsHAZZ6Osg8kMB1ni0AcR3ENKc2DyhRswQQkgAIBhhcdJWNkKaCmTZQEAwRH592UNI2wXWaViejRZluBBhVgOxEbowU2uAGQHMcsHfP5IqypK2CX28CSGNctlAyjlYdBtilSekPiCIYGV/++23+WW1Y+DmAuvza6+9pmaFvv76a2XxxkwcZlBaE8zMIZ89RTdpCehqQghpMAiGhC8vfGVRzZCQpgauK3BPgK8u3Bl0fAJpX0B0I24Drj4IZMTsBVx+kOq0NX9TZO2BKxuKgRHSEtDVhBBCCCGEkBaAriaEEEIIIYS0ABTehBBCCCGEtAAU3oQQQgghhLQAFN4hBpDhQQghhBBCSENhVpMQMJa0JoQQQgghpCHQ4k0IIYQQQkgLQOFNCCGEEEJIC0DhTQghhBBCSAtA4U0IIYQQQkgLQOFNCCGEEEJIC0DhTQghhBBCSAvAdIKEEEIIaVO4XC6pqalp7cMgRGJjY8XhcDTZN0HhTQghhJA2gcfjkdzcXCksLGztQyHER3p6unTt2lVsNps0FgpvQgghhLQJtOju3LmzJCUlNYnQIaQxA8Hy8nLZtWuXet2tWzdpLBTehBDSSKqdLomLabqpSEKi1b1Ei+6OHTu29uEQokhMTFTPEN9om411O6HwJoSQBpBbWC7z1+bJH1vzpdrplrgYuxzQK1PGD+wiXdOT+J0SEibapxuWbkLaErpNoo1SeBNCSAuzYste+WjBJnG5Pb5lEN9LNu6R5Zv3yrRxfWV4b1rsCGkIdC8hkdwmmU6QEELCtHSbRbcRLMd6bEcIIYQYofAmhJAwgHtJINGtwfoFa/P4vRISpUyaNEmOPPJIKS0trbPu5ptvlnPOOafB+16wYIEMGjRItm/f3sijJJp169bJDz/8IC0BhTchhIQBfLpDYUWI2xFCIpOcnBx58MEHm3y/Bx10kPz8889NkmGDeLn00ktlxYoV0hJQeBNCSBjZS+DLHdq2bqlxhbYtISTy6Nmzp3zwwQfy008/Nel+4+LipFOnTk1a1IW0HBTehBASIkgZiOwloW1rl1gHu1hCopWTTjpJJkyYILfffruly4kGKRTvvvtumThxohx44IHyl7/8RRYtWhSyqwncWt5880256qqrZMSIEXLEEUcowb906VI55ZRT1LIzzzxTtm7dqrbH+/D+Tz/9VB0jPvP000+XJUuW+LnDXHnllXLhhRfKqFGj5MUXX1TL4Y6BbWF1P+yww5RFv6qqyvee6dOn18nLPmTIEJk3b556vWTJEvnrX/+qPhOuODhv43cT7rmAvLw8ueaaa2TMmDEybtw4+fvf/y6bN2/2O5cbbrhBHnroIfV7YB+XX3657N692/eZO3bskGeeeaZRLkChwrsCIYSEAVIGhsLwELcjhERuJoz7779fiouLZcaMGQFzl0PcQmhDGH788ccyePBgOf/888NyfXjsscfk8MMPl88++0wJ2rvuukvuvPNOJTrfeustJTIfffRRv/fg2C655BL1mf369ZMLLrhAtm3b5ls/Z84cOeSQQ+TDDz9UAv2bb76Ryy67TA0QsOzee++VL774Qq6//nq1/amnniq///67bNmyxbePTz75RLp06aIE8erVq9V5HXrooWo5jmflypXq/FGopiHnguI2EMv4HrEOoj0jI0MNDiDINThODHCwDQT24sWL5YknnlDrZs6cqapS4jiefvppaW4ovAkhJAyQp9thD55aCuvHDezC75WQpmLYMOvH+vXe9XgOtI3mq6+s15900r5tnn028PsbQPfu3ZW1FeLOyuUEvtoQnxCb48ePl/79+8sdd9whAwcOlP/85z8hfw4swxCbcG8599xzxel0KkGKfQ4fPlyOP/54Wbt2bR2/5hNPPFF9JkQ0BOv777/vW9+hQwe5+OKLpW/fvsqfHFbvyZMnyxVXXKGE+lFHHaUE8ddffy0bNmyQgw8+WH0+LOka/H3yySeL3W5X5zNhwgRlbe7Tp4+yUOO8ly9fLr/99luDzuXzzz+XgoICtR8MWPC9YUCRkpLidy54fc8996hzhajHMUF8g8zMTOW2g1zdKA3f3FB4E0JIGKA4DvJ0BxLfWI71LKJDCAFwjQjkcgIBmZqaqgSj0VIOUbpmzZqQv0CIY01CQoJ67tGjh29ZfHy8VFdX+70HQlkTGxsrw4YN8xPnvXv3rnOscDsxMnbsWPWMY8Vxwx1EC+/s7Gz1HiwDq1atkl9++UW5qegHLOkAwr0h54J94jvFueh9jh49Wrm4GPeJc8E5avCd64JNLQ0rVxJCSJigOE6nDokqZeAKQ+VKuJfA0k3RTUgTs3Jl8PUDBtS/zbHH1r/NFVd4H83gcjJ16tQ6LidwsbAqzuJ2uyUmJnSJZrUtrMzhvAefaXyPFr3BjhUuHsZ9wd0ErhxwOYF7B4SwFtLY/9SpU5UPthlYnRtyLtgn9v/888/XWWesgIqA1LYCLd6EENIAIK5PPriv/Ou00XL79NHqGa8pugkhVi4nN954o3I5MQZOIsgRPuBmNxC4QQzAYKIZMfqQw4IMlxdYvQMBq7x2z9Doc4ELhz5PWJ+//PJLmT17thLimv3331/ly4b1WT8g3DEYQerFhoBj2rlzp7Jg633iGOB6snDhQmmLUHgTQkgjYfYSQkgoLicIVjQGMCLQEOL7uuuuU9lK4B6BTB8Q4uedd16zfqn//ve/VZYSfOatt94qFRUVyrc6EBdddJHy53722Wdl06ZN8v333yvfcPh6a+ENpk2bJu+++67yvZ4yZYpvOYIXs7OzlQ/7+vXrlW83AjOxL/h8NwS4qsAXHRlYli1bps7llltukR9//FEJ/VBJTk5WmVD27NkjzQ2FNyGEEEJIC3DfffcpkWd0q3j11VdVyj2k0Pvzn/+sRPdrr70mI0eObPaBAKzNEMq7du1SGUE6d+4ccHsENSKbCKzZcBlBYOUJJ5ygBLyRY+HSIyLHHHOMskRrcD6vvPKKOj98JjKqIIAS599QVxDsH5lKOnbsqAJBTzvtNJUaEIGc4QhvBG9iEILBQXNj8xhzuJCg0zGIpiWEEEJI01NZWamsn/DZNfsXk6YDebyPPvpoeeONN1SaP9KybZMWb0IIIYQQQloACm9CCCGEEEJaAKYTJIQQQgiJEpATO5wc4aRpocWbEEIIIYSQFoDCmxBCCCGEkBaAwpsQQgghbQYmWyOR3CYpvAkhhBDS6sTGxqrn8vLy1j4UQvzQbVK30cbA4EpCCCGEtDoOh0PS09NVMReQlJQkNputtQ+LRLmlu7y8XLVJtE200cZC4U0IIYSQNkHXrl3VsxbfhLQFILp122z3wruwsFAef/xxVaqztLRUBg0aJNddd52MGTPGcvt169bJI488IsuXLxe73S5jx46Vm2++Wfbbbz/fNpMmTVIlQ42gvClKnRJCCCGkbQILd7du3VTp8pqamtY+HEIE7iVNYeluM8L72muvlb179yrxnZmZKW+//bZcdNFF8tFHH0n//v39ti0oKJALLrhAie233npLqqqq5KGHHpKLL75YPv74Y4mPj1fifefOnfLiiy/KsGHDfO9l+VlCCCGkfQCh05Rih5C2QqsGV27ZskV++eUXufPOO5WFu1+/fnLbbbdJly5d5LPPPquz/TfffCMVFRXy4IMPyv777y8HHHCAsn5v2LBBlixZorZZu3at8skZNWqUdOrUyfdITU1thTMkhBBCCCGkDQjvjIwMeemll5SANk4zQTgXFRXV2X7ChAny7LPPKsu2Gb09qjFBaKelpTXz0RNCCCGEENJOXE0gjidOnOi37IsvvpCtW7fKYYcdZlnmFA8jcCmBEIf7ibZ4IxL6qquukqVLlyr3lWnTpsm5556rfMIbG9lKCCGEEEKIEWjPduHjbWTx4sVy6623ytFHH60CJOvjjTfeUD7ht9xyi3Ts2NEXfFlSUiJTpkyRK6+8UhYtWqSCKmERv/rqqxt8bAjyyM7ObvD7CSGEEEJIZDJ69OiQtrN52kiJKPhvX3/99TJixAhlxQ4WDIlDfvLJJ+X555+XSy+9VAVoGgUygi5TUlJ8y+DOgm0h7Bti9V6xYoX6zAEDBjTgzAghhBBCSCTTrizeyFBy//33y+TJk5V1Oi4uLuC2ENawcCP48sYbb1QZUMxpX8yVhQYOHKjcRGD1hl95Q4DveahfKiGEEEIIIW2uZDxcRe69917561//Kv/+97+Dim4Asf3ll1/KY489Vkd0u91u5aIC67bZYp2VldVg0U0IIYQQQkhjaVWL96ZNm+SBBx5Qlm64jCCftwauJrAw5+fnq1SAeI3c3rNnz1bi++CDD5bdu3f7ttfbHHvssfLKK69Inz59VB7vefPmqddIU0gIIYQQQkhr0ao+3i+88II88cQTlutOPfVUFRyJQMsZM2aozCQXXnihyvtthd7G6XTKyy+/LB9++KHk5uaqLCh43+mnn97g44TFHAwfPrzB+yCEEEIIIdFNmwmubMtQeBNCCCGEkHbv400IIYQQQkg0QOFNCCGEEEJIC0DhTQghhBBCSAtA4U0IIYQQQkgLQOFNCCGEEEJIC0DhTQghhBBCSAtA4U0IIYQQQkgLQOFNCCGEEEJIC0DhTQghhBBCSAtA4U0IIYQQQkgLQOFNCCGEEEJIC0DhTQghhBBCSAtA4U0IIYQQQkgLQOFNCCGEEEJIC0DhTQghhBBCSAtA4U0IIYQQQkgLQOFNCCEhUO108XsihBDSKGIa93ZCCIlccgvLZf7aPPlja75UO90SF2OXA3plyviBXaRrelJrHx4hhJB2BoU3IYRYsGLLXvlowSZxuT2+ZRDfSzbukeWb98q0cX1leO+O/O4IIYSEDF1NCCHEwtJtFt1GsBzrsR0hhBASKhTehBBiAu4lgUS3BusXrM3jd0cIISRkKLwJIcQEfLpDYUWI2xFCCCEU3oQQYpG9BL7coYDtalyhbUsIIYTQ4k0IIQbiYhwqe0koYLtYB7tRQgghocE7BiGEmEDKwFAYHuJ2hBBCCIU3ISTiaUjhG+TpdthtQbfB+nEDuzTiyAghhEQbzONNCIk4Glv4BtsgT3eglIIQ3VjPIjqEEELCwebxeILnzCKyYsUK9S0MHz6c3wYh7bDwjVkwh1r4BgIeKQNXGAQ83Etg6aboJoQQEi4U3iFA4U1I+wBC+cWvVwXNwQ3xfemfhoYtnJG9hIGUhBBCGgODKwkhEUNzFr6h6CaEENLuhXdhYaHccccdcsQRR8ioUaPkL3/5iyxatCjg9gUFBXLdddfJ2LFj1eP222+X8nL/ss1ffPGFTJkyRbmGTJ06VebOndsCZ0IIaW1Y+IYQQkhbptWF97XXXivLly+Xxx9/XGbOnCnDhg2Tiy66SDZs2GC5/T/+8Q/Ztm2bvPbaa/LUU0/JL7/8Infffbdv/fz58+WGG26Qs846S2bNmiWHHXaYXHHFFQH3RwiJDFj4hhBCSEQL76qqKtmzZ484nc4GvX/Lli1KON95550yZswY6devn9x2223SpUsX+eyzz+psv3TpUvntt99kxowZSqBPmDBB7rnnHvm///s/ycvzTh2//PLLMnnyZDn77LOlf//+ctNNN6ltX3/99cacKiGkjcPCN4QQQiIunSDcNj755BNlWd67d69aZrPZJCsrSw4//HA5/vjjlZU5FDIyMuSll16SAw44wLcM+0KilaKiojrbwwWlU6dOSlBrDj74YPWexYsXy3HHHSdLliyRm2++2e9948aNkzlz5oR7qoSQdgZSBi7ZuKfe7Vj4hhBCSJsW3gsWLFCW5jVr1shBBx0kJ5xwgnTv3l0SExOluLhYcnJylOj9+OOPZfDgwcoP+9BDDw26z7S0NJk4cWId/+ytW7daindYtbt16+a3LC4uTtLT09Xn4zjg7921a1e/bTp37qzWE0IiG+TpXr55b71ZTVj4hhBCSJsV3nDn+Oabb+Tcc8+VF198UbmCBGLXrl3y/vvvKxePY445Ru66666QDwZW61tvvVWOPvpomTRpUp31FRUVSmibiY+PV24vlZWV6rV5G72+McAKbw7iJIS0LdLiRKaM3E8+XbwtYB7vKSN7qu14PRNCCGkqkpKSmk54p6amyldffaWs2/UB6/KVV14p559/vnIjCRUI++uvv15GjBihAi2tSEhIkOrq6jrLIapxwhDYwLwN1ody7MGoqamR7OzsRu2DENIygStH9BDJ3lUtm/OrxekWibGL9MmMkyGd48VekiPZ2ZwBI4QQ0nSMHj266YT3NddcE/YBpKSkqIwlofDWW2/J/fffr4IiH330UUurNoALCQS6EYhspCSEFR4uJxDgsLobwWuz+0m4xMbGyoABAxq1D0JIy3FI7TML3xBCCGm3wZVwN0EWEmOAo2b16tUqld+nn34a8v7efvttuffee+Wcc85RbiZ2e+BEK8jbDWGObCi9e/f2+Z4D5ABHkCWekflk+vTpvvdhm1BHIoHAvkOdRiCEEEIIIaRBwhvZRODjDCBqFy5cKPn5+XW2+/7771WO7VDZtGmTPPDAA8rSfemll/qypGi3EghdfA5cXfAabigQ1rDAw3ccPpoYBJxyyik+v/MLLrhALrnkEhk6dKgqyvPhhx8qFxFY1AkhhBBCCGktbB6tqIOA9HwoRgOrbyD0bk488URllQ6FF154QZ544gnLdaeeeqryFUegJbKpTJs2TS2HOEfBnJ9++kn5dCOF4C233OLz7wY41ueee05yc3OVewis8Mj53VBWrFihnlEJkxBCCCGEkGYT3iUlJcpqjE3PO+88VeLd7O8MFxGkB9x///2DCvT2CIU3IYQQQghpEeFtBK4mqASZnJws0QKFNyGEEEIIaREfb7huoNANKk3u3LlTPYIBn2tCCCGEEEJImBZvVKJEUZwDDzxQ/R0MuJlEWr5rWrwJIYQQQkiLWLy//fZb6dSpk+9vQgghhBBCSDMI7+7du1v+jRLuCLxE4ZpARW8IIYQQQgghDSigA3799Vd5+umnZfny5SrTicPhkJEjR8o///lPGTNmDL9XQgghhBBCTAQuExmA2bNny4UXXihVVVUqzzYK2fz973+XoqIiOf/882X+/Pnh7pIQQgghhJCIJ+x0glOnTpWBAwfKY4895rccu7n88stl165dqlpkJMHgSkIIIYQQ0uIW7y1btqiqklbZTM466yxZt25dow+KEEIIIYQQiXbh3b9/f1m1apXlupycHOnVq1dTHBchhBBCCCHRF1xpLJgD/26UjEeJ+OOPP16lGYR/908//aQCLu+///7mPF5CCCGEEEIiu4AOXEk0+i3GZXo5C+gQQgiJdKqdLomLcbT2YRBCItHi/cADD9QR2YQQQkg0kVtYLvPX5skfW/Ol2umWuBi7HNArU8YP7CJd05Na+/AIIZGY1SQaYVYTQgiJblZs2SsfLdgkLnfdW6bDbpNp4/rK8N4dW+XYCCERXkAnPz9fXn31VVmwYIEUFxdLRkaGKpyDPN4dO7LjIYQQElmW7kCiG2A51nfqkEjLNyGkabOa5ObmqnSCr732msTHx8vQoUMlJiZGCfFTTjlF8vLywt0lIYQQ0maBe0kg0a3B+gVref8jhDSxxfuRRx5RQhsVLHv27Olbvm3bNpXx5IknnpAHH3ww3N0SQgghbRL4dIfCiq35cvLBfZv9eAghUWTx/vnnn+Uf//iHn+gGeH3FFVfI3Llzm/L4CCGkzWe3IJH9+yKQMrRt3VLjCm1bQkh0ErbF2+VyKZ9uKzIzM6W0tLQpjosQQtoszG4RPSBlILKXhCK+sV2sI2x7FiEkigi7hxg0aJD83//9n+W6WbNmycCBA5viuAghpM1mt3jx61WyZOMenxjDM15jOdaTyAIpA0NheIjbEUKil7At3pdffrlcdNFFUlhYKFOnTpWsrCzZs2ePfPrpp/Lrr7/KU0891TxHSgghrVz0hNktohPk6V6+eW/QAEukFBw3sEuLHhchJEryeMPijSBLCG4NBPh1112nMp5EGszjTUj7pSndQmb9tklZtutjdL8sBtlFGMzjTQhpFeENq/bIkSMlMTFRNm7cKEVFRdKhQwfp169fxFa3pPAmpH3S1GLpvpmLQ/b1/ddpo8M+XtL2B3FIGbjCMIiDewks3axcSQhpFleTG2+8UW666SblZtK/f/9w304IIS1CU7uFNCS7BQPtIgu0E6QLxIO/LyGkRYIr4+LiVOEcQgiJpqInOrtFaNsyu0Wkw0EVIaRFLN6XXnqp3HHHHbJ69WrZf//9lW+3mbFjxzboYAghpC0XPYFveCg+3sxuQQghpEmE95133qmen3vuOfVs9OuGuzheZ2dn89smhLQazeUWwuwWhBBCWlR4v/HGG436QEIIaa9FT+Dji4DM+gI2GWhHCCGkSYT3wQcfHO5bCCGkxWkutxBkQUFAJrNbEEIIaXbhDVatWiUvvfSSLFq0SIqLi6Vjx44yYcIEueyyy6Rnz54N2SUhhDQp2i2kxukWu93WpEVPmN2CEEJIiwjvefPmyd/+9jdJT0+XiRMnKtGNQjpz586Vr776St555x2WjSeEtImiOaWVNbJtT6mKPUlNjJWM5HiJj3U0qVsIs1sQQghptgI6p59+uiQlJcmLL77ol1awsrJSLr74YklISJBXXnlFGgICNiHs33zzTcv1Tz/9tDzzzDOW66ZNmyYzZsxQf0+aNEl27Njhtx55xx999NEGHRcL6BDSfovmVNW4pKCsSkoqalQAeM+sFDlscFcWPSGEENL2Ld5r166VJ598sk4ubwjuiy66SK699toGHchrr70mTz31VNBUhBdeeKGceeaZfstmzpwpL7zwgpx33nnqdWlpqezcuVMNDIYNG+Z3fISQ6CuaAws3rNpd00XcHo+yUNdXaRBZURCgSQghhLSq8O7WrZts377dcl1+fr5kZoYXqJSXlye33XabLF68WPr2DZ5LNzk5WT00W7duVQL75ptvlsGDB/sGBrBqjRo1StLS0sI6FkJIZBfNsdtsvqI55tzd2j3lD0M5cARowlecWUoIIYS0SuVKlIuHZfqTTz4Rl8vlW/7zzz/Lv//9b7n11lvD2t/KlSulQ4cOan8jRowI670PPvigKuJzxhln+JatWbNGOnXqRNFNSBQSTtEcv9db9sqLX69SWVB0CkI8L9qwWy3HekIIIaTFLd733HOPVFdXKwEOkZ2RkSFFRUVSU+P1n7zyyit92yKgCRlQggF/bDwa4nf97bffyuuvvy52+77xAyze8EG/6qqrZOnSpcoCD//vc88912+7cMG5lZeXN/j9hJDmBdlLKqpqQtq2wuWSopJS5Xayq6hCPvh1vZ9PeGF5jQrMhGsKrORP7y2VS47eX4b2yGjmsyCEENIegfZsFuENEdsWgE84LOTjx4/3W75u3TopKSmRKVOmqEEAUh4iqBKDg6uvvrrBn4eBBStyEtK2qSwvlVAKVsbYRdavXaP+/nVzmRQVV6u/y6vdkl/uErOzyp4apzw9+w+ZOixN+mbGNcehE0IIaceMHj26eYS30aLdWsDyPGfOHF/5eiOvvvqqVFVVSUpKino9aNAgKSsrk+eff15ZwRtq9Y6NjZUBAwY0+tgJIc3HuIrtsnxL/e4mI3tnypAhPdTfn67/Q1JS4pSlu7i0TBwBgiqr3Db5fY9dxhzQRzp3SGzyYyeEEBL5NKiATmvz008/idvtlsmTJ1sKZDyMDBw4UIl1WL3hGtMQ4DYT6jQCqQuzRJCW4IgDekj2zuKgAZbI3334AT3U9Yx26fbYxOFwSHFJFS50sS61I8oK7habLN9WLCd369hs50AIISRyaZfCGxlQkCrQnLUEYvyYY46R6dOnqyqaRn/wrKysBotu0jCYJYK0NMg+gqI45pSCGnPRHKQMRPYSBFIiz3cw4OuNBwIzzRlRCCGEkHYvvJE1BSkKU1NT/fJwr1692rI6JtxIjj32WFXAp0+fPkqcoyAPXiNlIWm9IiYA4gZZI1DGG+JneG9aDUnTg3bVqUOiShkIkYx2F+OwyYjeHS3zdyNlILKXIJAyGKh8qdtxjcvNipWEEELCpuFpPlqAnJwcOeyww2T27Nl+y1GiHiXrrbjuuutUBc3HHntMBVjC5xuiGxU3SesVMTGC5ViP7QhpDiCuIbIhqiG6nS6PEuHI021ud8jTjewmsGYHAmtQbh7AQs4y8YQQQpqtZPwdd9whEydOlAkTJkSlnzNLxofHrN82Kct2fYzul8Upe9JiMy5mdxPjjAu2f/bLlaq0vJXo7pqRJGmJ3mwmbLeEEEKa1eKN8vAPP/ywjBs3Ts4//3z573//K+vXr2/wh5LIpqFFTAhprRkXiPArjh+mrNra8o3nDklx0rtTqk90Q7TDkk4IIYQ0m8Vbs2XLFpk7d678+OOPsnDhQunYsaMcfvjhcsQRR8ghhxwiiYmRmWKLFu/QQZaI+2YuCXn726eP5rQ9aTMzLtpSDh9us+uJlaWcEEIIabbgyt69e8s555yjHpWVlbJgwQIlwmfMmCF5eXkyZswYJcIvuOCCsA6CRA7GLBH1b0tfWdK6My5m4W0VmIl2OrxXpmVgJiGEENIiWU2QZQR+33iAjRs3KhGOHNsU3tENAtpCsThCzBDS1DMuoQz6gmUngbiGIMeD2UsIIYS0yXSC/fr1Uw+KboIsEUgZWF8RE/rKkrY+48LsJYQQQqImnSBp30VMIK6tMBcxIaSpZ1xCgTMuhBBCWpo2XUCHtF/oK0taC864EEIIiYisJtEKs5o0HvrKkracx5sQQghpCWjxJi0CfWVJe59xQeAmfMgJIYSQZrV4I2d3OIwdO1YiCVq8CYnOGRcU2UGZ+T8M4h0+5HBnYYwCIYSQZhHegwcPFpuhmITxLebleJ2dnS2RBIU3IdEH3VUIIYS0iqtJbGys1NTUyNChQ+WEE06Q4cOHN/mBEEJIeys7D3cWWr4JIYQ0qfCeN2+ezJkzRz7//HN54oknZL/99lMCHI8BAwaE/GGEENIegHuJleh2uz1ir02TifXwITdXvySEEEKaLKtJYWGhfPnllzJ79mxZtGiREt4nnniiTJkyRXr06CGRCF1NCIku7pu52FeEp6rGJQVlVVJSUSNuj0fsNpukJsZKRnK8ev7XaaNb+3BJC8DgWkJIq6cT3LVrlxLhX3zxhSxfvlwOPPBAZQU/55xzJJKg8CYkugTWfTOXqL+LK6olt6BcrDpJ2L27ZiTJI+dOYNaeCIXBtYSQNpnHe+/evfLmm2/KK6+8Ii6Xi8GVhJB2b/GGhXvL7hJL0a1x2Gzy+AWH0M87AmFwLSGkTeXxzs/Pl6+//lpZu+FyEhcXJ8ccc4xyOSGEkPYMUgbOXrI1qOgGKYmx9POOQBhcSwhpE8K7oKDAT2zb7XY54ogj5JFHHpGjjjpKEhMTm+1ACSGkpUCe7vd/2RB0G7iawM8bBXoYYBkdwbVGGFxLCGk24f3BBx8osf3bb7+pPN2HHnqo3H///XL00UdLSkpKgz6YEELaasBcZkq8dE5PrNe/Oz7WoYIwG1qgh7RNUDApFDjoIoQ0i/C+/fbbxeFwyKhRo5Q7SYcOHVSxnG+++cZy+1NOOSXsAyGEkNYKmBvcPV0OG9LN56sNMZ6VmiDxMY6AGU0gur3b2im6I2wwpjPa1L8tB12EkGZyNUHQJErH11c+HhZxCm9CSFsPmDOmCYTl8qP5m+SwIV1l2vh+SoDDz3vJxj3q767p4hPeZob3ymyVcyHNAwZdGEyFIr456CKENIvw/vbbb8PeMSGEtNWAOas0gS6PR+auypFte8vkrMMGKD/v5Zv3+nx9rUS3w26TcQO7tOBZkJZAD7qCgWJKHHQRQppFeHfv3j3sHRNCSFsMmIOlO5DvNpbtLalUAv3SPw2VaeP6BiwdD9GN9SwZH3mYB10a4yyJysRr87YZbM92QAhp8qwmZWVlqmDO4sWLZc+ePcqtpEuXLjJu3Djl+x0fHx/O7gghpMUD5iCcguWrgKgyZqzo1CFR/b3C4BMOSycs3RRbkQl+V/OgyzhLooNrMQsCyzhEOrYf3rtjax86ISRSCuh89913cuutt6qS8UgjmJ6erpYXFRUp/++srCyZMWOGHH744RJpsHIlIZFTjXJdTpHy1w7G/t06SEKso045eGYviT73JAy6Fq7fJetzi5WxyRxca5wBwSwJB2OEkEZbvJctWyZXXXWVDB06VB5++GGVThBZTkBVVZXMnz9fnn/+ebniiitk5syZMnDgwFB2SwghLRowV1njqld0w4qJh85YAduETjnIlIHRBUQ0Zj1Ui6ltF4FgXm9CSJNZvC+//HLZtWuXvPvuuxITY63VYfX+61//Kv369ZMHHnhAIglavAlp/8z6bZNyC6jP4t0hKU5ZNOFa0D0z2edegoA7+vJGJ/fNXBxylhPzLAkhhBgJqeLD8uXL5bzzzgsougEs4Geeeaby/yaEkLYGRDPcAeAqEAhbrVV7y+4SZcHUYgvPEO0vfr1KpSQk0UND8noTQkijhDf8uLt27Vrvdj169JC8vLxQdkkIIa0SMNcxNUEJbDNYlpkar7KaAFi9zUCMI+AOvr8kutyUQtuWxZQIIcEJqTdxOp0hZSyJjY1VPt+EENIWQdaJq08YLhOH7ieOWn9d+O3CvaR3p1RxurwuKLocfDBfXhI9wM0oFJjXmxBSH6EN41uI5557Ts4555yg23z88ccyaNCgOo8tW7b4tvniiy9kypQpMnz4cJk6darMnTu3BY6eENJeLN+XHTdMHr/gEDnjkP4yrGeGWgYXlBi7TQnwtMS4oH7gSC1Ios9NKRgspkQIadI83j/++KNs3Lgx6DZbt26VhvLaa6/JU089JWPHjg263Zo1a+Tggw+Wxx9/3G95ZqbXIoEMKzfccIPcfPPNMmHCBJVlBdlWZs2aJf3792/w8ZHm8Z3U2SIIaRXXk/H9ZFptmkDEmd/+zkJfgRRdIt4qfZz25WWWk+jN622ExZQIIU0uvJ999tmQtkOe03CAT/htt92mgjL79u1b7/Zr166VwYMHS6dOnSzXv/zyyzJ58mQ5++yz1eubbrpJli5dKq+//rrcc889YR0baXrgG4sKgihmwmwRpK0AAY2gyW17SlXpeA3Ed1F5tRSXVyv3E1jCAX15o9NNicWUCCEtIry//fZbaS5WrlwpHTp0kE8++USJ+x07dtRr8T722GMt17ndblmyZImydhtBZc05c+Y06XGT8IGwMVuMdLYIVn4jrTmjggEh2mZKYqwS2hpVFby2LDiqFsbHOJTlm7680Z3XGw/OeBBCmk14d+/eXZqLSZMmqUco5Ofnq1L1CxculDfffFNV0RwxYoRcf/31ylpeXFws5eXldTKwdO7cWXJycprpDEg4wsZqmtaYLQIWJVZ+Iy09o4L3oA3CpaSwrEq9F4GW+GcTm8Q4bMoqDjcU5PZGuXgS3dDNiBDSrK4moKysTL788kvlFgIBDLeSLl26KIvyMcccE1Lmk8YANxOdM/yhhx5SIhsBmWeddZZ8+umnKvsKiIvzTgdrcFyNzbYC/098HmkYc//YLtU13t8nEC6XyE9/bJcTRvXg10zCZuX2Qvl08Ta/wV2FyyUL1+XJkg27ZOronjKsR7rle5dt2i0ul1sqqmvUs8rFXLsbiO8aJx7wAxf526T9JS1O2B8QQgjxkZQU2LjTIOH93Xffya233qqszHa7XdLTvTewn3/+Wd5//33JysqSGTNmyOGHHy7Nxfjx4+W3335TrikauKccddRR8tFHH8n06dPVsurqfVPFAKI7MTGxUZ9dU1Mj2dnZjdpHNLMgu0BCqUExP7tU+iWWtMQhkQiioNwpn2eXSIAJFcXbP6ySE4akSkaSf7cHy3ZBYbES23klLqW3Y+2YhfHOxBgD6Fwup+TnbJXsEs6gEUII2cfo0aObTngvW7ZMrrrqKhk6dKg8/PDDcuihhyqrsxa1yCTy/PPPq+whyCIycOBAaS6MoluPMHThHgwG8Brl7Y3gdSgFgOrLUT5gwIBG7SNagaUwYfUfIW8/YOAgTuOSsPhsyXZJSg6iumspsGXIIUPqzqhkbPpDtu8tF0eM/6Bdz3bpoHFkOSl2ZMrhFvsghBBCmkR4v/TSSzJkyBB555136pSNhxvHxIkT5bDDDpO//vWvKi3gAw88IM3B22+/LU8++aRKbZiQkKCWlZaWyubNm+W0005TN8dRo0Ypq7i2foMFCxaEPBIJBPYd6jQCqUtifGxIZZfhk9shNYVfIQmLtbklPmNAMNbklsh0i+t4ZN9OsmbnBsusTMZlaUlxAfdBIg+mPCWEtEoBneXLl8t5551XR3QbwU3vzDPPVP7fTYXL5ZLdu3dLZaW3hDNcSmB9uvHGG2XdunWyYsUKZYlHDu9TTz1VbXPBBRfI559/Lq+++qps2LBBWejhIoLjJ60HK7+R5hRHoQzq3G6PL/+2mdH9slTfEgzIbwRfBtoHiZwA3Vm/bZL7Zi6W+2YuUc94jeWEENIiwruoqCgkVw3t8tFUIBMJLOmzZ89Wr7t166bycSPI8y9/+Yucf/75kpqaKm+88YbPAo7tYXGHdR5iHG4wL7zwAovntDKs/EaaC6QMxEyJFVU1LskpKJN1OUWyLrdINuQWyeeLt9QRUT2zUqVnVooS11bYDGXkmcM7slOevvj1KpXiVA/mdMpTLMd6QghpDDZPfWYeEVWwBgGUBx54YL2WcVi9Iy0IEZZ1gBL0pGnzeJsrv6FIBSHhAoskxJG/4C6X/NIqv5SAmSkJ0qNjsmV7wz7mrcmrt3IlrOPI40wiCwzGIK4DpTwFaDeX/mkoU54SQlomnSAhjYGV30hzzqigCBNEU3FFtezYWyYV1U6dEVCJb2QvKamsluKKWFWB0pw3Xu8DArtruviEt1l4MYd3ZKJzuQcD6xeszePAixDS/MIbAY0bN24Mus3WrVsbfiQkKmDlN9Jc7QoW7Ld/Xq8qTMIH2yihIJ8hqCGkjRUojSJK70PPyliJbqxngafIBEWXQmHF1nwKb0JI8wtv5MsOBausACSyaKpIf1Z+I009ozJk8x7ZW1IppfleH25j1Um73ds3QZDDnQQC2iyiOCsTnYQaoOvd1htcy/6LENJswvvbb79t0M5J5NDQUtyEtCRbd5dK57REKSqvVlUmA9kB4MMNdxIrEcVZmegN0A015SlFNyGkWYV39+7dG/wBJDKDInWkP3xiGRRJ2pLVEpZtuIn4O5v4A/9tPBJiHUFFFAVW9ABDgjFANxDDe2W2yPEQQqI4nSAK44SbqQSZQJDyj7R/S3egTCQAy7GeOW5JW0oriEwkwYAwx6M+EQUxb/U3iTyY8pQQ0mYs3ueee65cfPHFcsABB8hJJ50kkyZNksTExDrboYrkzz//LO+9956sWrVK7rzzzuY4ZtKCMNKftEerJdL/FcPdJMB2EOaBMpQY3argkoIsKQCZUPA+ulhFJubgWjMMriWEtFgeb5Cfny/PPfecfPjhh+J0OmXAgAGqYA4EeHFxseTm5qpqkqhuiXLtf//73yUrK0sigWjO442qbaH6Pf7rtNEtckyEhJKLGYIZGUzMHRzcvvt2SZOzDhuggimNwcLararG6ZbSqhq/9+siOhDgzDsf2W0I2W5WGOJZMDOCQRrjWQghLSa8NQUFBfLVV1/JggULZNu2bVJSUiIZGRnKD/zQQw9VZd3xOpKIFuFtzlaC1yiZHCq3Tx9Nn1jSpmISUEjHWBDHYbPJ4UO6yWFDu8qG3GK/YOHM1HhZvGGPlFc5xelyqzzgMQ67ekBoA/y/d6dUlYqQxVQiH2YvIYS0uvCORiJZeNeXrYQWbxIJVssYu02160MGd5XdRRV13AlgHd+8q0QJbohqrIPoMuYAhwAHHZLifJZPVrEkhBASDqxcGcWEkq3EKtLfqqJfQyP9myonOCFGIIzhGoCW/fuWfCWoV20vkN+WLJcF2VtlQL++0q1bN7UtrOJwKUFlS0/ta2WOqG3iehlqFMDKrVMRAhZTIYQQEg4U3lFKqNlKpo3vq0Q4pt+NU/YQ3gg0QxBbUnxMWGW0mROctNagcsGGfPEkZsq6Tdt8whvtGpt5Zbco0W0eXGINxLvD7vClIsR6FlMhhBDS5OkESXRkK3GbXmP9xtxiGdGno2zdU6qKkkBwqG09HvUay7E+1KAjCCIEv8GKroM2tZUdy7GekOYaVNpiE1RVHXtyhrJiAwwmobFR5dK7kVd8m4HwNqYiBCymQpoCpqokJHqgxTtKgU83MAefGS3Z8GtduH6XxMY4pFdWSp3tUhJiJTMlXlnEQ6lgGaqVvVOHRGYPIE2eAhPt1mazi3hcSmSjPXfukOgbTKK0fI3L+zd0NcJf4F6iwRosS02K8y1jMRXSUDjzR0h0Qot3FFf4Q0DZlt0llpZsLMf6XcWVKsgMIhzCGgI8rbY4CdbD4r0jv0zmLN/epDnBCWnsoNIMBosez77UmBhEGq3XqFKpZbZxuQavsAyDUhAoDzgh9cGZP0KiFwrvKATBjG632zLHsQbLsR5FSLQA0UK9uNbqbRTqXyzZWq+bSCBBZAYBa4Q0ZlAZCFdVme9v7autq1yi1DwGmGjtsTF2398aZDVBHm+dShDBx8zrTMKF1YAJiW4ovKOUuFhHQNGtUcbpWuWhMz8Eeo/L45GZ8zcGLB1fnyDy39btS+VGSEPLxlvhLC/2OXBrqzYs2DaDuEawMFyosK/EuBiJc9jV3306pUpWaoJKIXjpn4aq4juEhAtn/giJbujjHaVA3EJsBBPfdsMG8IcNvi2m8UW5iZx8cN+AgijUKpiY9iekIVilwNR4XDVSUZAryVndfZZu5UaVkaQGlqBbZrKqTglgEUdbhHV7cI8MtkvSaMKZ+bPqSwkh7RuqmygE1mcIZYgNfy/WffjKYyfFKfEBf1gQqN6SFjHB3EQgiEKBAWvEqs2GCgJ9daVJI9o9qqaiVKr2bpfDh3T1WcdhyT5hdG85YXQv9TfAurH9O/ms2xwMksbCmT9CCC3eUYi2PsOqFx/jCJrVBJlLamrLZyOdmvY+MZbSxmsdcBYsrzEEETKgBAuwZMAaCTXrQ6DiS1gHCzUy5Jjzzydk9RIpKZCCFXPknIk3qu2t2itLhZPmoCln/vRglAXICGlfUHhH+XS8zlaCSnxWFSm7ZyTL+twiX1U/4KkVJrq8ds+sFPVc383CKIisxDcD1kh9BXDmrcmTL5duU4ND5X9tEOPaLxvAQp1fWinv/LzBb1CJ4MrqsiLpMGCs+oxAlmxat0lruELVN/OHwejXy7ep66CwrFotS0+OkwmDusqfRvRgsC8h7QAK7yjFyvpsFt0Q11v3lkpyQqx07pAg+aVVtQLco/IgI+8xxDKs5qG6iUDoIE83fMHhllJZ7ZKEOId6H1KzMUsECZT1AVl1dIBvYVmV9O6UqoJ+Zy/ZKu//skE6pycqNxEIm/5d0+SHlTkqT7fO1Y32vWXlXnE7q8WWkBAwZ3wgS3owGvIeEp00dOYPA8VXv18jO/PL/OJt0C/PXrxFlm/aI+cfNYhBv4S0cSi8o5RQrM9d05Mlt7BCve6Ulqim7T2x3qQQRo2OqXzsLxw3Ed8n1u6nvgwrJHqzPqCiKgaBxqw6eM4pKFeCVy/DegwCYU2EVRwDOh0kaR5UGnPGI4CtIcVMWACFNISGzPyhrb398/o6oluDZaingG2uZgEyQto0FN5RjNn6rAWHtj6/8k22b1u/zA8mDYOp/O6ZwfMaa4ug2YUAgkiXjIcVCPtgmrboBuLXXFG1usYrsOECgnzbEOOw9CUn7OvCPIZBIHLLF5Z5lBDXblBWoN3365Jm6dYSrE0GcoVhOyZN0fea+1EMCveWVAY1UGAdtgmUWYoQ0jaweQKlqaiHsrIyKSkpUYVYzOy3334SSaxYsUI9Dx8+XCIZY0AZhPJ9M5fU2SZQifk7po+WHh1TgloEsf2e4krpkBQXUAzB2oMsEnQ5iU7Q7m58c36dnPFllTW+wF60nYrKKtVeXVWmvPEej5Tv2iJJXfqol86KEqku3udPW13t9YsFcXFxYouJE09NlQhKyRs+EaXlfVUuPW7Z8fO7UlOyV66//no5++LL5cWvV9XrKsB2TEKlvmDe+2YulpXbCnyZeQKB/nhYzwz512mj+eUTEikW761bt8q1114rK1euDLhNdvY+SylpPxg7/kDR91bBmNjOLLqtLIK7iipqLZFV3lSFtW4Agab/Q4X+tZEDrNi7Civ8RDdsA0YXEwz+3B5v3nibI1ZsDoPV2+UUNFm3y6X8oWyxSX5i2wi2S07rLDa7vdZ3yga/Fq8It3nE4/Yo4V5VWiC2jv0kb/0qKS0tDasACi2PJBTqy16CWJj6RDfANpU1LmblISSShPfdd9+txPell14qPXv2FDtuWiQqo++136w5oDJQcJwvF7jBH9fK8h1K4Qj610YmELUpibFqgKaxQUAb7NHQHx5UnbTbxWPz5o/3beuIlbSuvVUApT02XlUqiI+P9+Wf1yI8NjFVkjt2E3tMnLhdNWKzx6i/9Qe5nVXicbvEkZou8SnpEh+fINWbF0pKSgoLoJCwaYxxAO9DvAL621As3gmxDmblISSShPeSJUvkzjvvlFNOOaV5joi0++h7K4ug0+11NbHyxzUTLBc4oH9t5AK3JOSELy6v9rN6I2c82sQ+bBITE+O1hhs2RKadxNQkZcDGcrxv/xH7+9Z/8803qn0lZ+0nnTp1Ur7iAHnqjZ9ni4+ThLgYXyEeW+dO8t7K9SpDipULlhX1tWMS2TSlcQDv27qn1G9AagXc/liAjJC2Tdh3hOTkZHXDItETfW9VBTBY3m1dEhkuAbj5rMspkg25xVJW6VTLtCjXFnAzwXKBB7Kma7Ac67EdaZ9V/XQgr7HVQUDr1xjA4W+IZrMBEOsgotEOEuNjVDyBmfiUDPXcLSPJJ+jNrQmvkafeaHVfvGG3zwUrFOorgEIiFxgHEAeAGUPtrqeDb7Ec68MBYr1jakLASsMA67BNqJmlCCGtQ9h3hZNPPlneeOMNccGHkkRF9D2CxEb3y/IJDjzjtS6l7S+cvOIJOZe37C5RFhpt6YY1EiKnsrYKJpZbTZ0Gs9iE419L2hdGUQv/f+TphnDG9LnKF187hQ4RbIXXVdvm8wPH44rjh/m1XbezRtxup1Tl71SfAQshctNbYRTe2A4uUNr6GAq0PEYnzWEcgHHjrMMGyH6ZyZbiG8u6ZyarbRiYTkgEuJrccsstvr+dTqf89NNPMnnyZDnwwAMlMTHRb1vc+B544IGmP1LSaqAjh881Huapc6vp1J0FZcpVwCyQ8D5ddh6iyGG318mvXF8ucG1Nr49Q/MRJ244rgNCG2wlAe4J12mH3qLYDdFpBNCHvw78tIYagf5c0Gd6ro6/tvnrjdOl0xHmSkJCgtoGwR25kK9BO4cqCNorj0K4jDXXBItFBcwXfwshx4ykjZc7ybfKroXJlRm3lysmsXElI5AjvBQsW+L3u2rWrev7999+b9GCee+45mTdvnrz55psBt1m3bp088sgjsnz5chVcNXbsWLn55pv9UhhOmjRJduzY4fe+qVOnyqOPPtqkxxuNGEV3IF/r0ooaVWwHwgliSQOhhGUQTniHOS63vpLx2poeCvSvbZ8YRa2xUqVGW7S9FnAIca+riLGiqm5XiCF48ONlfr61wFVTLVIrvFF2Hr7cmIUxSyXIeIjuLumJqt1q15GGFEAh0UNzGgfQps6ZOEg9dMwD3ZkIiUDh/d133zX7gbz22mvy1FNPKSEdiIKCArngggvUNm+99ZZUVVXJQw89JBdffLF8/PHHKnsB0n3t3LlTXnzxRRk2bJjvvdrCRZp/OlUvgsCGcIHg1kCIY5m6adRuF6xwhBFjikNYOo37rbst/WvbI1rUogKfWXRrIDRUUR2nWxLi7EoUx9dWVHW53b6Bnd4OsQS6rHyfqdeqNIKxCQlqO7w3MyVeCkrFT8Djv5gYvB9tvUJ2FVXK0B4Zqt3jGMMtgEKig5Y0DlBwExIlWU3gdnL55ZerVIJmNm7cKA8//LC88MILIe8vLy9PbrvtNlm8eLH07Rt89I+MBBUVFfLggw8qkQ1g/Z44caLKtjJhwgRZu3atmh4eNWqUpKWlhXt6pJHTqXrqX1u2cWOJtzvqWAS7d0xVPrY3TztIkuJCa4bb9pQoIYVgTWPxHrgBmNMS0r+2/QJRO2TzXlWFz1yoCb/1pl3FakZFB0A6atsXttOiG0MyCOHSyho/AR+bliVVu3eIIz1LxSB0SU/yZVGx270CHm0WlTJVQGft2A59CvaFwDhdyTKYCxaJTgLVP7DelsYBQqKRkBQPLMj65gPL8jHHHCMOR92cpHPnzpVff/01rANAIZ4OHTrIJ598Is8++2wdFxEjENbYRotuI0VFRep5zZo1KusKRXfz5psNNJ0KKzREkrZsQxjp/LNmoYwbT32iW/uQ/7I6V7btKVUWSByjEjl2UcGbEE3Ggjz0r23/bN1dooRt5w5eyWyMBUhPjleDPohstC896NLxA7q6ZUpCbF1XFbFJXFKayvNdWumU0twiSYqPVbmPK2qcytIN0Y33+1IJwhKfkaSW6cA4WLuNVm2KbhJq/QMNjQOERCchCe977rlHfvzxR5+P5ZVXXmm5HYT5oYceGtYBwB8bj1Do0aOHehiBSwmEuHZRgcU7KSlJrrrqKlm6dKlkZmbKtGnT5Nxzz21UsR+cW3l5ZKaoQ0XJ3zbslVU7CqXG6ZbYGLsM7Z4uB/fvqPIWm8E2FVXWqQBBcrxDiitqlLUQ++rTKbmOeEJWnEE9OgT9TlduL5RPF29T1s1te8t8Agr7Qco4DBCQKQXLc/LLJKajSFJ8jEwZ2VPS4iRif69IZ0d+mWzeVaIszHrABhGdnhSrxG9aQowUlaEqpaPWZclb2RLCO8ZuUyIYohmVJ+tkzbE7JCG9s7hrKiQh1usfjoEcFDdaJ1IQAhWsKSIpCTGSnhTnFd21mZzw9NMf2+WEUT181wshmpE902TJhl31Bt+O6JnGPoqQCALas8mEN6pVwpIN8XnrrbfKZZddJr169fLbBqIWVuZx48ZJS4G0hm+//bZyf+nYsaMv+LKkpESmTJmiBgiLFi1SQZWwiF999dUN/qyamhrJzs6WSGNTfrX8sqnM55et+aWwWH5duVUO65csfTO9lmT4v0LogsryUuUra0WseMTlrA2gtImUl9XNGoHlGR6bZGeXWO6joNwpn2fDtUQkv9wpNU7/A4y1YwDgEsExwI8c1b5rKuSI/qliL8mR7OycBn4jpC20x8JSiO59y/NrnFJQUiGZSQ5JirOrgVV+uVu1g64pduXbvdPpbZset1vSEhxSUFHltw+VoSQmzuvDrdyi3ILmbLd5pGuyTRkVdhRVSaeUGLVfb5YUl9RUVUhN1b79VLs88vmiQpmfvVVdA9DdfTLjZEjneMlICtt7j0QgB2a5LftVgL7q0L7JsnfnZtnrnUyu078GW0YIaZuMHj06pO1Cukt06dJFTj31VPU3bkZHHnmkZGR4i1C0BriBPvnkk/L888+r0vXnn3++b92rr76qgi5R2hkMGjRIysrK1LawgjfU6h0bGysDBgyQSLN0/9/a9ZKU7P2uAKbvC8trpKTCWznw/d9LpG/nVHWzgOVRW8OH9YmXDbtKA+47Nq5G8ooqJDUxTlJSEupYe6aO7inDeqQHfP9nS7ZLUrL3rpVXViIxMXXvYCgWDveSTmnxvmM7ZPQBDfw2SFtqj+nOCjVrYqa4WqRDaqJ0TnFIh1SXsoTDNA3L897KEkmO91rG0RaKqkv8ChWoNISG2vMxBpeq5Nr+wl5aIjUSI5mpdWd6AHzO80srxCN26ZaYLAm1szi5lSK7t4tMHd0taLsm0cEQERlzQIUs3LBXVhpmEod1T5exhplEq9nGnipXt0e25peHNANJCIlgH28AizYCHPEIhDG1X1MDyzMs3J999pnceOONctFFF9URyHgYGThwoJrSg9W7oQMGDDhCnUZoLyz7I0/EZhftro9gNkzz68wOmKXHIGf1jkKVdk35UTsc8sf2otoANLckQ/hYkJ7ikOTEOJUJAkFs4WZ9WJtbouIIVHXC2u/firIqp+wX43VlgXUpNj6B/rYR0B47piYqH2yryfriSqd0TYiT1NgYVcQJbQnt8fPFW/x8a5Fq0OhqAq8U+Hi7XDVit+0rxOMdtMX43lNW5bKMYYFw31VcqWZYHIb3GJm9bKf07JzOrCZE+iQlSZ9uHWU67lsWwbf+6Vhtqs0VllfL6h3e+62OW8Fq9LnZO4t9gb2EkAgX3vDBDiR8rGhOlwyI7Tlz5shjjz0mJ5xwgt86TB0j8HP69OnKHUazYsUKycrKalUrfVvEGCAJ0b1ld6lvGh56BaIXVNW4JcbhVoFqKEqiKwjqrCXmGwreByvN9AkDfDeJcLI+GFNy6WBNqwqX6rNqq19iG2YJiJz2qMvGW6UUhNW5e6Z/rmy0LXNhGwTyIvgWqEGkeJS48Tg9klRr4dbbGf/W1VbNxZ2QF9xj8Z7GFkYhkY+577NKx4r+1Njejf0tCBTYSwiJQOGNSpRaeMNqDJ9pZBg5/vjjVQaRwsJClev7hx9+UMVsmgoEM+Xn50tqaqrKw/3RRx/J7Nmzlfg++OCDZffu3b5t9TbHHnusvPLKK9KnTx+VxxsFefAaKQuJtbhFhw9LtxbdAJZuIygwYo+PVeJDd/qY5t+/W5qyyixcv0tZA8sqa5QVHFOiG/KKfTeJcLI+mFNyGQWUGZUnvLZtMktAZOU/RruC8ECbM6cVvOjowdKj4z7xDMyFbXSaQO+MidfanZQQKzHJ+wbgaDm6OqbU/o0CUGbRDXAMVu8xw6qppCHpWI0DO4C/jf0t4MCOtNdMaCRM4Y2sIJorrrhC+Xvfe++9dSpD3n///fLFF1/IGWecIU1BTk6OHH300TJjxgx1DHAvAcgVjocRvc11112ngjxhEc/NzVVZUCC6Tz/99CY5pkjBKG7RucO9xEgdA3Nt0RuIj64GF1ZYyU8e20eWbd6rxLbdcJPAtD8skMGmRwNdtMaUXEYBZUZbHplCsH0TKP+xsnynJ6k2Z5zZMItujbmwDazmuworJCUxVpLjY5RrksaYJtD4eYcP6Sa7iiv8hBFmcfD5Vu8xw6qppCHpWPXAzrzM2N8CDuxIa6PT/KIdazdSXR2YszH1Y/OYTZv1MHLkSJVL2ypt4C+//KKK66CceyQBVxUwfPhwiSRm/bZJidu1OwulpNLQ6asKgPuaBYx/cPnAv+SEGNm/WwefRRDWcpTcDtaMIIq1L26oFy22QbESfRyW5cNFpHenVJVCkL6PkdMe62N0v6yQXTkwWIQbFYQ4ZmXW5xar2btAhZd0WwXmqpQ788tUsHAw0Q2w7b9OCy26nUQfMDbcN3OJ3zIM7NblemtRmDH2t5rbp49mLAtpFfxjE6RO/8l7cf2EnfsKftLLli2zFN7z589XGVBI+wBCd+mmPbVFRxBHX3shmWbZfdX7anMdG28CEMT1CRHj9KjVRQthY7aOm90GzG4HEPo9s1LkkEFdWKI7QjD7aFsR7swGXJyMFSbR3j9ZuDnoTUMP/sxVKUMdGNDliYQyu1NZ7VIGjWCxLEZXun3vZ8VL0jpYxSYYYRxCMwlvBC4+99xzKqsJgi5RoGbPnj3y5ZdfyjvvvKPyfJP2AQTGaeP7yYyPlqpcsTUGdxP09bgHIPui9u+HOE9L8ub09m0X4mct37JXCaZwLlqz2wDolZWihM3o/p0CuhuQ9ol5sFWfMG6IpfGgvlnSLSOpjjV7SI8MOXRwV8t96/iE5hgYkOgTLj9n56iYmvzSKr9qvlaxLFZBvBzYkbYUm2CGcQjN4GqCzeFf/eabb/oquWEZAhvhZnLJJZdIpBGpriaaN35cIz/8sVPllNUlt2GBcan8azY/ATJov3SfhRtrKmvLa1sBNxRjYBxuLvo5mJU8kCtBOJlRSPsWJ2ZhbE5DGSygx7gumFsT+GV1rmRvLwjZT5HTrKShbfqj+Rvl5+xccaHSqsfbTtGfed34RDJT4yW/ZF+ApXalM/aVZrc9QlqS+2YurhOHYwXd7ZpYeGtQHRIl2YuLi5X7yUEHHRRxea6jRXhrf2qIZJRe12IbJbghnrUrSu9OKdIxNcHP+vh/CzdbXohmn2w9XWoMUIP7iBW8aInVYKs+EW1el5kSL9v2llkO1pB9B21TFeAJ008xlIEBiQ5CyeiAwdrbP6+XTXnFfjEq3v7VLfGxdolx2FW/iP4VMQli0UfSf5a0tdiEYDAOITANrm+M9H1HHHFEQ99O2ugUv9GPGjcDFBRBh4+MJR2S4uqIDKQMNPu9mvPRgpSEGF8lQo9FjlojzApBNFo0B4sNmLsqR4kWYzEntF+dOcIsYHT6TLGwKIbip2j0GecsTPSJD7iIhJrRQfvEQkybLVzoX2GQQBty2D3K4AHDxAmjeylTR0MKjxHS0pmnjAHCOmaBcQhNILyR0g+ZTAYPHlxvMR2s++abb0LZLWlDmP2pcXHhxjCi974O30pkGP1edbo3cz5aNY2akqAqEergIasctRpetCTUgB6IaGQbMYtoYxs0D/KM6wK1wVD9FOn6FPkYZ1r2lFT60lNqlzmr4HCzT6xVqkAAoRJvdyijBowbCbEOOWfiIN96DuxIW8KY5tfKnVTHLBw+pGurHmdECG8Uq0lOTvb9HU4VS9J+qM+SF0hkdEpL8Pku4sJDsZ0Yg++izntsDh6yylELGDxEQg3oCSSijULHE2RdoDYImC+ZGGdajDN56MdQW8A4m2I1UwKxrnPAB8PbDpPqzPZxYEfaEkZDm1WKX7RzXBertheqayeQu160E5LwRnEazd133y3x8YGrtpHI8FcMpcM33pSQ2g/iBhddtct788hMTZBu6fuKjZgL4RjLvWuYFYKEUmxEYyWirYSOb11tm5MgbXDf9eAvgkh0z7RYVZa0KuuuZ0p0NdZAqQKN6HYIizfbG2nrbqmIWTCLbqANbWjDwdz1op2wfbxh8T7kkEOUy8mRRx6pSsaT6KtAZb4p7aswmKQK8mBpdY03640WNmqbjCTfBWsWO8HSxbEsbXShf2+rUvIas8D2iWgLoWMU2MZ1VnmSNXR5im7MMy1W7iJWLnN6psToE2uVKtCIboec7SNtHVixh2zeq+IWzC4mxoxlMFqE4q4XjYQtvG+66Sb56aef5IEHHpA77rhDhg4dKkcddZQS4vibtB9CLWYT6vS/vgCR67ugtEpdeBDhcbEOvwsTN6nconI1dbsup0hi7DYZ3D1DThnXR4b32vd5LEsbXQT6vd1uWA3rWp3NAtsoos1CJ9A6qzzJmkAiiIPA6JtpCeYuYnZXMs6UaJ9Y82yfGbRDzvaR9sLW3SW1hrZ9932g3LEKy32CXGfyYSn5Rgrvs846Sz1qamrkt99+UyJ89uzZKviyc+fOygoOdxQS2RWo9E3JKrgCgc3l1U61Hl7ecbEi5RWVsmfPXslJSpb4uDhVsAelkGNj9kX2fzR/kzIhDeqeLmt2FDZ4UEAiaxC4p6RKEuIcluknA4los9CxWqf/NkfkA7MI4iAwujDPtARzFzG7KxlnSrRPrHm2z4hOI9iY4lCEtNq1UdvurXy+nW6PLNqwm/fspkonGBsbq8rG9+zZU/r37y+zZs2SxYsXy/vvv0/hHeEVqPSFZ3mhudxSUe1URZXULcXmLRZRVloqLjfy1jrFY7NL73T/NG5awKOKJqL7UczHmDnAfFz0H4ueQSAyPmzdU2qZftJKRAOj0LFa1z0zWaqcbsuIfIig6RMG+ERQY2aGSOSkTgvkLmJ2V9IzJegnjalaMXA0pmtVbU5Ejhi6n5w6nqKbtG30TJ/VtWGVQth4bfCe3UjhvXnzZlm4cKEsWLBAPe/atUtlPBkzZozcfPPNMm7cuHB3SdpYwFoomR1w07C60GC5VpK7tuZ8jN2hrIcQ4nZHjLhdTkmI86Ym1JgFPIRYoMwBGpaljZ5BIIRyl/RE1R4wKDOv2y8zWVkNzaIcbSYxLkZ6dUyRvaWVfjmR05Pj5Mtl22VviTcOoblmhkjkpE4L5C5inE1Bv1ZUUe2r8KfdpaaN7ysbc4tVfwrQJ2JHcMtD28Q1wOl40tYINNPXq1OqrM8p8m1nDjy2ujZ4z26E8D7uuOOUqBo2bJice+65MnbsWDnggAMsfTBJ2yRYwFrdbb3+igikMF6AG/OKVbl4b5GdfdYep8tw+dlskhjvUC4lW1YukrjM7uJwxKjttV+k1UgZ+b6TE2LqLbbDdG/RMwiEiE5J8MjoflmWFSNBfdUktd+trtSKv638FIEW06HMDNU4GUQU6anTgJW7iM0wm6Kroa7PKQ44M9KvS5rMnL/Jry/j7AlpiwSb6UNfiuQJumiZVeCx8dqoz5AXbYQtvKdPn66s3X/88YcqG79lyxbZvn27EuDMcBIZFaj8t7XL6u0FdS5AuJToB24iEOBwKfEYJDQuPLfxIwzCRvtF1knR5cEevO4pevNAxXaY7i26BoEQxlNG9w6YZ76+apJ6mZWYNmc20daZQIMCc2zDpl0MIoo0jG4iur0Y3UVKK2qkc3qisuqheNOq7QUBUwHi/UjBBoJtw9kT0haob6YPbRh9oLfqat3YB2P9DiO8ZzdQeN97773qeefOnTJv3jz1QJ7vPXv2SN++fZWryZ133hnubkkrT6MGAjcU8wWIiwyzHriocPHhgde4APFPi2+sh57xXpRQ5Z46vl/mkTL2o/7ZgmcOYM7b6BwEhlJYpL48yKG6WcFKieAgMwwiiu6KvhDahwzqomZTEA+A9jbrt031tjvMGoJgLkmcjidtgVBm+lISYmX/bmlqMIrsJegrrdIKGmGK1kYGV+63337y5z//WaZOnSqLFi2Sd955R+bMmSObNm2i8G6H06hWKN9s8VhaB/FQ1SlrM5K4XB41yoXARoYICGcIcvyNqXhQU14ijtQM9TcuzkApulISYur4i2E7VMQsLK/2WRkzU+LVDY++kdExCGyKHMfhWNhxI0Ebx6yOhkFE0Ud9FX1DHcxpI0OgSqkaTseT1iZU48SW3aXyr9NGq/s1spcEqomgYZ56Lw1yzIabyUsvvSQXXHCBcjG5+OKLlQX8iiuukPfee68huyStNI1q9M+2KmazdXep5XqIY51mCyNbh8ObRhDBbMrybYNo97qJbNldIo74ZKkqLVBCXvt+6RRddaao0pNqRfw+IP6R2QIBdsriXjvtC8EGf134o5H2CQZOgdphoPR+ENCNsbCHtq1dDuztL/bDCSIikYeV6A5lMKeNDObKqVbo6XhCWoNA7RltOFBbRR9e34wP89Q3wuINV5Li4mJJSkpS6QRRRGfixImSlZUV7q5IG5xGNQalwaJcPc/f2qfLwkNcIG2gCq602aTK6ZKk+BifEEfgJaQULkZcrrFpWVJRViLVRXnSfVAf3zSUMUWX9gtTGUxs3qBKT+0FD6ujw+6w9B+jb2Tk+dJaDQIBZjjCrbLaGAv7ONPMEIOISEPcpYxGhvqsgpyOJ22lPVvV6UiJj5HM1AR1/9VtNdQ+nJmfGii8TznlFFUkB5bumJgGe6qQdjKNqi9A+CfuyC+TyuoacblctS7bbrHZ7WKz2cXj9kh5abFKGWizxyh17HG5pbLcLR63U9wul9RUlkr2p8/IljmZ0qHPCEneb5DY45MkIaObuKrKxFleLJud+/Lk2mLiJDYpTeJSMiUhHqnhki39x5QFye3NasGI6cgcBO4uqlAzG02RSztUNyudEUXfUOAyxSAi0tDBXLAqqQ2djmcVVdJc7fmHlTv9jF/QB8hahhiXnMIKZZibPKJ7yH04Rfc+bB5vpRMShBUrVqjn4cOHR933BAvj18u3KZcTNBSX0yVOl7cqJUAgpCCVpBLitYIcQttZo4S5wiNSUZAjlcX58vv7D/rt3+aIkfQeg6TXhFPE7qgbjAHBHpuYLP369JJBAwf6lluNxJGb+c7Tx/ACjwCMg0Cd/q8+oXzpn4aG/Ntbpcoy7sss5HEMuKG8/+uGkIOI4PtIooNQ2qh2Hwk2Ja/bMUQNLI/h5FZmrAtpKlZs3SsPfLhU3Vsx24z7rVXF1YH7pcsFRw2yNHoEiocgjQiuJNFB/65pkvO9Ic82ilHW8cuGP7cbKlnctRZrbAJLuCYxo6u4aqrFERsnWZneAEsflXtk76KPfVZwiHGPyyllO9dI8dY/ZL9Dz5AYgyi3yiqBDiK/tEqe/2qlnDa+HysJtnOMHXZjqqwGIlzrjJ4ZYhARsSKUqXZUQwWBtoFQ6ZqeLK98kx1QULOKKmkJNuQWq6JlmOUOJLphcCitrAmYApOiOzAU3qTeC9DhsIuttiKlw4FASoffBei9idjFHhsrsTEpaoRcWzBe+YDjUbB3r8QmpUq3rt1k29bNQT/TPFLWVeCCZZUAsEJi/oa5cCOLxlZZbUy2isa4qZDoItTBnNU2HVMSZOveUtmZX678wa1cqfA+VlElLdXnItaqOKFGudjBxcSbGAHZzGyqr0Q7xYwzU2CGD4U3Ccrvm/cqIYFsJdrHy3wBYtQLHaKK3zi9Ob0BpAnegwvXFhsvieldpOefLlVCOtjUqFkAGf0nA2WVMPpQsiOI7iqrDbG0hPoeBhGRxg7mzNus3lEgz36x0pexyezGpIPH+3ZJa/KZH0KC9blIoIA2GB/rLcNhjgvWWXqYAjM86IBDgl6A2p9VZytBKffk+Bj17C2Q400dqFFpBA1hA/gTNwN7TGytz7fNZ8kJNQ2gMd2cVVYJq/K06AhI+0WnCww3/V9LTG/Csgk/XJSv18eGZ7zG8lCDPElkE0pbRFVgiG4YFHTgLp4hwpGGFW51AH3o/DW5IX0u+z4SjPpSseo+15z60ioZj67pwRSYzWDxRo7ucIvrkPaPvgBhfSkorQpo8TYKbRTS8YLhce0oGS/dbrHZ4KLiCbtEsrYyzpy/0TIHrlV5WpambX8EChrr1SlF1ucU1/v+lizO0BA3FUKsynLrdKpm0NPBrQ4l6tG+CsqqJSstsd4vkX0faWxArp5lhqgOlndezzIzBWYzCO9JkybVCagLRnZ2dpiHQdoquAB3FpSpKSfj5QfxXePyRjzr6xJGaVU0p/a1cbkKz7CFNzVqTJWl/Sfvfn+RFJZZT8kaYUfQvggWNAZhC99+lCgOxa+6qVOs1bc/im7SkDYHIYS2HUjYqMU2r3udFke63wsG+z5ipCEBuTqWxVhnI9gsMytSNoPwfuCBB8IS3iSyspoU/FKthK1VdLPy7a4V1zZUrNQral1M9DZqkUpDWLcdGf3D6huZHzuyp+ow6rsBsSNof5a/QP6rELbVNa6AlmWI7iOHdWvSFGtM2Uaau43gfXqq3udmUpsvGXExKoMUXPNqXErgIF1qfaIbsO8jxjbakIBcPcv89s/rVcE8T5BZZgaTN5PwnjZtWgN2TSIprRCmPDH4goVbZy3x5u72XpIqx7fLo/zALG8Obo943C6JsbAK6alR+DvWNzLXI3FkLwwEO4L2RSjpApMTYmX/bh0kLTG2TsaIDsnxqthDUxTXAUzZRpq7jRgD2LRVEQIbFX+NBnCbDX62Ipt3FcvYAZ2lrMrJjDok7L4VAzqdLSfUWWe036s7JMrH8zfJT9k54rKYZWZFyhbMapKTkyO7d+9WQqxr167SqVOnBn48aetTpjqtEPwMjQVrnE63uhCVZQaOJx6v4MU6l+Ei10LcFhsrngqn9B00qM5nQkChMmYoI3MEr7E0bXSmC0SwGYrSGP2q6ytcEmocQWMtRCR6aIo2YizLDRGzu7hCKqrrBr2hX8UDQe57S6vUjN+PpkGmhiKImNvpV8u2heSaGSgrCdrvZccNk1PH95VfV+fKqu0FrEjZksK7urpaXn31VXn33XclN9c/urpXr15y1llnydlnn+2X4zlcnnvuOZk3b568+eabAbcpKCiQ++67T+bOnateH3fccXLLLbdIUtK+Du6LL76Qp59+WrZt2yZ9+vSRG264QY444ogGH1e0TpmO7t/JZ5XBhar9uQrLqqTK6bXM4II2ZjIxjqrjY+3qhgGQC/yA4YNVWkIzsFqGWySFpWmjO12gdjkJt93U54vbHMV6SGTRVG3EmCZ1X1B6XbwTiDZltSwqq1LGB5blJvXNyCAZAYrKaXS2HLiOwE0EBrVQA3KV68n4fgLfBwaTt5Dwhui+8MILZdGiRXLggQfKqaeeKllZWWpdXl6eEsszZsyQH374QV566SWJjQ0cBBWI1157TZ566ikZO3Zs0O3+8Y9/SFVVldq+uLhYbrvtNrn77rvloYceUuvnz5+vhPbNN98sEyZMkJkzZ8oVV1whs2bNkv79+4d9XNE+ZYqgSohlY7VIrFdbI5jS7XU38VaqNOYs2VfTEiTExViKbu0WgmptIR1v7cicWSUiA6Plz4iVD79V0Fgo1nLEJny5bJufi0ogX9zmKtZDIoemaiP56xbLT79sli69B4nbE+ObMfR3NRFJio9RGaQw46j3yYw60U0wA4KekUE7sspKorPloC/V9+RwAnKN2zV1IHu0EJLwfuWVV2T58uXyzDPPyDHHHFNn/TXXXKNE9z//+U/58MMP5cwzzwz5ACDcIZ4XL14sffsGv5EtXbpUfvvtN5k9e7ZPRN9zzz1y8cUXy7XXXitdunSRl19+WSZPnqys7+Cmm25S73v99dfVtiS8KVO4lmCEiwtVW7eRUlCjxHbtS13sQaccxN+ZKQlSUeP0y7FtnhrNTIkP2+oJsa8veGaVaN9oyx8EstGdyTw1ag4aC8VabhwwdkxN8OWctfLFbUyxHt6AooOmLOj0yH23S16FQw76y62SkJquDBm+wWZtsRK0e1T+BbguzPtk3xc9hBrMa5yRMWcl8d6fvXFa63KKlPDGNocP6drkx0EaKbzhunHBBRdYim7NkUceKeecc46yLIcjvFeuXCkdOnSQTz75RJ599lnZsWNHwG1hcYc/udFyffDBBysxCOEOt5MlS5Yoa7eRcePGyZw5c0I+pmghlCnThFiHbNlV4hW7tcsw5em1cKN4jq5oZVP7Mle5Qg5mLB/aI1227C4N6B9mZfU0A2EGIfXQx0t5wUcQ6LB/WpUjO/LL/GZMjFOj3TOT65RhD2QtN7YXLbp19ohgvrj17c8qLoE3oOginDai6xwEoqSkRApz86Qyf6ekpWeq9Ky+Ggkx3vcamyyuh/r2SSKTcIJ5jTMyMFrorCQQ28bMZDCQoU1h/artheoz6gtCZ+B5Cwrv7du31+sCArDNRx99FNYBIEc4HqFax7t16+a3LC4uTtLT01XAJ1xPysvLVcCnkc6dO6v1JLwpU2UtLCxXF7jxBqADfux2rwU8Ic5eO4qG8Ba/oErcJM44ZF+nEMgCZPR3DHgsBeWSlhTnu+k1JnMFaVt4Grg+WLuB9dxjKvRQny9ufe1Q0zElvk5QJ9tjdDCkR7os32zddxpnbdDm7nl/kRzYp2NAayBSrLqrK9TgD4NMHTeD/lTXTkDXC6s3RP8I9nFRRzjBvObZYxjC4M+9Y29ZnXTAGOQBrMc9GT7hwQKCGXjewsK7srJSUlNT690uLS1NBT82FxUVFUpom4mPj1d+3zhOYN5Gr28U27eL+4wz6iyueu898fTvL7YNGyTeYj2oXLRIPdvnzJG4W26ps97dt69Uf/CB+jvmxRcl5uWX62zjmjRJah5+WP0de+ON4vjuuzrbOP/2N3Feeqn6O276dLFv2lRnm+oZM8Q9ebLKRlL1Td19qOMZNUqq4hIkZ3exeAqLROISJaGmUmocMeKyo0y8Xdx2uJWI2MUtzupqsbndAjltK4XYsYnNYZdBw/rIeYf3lf3efUPctecE5xC3xTmNees5WVaZJS67vyj39Oollfv1kJz8MrEVFkr6hp3icfmXja8ZPFg++NUlqXEivY4+3PKc2uvvBBLGjInoc5o38FBJ7jpQejliJX//IVLqtou7xin2okJJrSqTjMpSiV9bI/M+flVOXveL3zmNu+9hWXbQSf7tJilJ3KNHey09ZeViryiX9KI8v3bjycoSz5Ah6u/l//etTD3/cfX3uOQM3/7Q9jy9e3s/a/FikfJy9XeNzSFbbSIxbpd4Bg8WT21WJ/tPP3m/CxH58JtvJWvpJ9KlrCBificj0XZOeckZMr/7UFn+l79JaUWNbM8pkNQ9eZJRWSLxte2qJC5Jcjt0EmdaByWcXaVlUrQ1R1YtWydfVJbKmSu/lYnbflfntOO2uyVj2FEy6dAeYkvpKIXbcqTaHis2j1tqYmLFgyQFdq8rncfplJpqj9hcNdLh9lvEve33JjmnSPydIvGc0D86uw4U9+Hee5tn925xrF7tfxwi8uuXM+W4Z+4Qu80jzg2bxLZ1q1oH5ZaSmiU1iWniVG0rRhnGYmuqpVvuRinfkSi58YniEbvcPX+pHN01Vsace5J07pDod076ONQxWPR7+jjm/e9FOX7DfPG883ZU/U7AvmqVNJnwhj+t3SSIrDBmt2gOEhISVKCnGYhqZDWBwAbmbbA+MbH+UrvBwHlZifcNGzZIVXW1xG/bJv0DiHtdyTNt2zbpYbFNVUmJbKjdplNurnSy2KY4P1+2127TIz9f0iy22Z2bK7trt+lfUiLxFtts37ZNimu3iampkmpHXUtgWVm57C2rEafTJQ7IaI9b7B63xDurpcYRK86YfQMbJaJtdlU8B99R55I9klxVIY74WDmse2/Zu3Oz2EM5pz075MTNy+WToUcqca+prqqSvIIScTo90q28QGJrquqk8MaAy1lcIp/+ulIuDvAbtOffaWiEn9PvHXuL2+2WWHeVpEq1JCcniq3cKcl7vDcOgN/896zectwf3/mfU36unPjHt37txu10SmlJiVTXOMXhdqs2aW43GCxWlpZ6/65xSWmNU2LdLkmv2re/yqoqqa7dJsnpFLvbLQ63S/Yr2yV5qVlqf6rt1W6T4t73Cfjr584D5cTVcyPmdzISTee0skt/1R7KYxMkb2e+VNR406mWpnSU3UkdpFvxbkmurpCc5AypFrtUVTkl1oEsJDBGeO2KBQkp8uKok6TG6ZT4ihiZ9elSSe4+WNzV1ZJeUiCVKRkiNrdUxCWodmYsAWxDbI14pGvxbvmu54HSJ3eDdCnN5+8UBW1P948VNofk7SpUbU+qPRKT0V1SqsokvWLfwG9ZUifpnZ0tHWMqZWtVlcQZ+qOKmHiJc1ZLrM0jrnjIPpskVlRJTnKmeHzT2R7VTheUO+WXT5fKoX2T5RjDOel+WgL0e1WOWClMTJUPhkyURV0GSPV3m6XX2gIZ4cqXsVHwO4FQVabNE4JSHjx4sLz//vsqo0kwEIAJ/+6GloyHbzZ8vAOlE0Tg5FtvvSU//vijbxlE9ogRI+Txxx9XPt6jRo2SW2+9VaZPn+7b5oknnpBvvvlGPv/88wYd14oVK5SoHDBggEQSny3ZLsu3eKdMMQ1VWF4jpZXewLbyKqc47Ih0tqlUgDh/WMl1rlmrSlaxMXZJinNIn04pctr4PjKsR3rYx7SrqEIWbtgrK3cUqs/DPnMKKiQ1IaZO7lEz2PbGqQeE/Zmk9cBv/PCnf4S8/Y0nHWDpqtTU7cZqf8O6p8vY/h3ltbkbavPXB4ftsf2DdvDfH9ZLYXm15BVW+McgqCqTCCJ3K7cSuNqhXcAPO1CxkpT4WOmUFq9cR7777jtlzIHBaOyEw2XrnlKphp+3IaMPnmLsdumW4XUjACN7Z8oJo3qov3XbJJEJft/bP1hap+1p0EpQ4C41IdbXPxaUVqk2u69ytEc25JX4vU+rPqtad/27pKr2h+QHFx45QFm+6+un4VqVV7TvGPU+9GecPKZXg/RAe8OY1rpJ8ngjJZ+Vm4cRK2t0UwIf8kcffVS2bNkivWungBcsWKCeIbhhccczMp8YhTe2GT16dKM+G/sO9UttLxxxQA/J3lmsfBJ1EJpGBWO43eJy22S/1AS1DaqqBUIHb8THxssBvTvK2IH7NeiY+iQlSZ9uHQW/ns5ect/MJSG9F/1MbHwCg4/aGYnxsSEHNHZITQmp3UCcz/ptU0j+2gf1zapzbVvtT2e1cHswGK0/hRbbY/tn2R95UuX0yK6iSqUgjDrFoYwS6i+prHZLRkqcqiwZDPSjMTF25UeLewrAc3pKguSqz3ApAQ/Rg4wTaUl1C54s31YosXGxzCoRBcCvek9xVZ22ZwRtMzEuVpLjY1T/iMf0Q8TnF46WAyOaTiuI/cTFOlQtDjMqLitmnyxcvq1YTu7WMWg/DaPdruJ914c3haFNFXzSGaqe+HyVHDeqp/xpRE9mPglVeCNvd2vgcrkkPz9f+ZfDzQSWbQhrpC+86667VCDlnXfeKaeccopKJQiQfeWSSy6RoUOHqqI5SG8IC/z999/fKufQlkHnP3HYfipIzCi6VbYSQxEc+MrGORzi8dQEtHarYEq7TQVB/l5rRW8sWuyEk2mCEf/tj1ADGs3pBAOh2wAC2hB4Gyxzj84jH8r+QLiZT9ge238AujFI1wromRqXS1kaIWiCAQu5KmBiCmCD9dw4+QwrupU1UgeZA21RZFBv5IKsSSmmlIBGdHrAtTsLVWDkfTMX+1L7GQstYUZGB/xiIIfZFSvMQejGXPSB+mnz9YE+D1WGjctQ5frn7FzZlFfCRAihCm8Ux2kNkInk6KOPVp8/bdo0JQiRSxwFc8477zw1RacrV2oOO+wweeCBB1QVTLiYwD3khRdeYPGcAKAKZa+slDr5k1MSYn2l33EBlVfVWBd4qBUvWqnDWrNtT6ls31sqPTpaWydbW5iRtkVTCWQjsEyramvj+gbMCNDQEttsj9GVsxv9YjB0SlXMjAQT3rrfxJO5qElpVY0vi4kGbdZYaTA+xqFEt80iNWaopepJ+xv4GVMCGjGmB4SpDNuZB2G60BLux//5drVqI2h75vYH0KLM9TaMeeMD9dPG6wP7Rd0Oq/aJ7dhGw/Dxjnbg4w2GDx8ukQZGyEbrnRbeuKD1qBUtBD7f2hJjbDGIudVTpprUhDj5y2H9m6yyH6bbzKnbrEQURvi84bRPrPLDmgVyfekiAxV26N81TTbmFvtVrjTnkQ8Htsfo4Z4PFsnKbcEzdSEYvbKqRmzI+lTttUYHwu6IFbezWsp3bfa5Ztpj4iStax+xx8arfSg84r8v9RpB64nirCiR6uI9yphkVY15dL8sVlWNkIGfdrM0FgID6CcrDekm4Yo0pEeGn+A13xONfSyK5+BZb44nYxl5LcxRx+Nfp40O2E9jpmZdbpFvH5gRtHJh0ezfrYM6xmhvoyFZvM8999yQdwgRhiqRpH1WYdMXrs7/qaY1bfsuZOM1ZSW6MfKGX2JTltRuLsslaTtAVMNSp6dGwxXIoRR2QHsMVkkwVNgeo4cDe3eU7O2FlhZCHx6PVBbvlbjUDF/Wh0DbVZeXitvl9IuHik/J8Foha6olRqcQRH5v074cianirqmS8sI9SrzDFdMMhFBT9r2k9TC6tUEQY8ZDz0xXqd8edTK8tTIQrGtVIOyX1bny5/H91GtluLCJzFqwWRnWIJBxv05JjJGuHbwzKtv3lklBaaVyiQK4l7/x4xqfb7ZVPx1jt0lyQnAXFjEVMVsR5W005HSCZhYuXKj8qJOTk+vdlrRN6vNXNV7siGqG2wkuWG/VyrqiGyCiX095NYXIaSphRto++A311Gg4bac1CjuwPUYHmF7/cuk21QcGAt2gq7JYindvkrTew61TRSALXMlucdXsqzWBbheiOzGzm0rHim08Hvh228XjdplS+NrEERMrVQU7RSUxiYvzBfgai/boGcsP52+UQwd3Zb/YzjG6tSljWHqSdE7zyPrcIvHEWvtmG9sDLNvZ2wvUftKT4+XHlTtVfwjL8+ZdxaqtYnwHwY04BazTvShaMe6zsxdvlWWb9soFRw1S/Z65n/588RZ1jIFcWKyOsbqJ9UFUuJo4nU454IADVODisGHDJNKJZFeTUDM/7N8tTdbnFqtpVz3FZQaj596dUqRjaoISxcYpqqYmmi9a0rA23JzTm2yPkcsPK3cqV7dAfrGYGYSxAdP6q3cUyrs/r1e+2VoE64C2pPgYNfMCXv1+jSxbs1lZqGMTk30Zw/R0PWJs0KaM+ygsq5aB+3mn6jVmFwSA9RBWobpokbaLdmuDq6cW03D3LK9yKSMX7oH4nXt3SlXC3Ko9oC3AcAZrNFIPwqAGcZ5TUC57SyprBfO+7TEGxCMhLkZZ0gFaXN8uaXL1CcNVWku0UfMxaheWQNeJPkbQ3PqgrRNyOkEjVpZOEtmBbZNH9JSRfSokr3ClulhxU0AgJf5BcKMT6J6ZrER3SwQ6UnQTDXy6Q6E5pzfZHiOXI4ftpwrYvPPzhqCCGpZAPAZ3T1czc6iRgAA488zciq171X7g2223oRKwQwlwbAdBg5lFuAFCqCBHtxba6IdVqrbaoHeIJ7PIMloWGcjW/kF7GdGno7z3y4Z96QBVe/DmkMc9eL/MJCVordqDdu+AaMf7sR4iXN/DgVkn70s6uA+n2yOb8orlnvcXSVZaoi9+BvrB6HqXapGBRQ9OjSkxh0d5IoQGCW8SOYTjr4rHFccPk2e/WOm7AeGiSkuK88s1G24GCkKaMk4h8LbRPb1JGs7EYd1lUPeMWkG9VwmeQK5uwVymEIvw7JcrVaEyjxPWy0qJiUuU2NhEX8YJu5L53jRter8QVZkpCbIeQXG1wh+T1RBEKqtUgMwU2CeOOZr9adszsCbDMGbOPIY2hTaCZ2Q8wW9ulfZSD8J05hG0hx35ZcqSjTZlmRy8NmLT61bqbWdqW5jYbDYlvK1SWMKVb87y7fLFkq2qjVppA+CgPqDwJuH5qw7v1VGuOG6YEuq4qZgDOhjoSFoS5tUmbTkGwbiNjkUwWwQ9rhoU8FZ6BwJHWSntNiWWuqZ73UlQuRDiq3N6orJaQnyVVSLTlEeJGggpK8siiPZAtvYMsjRBLGv/brQHuJpg4OfLOlY7SDOnvdSDMMyQaGu5nqkWcXtFusVEt053iXaoCob5/L49+waHtfd986zK5BE9pKSiWn5alSMwh+hj0uKb+sALLd4k7JtKfUJdlzYmpCVgXm3S0jRk1gQiCtP8dXxgPV5hpXMyo/+Ntzu8OZGrnUp0wzcX26hHjEPySyuV8PaKdbd0SIqTTmnebcxwpqf9u9FZBdDC6FBZ41RWaHOeb/MgzFtN0uuagn86a4mVxVs5stSu1kkVvMu9ZeStsqdAC/TrkuabOe/VKdV3vBhollbUyOFDusmp45l9rNHCm77e0XtTMQt1+IzhxvLKN9l+OZS1DxhGzsaAjIbSVPshkUNzFOAhpDlEFESMFkFGdDo4bZGMjxWVpg2BcL2ybH6CGn93y0iW0kqnLxcz3m8lugErqLZvNzodMInuTWtetB+kA8SypDiHEshQyzZD7IGxPejgXGW71sm/TQWg6qCy7Hgz1WG/iOMyV7bULNywW7me6N0YLfR6oLCruKJJv5+IF96TJk2yFNl///vfJTbW/4fAdt98803THSFpNUIVuau3F1jmUJ63Jk+l4sLFmhgXU0eMh0qgwijh7odEJsyrTVqDcIwAxlgEqwA0AFEOSzdE94CuHWRUvyyVCg4ixji9rzHuR7ulWBHtgWztFbQtzHhs3lXil8hA28UQG4llldUu6ZaRJIcM7qJKsluhq1+qRAgxqMdR62pSaw2HuA5ktoCmQ8tDoK+5sqW2xO8uqlDrHXZ7XeFfK/QZbxCm8D744INp3Y4SwhW5gXIoG9MaoSw9IvSBOSCjPkIpjMJ0WaQxebU5i0Kaq380iihYsBEMqUWQFfCnhShCBonSymrZuttbkMThsKuZSKOoMZYS1zmUrWJuONPTPsG9z5tf27+ytLPG4xuoKWu1eKSkslrW7ixSQhipKPW25oJ4W3aVKss18O0X6QPtteJbe6BgE12vQ70/RmUtM1rRcY/fmV+mroPKGpdUu9xK2GOwkF9SKUnxdVNiLly/i/EGLBkf4gUQwXm8G1u22yqHsrHcvAY+iMYbUygl3lmamzSU+uIUOItCWqJ/NLYz5FGGZRoCBG0ze+M2VZ3SYXdIVqcsJbjRd8JtpHOHRCWq4U7itXR6xTusisby3oXlVbKrsEIZxpCvOZRjIm0ffe9D3YzyqhpvEKXHOzAz/8YQxxC5SGNZUFol5dVOlffbKu3lyD6Z8sXS7ZKP7aoQ+Oh1IXE7nVJRUS5xCUnittkFPSfaVHJCjCTHx9SJH0A7RV2Pqhqn97jwWbW+4BDwKMqD13ivXg7w102nHiQH9c2SaIbBlaRR1f+scihbpTUyT4WGMu2kI7qDwekrYkUw0R1sFmXpxj1y2oR+FCuk0f2juZ1pCzXcQyBA3BUlarYlKQ2l5j0+0Y28zPkl3j4Uzbja6bVEOl1OcdTYxOGwyca8YkmIjfH553ZMjlMiHtUuWdG3/aMCcWFBtnmt1WgbVoVpsCwpztsO0K52F1cogQ2xrQMx8QwBPmVUT5UWs1+XDqpdIkAXgbvY696iQtW+UDH1gP17SYekeDnl4D7KhUUXxzGCz1Giu9Y67h0O7hPd6tjcHtW+UYhHAzH/ycLNar/R7CbKhLYkbJFbXw5lc1ojYFVOFi4BTVUYhZCGCihVeKKwXFVdW72zUGZ8tFTe+HGtWkaIsX8MVhLb2D9atTM93e8tfyKS3rm7JNhd0rdTsqQnxykXgT6dU2v9eSG0a4uUGT4S+ZGra9zqgSl9AIMi8iXHxjjk1HF9VUVAGDSiWdhERCBubfEbDMaM4lWjBG/tegjevEJvAGZFtUv99pgB0Q/MoPywMke1S8yAYLZ50gHdZUDXNDUb7XY5pbqsSMp3bZGjhu0nlx07VFmldfyMMVc8gGVdx2jGxXpnYoD58jDrAwwIXCYdEY3Q4k0aXP3PKoeylcAGuhMJNc0VC6OQlhhgWpVYhrj5OTtH+dlyqp5ArHy1bJvKCmGevg+UMzuQIQPuIUgHqFKt2WzSeeiBKj83fGV1SW1YIbUFXPvf+nIpI7NF7b5UasI4j/K9hfDB59Ga2P6xCsRVZdwNrhzGZBf43V0u3Ev3BWDmFJSpgkvG9mmcHTZnJevbu5fszM2T/bp2kZNfuTlo/AyCOZU1vlZoqzSYtZZuT4DMKNpXXAdnrojy3PK0eJMGiVwNAovqE9jAKg1RsDRXWtSDYFam+vZDSKABZqCS23rWRrsP0PIdvcBd5PkvVyqfWN0P4RliCHEsGLhZ9Y/BDBk61RoskT06psiNp4xU1QB19hI8sA+P0bIJX97a18blAEIdszV4oCohqgeS9ovx3gehajNkFgG6miTAE4Qw2t2+vCQeKa6o8Wuf2jfcanZY3zs9Lu8MihVaqKvZlLG9vfus9e3GrIw3w0pd1MyN210nr3i1SUdEG1QrxO9Crw+zyEU0P6ahjOLYLLLNZYxDSXMFsYN96hsKHljmLV0b+n4ICTTAtIpF0GgBxGnR6EW7i6CNWBkTsBwDN2OfhP4RoihUQwbcSSCkdP+rDRfe6oL70B+PJ2RH8WahEKmoctUZEKBkNwYMpP2iDVpG9yQ98+Fyea3L3mePN0uJoXnC9QQggw5SEa7eUSjrcr33UAT4bt/rzZSj+8Rwr4nZS7f5jqUe71QFttPBwJq4KDeW0dWEhF39T6dfw0X4y+ocKa2skW17Sn3J+xHcodNcBSpjHCzNlQ5KQmS2x3RTwX6NFzHTZZFQMbtGWcUiWM3cRPu0aLRidBcx597W0/26XLf2p0b/aOWCFwgtQIz9b0pCTB1Luh4h+gqo1BbOUc4FpnVwlZo5f2OdQHjSPouC4V4Ht6KKvWV1fKghgFVO7lp3DpfLJRWlRWJDZpKYeHUDLnU5xe30tqc8j0em3zxfqgpyJDY5Q2yOGGXpjuk1WhLKaiQnJ0e6du0a8LiyDjxaUvuMlOQu/cQei3twbVVL9eQ/OPWmIfQWdsK93Ci8h0e5sYzCm4RU/Q9WHdx4kIfz1zV5squoQuXn1vllIba1mIE4zkiJV50BAjesRDd8Z+vLC65H+0Z3AG1lgp8kPjPQfgixQgscr7UmsLnGOGvDktvRidFdBDN2CCjzVpb0uoEo67PDrny/0QcZjQDhGDLM/S98c3MKkG3C0D61xdu2z/UOwh/tWBkoan17Yf2E8Mcm9WWNIu2jKBh+X1SGTohzqN8XObNVVpxase1NNehNOWn3uL1W8Pi4fVUq7Q6V8Qa4qqskPmM/ic/oLqW7toi72ivIM/uNlPTew2XrvFmSt2VlwOPqktFL7HHJ4qqpUvu1O2olpKkapsLmNXaYs5o5mFuewpvUvdCtiuHAj7BLeqKUVjmVH2GF6uxFbLgJ1fomaus2ovMhxqeN7ysbc4vDKmhiDkrSwUjwsYRlXX2OzaY+42+Th1B0k7DQAgdYle62co2K9mnRaMTslqTLcxsr/uFZC3EIiwsnDfL1R7qdYb2Vm4pZgBj7XxgcMlPiVbo3nz93beVA5QJVa+02Wr29xwPfcI+vz+ZMTftGBzW+PGeVz68bQhbGLHRbSOnn1bsYbHnbGNymY2Lj6hQ8RJpJm80hMSmJYrM7VINK7zFQqksLxFleJJXlpWJ3OKT3IadIcqxHakrquirZ7DGSmJwi8Skd1Ps9Tq/4rnVCt541rA0I1a57SheMo7GMFm8StPofLhYEbyDyHiBgA9NexpsPrOG40HEjgTUa0fm46CC6jZHToYgXc/Cbiv6vzUeKfaYlxiqLEK5zWrpJuBgFjlXpbivXqGifFo1GjO4iOggX1ka7LaZWbO/LIBHrsClrJPpOY9EcswueMQuK1ayfsf9Ff1dR4xSnEv/e3N3o/zAg8NRaNYFZ1GsrvJ4VDLXfJW0TtA9dHEkF3TrdvnuiV3J7fbzxG6NFJMYliTi8BXc02Co+OUlqnC7lhuRbHhcnHTPTVZvZsSFbyor2SmJSsjzzv88CzpTc8/4iWbm9wPe6tPbejN1qo7fOYILrR6fORFzC2P6d6q0iHC1QeBM/zGmGPl+8xTdlihuKN7+sv5VQ55xFBTajz6PR4hJK52+0MlmlecMFjmhtNW2VkcSbCmkQWuDMWb5NvliyTd2MAqWI47Ro9KLdRYxBuLDgxdsdEh+7L00aLJDo3yCY+3VJ880aJsbFKCOEFkpwweuZlSKHDe4aUIAY+9+lG3fLJ4u2+PKHo42iX1y7o9B3LMbpfa9PrUOlnsPxYluK7vaN8Z6IgZzxngjBjRkOPeuC3757xxTZkFvsNzBMivfel81epNpFBY04Ni1L7GUlanmwmZID+3SU7B2FvplCzML4spMY3E3Q7tD+9YABoptuT/ug8CYBwcVjtEDj5uEd2dadntcXvtGfK1zfWG1lwvsDpXkDWI4yyfB74+iZNAS0m3MmDpKRfbJk5vxNviDeUGMRSOSj3UUCBeGacxMv3LBblpniZHTqQPSHeqo9VKvfQf06SbfMZL8ZyKzUBMlJipPKaqf6HK119JS+yqlcu0zNQBaWs/22Y4LdE9GWtOuTnnmOsXsFr7ZC22vLyUO0m+/bOk2h92+bxKWki1QWB7xvYxCAa+LLpdvUYFJqZ1d87leGzDt4r46T0W2e7IPCm4Q02tY+Wt6bDSa46lq9daS/3jYBFa3CnOaElWn2kq0BRbcmJTGWwUOkWdyrWHKbAIjjk8b0lmzD1HowtyQEnKNCYCCfbiw3FjFpyAwk+tjb31moXP50n4t9asFtBG4Jz3+1Sk4b31e1c9I+CXRPVLMvteXklTuHw64EsbHoDton4rOs7ts67SDAfTs2MU08znK/mBbtNvWHoW9Etcvf1u9W+3SYjkG7mGA5BqQ0XlhD4U1C8nPU6dUgqPUUV6DRs962Ib6xGFG//8uGoNtoKxODh0hTYBY3nJ4nRqvzgG4dlKg2xppYuSWVVdaIPQRLdkP7Ld0u8dk62xO6YbPo1i4nupQ4XF+YWrD9EuyeiN8Y7RH9Fu7XaKP9u3hjrJQLVIxdcjzltdvuu2/rWABNckqKlJeXSe9ug3z3bZ3W1ziDg4wq0AMYYJZWVEuV0632A1GPQjk4juraZUiLecq4vjK8Fwd9Zii8SVCMabH0SNo4xeVrSIaLGNs11DcW0fyd0xMDupoYrUxM80aaGopuYmZM/06qD9TuIlYWbSxPTqhbndeKxvZbuk9G8CQs395ecV86QewX1lA91R+ulZ20LYz3RJ1C0Ajutd07pqoMYGiHd54+RlZvL/CJZm0w0/dtgPsn3qdJSIiXpMQE6dk9Xd23jWl9rZIcoG1lpibIQX2zlHvVjr2lEhsTI50TYyU9KU4S4hCE7JGP5m9S0+GccfGH4c4kKLoypbF8rZ7i8pWwNQhv/N0xNaHBvrEYtcOPEUFJGLHrm5wewWO5TsTPNG+EkOZE+7XqPjCQGwlETee0hJD22dh+Sx+P6oNtNklOiJHk+Fj1rIIra8vLG1NiWpUKJ+0DpNKF+IXoRnssq3R6Zzlq62QY74navRNC99I/DZXR/bIkPdm7DvdoiGX4gBsNZQAzI9iXvm/rtL4I0MXgDgY3c4XUnfllkldYrjKNYVYIgZR4L0S3BvuAgIeQJ/ugxZsExZxfVk9xGqe4vH+LOGw2OXxIVzl1fL9GBfRoi44xKMnqhsc0b4SQpsbKr7VTWoJs21tmKZi1H+uGvOKwiuY0tk+eOW+joZhO8JSYnB1snxjdPfAb4zf1ZtTx1AZOegdbVm1Lu9DBgv38Vyt9VS+1BRuFnyDkkQEFMyWwYqMNwy0JbV+n0QyW5OCX1bkycL/0OkXyjHDGpS4U3iTsADRMcZZUVCvRraeiEDmNAjuIwm/qKppWoptp3gghTY2VXytE684Cr8WuW3qS7C2ttAzCRR8ZrPpvU/Zbuk9GXuWCsmo/FwBYLmHVNMLZwfaH0d0DsxdIR+krqISCOoYqztp1xKptoW2eNr6fX0VoCHbsDzPM8JDCQA1tBgPHpZv2KIs2YhaMSROsgAaARb5bRnBDG+Ox/KHwJg0KQNM+ZObKbLhwcfOBRaahfl3BqmgCRkqTlgDWIF3ymESX0AFG31YsQ7+DsvFXHD9M5euGe0eo/VZzVO3Dfv40sqfqc5FesLC8Wh2rN6OFfwAoZwfbH8YqzsbZZl29Gei6Gd0zk4O2LaPxbOH6XWo/2Ed6kn+QsG7zcCPR9TrwUWhPCNTE7LZ2u/KmYLWpVIX1wRkXfyi8Sdggf7YxcMOM9utqTCQ907yRtuJmANcnzMKY2zKFeeQKHfi27sgvUyn5dDES/IeZPqTz69M5VcWimNuGn8DZsFtlQ4HlEIGX8AHXU/mNFd+67eGzf1qVo47VXGwMAhxWTYgy5lFuf5irOJdXOWv9vN0+9xC0AVSFhD93fW1KG8+8Obe9mceM6KJ1eiZbpweEvkaRMY/6XG+9Du0jjqqtOn1woPgHwBkXfyi8SaNuUM3p18U0b6QtuBkYZ3EgmkIV5qR9Ch2InO17yqSyxukTsxAfuky70+Vdjyl+qxk+tANYxJHtQeX1NrSLxswIWg0Ke3VKlSqnK+j76quJQNoegao4+/t5ewvkxMU6VEKDUPl9y946Iln7c2vXUZ0JxYhq/3abGgCooqk2mzjsdqmucakBajA/b864tDHh7Xa75ZlnnpEPPvhAiouLZfTo0XLnnXdK796962z79NNPq22tmDZtmsyYMUP9PWnSJNmxY4ff+qlTp8qjjz7aTGcRvSPxlvLrYpq36KE1LMlmNwMzWP7q92vUzcXYFs3CnGmz2r/Q2V1cGVB0ayqqa9R2PTom15nhM7alppoRDDQo/Dk7R1m1IbxgqQyUa5zpBNsXoVRxRtMKtYqzHrT9vnmvrNxeUKd9wL0E+1JFmtTOa/eP9IW1+8ByFeQJ3SYiSXHerDqwgiPzCVxhdHYVI4zHaoPC+7nnnpN3331XieYuXbrII488In/729/ks88+k7g4/x/xwgsvlDPPPNNv2cyZM+WFF16Q8847T70uLS2VnTt3yosvvijDhg3zbZeQEPqIkIR2g6oP+nWR5nDxaI1ZHFiB4PeYlhRneTxN4V5F2kaxsILSSj+h4/VlNWGDv7dXeJtn+Jp6RjDYoBDCDEshvHpmpai2ZzXtz+C29kdTVXE2D9q0NVu7InVJT1LtCGi/boAsZVDZuvy8Rvt8ewvniHTpkKzanzHQ05zxh31iG8rjXV1dLf/973/lqquukokTJ8rgwYPliSeekLy8PJkzZ06d7ZOTk6VTp06+R0VFhRLYN998s3ovWLt2reooR40a5bdtampqK5xh5N6gWiJfLYkOcGN48etVynKsB3XakozlWN/aszjaIqRvUMHEFGm/Qgciw1yV16y7tabFdsbpeJ0rO5wZwVAIJOTx2bA2YlBYWumUdTlF6gG/ciyzMoKQ9gOMDqVB+htzFedQB22wdGMGR7eb9blFSoBXViM3+L7tEEyZEOfNllKrwb11PPQLj0dZuTHbonKJJ8WpGAh970cOcfiecxawLq2qilavXi1lZWUyfvx437K0tDQZOnSoLFy4sN73P/jgg7L//vvLGWec4Vu2Zs0aJbSxH9J8N6hQoF8XaQoXj+YuwBDKLI4W3DqQKBAsVNJ+MRbK8WFt7LYEbaisqsbXlszuKVbbhyKGAwl5ZJOoqHbVugd4Rbi2ZGLqH77BGhpB2m/FSnNz092PVRXnUAZtCMqsqHb62g3AJij5roIqa91LYNHGw5yaEu4lcQ679Omc5nMtUVlX0pNkv8xkuX36aPnXaaOVBZ6W7jboapKbm6ueu3Xr5re8c+fOkpOTE/S9K1askG+//VZef/11sdv3jR9g8U5KSlJW9KVLl0pmZqby/z733HP9tgsXjATLy6Oz+hICJzD61YzsmSZLNuyqN1/tiJ5pUfudkdCY+8d2qa5xBt3G5RL54fetctKYXs32tdptHtXOrYCYcbm962Dt8eAGFWA/FS6XFJWUcqanHZIWJ3LiQd2VH2y107pvU0FlkCUekZgY/7aAPnJ3frHkFZZJUfk+X+uUhBhVRtscfIbta6oqJZhNE22yoqruFrBW5uSXCSYUdWylpzZmSud4xvqYjl5RNKhHB/bF7RCk+4uxJcnekiopKK/2lXyHIM5IipMYG/pHV8C2tGzTbnEZBDnazd7iSjUQq0J/52vmyNrjtWxjwBgX51BtXVvAtZUboh3tCdUqIQnw2UC3dfR/uOebZ7rNGiJSgfZs88IbriLA7MsdHx8vRUVFQd/72muvyYgRI/ys5WDdunVSUlIiU6ZMkSuvvFIWLVqkgiqxv6uvvrrBx1pTUyPZ2dkSLRSUOyV7V5Vszq+WapdH4hw26ZMZJ0M6x0tGUowcmOWWXzaVqZGyGVywh/ZNlr07N8vena1x9KS9sCC7QAIZm9HuSqtcUlHjkTU7CmThmu1+bbAp6RhTKesL91kIzbhd3pLNyXE2FUcSCNxb1q9d06THRloOSIMR3eJk8fbKOoYFX5BZrRhBEzS2hYxEuzz56VIpr3BKdc2+9+bXOKWgpFIykxySFLdPfAzIigvpnlJZXlrnGskvd0qN0yN22CwNMzBGkQXyCkokKzlGMjw2yc4uCfVrIG0E9Es7SiqluNyl2macGrspPw8pKq+S4vIq1a4O3C+hTluCv3ZBYbFFu0EbsUkshLPbO6sI0I4cEPJIH+hyy77wYvGl08QxuJwuiRWb5BeV+Prn2oQnkhJnk4XLVqr+2agh8JHoG5ur/24rIDlIKLTq2euAR/h6G4Mfq6qqJDExMeD7MKKCDziyn5h59dVX1ftTUlLU60GDBil3lueff15ZwRtq9Y6NjZUBAwZINLBye6HMWblZ9pbCB0zn6BQpQpqtMpecNr6nTBmSLmMOqJCFG/bKyh2FvhHtsO7pMrZ/R5VGi5BgoM0krP4joGtHfmmFQFrYaw2FcYnJkltpk93bRaaO7ibDeqQ32Rfccb8K+e8P6wPO4qS7KtQxdclIDpo2a2TvTBkypEeTHRdpedAWCr/IlpzaYiXQtRXwmfZqD18lv2q3TfLKPJKSECspCQ6pEIckJdulS5xLlZc3t6TiapEOqYm+KoNTDxkQUj85rmK7LN/i726SV1YiMTG1BU7sXn9dWCNjTNmAajw2OevIoU16rZCWw5NaIL9sWSWOIFmeSmpsMmF4fxnSI6POuoxNf6h+Fu2jsLxGCiqqva4kNhSEskt8rE3s8OFGkGR6ouQVVigXFLiuGNtSnMe7HfJ2d6lts3lF+/pnrapi4uJk7naRA3qmyh/bC8TljpOEpH2G1dxKaZb+u73RqsJbu5js2rVLevXaN42M1zpY0oqffvpJTalNnjzZUiDjYWTgwIFKrMPqnZFRt3GGAjraUKcR2jPwpX3n180qg4O5PG1JpVMFY2D9jaeMlD7dOqrH9No0RAykJOGSGB9bx78aN4ldxZXKmVZbGb2V0/Z1V7OX7ZSendObzIewT1KSTD9EAvqbd+qQJB2SXJKE+soBgJg6/IAeUdFPRDJoCxcdPUTe/nm9ytagM4fo4DO0RRV05oC92etrjf4vI8Uu8XEOSXI4pFumt5x3HfFd6ZTuSfFy0pjequ8MhSMO6CHZO4t97VINBmr7ZRAbgwwTcGmJVT7f5nSCI/t3Zd/cTtlemOerWGmZUrDWz3tHYbWMGVi33xnZt5P8sHJnbcXLWs+S2jSEXp9urysSAiTTkxMkMS5WBZIXllVLh6Q4Ka/yFn/C37gO8AwQQ2Dsn/WxYD9VTo98vHCr9MpKCWikmN3E/Xd7o1WdbiCuYZlesGCBbxlyea9atUrGjBkT8H2LFy9WqQLNAZQQ48jhDeu22R88KyurwaI7mvh6+TY/0W0Gy7F+zvJtfsspuklTBerqDCJGICKaO4MIou8RhY9ofJ25R0fnX33CcDn/qEF1g+9qYdqsyAJtAb/5CaN6ybCeGaogDoRth+Q4JUTgYwtxCyGCjA4YPELc6GwiCDrDcqzXqf1gadQi/ePfNst9MxfLrN/qDxzWpeh128P+jOkC8ReC2np0TJH9u3XwPfA+XDfsm9svCKy1akvGtof1gYK6+3dNU1ZsXXzHXyp77+dos0m1AZQ6SPKAnhny3CWHy//+eYw8+7fD5ZFzJ8glk4dIUnyMZf9sDPTEegz+8BwIV5RngGpVizd8u88++2zlg40gyO7du6s83l27dlXWbDju5+fnq1SARlcUZEOBFdsM3EiOPfZYeeWVV6RPnz5KnM+bN0+9vu2221r47Non89bk1Zs3FOt/XZMn50wc1EJHRSI5kwSKzxitzOaUfTpllpnmyE0crFoq1uly4CsM+caRvQcluaPVehOpGNvCh/M3+mV3MObKRjCaznQDsaHbgRYxXdNF+eNCAAWrihos7ZqxFD3aHgQ1rhNjERSNUZQzs1T7xZhtydiWrPK0B6qZsSG3WLmQaIs5ZkaM6TJttfsur3b6RLNuVw99vFRVRsUdf+vuUvUZMG5i8Oh1V/G2NXMb1P03nnG8gVjRDP13e6HVPdz/8Y9/iNPplH/9619SWVkpY8eOlf/85z9KlG/fvl2OPvpoVVwHmUk0e/bsUYGVVlx33XXKEv7YY4+prCk9evRQovv0009vwbNqvxc6pphCAdvRvYQ0Fm3N0y4eRhFjtqTUba/WN5umwmq/wYQ5iVyytxf4vTYKH/i+6qIkVmIDFkVtdTRbq8MpvmRse9v3lsp/vl1db2YpDAhJ+y/qZMSqGmqgdJHaYo7CNtqFxJgZBQ+0k/xSBGpWey3jtYaOPSWVvlSW+6pS2morssInHO53+/y3Vb+NKq+1/bdOvWp1vNFeYK/VhbfD4ZAbbrhBPcxANCMvt5nZs2cH3F9MTIxcdtll6kEIafuYrXm6o7ay5hlp7dzE0XjDiEZCyfOOtor82VZiwzg1b3aZakglSwC3EuOA1QxdnyLHFQ+zIvVhNbNhbTFP8s2+6FaD9lNZ7ZSk+FglqCGygdGv3FiVUg8y8wrLldW7vMqpBpy63VerQF+vqA8kuttC/92aROdZk4Aj7PRk/9SOgchIjovai4Y0Pdqah8ILp03o5/NRDZZBhNPopK1U68UAUVf1M4sNPfUeyGWqocWXgsUksGJgBBd1CnFmI1C77ZAU7+cz7rWA29S9X/uM68GinnzEk/bZ1oNHWKs37yrxDTiNgb8o0FOfPhgeYiG+SKTVLd6kbTFhUFeZvXhLUD9vW+12hDQHhw7uqqY4OY1O2ovlUVkUM5JU5hMj2nXKymXKbBlvyNQ7XZ+iyxXPjEpLOaZ3QBelQO3W6DO+dmehpHWI8+0DrlG7i5BWECIabdebNQduKtgGg8fCsqraQGIU1PHfNyzdEPMVNU61jZXxxBHlblA0WRI//jSih3TPTK5TplaD5Vg/eQRzFZPmwZzFwQyn0UlLE4rlEYLkiuOH+VmgkXawY0q8z5IIIYIsJutyinwPvMbyxk69m98LVwPS/rGa2cCgDb93jdMls4JkyKmv3WJgKIaZmOKKatm8u0SqDeXk8YwBIVILwk0FQjoh1muzVdLcNB6IsdtUlh2HzWaZ2cRht6n+PZqD0W0eY9krYgnSEYLhw4dHxTe0Ystevxy2xrywyNN51mEDgkbgE9IU4CbCDCKkLfWL9flUG/tFbb2GIILVEaImWD7miUP3k8uOG9boa2b+2jw1Y6Sz7sDqCQEWzUInkli6cbfMWri5juAN1A7ra7fIUoKMPRj8IT83tiqrRN3KupVbkVITg8ite0pV+3a5PGpwac4dD3GO/aHNw1DHDFD+UHiHQLQJb7PoqaxxSUKsg2nTSKsRrdHvpP0PBvGeJz9fIZvyigO68EHU9O2SpnKHN1QghzswIO0LtCPU2fhyyTZxWQhd428NC7mxHQVrtxioYWCIbeCvDSCa0ecaQf+Lz8HMDQQ1gJ84PidY9pLbp3vLqLP/3geFdwhEo/A2QtFDCCEN7xef+/IPmbsqJ2j1QQgauBM0JLcxRNOLX6+qNy7CLMhI+0APqnbkl/nEsVX70QRrR+Z2q9vO6h2F+4Ik3R4VIOmrXq3cpmJUG9pXEMqjrN/BAuAh8BEwT/yhCYnUC0eqhBDS8H4ReZLrqz7YkMwmGlgtg4luEO3VAtsrEMZ6JsNcXAzgVzdWTa2vHZnbLQZiCNA0eh0jNz0Etc1QZEf7ikOcpyXGBqyvYCSaM5cEg8KbEEIIaSZ0PmWdScJc1t0oXnRmk3DRhU7qo6HCnrQeelBlLi5mxJjuryHtaFS/TjKga5rfwBDpCDt3SFQP/A2wLjMlXq6cckDQ1JgAe4nmzCXBYDpBQgghpAUrEAbyh21IZpNQCvzs2zZ6qwW2V/Sgylgh1Qpj1dSGtKMxAzorX+9AZen1MrixDO/VUal9c0wBrO4YAJRW1Ejn9ER55ZtsBvdawKuPEEIIaUaQWSQUGjI1H0qBn33bRm+1wPaIeVAVqPIp0FVTG9qOjKkHrQaGWGbMv21Oc4iAy217StW6nlkpyn0Kxw4xDx9y+KkTL7wCCSGEkCbEnEO7MRUIW1vYk9bDPKjSFVKt0FVTG9qOGlI/QRdwuviYIaoi5oAAFYdhFYd13JxnPFqhqwkhhBDSSOrLoV1fBcLGFBXBZyzfvJfVXiMQY/VJXSHVKh88rOGNbUewYnfqkBh2yky0+/pKwujg3pMbkLUn0mA6wRCI9nSChBBCGp9DuzmLQjGPd2RilSpS+1LrAneoEnn8qF6qonRTposMNR4AlTNDiTNgekEvtHiTkKZNdVQzIYREC6H0fcZ0b1boaXZYEvXUPB7BRE1D+lwrayXKd4/o07FJhD1pHaxmS3SGHARCwh37tPH9wiqOFGr7CkV0M7g3fCi8iSUsPUwIiUbC7fvCyaFtnGY3i5qm6HOxHUQ2jub3LfnidLmVCMdrlo1vvzTUBaQl7ulWWXsCb8vgXkBXkxCINlcTTlkSQqKRhvR9TTHN3lR9Lvvu6CDclJDN3S5m/bbJ54cejIZWZo00mNWENGjalNHJhJBo7/saMs3eFJ/bVMdP2ifhiO6WaBfNnbUn0qDwJn6w9DAhJBppSN/XFDm0m6rPZd9NWqtdNCQVYTRD4U38YOlhQkg00tC+r7E5tJuqz2XfTVqzXZgL6gA84zWWN8aVJdJgcCXxwehkQkg00pi+rzE5tJuqz2XfTazbTNO0r1AJNWtPtMNvhfhg6WFCSDTSmL6vMdPsTdXnsu8mba1dUHQHhsKb+MHSw4SQaKQxfV9jptmbqs9l303YLtoHdDUhfrD0MCEkGmls39fQaXarz3W7PWI3WNBDyQjBvpuwXbQPaPEmfjA6mRASjTRl3xfONLv+XIh1pHRbl1Mk63KL1DNeY3kon8u+m7BdtA9o8SbNUiWLEELaG+2972vvx0+aB7aLtgUrV4ZAtFWuNMPoZEJINNISfR+s2i9+vcrf1cTjEbvN39UEvuLhCmf23YTtou1BVxNSL4xOJoREIy3R91kVODGK7sYUOGHfTdgu2h4U3oQQQkgrwcI3hEQXFN6EEEJIOylwQghp31B4E0IIIa0AC98QEn20uvB2u93y1FNPyeGHHy4jRoyQCy+8ULZs2RJw+48//lgGDRpU52F8zxdffCFTpkxRwZBTp06VuXPnttDZEEIIIaHDwjeERBetLryfe+45effdd+W+++6T9957T2w2m/ztb3+T6upqy+3XrFkjBx98sPz8889+jx49eqj18+fPlxtuuEHOOussmTVrlhx22GFyxRVXyIYNG1r4zAghhBCpt/BNoNzh4RTQIYS0D1pVeENc//e//5WrrrpKJk6cKIMHD5YnnnhC8vLyZM6cOZbvWbt2rdquU6dOfg+Hw6HWv/zyyzJ58mQ5++yzpX///nLTTTfJsGHD5PXXX2/hsyOEEEKCw8I3hEQXrSq8V69eLWVlZTJ+/HjfsrS0NBk6dKgsXLgwoMV7wIABAd1WlixZ4rc/MG7cOFm0aFETHz0hhBDSNAVOkKd7dL8sVfAG4BmvsRzrCSGRQatWrszNzVXP3bp181veuXNnycnJqbN9fn6+7NmzR4nyN998UwoLC5Vf+PXXXy99+/aV4uJiKS8vl65du4a0P0IIIaStWL5PPriverDwDSGRS6sK74qKCvUcFxfntzw+Pl6Kioos3UwA3EoeeughJbLhIw5/7k8//VScTmfA/VVVVTXqWD0ej/o8QgghpLmp4VdMSLsiKSmp7QvvhIQEn6+3/htAJCcmJtbZHi4kv/32m3To0MG37Nlnn5WjjjpKPvroI5k+fbpvf0YC7S8campqJDs7u1H7IIQQQgghkcfo0aPbvvDWLia7du2SXr16+ZbjNQIorTCKbj3CQEYTBGSmp6er13i/Ebw2u5+ES2xsbEDfckIIIYQQQtq08Ia4TklJkQULFviEN/y0V61apbKSmHn77bflySeflB9//NFnIS8tLZXNmzfLaaedplIRjho1SlnFtfUbYP+hjkQCgX2HOo1ACCGEEEJIm8pqAl9sCOxHH31Uvv32W5Xl5JprrlHWaaQEdLlcsnv3bqmsrFTbw6UEvtY33nijrFu3TlasWKFSEWZmZsqpp56qtrngggvk888/l1dffVXl7n744YeVi8h5553XmqdKCCGEEEKiHJsHSrYVgbh+/PHHlY82BPbYsWPljjvuUO4j27dvl6OPPlpmzJgh06ZNU9tDREOoL1++XInwQw89VG655Ra/zCgonIOgS2RNgXsICupMmDChwccIgQ9QCZMQQgghhJB2KbzbAxTehBBCCCGk3ZeMJ4QQQgghJBpo1eDK9gJSCWJiQFu+CSGEEEIIMcYtDho0SOqDwjvEjCaEEEIIIYQ0Bvp4E0IIIYQQ0gLQx5sQQgghhJAWgMKbEEIIIYSQFoDCmxBCCCGEkBaAwpsQQgghhJAWgMKbEEIIIYSQFoDCmxBCCCGEkBaAwpsQQgghhJAWgMKbEEIIIYSQFoDCmxBCCCGEkBaAwpsQQgghhJAWgMKbEEIIIYSQFoDCux2Tn58vhx12mCxYsKBFP/ejjz6SQYMGBd0G67FdJLN9+3Z1ni39/bclvvvuO/nzn/8sBx10kEyaNEkeeughqaysbLHPZ1v0wrbYOoTS/ghpSdpqm2QftQ8K73bKzp075fzzz5fdu3e3+GdPmTJFfv755xb/XNK2WLRokVx55ZVy7LHHyqxZs+Suu+6SL774Qu6+++4WOwa2RdKasP2RtgbbZNuHwrsd8sEHH8gpp5wiMTExrfL5CQkJ0qlTp1b5bNJ2ePfdd2X8+PFyySWXSO/eveWII46Qa665Rj755BOprq5ukWNgWyStCdsfaWuwTbZ9KLzbGJgiev/99+WCCy6QAw88UA4//HB58cUX/bb5/vvv5YYbbpAnn3yywZ+DfUybNk19xuTJk+Xf//63TyzdfPPNMn36dL/tc3NzZciQITJv3rw6U1lYd9lllyl3gyOPPFI+//zzsD4v1PP+5Zdf5Mwzz5QRI0YokffYY4+Jy+VS67CvRx55RL0Px3H66aeHbZX//fff5ayzzlLvHzt2rFx11VVqZkGzdu1aOffcc2XkyJHKyjt//nyJZOr7TS688EK58cYb67zP6XRKaWlpyJ/DtlgXtsXWae/vvPOO/OUvf1HtferUqfLtt9/61j/99NOq/7n22mtl1KhRambH3BcWFhaq5RMnTlT7wL4wMxRsH6EAN64333xT9Um6/4MBZunSpcoIg2XY79atW4NO60eDC2Ak0ZbbJPuoRuAhbYqBAwd6Ro8e7Zk1a5Zn48aNnieeeEItW7hwYZ1tt23bptbNnz8/rM/48ccfPcOHD/e8/fbbni1btnh++uknz5/+9CfPP/7xD7Ue+8N+N2/e7HvPiy++6Jk4caLH5XJ5PvzwQ7Ue1NTUeE444QTPGWec4fnjjz88S5Ys8Zx88slqPbYL5fNCOe9ly5Z5Bg8e7JkxY4Zn/fr1ah/jxo3zPP7442r9tdde65k6dapn3rx5nk2bNnn++9//eoYNG+b5/vvvQ/pOcF7jx49X+9u6das6l2nTpnnOO+88tb64uNgzYcIEz2WXXeZZu3at5+eff/YcddRRDfr+I7EtgqqqKvXbn3rqqSF/BttiXdgWWwe07ZEjR3reeustz4YNGzyPPPKI6nMWL16s1j/11FNqm/vuu0/1EehnjH2h0+lUbf/EE09U/RD6qbvuukv1Q7///nvAfYQC+poRI0Z43nvvPfW+22+/3TN06FBfn4f9T5o0yXPVVVcFvTcY+2XS9mmrbZJ9VOOg8G5j6AvAyNixYz0vvPBCkwnvv/zlL567777bbxkuSuwL+3S73Z6jjz7a8/TTT/vW48LVItd4Yc+dO1f9DUGtWbVqlV8HX9/nhXLeENann3663/qvv/5adUgYIOD9uiPR3HjjjZ6zzz47pO+ksLDQM2jQILU/nD9AJ7R06VL19zvvvKM6QAhwzZw5cyJeeIfaFjEAu/rqq1WHjkFSqLAt1oVtsfXa+7333uu3DH3ONddc4ydQjH2AsS/84Ycf1N9r1qzxrUdfAuGDayPQPkIV3lpUg3Xr1qn9vP/++75lEGXHHnus+pvCOzJoq22SfVTjaB0nYRKU/v37+71OSUmRmpqaJvvWVq1apaaJPv74Y98yDMLAhg0bpEePHmr68tNPP1XBc9nZ2crN4qmnnqqzLyzv0KGD9OrVy7cMLimJiYlhfV59571mzRo55JBD/NbDZQUgoA/ADcQI3puWlhbSd4JzuPjii+Xee++VZ555Rn0WpnPhUqLPs0+fPpKamup7D1xSIp1Q2iLcSv75z3+qaW20EUx7hwrbYl3YFluPgw8+2O812vKvv/7qe92xY0e/PsAI+gisGzhwoG+ZzWaTMWPGyE8//RTSPoLRt29fPz9eoPtOEB8f32KxFSS62yT7qMZB4d0GiYuLq7NMC9WmwO12K5F56qmn1lmngyaxDgIUghnCFiLT2PHXd2zGwM9QPq++88b+0GEE+/z//e9/kpyc7LfObg89jOH6669XPt4//vij8mVHlg74NCNjh9V5tlZwa1tqi7t27ZK//e1vyqf05ZdfVsGW4cC2aA3bYutgvqbRPo19iBa8VuC6sOqjsA/jfoPtI5xjC6V/M16rTWm8IS1HW22T7KMaDoMro5D9999fNm7cqDJR6EdeXp48/PDDUlZWprbp3r27Gml/+eWXMnv2bEvRDIYOHSrFxcWybt0637JNmzZJSUlJWJ8XiuV1xYoVfstee+01dVzYvxaBxs9AkMmHH34Y0v5xfHfeeaca+SP4BJbbV155RVnkV69eraz4OC/kTteYjyfaKCoqkvPOO099J2+//XbYohuwLdaFbbH1MF/Ty5Ytk2HDhoX0XgS0oS+EldHI4sWLZcCAAdKSxMbGqmdjkLMOvCTti7bYJtlHNQ4K7ygEFsqvv/5aRTNDTMK6e8stt6gL1GiBRhYSpIwrKChQuUGtGDdunJr6QnYLdAjoJJAVxTgiD/XzggGLOfaPbCjYB6zSsEYfffTRSrwdddRRSjgj4nvbtm3yn//8R63v2bNnSPtPT0+Xzz77TO644w4ltvEZEO2YUuvXr5+ccMIJSpRfd911Soj/9ttv8sADD0g0M2PGDPVdI5tMZmamyimvHzrbTH2wLdaFbbH1eP3115WLHa5/FIPCtY7BZSgceuihSuigj4DbFfoRZIiA6Al1H01F586dVd/36quvyvr161W/fPvtt1vOYJG2TVtsk+yjGgeFdxRy3HHHyRNPPKFEKtITYcpowoQJyrXEiPZvPuaYYwL6f0FgQ+BCnCK93KWXXqpEOoRYuJ8XDFicn3vuOZk7d67aB9xAzjnnHLn88svVeuwfxwvxjc+HaIa/NqoqhgKOFxbuHTt2qFSEsKQjlSBuXPBrTkpKkjfeeENZkmARx0ADojFawVQlZkIwfY0OHBVUjY+cnJyQ9sO2WBe2xdbjjDPOUNf8SSedpFKuYQA/ePDgkN6LqXu8F30V0v6h74HAwcwcUpC2JHAvwIC4qqpKxetAeMGNrmvXri16HCQy2yT7qMZhQ4RlI/dBCCGEtGtgGcQsDmb6CGkLsE1GJrR4E0IIIYQQ0gJEflqGKAJVzODuEQy4jWAKMpq45557/FIZWoEqoEgfSJoGtkW2RVI/7JtIW4Ntsvmhq0kEAX8+lG8PBnyVQw1ojBSQdcOYZSVQMJIx9zhpHGyLbIukftg3kbYG22TzQ+FNCCGEEEJIC0Afb0IIIYQQQloACm9CCCGEEEJaAApvQgghhBBCWgAKb0IIIW0ClpUghEQ6TCdICCFtGFRo/e233/yWoYJqVlaWHHXUUfLPf/5TOnToIO2Z4uJiuf/+++W0006TsWPHtvbhEEJIs0HhTQghbZyhQ4fKnXfe6XtdU1MjK1eulMcff1yys7PlnXfeUWXC2ys4h1mzZrFqJCEk4qHwJoSQNk5KSoqMHDnSbxksw2VlZfLUU0/J8uXL66wnhBDS9qCPNyGEtFMOOOAA9bxz505xuVzy0ksvyYknnigHHnigEuJnnnmmzJs3z7f9008/LZMnT5ZnnnlGxo0bpyrZFhQUSGVlpTz22GPypz/9Se1z1KhRcsEFFyhLtObmm2+Wiy66SN5//331PnwG9r9p0yb5/vvvZerUqTJixAiZPn263/vAokWL5Oyzz1brDz74YLnppptUoQ6wYMECOffcc9XfeIZrjeabb75RVvDhw4fLoYceKvfdd5+Ul5fXez6EENJWocWbEELaKRC9oGfPnvLoo4/K22+/Lddff70MGjRIVbF99tln5eqrr5YffvhBVa3VIn3OnDnKTQUiNSMjQ/7xj3/IwoUL5brrrpNevXrJ5s2b5cknn5RrrrlGvvjiC58by7Jly2TXrl1KhEOs33XXXXLJJZeo9diH3W6XBx54QB3D559/rt6D/ULEjx8/Xv79739LUVGR2jdE9syZM2XYsGFyxx13qFLVeIaABp9++qnaDwQ9/Nh37NghTzzxhKxfv15effVV3zFZnQ8hhLRVKLwJIaQdZPtwOp2+1xCvCLh8/vnnlWUbVurXXntNCWWjxTghIUGuuuoqWbNmjRx00EFqGfYDi/MhhxyiXldXVyuXldtvv12mTJmilsEqjWUPPvig7N69Wzp37qyWl5aWKvHcv39/9RrH8N5776nPnjBhgloGwf/QQw+pgMm0tDRlSe/bt6+8+OKL4nA41DawfJ9wwgny4Ycfyl//+lcZMGCAWo5nPHC+GEgcfvjh6lnTp08fOf/88+XHH3+UI4880vJ8CCGkLUPhTQghbRxYjWEZNgLrMsTuvffeq6y/ELgALhxbtmxR1vDvvvvOF4xpZODAgb6/4+Li5D//+Y/6G9ZsvHfjxo3KfcT8XmRP0aIbdOrUST0b/cvT09PVM4Q3sq/A/xwuKsbBAyz02M8vv/yihLcZfD4E/KWXXuo34IBfO/zd8T4tvM3nQwghbRkKb0IIaeNAdN99993qb4js+Ph46datmxKhmhUrVqht8AxLNyzH3bt3t8yPjVSERn766SflIgLBm5ycrFxV8Gx+r/HzjCQmJlouh/h2u93y8ssvq4cZnIcVhYWF6hnno8/bCAYIwc6HEELaKhTehBDSxoEIRoBhIOACcvHFFyvB/NlnnylrMizicMn46quvgu5769atcsUVV8jRRx+t3EHg4w3+97//KUHe2OPGQAHuIXAtCVWww0UF3HjjjcrtxUx7z1tOCIleKLwJIaSdA0s1rMQIWNx///19y+fOnaueYXUOxB9//CFVVVXKrUOLbqBFd2OqScJCjhzkOD7jwAGBmQj6POKII5RlXvt+a/r16ycdO3aU7du3KzcVDfzNb7jhBpVNxXishBDSXqDwJoSQdg6CFyFyX3jhBYmJiVEPWLqRNQRUVFQEdWPB9o888ohceOGFKtjyo48+UplQgDF9X0O49tprVeYTZEw56aSTVNrD//73v8r3+7LLLlPbpKamqmd8JqzZgwcPVoGiyHICUY4KnXBbee655yQvL6+OvzshhLQXmMebEELaORCuEKWwTsOSDBcNpNl76623lLsH8mgHonfv3iowE4IWQhhiF7z55pvKTSTYe0PhsMMOU8GbCJZEykEcG8Q0UgLqoExY6ZF/HO4tSCEIkA8cx7VkyRL5+9//rlIX9ujRQx0XgjMJIaQ9YvM0Zh6REEIIIYQQEhK0eBNCCCGEENICUHgTQgghhBDSAlB4E0IIIYQQ0gJQeBNCCCGEENICUHgTQgghhBDSAlB4E0IIIYQQ0gJQeBNCCCGEENICUHgTQgghhBDSAlB4E0IIIYQQ0gJQeBNCCCGEENICUHgTQgghhBDSAlB4E0IIIYQQIs3P/wOe9GYoEf7TigAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ratio = joined.copy()\n", "ratio['hdi_ratio'] = ratio['hdi_width_ddm'] / ratio['hdi_width_probit']\n", "\n", "fig, ax = plt.subplots(figsize=(7.5, 4.5))\n", "sns.stripplot(data=ratio, x='parameter', y='hdi_ratio',\n", " order=shared_params, color='steelblue', size=8, alpha=0.7,\n", " jitter=0.15, ax=ax)\n", "sns.pointplot(data=ratio, x='parameter', y='hdi_ratio',\n", " order=shared_params, color='black', errorbar=('ci', 95),\n", " markers='_', linestyles='none', markersize=22,\n", " err_kws={'linewidth': 2}, ax=ax)\n", "ax.axhline(1.0, color='red', ls='--', lw=1.2, label='No improvement')\n", "ax.set_ylabel('HDI width (DDM / probit)')\n", "ax.set_xlabel('Parameter')\n", "ax.set_title('Per-subject HDI-width ratio (lower = DDM tighter)')\n", "ax.legend(); sns.despine(); plt.tight_layout()\n", "\n", "print(ratio.groupby('parameter')['hdi_ratio']\n", " .describe()[['mean', '50%', 'min', 'max']])" ] }, { "cell_type": "markdown", "id": "22", "metadata": {}, "source": [ "Each blue dot is one subject; black bar is the across-subject mean ± 95%\n", "CI. Ratios below 1.0 (red dashed line) mean the DDM produces a tighter\n", "posterior for that subject and parameter.\n", "\n", "How much RT actually tightens the cognitive parameters depends on the data.\n", "Several things can keep the ratio near (or above) 1:\n", "\n", "- The DDM adds **two parameters** ($a$, $t_0$) that compete with $\\nu_k$ for\n", " explaining choice/RT structure — a known posterior trade-off in\n", " diffusion-style models.\n", "- With limited subjects or trials, the *hierarchical* group-level pooling\n", " in both models may already constrain $\\nu_k, \\mu_p, \\sigma_p$ well, leaving\n", " little room for the marginal information in RT to tighten them further.\n", "- $\\mu_p, \\sigma_p$ enter through posterior *shrinkage* — that mechanism is\n", " already pinned down by choice alone, so RT typically helps the noise SDs\n", " more than the prior params.\n", "\n", "But — and this is the punchline — **even if the marginal HDIs don't tighten,\n", "the DDM gives you something the probit literally cannot**: a clean\n", "separation between sensory acuity and response caution. The next section\n", "makes that concrete." ] }, { "cell_type": "markdown", "id": "23", "metadata": {}, "source": [ "## Bonus: acuity vs caution — what the DDM disentangles\n", "\n", "This is the deeper reason to fit the DDM, beyond any HDI-tightening.\n", "\n", "In the probit, a flat per-subject psychometric (\"noisy\") can mean either:\n", "\n", "- **Low sensory acuity** — the subject genuinely perceives the magnitudes\n", " imprecisely (large $\\nu_k$).\n", "- **High response caution** — the subject perceives well but is *not using*\n", " much of that signal because they have a permissive criterion / give early\n", " responses (would map to a small boundary $a$ in DDM terms).\n", "\n", "The probit's single scalar noise term cannot distinguish these. The DDM can:\n", "$\\nu_k$ is the *perceptual* SD (drift denominator); $a$ is the *decision*\n", "threshold (response caution). They're separately identified because they\n", "have different fingerprints — $\\nu_k$ controls SNR (accuracy at fixed RT),\n", "$a$ controls overall RT magnitude (caution at fixed accuracy).\n", "\n", "Two empirical checks that this separation works on these data:\n", "\n", "1. Per-subject **DDM $a$ should correlate strongly with mean RT** — that's\n", " the operational definition of caution.\n", "2. Per-subject **DDM $\\nu_k$ should NOT correlate strongly with mean RT** —\n", " acuity should be largely orthogonal to RT magnitude." ] }, { "cell_type": "code", "execution_count": null, "id": "24", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "corr(mean RT, DDM a) = 0.94\n", "corr(mean RT, DDM nu_1) = 0.34\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mean_rt = df.groupby('subject')['rt'].mean().rename('mean_rt')\n", "\n", "a_post = get_subject_posterior_df(idata_ddm, ['a', 't0'], hdi_prob=0.94)\n", "nu_post = get_subject_posterior_df(idata_ddm, ['n1_evidence_sd'],\n", " hdi_prob=0.94)\n", "# Subjects in get_subject_posterior_df are 0-indexed positions; map back to\n", "# real subject IDs by ordering.\n", "subj_ids = sorted(df.index.get_level_values('subject').unique())\n", "def attach_rt(d):\n", " d = d.copy()\n", " d['subject_id'] = [subj_ids[s] for s in d['subject']]\n", " d = d.merge(mean_rt, left_on='subject_id', right_index=True)\n", " return d\n", "\n", "a_df = attach_rt(a_post[a_post['parameter'] == 'a'])\n", "nu_df = attach_rt(nu_post[nu_post['parameter'] == 'n1_evidence_sd'])\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(11, 4.5))\n", "for ax, d, ylab, title in [\n", " (axes[0], a_df, r'DDM $a$ (boundary)', 'Boundary $a$ vs mean RT'),\n", " (axes[1], nu_df, r'DDM $\\nu_1$ (encoding SD)', r'Acuity $\\nu_1$ vs mean RT'),\n", "]:\n", " ax.errorbar(d['mean_rt'], d['mean'],\n", " yerr=[d['mean'] - d['lo'], d['hi'] - d['mean']],\n", " fmt='o', ms=6, capsize=0, lw=0.8, alpha=0.7,\n", " ecolor='steelblue', mfc='steelblue', mec='steelblue')\n", " r = np.corrcoef(d['mean_rt'], d['mean'])[0, 1]\n", " ax.set_xlabel('Per-subject mean RT (s)')\n", " ax.set_ylabel(ylab)\n", " ax.set_title(f'{title} (r = {r:.2f})')\n", " sns.despine(ax=ax)\n", "plt.tight_layout()\n", "\n", "print(f\"corr(mean RT, DDM a) = {a_df[['mean_rt','mean']].corr().iloc[0,1]:.2f}\")\n", "print(f\"corr(mean RT, DDM nu_1) = {nu_df[['mean_rt','mean']].corr().iloc[0,1]:.2f}\")" ] }, { "cell_type": "markdown", "id": "25", "metadata": {}, "source": [ "Left panel: per-subject DDM boundary $a$ vs mean RT — these should be\n", "**strongly positively correlated**. Subjects who take longer have larger\n", "boundaries, by construction of the DDM. That's response caution.\n", "\n", "Right panel: per-subject encoding noise $\\nu_1$ vs mean RT — should be\n", "**much weaker**, ideally near zero. Acuity is largely orthogonal to RT\n", "magnitude.\n", "\n", "**A choice-only probit cannot separate these.** If you took two subjects with\n", "identical probit psychometrics — but one was a slow, careful responder with\n", "high acuity & high $a$, and the other was a fast, sloppy responder with low\n", "acuity & low $a$ — the probit would assign them the same noise parameter\n", "and you'd never know. The DDM gives you both numbers as separate, identified\n", "quantities. This is especially important for individual-differences research,\n", "clinical comparisons, or any analysis where response caution may itself\n", "covary with the experimental manipulation (e.g. TMS, drug, instructions to\n", "\"go fast\" vs \"be accurate\")." ] }, { "cell_type": "markdown", "id": "26", "metadata": {}, "source": [ "## Bonus recipe: regression DDM for between-group or within-design effects\n", "\n", "The acuity-vs-caution decomposition really pays off when you want to ask\n", "**does my experimental factor shift one parameter but not the other?** —\n", "e.g. \"does a dyscalculia diagnosis specifically inflate the *encoding noise*\n", "on numbers, holding response caution fixed?\" or \"does an instruction to\n", "respond faster reduce *only* the boundary $a$?\". The probit can only\n", "collapse these into a single slope; the DDM regression separates them.\n", "\n", "bauer provides `DDMMagnitudeComparisonRegressionModel` for exactly this.\n", "The only thing that changes from the basic fit is **(i) you add a column\n", "naming the condition for each trial, and (ii) you pass a `regressors=`\n", "dict** keyed by parameter name with a patsy formula on the right.\n", "\n", "For a clinical 2-group comparison (the typical case you'd send a colleague\n", "this notebook for) the recipe is:\n", "\n", "```python\n", "# Suppose subject_info has a column 'group' ∈ {'control', 'dyscalculia'}.\n", "df['group'] = subject_info.loc[df.index.get_level_values('subject'), 'group']\n", "\n", "from bauer.models import DDMMagnitudeComparisonRegressionModel\n", "\n", "m_reg = DDMMagnitudeComparisonRegressionModel(\n", " paradigm=df, fit_separate_evidence_sd=True, fit_prior=True,\n", " regressors={\n", " 'n1_evidence_sd': 'group', # does acuity on stim 1 differ?\n", " 'a': 'group', # does caution differ?\n", " },\n", ")\n", "m_reg.build_estimation_model(data=df, hierarchical=True)\n", "idata_reg = m_reg.sample(backend='numpyro', target_accept=0.95)\n", "\n", "az.summary(idata_reg, var_names=[\n", " 'a_mu_group[T.dyscalculia]',\n", " 'n1_evidence_sd_mu_group[T.dyscalculia]',\n", "])\n", "# If the 94% HDI of either contrast excludes 0, that parameter differs\n", "# credibly between groups.\n", "```\n", "\n", "Garcia 2022 doesn't have a clinical-group covariate, but **it does have an\n", "inter-stimulus interval (ISI)** that jitters between 6 and 9 s across\n", "trials (loaded as `df['isi']` since the bundled CSV now carries it). That's\n", "a natural within-subject covariate: longer ISI means more time over which\n", "the first stimulus has to be held in working memory before $n_2$ is shown.\n", "*If* memory decays during the delay, we'd expect the encoding noise\n", "$\\nu_1$ on the first stimulus to grow with ISI. The expected null is also\n", "informative: a clean \"no effect\" would mean memory for these number\n", "displays is stable across this delay range — and the DDM regression is\n", "the right test, because a probit would lump any ISI effect on $\\nu_1$\n", "into the bigger pot of trial-to-trial choice variability." ] }, { "cell_type": "code", "execution_count": null, "id": "27", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'long': 6569, 'short': 6556}\n", "ISI range short: 6.0–7.5s\n", "ISI range long: 7.5–9.0s\n" ] } ], "source": [ "# Categorical ISI: short = 6–7 s, long = 8–9 s (median split).\n", "df['isi_cat'] = pd.Categorical(\n", " np.where(df['isi'] >= df['isi'].median(), 'long', 'short'),\n", " categories=['short', 'long'], # 'short' is reference level\n", ")\n", "print(df['isi_cat'].value_counts().to_dict())\n", "print(f\"ISI range short: {df.loc[df['isi_cat']=='short','isi'].min():.1f}–\"\n", " f\"{df.loc[df['isi_cat']=='short','isi'].max():.1f}s\")\n", "print(f\"ISI range long: {df.loc[df['isi_cat']=='long','isi'].min():.1f}–\"\n", " f\"{df.loc[df['isi_cat']=='long','isi'].max():.1f}s\")" ] }, { "cell_type": "markdown", "id": "28", "metadata": {}, "source": [ "**Fit the regression DDM.** This adds one regressor on\n", "`n1_evidence_sd` — the encoding noise on the first stimulus, the\n", "parameter most likely to grow if working-memory representations decay\n", "during the longer ISI delays. We don't regress on `a` (response caution\n", "shouldn't depend on the ISI scheduled by the experiment) — pre-registering\n", "which parameter the covariate is allowed to move keeps this honest." ] }, { "cell_type": "code", "execution_count": null, "id": "29", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading cached ddm_isi fit from /Users/gdehol/.bauer_tutorial_cache/garcia_n64_rtmin200_ddm_isi.nc\n" ] } ], "source": [ "from bauer.models import DDMMagnitudeComparisonRegressionModel\n", "\n", "m_isi = DDMMagnitudeComparisonRegressionModel(\n", " paradigm=df,\n", " fit_separate_evidence_sd=True, fit_prior=True,\n", " regressors={'n1_evidence_sd': 'isi_cat'},\n", ")\n", "idata_isi = fit_or_load(m_isi, 'ddm_isi')" ] }, { "cell_type": "code", "execution_count": null, "id": "30", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_bulk ess_tail r_hat\n", "n1_evidence_sd_mu[Intercept] -0.878 0.053 -0.971 -0.772 0.002 0.001 999.0 1614.0 1.0\n", "n1_evidence_sd_mu[isi_cat[T.long]] -0.004 0.031 -0.063 0.051 0.000 0.000 6203.0 3382.0 1.0\n", "\n", "94% HDI on long-vs-short ISI effect on n1_evidence_sd_mu: [-0.062, +0.053]\n", "→ HDI includes 0: no detectable ISI effect on n1 noise across 6-9 s delays (the expected null, given the flat empirical RT / accuracy seen earlier).\n" ] } ], "source": [ "# bauer's regression model stores coefficients as a *coord* on the\n", "# parameter posterior (here 'n1_evidence_sd_regressors'), not as separate\n", "# variables. The 'isi_cat[T.long]' level is the contrast vs the 'short'\n", "# reference level (same convention as patsy / statsmodels).\n", "print(az.summary(idata_isi, var_names=['n1_evidence_sd_mu'],\n", " hdi_prob=0.94).to_string())\n", "\n", "# Plain-English readout of the long-vs-short contrast on the\n", "# untransformed (pre-softplus) scale.\n", "post = idata_isi.posterior['n1_evidence_sd_mu'].sel(\n", " n1_evidence_sd_regressors='isi_cat[T.long]').values.ravel()\n", "hdi_lo, hdi_hi = np.percentile(post, [3, 97])\n", "print(f\"\\n94% HDI on long-vs-short ISI effect on n1_evidence_sd_mu: \"\n", " f\"[{hdi_lo:+.3f}, {hdi_hi:+.3f}]\")\n", "if hdi_lo > 0:\n", " print(\"→ Long ISIs INCREASE n1 encoding noise (memory decay detected).\")\n", "elif hdi_hi < 0:\n", " print(\"→ Long ISIs DECREASE n1 encoding noise (unexpected — investigate).\")\n", "else:\n", " print(\"→ HDI includes 0: no detectable ISI effect on n1 noise across \"\n", " \"6-9 s delays (the expected null, given the flat empirical \"\n", " \"RT / accuracy seen earlier).\")" ] }, { "cell_type": "markdown", "id": "31", "metadata": {}, "source": [ "### From contrast coefficient → on-scale noise per condition\n", "\n", "The summary above is on the un-softplus scale (so contrasts are\n", "additive). For interpretation it's easier to read the actual\n", "$\\nu_1$ in each ISI condition. bauer's\n", "`model.get_conditionwise_parameters(idata, conditions, group=True)`\n", "does that for you — it rebuilds the design matrix at the conditions\n", "you pass, multiplies in the posterior coefficients, and **applies the\n", "transform** (softplus here, so the result is in the natural noise\n", "units used by the cognitive model)." ] }, { "cell_type": "code", "execution_count": null, "id": "32", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Group-level n1_evidence_sd per ISI condition (natural scale):\n", " mean median hdi_3% hdi_97%\n", "short 0.348 0.347 0.320 0.379\n", "long 0.347 0.346 0.317 0.378\n", "\n", "long − short difference: mean = -0.0012, 94% HDI = [-0.0179, +0.0158]\n" ] } ], "source": [ "# Group-level n1_evidence_sd per ISI condition, with the softplus\n", "# transform applied — i.e. the actual noise the cognitive model uses.\n", "isi_conditions = pd.DataFrame({'isi_cat': ['short', 'long']})\n", "cond_pars = m_isi.get_conditionwise_parameters(idata_isi, isi_conditions,\n", " group=True)\n", "# cond_pars rows are (parameter, posterior_index); columns are conditions\n", "nu1 = cond_pars.loc['n1_evidence_sd'] # shape (n_post, 2)\n", "nu1.columns = ['short', 'long']\n", "diff = nu1['long'] - nu1['short']\n", "summary = pd.DataFrame({\n", " 'mean': nu1.mean(),\n", " 'median': nu1.median(),\n", " 'hdi_3%': np.percentile(nu1, 3, axis=0),\n", " 'hdi_97%': np.percentile(nu1, 97, axis=0),\n", "})\n", "print('Group-level n1_evidence_sd per ISI condition (natural scale):')\n", "print(summary.round(3).to_string())\n", "print()\n", "print(f\"long − short difference: mean = {diff.mean():+.4f}, \"\n", " f\"94% HDI = [{np.percentile(diff, 3):+.4f}, \"\n", " f\"{np.percentile(diff, 97):+.4f}]\")" ] }, { "cell_type": "code", "execution_count": null, "id": "33", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Figure: per-condition posteriors of n1_evidence_sd, plus the difference.\n", "fig, (ax_pdf, ax_diff) = plt.subplots(1, 2, figsize=(10.5, 4.0))\n", "\n", "palette = {'short': '#377eb8', 'long': '#e41a1c'}\n", "for cond in ['short', 'long']:\n", " sns.kdeplot(nu1[cond], ax=ax_pdf, fill=True, alpha=0.25,\n", " color=palette[cond], label=f'{cond} ISI', clip=(0, None))\n", " ax_pdf.axvline(nu1[cond].median(), color=palette[cond], lw=1.2, ls='--')\n", "ax_pdf.set_xlabel(r'Group-level $\\nu_1$ (n1 encoding SD)')\n", "ax_pdf.set_ylabel('Posterior density')\n", "ax_pdf.set_title('Per-condition posterior')\n", "ax_pdf.legend()\n", "sns.despine(ax=ax_pdf)\n", "\n", "sns.kdeplot(diff, ax=ax_diff, fill=True, color='#666666', alpha=0.4,\n", " clip=(diff.min(), diff.max()))\n", "ax_diff.axvline(0, color='black', ls=':', lw=1.2)\n", "hdi_lo, hdi_hi = np.percentile(diff, [3, 97])\n", "ax_diff.axvspan(hdi_lo, hdi_hi, color='gray', alpha=0.15,\n", " label=f'94% HDI [{hdi_lo:+.3f}, {hdi_hi:+.3f}]')\n", "ax_diff.set_xlabel(r'$\\nu_1$(long) − $\\nu_1$(short)')\n", "ax_diff.set_title('Long − short contrast posterior')\n", "ax_diff.legend(loc='upper left', fontsize=9)\n", "sns.despine(ax=ax_diff)\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "id": "34", "metadata": {}, "source": [ "### Visualising the coefficient posterior\n", "\n", "The `az.summary` table above is the formal test; a forest plot makes the\n", "same contrast legible at a glance. Both group-level coefficients on $n_1$'s\n", "encoding noise are shown on the model's internal (pre-softplus) scale: the\n", "`Intercept` is the short-ISI baseline, and `isi_cat[T.long]` is the\n", "long-vs-short contrast whose 94% HDI relative to 0 *is* the test." ] }, { "cell_type": "code", "execution_count": null, "id": "35", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "az.plot_forest(idata_isi, var_names=['n1_evidence_sd_mu'],\n", " combined=True, hdi_prob=0.94, figsize=(7, 2.4))\n", "plt.axvline(0, color='black', ls=':', lw=1)\n", "plt.title('Group-level regression coefficients on $n_1$ encoding noise')\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "id": "36", "metadata": {}, "source": [ "### Does the fit still track the data *within each condition*?\n", "\n", "A regression fit is only trustworthy if it reproduces behaviour at every\n", "level of the regressor — not just on average. Below we run a posterior\n", "predictive check and split both the data and the predictions by ISI\n", "condition. Two things to look for: (i) the bands cover the data points in\n", "**both** conditions (the fit is adequate on choice *and* RT), and (ii) the\n", "short and long curves nearly coincide — the visual counterpart of the\n", "near-null contrast we just measured." ] }, { "cell_type": "code", "execution_count": null, "id": "37", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# PPC on the original paradigm; DDM PPCs carry simulated_choice + simulated_rt.\n", "ppc_isi = m_isi.ppc(df, idata_isi, n_posterior_samples=60, progressbar=False)\n", "d_isi = add_bins(df) # keeps isi_cat; adds diff_bin, correct, stake\n", "\n", "# Attach difficulty + ISI condition to every PPC draw (same join as the\n", "# size-effect PPC earlier — d_isi is indexed by the trial keys).\n", "p = (ppc_isi.join(d_isi[['diff_bin', 'isi_cat', 'n1', 'n2']], how='left')\n", " .reset_index())\n", "p['sim_correct'] = p['simulated_choice'].astype(bool) == (p['n2'] > p['n1'])\n", "\n", "# Per-draw condition means → spread across draws gives the posterior PI band.\n", "acc_pp = (p.groupby(['ppc_sample', 'isi_cat', 'diff_bin'], observed=True)\n", " ['sim_correct'].mean().reset_index())\n", "rt_pp = (p[p['sim_correct']]\n", " .groupby(['ppc_sample', 'isi_cat', 'diff_bin'], observed=True)\n", " ['simulated_rt'].mean().reset_index())\n", "# Observed condition means (correct trials for RT, matching the PPC).\n", "acc_obs = (d_isi.groupby(['isi_cat', 'diff_bin'], observed=True)\n", " ['correct'].mean().reset_index())\n", "rt_obs = (d_isi.query('correct')\n", " .groupby(['isi_cat', 'diff_bin'], observed=True)\n", " ['rt'].mean().reset_index())\n", "\n", "isi_pal = {'short': '#377eb8', 'long': '#e41a1c'}\n", "fig, (ax_c, ax_rt) = plt.subplots(1, 2, figsize=(11, 4.2))\n", "\n", "# Left: choice (accuracy) PPC.\n", "sns.lineplot(data=acc_pp, x='diff_bin', y='sim_correct',\n", " hue='isi_cat', hue_order=['short', 'long'], palette=isi_pal,\n", " errorbar=('pi', 90), err_style='band', err_kws={'alpha': 0.18},\n", " lw=2, ax=ax_c)\n", "sns.scatterplot(data=acc_obs, x='diff_bin', y='correct',\n", " hue='isi_cat', hue_order=['short', 'long'], palette=isi_pal,\n", " s=90, edgecolor='black', zorder=5, legend=False, ax=ax_c)\n", "ax_c.set_xlabel('Difficulty'); ax_c.set_ylabel('P(correct)')\n", "ax_c.set_title('Choice PPC by ISI')\n", "ax_c.legend(title='ISI', loc='lower right')\n", "\n", "# Right: RT (difficulty) PPC, correct trials.\n", "sns.lineplot(data=rt_pp, x='diff_bin', y='simulated_rt',\n", " hue='isi_cat', hue_order=['short', 'long'], palette=isi_pal,\n", " errorbar=('pi', 90), err_style='band', err_kws={'alpha': 0.18},\n", " lw=2, ax=ax_rt, legend=False)\n", "sns.scatterplot(data=rt_obs, x='diff_bin', y='rt',\n", " hue='isi_cat', hue_order=['short', 'long'], palette=isi_pal,\n", " s=90, edgecolor='black', zorder=5, legend=False, ax=ax_rt)\n", "ax_rt.set_xlabel('Difficulty'); ax_rt.set_ylabel('Mean RT (s, correct)')\n", "ax_rt.set_title('RT PPC by ISI')\n", "\n", "for ax in (ax_c, ax_rt):\n", " sns.despine(ax=ax)\n", "fig.suptitle('Regression DDM PPC — points = data, bands = 90% posterior PI',\n", " y=1.02)\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "id": "38", "metadata": {}, "source": [ "Three practical notes for your own data:\n", "\n", "1. **Pick the right parameter to regress.** ISI here plausibly affects\n", " only $\\nu_1$ (memory for the first stimulus across the delay) — there's\n", " no prior reason ISI would change response caution. Adding `'a':\n", " 'isi_cat'` would be data-mining; pre-register which parameter the\n", " covariate should move and only regress that one.\n", "2. **Continuous covariates work too** — replace `'isi_cat'` with `'isi'`\n", " (the raw seconds column) to get a linear ISI slope on $\\nu_1$. For\n", " non-linear effects, patsy formulas like `'bs(isi, df=3)'` give a\n", " B-spline; bauer auto-expands the design matrix.\n", "3. **Priors are conventions + judgment, not derivations.** bauer's `a`/`t0`\n", " priors mirror HDDM (Wiecki, Sofer & Frank 2013): wide group-mean +\n", " tight group-SD. The front-end (`n*_evidence_sd`, `prior_*`) priors are\n", " bauer-specific judgment calls and were tuned partly to make this\n", " tutorial converge. For a real publication, run a **prior-sensitivity\n", " check** — refit at 2–3 prior strengths and confirm the contrast HDI\n", " barely moves.\n", "\n", "The regression DDM fit takes about as long as the basic DDM (one extra\n", "parameter, vmap dimensions unchanged) — budget another ~45 min on a GPU\n", "L4, or pre-fit on the cluster with `fit_for_lesson8.py` (which now also\n", "produces this `ddm_isi` cache)." ] }, { "cell_type": "markdown", "id": "39", "metadata": {}, "source": [ "## When is RT modelling worth it?\n", "\n", "| | Probit | DDM |\n", "|---|---|---|\n", "| **Likelihood** | Bernoulli on choice | Wiener WFPT on (rt, choice) |\n", "| **Extra params** | — | $a$ (caution), $t_0$ (non-decision time) |\n", "| **Fits choice?** | Yes | Yes (essentially identical) |\n", "| **Fits RT?** | No | Yes (size effect, difficulty) |\n", "| **Acuity vs caution** | Confounded into one slope | Separately identified |\n", "| **Front-end HDI width** | Baseline | Sometimes tighter (depends on $n$ and posterior trade-offs) |\n", "| **Regression on caution / acuity?** | No clean way | `DDMMagnitudeComparisonRegressionModel` + `regressors=` dict |\n", "| **Sampling cost (n=64, this dataset)** | ~5 min CPU | ~45 min on GPU L4; many hours on CPU (see below) |\n", "\n", "### A note on wall time: budget honestly\n", "\n", "The hierarchical DDM on n=64 with 4 chains × 1000 tune × 1000 draws is at\n", "the edge of CPU-feasibility. From actual cluster runs:\n", "\n", "- **n=8 (lesson 9):** ~15 min on CPU.\n", "- **n=64, GPU L4 (numpyro vectorized):** ~45 min, sampling ~1.4 s/iter.\n", "- **n=64, CPU 16-core EPYC, default `chain_method='vectorized'`:** 30–35 s/iter, 12–18 h total; tight on a 24 h slot.\n", "- **n=64, CPU 16-core, `chain_method='parallel'`:** pass `m.sample(backend='numpyro', chain_method='parallel')` — each of 4 chains gets its own process + XLA threading, much better core use. Fits in 24 h on any CPU node.\n", "\n", "Validate your pipeline on n=8–16 first (minutes), only then scale to full $n$.\n", "\n", "### If your diagnostics look bad\n", "\n", "When `r̂ > 1.01` or ESS bulk < 100/chain after a full fit, the usual\n", "escalation (in order, cheapest first):\n", "\n", "1. `tune=2000, target_accept=0.99` — bauer's escalation default.\n", "2. Check that you ran the RT filter above (`rt >= 0.20 s`). If you didn't,\n", " you're hitting the gradient-flat region described earlier and *no*\n", " amount of warmup will help.\n", "3. Tighten the priors on $a$ and $t_0$ (bauer's current defaults follow\n", " HSSM/HDDM: wide group mean, *tight* group-SD prior). If you've been\n", " editing those, restore the bauer defaults.\n", "4. For datasets with **subjects who have <50 usable trials**, hierarchical\n", " pooling usually rescues them — bauer's per-subject parameters are\n", " regularised toward the group mean by construction. Only drop a subject\n", " if its posterior obviously bimodalises (visible in a per-subject HDI\n", " plot) or if the subject has near-chance accuracy across the board.\n", "\n", "**Next:** [Lesson 9](lesson9.ipynb) extends this with the race-diffusion model\n", "— two parallel accumulators rather than one signed accumulator — which\n", "captures the slow-error pattern in correct/error RTs that single-accumulator\n", "DDMs (without across-trial drift variability $s_v$) cannot." ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.15" } }, "nbformat": 4, "nbformat_minor": 5 }