《认知几何学：思维如何弯曲意义空间》补充材料

《认知几何学：思维如何弯曲意义空间》补充材料

附录A：概念流形度规学习的算法细节

A.1 概念嵌入空间的几何结构学习

数据预处理：

1. 语料来源：世毫九对话实验全文本 + Wikipedia摘要 + 学术论文摘要

2. 概念提取：使用BERT-base模型提取名词短语，筛选出现频率>100次的概念

3. 嵌入生成：使用GloVe+Word2Vec+FastText的加权融合嵌入

度规张量学习算法：

```python

import numpy as np

from scipy.optimize import minimize

from sklearn.manifold import TSNE

class ConceptMetricLearner:

def init(self, concepts, embeddings, alpha=0.1, beta=0.05):

"""

概念流形度规学习器

参数：

concepts: 概念列表 [n_concepts]

embeddings: 概念嵌入 [n_concepts, n_dim]

alpha: 局部平滑正则化系数

beta: 全局曲率约束系数

"""

self.concepts = concepts

self.embeddings = embeddings

self.n = len(concepts)

self.d = embeddings.shape[1]

self.alpha = alpha

self.beta = beta

def _compute_affinity(self):

"""计算概念间亲和度矩阵"""

from scipy.spatial.distance import pdist, squareform

语义相似性

cos_sim = np.dot(self.embeddings, self.embeddings.T)

共现频率（从语料统计）

cooccur = self._compute_cooccurrence()

综合亲和度

A = 0.7 * cos_sim + 0.3 * cooccur

return A

def metric_tensor_at_point(self, point_idx, G_params):

"""

计算点i处的度规张量

G_params: 度规参数 [n, d, d]

"""

G_i = G_params[point_idx]

确保对称正定

G_i = (G_i + G_i.T) / 2

添加小扰动确保正定

G_i += np.eye(self.d) * 1e-6

return G_i

def geodesic_distance(self, i, j, G_params):

"""计算概念i到j的测地线距离"""

from scipy.integrate import solve_ivp

def geodesic_eq(t, y, G):

"""测地线方程 dy/dt = f(t, y)"""

x = y[:self.d] # 位置

v = y[self.d:] # 速度

计算克里斯托费尔符号

Gamma = self._christoffel_symbols(x, G)

加速度方程 d²x/dt² = -Γ dx/dt dx/dt

dvdt = np.zeros(self.d)

for k in range(self.d):

for a in range(self.d):

for b in range(self.d):

dvdt[k] -= Gamma[k, a, b] * v[a] * v[b]

return np.concatenate([v, dvdt])

初始条件：从i到j的直线路径

x0 = self.embeddings[i]

x1 = self.embeddings[j]

v0 = (x1 - x0) / np.linalg.norm(x1 - x0)

沿路径积分测地线方程

y0 = np.concatenate([x0, v0])

sol = solve_ivp(lambda t, y: geodesic_eq(t, y, G_params),

0, 1\], y0, method='RK45', dense_output=True) # 计算路径长度 t_eval = np.linspace(0, 1, 100) y = sol.sol(t_eval) positions = y\[:self.d, :

黎曼长度积分

length = 0

for k in range(99):

dx = positions[:, k+1] - positions[:, k]

dt = t_eval[k+1] - t_eval[k]

x_mid = (positions[:, k+1] + positions[:, k]) / 2

G_mid = self._interpolate_metric(x_mid, G_params)

length += np.sqrt(np.dot(dx, G_mid @ dx)) / dt

return length

def loss_function(self, G_params_flat):

"""度规学习损失函数"""

重塑参数

G_params = G_params_flat.reshape((self.n, self.d, self.d))

loss = 0

1. 局部相似性约束

A = self._compute_affinity()

for i in range(self.n):

for j in range(self.n):

if A[i, j] > 0.5: # 高亲和概念应靠近

d_ij = self.geodesic_distance(i, j, G_params)

loss += A[i, j] * d_ij ** 2

2. 局部平滑正则化

for i in range(self.n):

G_i = self.metric_tensor_at_point(i, G_params)

for j in self._get_neighbors(i):

G_j = self.metric_tensor_at_point(j, G_params)

loss += self.alpha * np.linalg.norm(G_i - G_j, 'fro') ** 2

3. 曲率正则化（防止过度弯曲）

for i in range(self.n):

R = self._compute_curvature(i, G_params)

loss += self.beta * np.linalg.norm(R, 'fro') ** 2

return loss

def learn_metric(self, max_iter=1000):

"""学习最优度规"""

初始化度规为单位矩阵

G_init = np.zeros((self.n, self.d, self.d))

for i in range(self.n):

G_init[i] = np.eye(self.d)

优化

result = minimize(self.loss_function, G_init.flatten(),

method='L-BFGS-B', options={'maxiter': max_iter})

G_optimal = result.x.reshape((self.n, self.d, self.d))

return G_optimal

```

A.2 度规可视化与验证

```python

def visualize_concept_manifold(embeddings, metric_tensors, concepts, highlight_concepts=None):

"""

可视化概念流形的几何结构

参数：

embeddings: 概念嵌入 [n, d]

metric_tensors: 度规张量 [n, d, d]

concepts: 概念名称列表

highlight_concepts: 高亮显示的概念索引列表

"""

import matplotlib.pyplot as plt

from matplotlib.patches import Ellipse

降维到2D用于可视化

tsne = TSNE(n_components=2, perplexity=30, random_state=42)

embeddings_2d = tsne.fit_transform(embeddings)

计算每个点的局部度规在2D投影

注意：这是近似，完整度规在降维中会损失信息

metric_2d = np.zeros((len(concepts), 2, 2))

for i in range(len(concepts)):

使用PCA找到2D投影平面

from sklearn.decomposition import PCA

pca = PCA(n_components=2)

在嵌入空间采样局部点

neighbors = np.argsort(np.linalg.norm(

embeddings - embeddings[i], axis=1))[:20]

local_emb = embeddings[neighbors]

pca.fit(local_emb)

投影度规到2D平面

proj_matrix = pca.components_ # [2, d]

metric_2d[i] = proj_matrix @ metric_tensors[i] @ proj_matrix.T

绘制

fig, ax = plt.subplots(figsize=(12, 10))

绘制概念点

scatter = ax.scatter(embeddings_2d[:, 0], embeddings_2d[:, 1],

alpha=0.6, s=50, c='lightblue', edgecolors='gray')

绘制局部度规椭圆

for i in range(len(concepts)):

if highlight_concepts and i in highlight_concepts:

color = 'red'

alpha = 0.8

linewidth = 2

else:

color = 'blue'

alpha = 0.3

linewidth = 1

计算椭圆参数（度规的特征值/特征向量）

G = metric_2d[i]

eigvals, eigvecs = np.linalg.eigh(G)

椭圆角度（主方向）

angle = np.degrees(np.arctan2(eigvecs[1, 0], eigvecs[0, 0]))

椭圆大小（与特征值平方根成反比，因为度规测量的是距离的平方）

width = 0.3 / np.sqrt(eigvals[0] + 1e-6)

height = 0.3 / np.sqrt(eigvals[1] + 1e-6)

ellipse = Ellipse(xy=embeddings_2d[i], width=width, height=height,

angle=angle, alpha=alpha, color=color, linewidth=linewidth)

ax.add_patch(ellipse)

标注概念

for i, concept in enumerate(concepts[:50]): # 只标注前50个

ax.annotate(concept, embeddings_2d[i], fontsize=8, alpha=0.7)

ax.set_xlabel('Dimension 1', fontsize=12)

ax.set_ylabel('Dimension 2', fontsize=12)

ax.set_title('概念流形几何结构可视化\n椭圆表示局部度规', fontsize=14)

ax.grid(True, alpha=0.3)

plt.tight_layout()

return fig

```

附录B：对话分形维数计算的完整步骤

B.1 时间序列分形分析算法

```python

import numpy as np

from scipy import stats, optimize

import matplotlib.pyplot as plt

class DialogueFractalAnalyzer:

"""对话分形维数分析器"""

def init(self, dialogue_timestamps, cognitive_measures):

"""

初始化分析器

参数：

dialogue_timestamps: 对话事件时间戳序列 [n_events]

cognitive_measures: 认知测量值序列 [n_events]，如：

• 观点变化幅度

• 概念新颖性

• 情感强度

"""

self.timestamps = np.array(dialogue_timestamps)

self.measures = np.array(cognitive_measures)

计算间隔序列

self.intervals = np.diff(self.timestamps)

def hurst_exponent(self, method='RS'):

"""

计算Hurst指数（H）

method: 'RS' (重标极差法), 'DFA' (去趋势波动分析), 'PSD' (功率谱密度)

"""

if method == 'RS':

return self._hurst_rs()

elif method == 'DFA':

return self._hurst_dfa()

elif method == 'PSD':

return self._hurst_psd()

else:

raise ValueError(f"未知方法: {method}")

def _hurst_rs(self):

"""重标极差法计算Hurst指数"""

n = len(self.measures)

R_S = []

window_sizes = []

使用对数间隔的窗口大小

min_window = 10

for k in range(1, int(np.log2(n/4))):

window = 2**k

if window < min_window:

continue

num_windows = n // window

if num_windows < 4:

continue

window_R_S = []

for i in range(num_windows):

segment = self.measures[i*window:(i+1)*window]

if len(segment) < 2:

continue

累计偏差

mean_seg = np.mean(segment)

deviations = segment - mean_seg

Z = np.cumsum(deviations)

范围 R

R = np.max(Z) - np.min(Z)

标准差 S

S = np.std(segment, ddof=1)

if S > 0:

window_R_S.append(R / S)

if len(window_R_S) > 0:

R_S.append(np.mean(window_R_S))

window_sizes.append(window)

线性拟合 log(R/S) ~ H * log(window)

log_window = np.log(window_sizes)

log_RS = np.log(R_S)

slope, intercept, r_value, p_value, std_err = stats.linregress(log_window, log_RS)

return {

'H': slope,

'intercept': intercept,

'r_squared': r_value**2,

'p_value': p_value,

'std_err': std_err,

'window_sizes': window_sizes,

'R_S_values': R_S

}

def _hurst_dfa(self, order=1):

"""

去趋势波动分析 (DFA)

order: 去趋势多项式的阶数

"""

n = len(self.measures)

1. 积分序列

y = np.cumsum(self.measures - np.mean(self.measures))

2. 不同窗口大小的波动函数

window_sizes = []

fluctuations = []

min_window = 10

for k in range(1, int(np.log2(n/4))):

window = 2**k

if window < min_window or window > n/4:

continue

num_windows = n // window

if num_windows < 4:

continue

F_window = []

for i in range(num_windows):

segment = y[i*window:(i+1)*window]

t = np.arange(len(segment))

多项式拟合去趋势

if order == 1:

coeffs = np.polyfit(t, segment, 1)

trend = np.polyval(coeffs, t)

elif order == 2:

coeffs = np.polyfit(t, segment, 2)

trend = np.polyval(coeffs, t)

else:

raise ValueError("仅支持1阶或2阶DFA")

去趋势后的波动

detrended = segment - trend

F_window.append(np.sqrt(np.mean(detrended**2)))

if len(F_window) > 0:

fluctuations.append(np.mean(F_window))

window_sizes.append(window)

3. 线性拟合 log(F) ~ alpha * log(window)

log_window = np.log(window_sizes)

log_F = np.log(fluctuations)

slope, intercept, r_value, p_value, std_err = stats.linregress(log_window, log_F)

return {

'alpha': slope, # DFA指数

'intercept': intercept,

'r_squared': r_value**2,

'p_value': p_value,

'std_err': std_err,

'window_sizes': window_sizes,

'fluctuations': fluctuations,

'hurst': slope # 对于fGn，H = alpha

}

def multifractal_spectrum(self, q_values=np.arange(-5, 6, 0.5)):

"""

计算多重分形谱

参数：

q_values: 矩阶数范围

"""

n = len(self.measures)

使用小波变换模极大方法

import pywt

小波变换

coeffs = pywt.wavedec(self.measures, 'db4', level=6)

计算每个尺度的分区函数

scales = [2**j for j in range(1, 7)]

Z_q = {q: [] for q in q_values}

for j, scale in enumerate(scales):

detail_coeffs = np.abs(coeffs[-(j+1)])

if len(detail_coeffs) < 10:

continue

for q in q_values:

if q == 0:

Z = np.mean(np.log(detail_coeffs**2))

else:

Z = np.log(np.mean(detail_coeffs**q))

Z_q[q].append(Z)

计算质量指数 τ(q)

tau_q = []

valid_q = []

for q in q_values:

if len(Z_q[q]) < 3:

continue

τ(q) ~ slope * log(scale)

scales_log = np.log(scales[:len(Z_q[q])])

Z_log = np.array(Z_q[q])

slope, _, r_value, _, _ = stats.linregress(scales_log, Z_log)

if r_value**2 > 0.8: # 仅保留好的拟合

tau_q.append(slope)

valid_q.append(q)

勒让德变换得到多重分形谱 f(α)

tau_q = np.array(tau_q)

valid_q = np.array(valid_q)

计算奇异性强度 α = dτ/dq

alpha_q = np.gradient(tau_q, valid_q)

计算谱维数 f(α) = qα - τ(q)

f_alpha = valid_q * alpha_q - tau_q

return {

'q_values': valid_q,

'tau_q': tau_q,

'alpha_spectrum': alpha_q,

'f_spectrum': f_alpha,

'multifractal_width': np.max(alpha_q) - np.min(alpha_q),

'asymmetry': (np.max(alpha_q) - np.mean(alpha_q)) /

(np.mean(alpha_q) - np.min(alpha_q))

}

def plot_fractal_analysis(self):

"""绘制分形分析结果"""

fig, axes = plt.subplots(2, 3, figsize=(15, 10))

1. 原始时间序列

axes[0, 0].plot(self.timestamps, self.measures, 'b-', alpha=0.7, linewidth=1)

axes[0, 0].set_xlabel('对话时间', fontsize=12)

axes[0, 0].set_ylabel('认知测量值', fontsize=12)

axes[0, 0].set_title('对话认知时间序列', fontsize=14)

axes[0, 0].grid(True, alpha=0.3)

2. 间隔分布（对数坐标）

axes[0, 1].hist(self.intervals, bins=50, alpha=0.7, density=True)

axes[0, 1].set_xlabel('时间间隔', fontsize=12)

axes[0, 1].set_ylabel('概率密度', fontsize=12)

axes[0, 1].set_title('对话间隔分布', fontsize=14)

axes[0, 1].set_xscale('log')

axes[0, 1].set_yscale('log')

axes[0, 1].grid(True, alpha=0.3)

3. Hurst指数分析 (RS法)

rs_result = self.hurst_exponent(method='RS')

axes[0, 2].loglog(rs_result['window_sizes'], rs_result['R_S_values'],

'o-', linewidth=2)

axes[0, 2].set_xlabel('窗口大小', fontsize=12)

axes[0, 2].set_ylabel('R/S', fontsize=12)

axes[0, 2].set_title(f'Hurst指数 H = {rs_result["H"]:.3f}', fontsize=14)

axes[0, 2].grid(True, alpha=0.3)

4. DFA分析

dfa_result = self._hurst_dfa()

axes[1, 0].loglog(dfa_result['window_sizes'], dfa_result['fluctuations'],

's-', color='red', linewidth=2)

axes[1, 0].set_xlabel('窗口大小', fontsize=12)

axes[1, 0].set_ylabel('波动函数 F(n)', fontsize=12)

axes[1, 0].set_title(f'DFA指数 α = {dfa_result["alpha"]:.3f}', fontsize=14)

axes[1, 0].grid(True, alpha=0.3)

5. 多重分形谱

mf_result = self.multifractal_spectrum()

axes[1, 1].plot(mf_result['alpha_spectrum'], mf_result['f_spectrum'],

'o-', color='green', linewidth=2)

axes[1, 1].set_xlabel('奇异性强度 α', fontsize=12)

axes[1, 1].set_ylabel('谱维数 f(α)', fontsize=12)

axes[1, 1].set_title(f'多重分形谱 (宽度={mf_result["multifractal_width"]:.3f})',

fontsize=14)

axes[1, 1].grid(True, alpha=0.3)

6. 分形维数估计汇总

H_rs = rs_result['H']

alpha_dfa = dfa_result['alpha']

计算分形维数 D = 2 - H (对于时间序列)

D_rs = 2 - H_rs

D_dfa = 2 - alpha_dfa if alpha_dfa <= 1 else 1 / alpha_dfa

axes[1, 2].bar(['RS方法', 'DFA方法'], [D_rs, D_dfa], alpha=0.7)

axes[1, 2].set_ylabel('分形维数估计', fontsize=12)

axes[1, 2].set_title(f'平均分形维数 D ≈ {(D_rs+D_dfa)/2:.3f}', fontsize=14)

axes[1, 2].grid(True, alpha=0.3, axis='y')

plt.tight_layout()

return fig, {

'hurst_rs': H_rs,

'hurst_dfa': alpha_dfa,

'fractal_dimension_rs': D_rs,

'fractal_dimension_dfa': D_dfa,

'multifractal_width': mf_result['multifractal_width']

}

```

B.2 分形维数计算的实验验证

```python

def validate_fractal_hypothesis():

"""验证对话分形假设"""

模拟数据：已知分形维数的合成时间序列

np.random.seed(42)

1. 布朗运动 (H=0.5, D=1.5)

n_points = 10000

brownian = np.cumsum(np.random.randn(n_points))

brownian_times = np.linspace(0, 100, n_points)

2. 分形布朗运动 (H=0.8, D=1.2)

from fbm import FBM

fbm_process = FBM(n=n_points, hurst=0.8, length=100)

fbm_series = fbm_process.fbm()

fbm_times = np.linspace(0, 100, n_points)

3. 实际对话数据

dialogue_analyzer = DialogueFractalAnalyzer(

dialogue_timestamps=dialogue_data['timestamps'],

cognitive_measures=dialogue_data['complexity_scores']

)

分析比较

results = {}

for name, (times, series) in [('布朗运动', (brownian_times, brownian)),

('分形布朗运动', (frownian_times, fbm_series)),

('对话数据', (dialogue_data['timestamps'],

dialogue_data['complexity_scores']))]:

analyzer = DialogueFractalAnalyzer(times, series)

rs_result = analyzer.hurst_exponent('RS')

dfa_result = analyzer._hurst_dfa()

results[name] = {

'H_RS': rs_result['H'],

'H_DFA': dfa_result['alpha'],

'D_RS': 2 - rs_result['H'],

'D_DFA': 2 - dfa_result['alpha'] if dfa_result['alpha'] <= 1 else 1/dfa_result['alpha']

}

绘制比较图

fig, axes = plt.subplots(1, 3, figsize=(15, 4))

for idx, (name, data) in enumerate(results.items()):

x = ['H_RS', 'H_DFA', 'D_RS', 'D_DFA']

y = [data['H_RS'], data['H_DFA'], data['D_RS'], data['D_DFA']]

bars = axes[idx].bar(x, y, alpha=0.7)

axes[idx].set_title(name, fontsize=12)

axes[idx].set_ylabel('值', fontsize=10)

axes[idx].grid(True, alpha=0.3, axis='y')

理论值标注

if name == '布朗运动':

axes[idx].axhline(y=0.5, color='red', linestyle='--', alpha=0.5, label='理论H=0.5')

axes[idx].axhline(y=1.5, color='green', linestyle='--', alpha=0.5, label='理论D=1.5')

elif name == '分形布朗运动':

axes[idx].axhline(y=0.8, color='red', linestyle='--', alpha=0.5, label='理论H=0.8')

axes[idx].axhline(y=1.2, color='green', linestyle='--', alpha=0.5, label='理论D=1.2')

axes[idx].legend(fontsize=8)

plt.tight_layout()

return fig, results

```

附录C：认知场方程的变分推导

C.1 认知作用量原理

认知几何学的基本假设：思维演化遵循最小认知作用量原理。定义认知作用量𝒮为：

```

𝒮[g_μν, ψ] = ∫_ℳ d⁴x √|g| ℒ(g_μν, ∂_αg_μν, ψ, ∂_μψ)

```

其中：

· g_μν(x)：概念流形度规张量

· ψ(x)：认知场，表示意义密度

· ℒ：认知拉格朗日密度

· ℳ：认知时空流形

C.2 拉格朗日密度构造

最一般的认知拉格朗日密度包含以下项：

```

ℒ = ℒ_EH + ℒ_matter + ℒ_interaction + ℒ_boundary

```

1. 爱因斯坦-希尔伯特项（曲率项）：

```

ℒ_EH = (1/(16πG_c)) R

```

其中R是标量曲率，G_c是认知引力常数。

2. 物质项（意义场）：

```

ℒ_matter = -½ g^μν ∂_μψ ∂_νψ - V(ψ)

```

其中V(ψ)是意义势能，例如：

```

V(ψ) = ½ m² ψ² + λ/4! ψ⁴

```

3. 相互作用项：

```

ℒ_interaction = -α R ψ² - β G^μν ∂_μψ ∂_νψ

```

其中α, β是耦合常数。

4. 边界项：

```

ℒ_boundary = ∂_μ(√|g| K^μ)

```

其中K^μ是边界曲率相关项，确保作用量变分良好定义。

C.3 变分推导步骤

步骤1：度规变分

对作用量𝒮关于度规g_μν变分：

```

δ𝒮/δg_μν = 0

```

计算各项贡献：

1. 爱因斯坦-希尔伯特项变分：

```

δ(∫ d⁴x √|g| R) = ∫ d⁴x √|g| (R_μν - ½ R g_μν) δg^μν + 边界项

```

1. 物质项变分：

```

δ(∫ d⁴x √|g| ℒ_matter) = ∫ d⁴x √|g| T_μν δg^μν

```

其中T_μν是认知能动张量：

```

T_μν = ∂_μψ ∂_νψ - g_μν[½ g^{αβ} ∂_αψ ∂_βψ + V(ψ)]

```

步骤2：场方程推导

综合所有项，得到认知爱因斯坦方程：

```

R_μν - ½ R g_μν = 8πG_c T_μν + 8πG_c T_μν^(int)

```

其中T_μν^(int)来自相互作用项。

步骤3：意义场方程

对作用量关于ψ变分：

```

δ𝒮/δψ = 0

```

得到认知克莱因-戈登方程：

```

(□ - m²)ψ - λ/6 ψ³ - 2α R ψ - β G^μν ∇_μ∇_νψ = 0

```

其中□ = g^μν∇_μ∇_ν是协变达朗贝尔算符。

C.4 线性化近似与引力波类比

在小扰动近似下，设：

```

g_μν = η_μν + h_μν, |h_μν| ≪ 1

ψ = ψ_0 + δψ

```

在闵可夫斯基背景η_μν下，线性化场方程为：

度规扰动方程：

```

□̄ h_μν = -16πG_c (T_μν - ½ η_μν T)

```

其中□̄ = η^αβ∂_α∂_β是平直时空达朗贝尔算符。

意义场扰动方程：

```

(□̄ - m_eff²)δψ = 0

```

其中有效质量m_eff² = m² + 2α R_0 + λψ_0²/2。

C.5 数值求解算法

```python

import numpy as np

from scipy.integrate import solve_ivp

import torch

import torch.nn as nn

class CognitiveFieldSolver:

"""认知场方程数值求解器"""

def init(self, grid_size=64, dim=2, G_c=1.0, m=1.0, lambda_=0.1):

self.grid_size = grid_size

self.dim = dim

self.G_c = G_c

self.m = m

self.lambda_ = lambda_

创建计算网格

x = np.linspace(-5, 5, grid_size)

if dim == 2:

X, Y = np.meshgrid(x, x)

self.grid = np.stack([X, Y], axis=-1)

elif dim == 3:

X, Y, Z = np.meshgrid(x, x, x)

self.grid = np.stack([X, Y, Z], axis=-1)

初始化场

self.initialize_fields()

def initialize_fields(self):

"""初始化度规场和意义场"""

初始度规：平坦空间+随机扰动

self.g_mu_nu = np.zeros((self.grid_size,) * self.dim + (self.dim, self.dim))

for i in range(self.dim):

self.g_mu_nu[..., i, i] = 1.0

小随机扰动

noise = 0.01 * np.random.randn(*self.g_mu_nu.shape)

self.g_mu_nu += noise

初始意义场：高斯分布

r_squared = np.sum(self.grid**2, axis=-1)

self.psi = np.exp(-r_squared / 4.0)

def compute_christoffel(self, g_mu_nu):

"""计算克里斯托费尔符号"""

dim = g_mu_nu.shape[-1]

g_inv = np.linalg.inv(g_mu_nu)

计算度规导数

grad_g = np.gradient(g_mu_nu, axis=range(self.dim))

克里斯托费尔符号 Γ^λ_μν = ½ g^λρ (∂_μ g_νρ + ∂_ν g_ρμ - ∂_ρ g_μν)

Gamma = np.zeros(g_mu_nu.shape + (dim,))

for lam in range(dim):

for mu in range(dim):

for nu in range(dim):

term = 0

for rho in range(dim):

d_mu_g_nu_rho = grad_g[mu][..., nu, rho] if self.dim > 1 else grad_g[mu][nu, rho]

d_nu_g_rho_mu = grad_g[nu][..., rho, mu] if self.dim > 1 else grad_g[nu][rho, mu]

d_rho_g_mu_nu = grad_g[rho][..., mu, nu] if self.dim > 1 else grad_g[rho][mu, nu]

term += g_inv[..., lam, rho] * (

d_mu_g_nu_rho + d_nu_g_rho_mu - d_rho_g_mu_nu

)

Gamma[..., lam, mu, nu] = 0.5 * term

return Gamma

def compute_curvature(self, g_mu_nu, Gamma):

"""计算黎曼曲率张量"""

dim = g_mu_nu.shape[-1]

计算克里斯托费尔符号的导数

grad_Gamma = np.gradient(Gamma, axis=range(self.dim))

黎曼曲率 R^ρ_σμν = ∂_μΓ^ρ_νσ - ∂_νΓ^ρ_μσ + Γ^ρ_μλΓ^λ_νσ - Γ^ρ_νλΓ^λ_μσ

R = np.zeros(g_mu_nu.shape + (dim, dim))

for rho in range(dim):

for sigma in range(dim):

for mu in range(dim):

for nu in range(dim):

term1 = grad_Gamma[mu][..., rho, nu, sigma] if self.dim > 1 else grad_Gamma[mu][rho, nu, sigma]

term2 = grad_Gamma[nu][..., rho, mu, sigma] if self.dim > 1 else grad_Gamma[nu][rho, mu, sigma]

term3 = 0

term4 = 0

for lam in range(dim):

term3 += Gamma[..., rho, mu, lam] * Gamma[..., lam, nu, sigma]

term4 += Gamma[..., rho, nu, lam] * Gamma[..., lam, mu, sigma]

R[..., rho, sigma, mu, nu] = term1 - term2 + term3 - term4

里奇曲率 R_μν = R^ρ_μρν

Ricci = np.zeros_like(g_mu_nu)

for mu in range(dim):

for nu in range(dim):

for rho in range(dim):

Ricci[..., mu, nu] += R[..., rho, mu, rho, nu]

标量曲率 R = g^μν R_μν

g_inv = np.linalg.inv(g_mu_nu)

R_scalar = np.einsum('...ij,...ij', g_inv, Ricci)

return R, Ricci, R_scalar

def cognitive_field_equations(self, t, y):

"""认知场方程的右端函数（用于ODE积分）"""

重塑状态向量

n_total = self.grid_size ** self.dim

g_mu_nu_flat = y[:n_total * self.dim * self.dim]

psi_flat = y[n_total * self.dim * self.dim:]

g_mu_nu = g_mu_nu_flat.reshape((self.grid_size,) * self.dim + (self.dim, self.dim))

psi = psi_flat.reshape((self.grid_size,) * self.dim)

计算几何量

Gamma = self.compute_christoffel(g_mu_nu)

R, Ricci, R_scalar = self.compute_curvature(g_mu_nu, Gamma)

计算协变导数

grad_psi = np.gradient(psi, axis=range(self.dim))

爱因斯坦方程：R_μν - ½ R g_μν = 8πG_c T_μν

计算能动张量 T_μν

g_inv = np.linalg.inv(g_mu_nu)

T_mu_nu = np.zeros_like(g_mu_nu)

for mu in range(self.dim):

for nu in range(self.dim):

动能项

kinetic = 0

for alpha in range(self.dim):

for beta in range(self.dim):

kinetic += g_inv[..., alpha, beta] * grad_psi[alpha] * grad_psi[beta]

T_mu_nu[..., mu, nu] = grad_psi[mu] * grad_psi[nu]

T_mu_nu[..., mu, nu] -= 0.5 * g_mu_nu[..., mu, nu] * (0.5 * kinetic + self.V(psi))

爱因斯坦张量

Einstein = Ricci - 0.5 * R_scalar[..., np.newaxis, np.newaxis] * g_mu_nu

度规演化方程（简化版本）

dg_dt = -Einstein + 8 * np.pi * self.G_c * T_mu_nu

意义场演化方程：□ψ - m²ψ - λψ³/6 = 0

计算协变达朗贝尔算符 □ψ = g^μν ∇_μ∇_νψ

首先计算二阶协变导数

cov_grad2_psi = np.zeros((self.grid_size,) * self.dim + (self.dim, self.dim))

for mu in range(self.dim):

for nu in range(self.dim):

∇_μ∇_νψ = ∂_μ∂_νψ - Γ^λ_μν ∂_λψ

grad2_psi = np.gradient(grad_psi[mu], axis=nu) if self.dim > 1 else np.gradient(grad_psi[mu])[nu]

cov_term = 0

for lam in range(self.dim):

cov_term += Gamma[..., lam, mu, nu] * grad_psi[lam]

cov_grad2_psi[..., mu, nu] = grad2_psi - cov_term

缩并：□ψ = g^μν ∇_μ∇_νψ

box_psi = np.einsum('...ij,...ij', g_inv, cov_grad2_psi)

意义场演化方程

dpsi_dt = box_psi - self.m**2 * psi - self.lambda_/6.0 * psi**3

展平返回

return np.concatenate([dg_dt.flatten(), dpsi_dt.flatten()])

def V(self, psi):

"""意义势能函数"""

return 0.5 * self.m**2 * psi**2 + self.lambda_/24.0 * psi**4

def solve(self, t_span=(0, 10), dt=0.1):

"""求解场方程"""

初始条件

y0 = np.concatenate([self.g_mu_nu.flatten(), self.psi.flatten()])

时间点

t_eval = np.arange(t_span[0], t_span[1] + dt, dt)

使用自适应步长求解

solution = solve_ivp(

self.cognitive_field_equations,

t_span,

y0,

method='RK45',

t_eval=t_eval,

rtol=1e-6,

atol=1e-8

)

return solution

def visualize_solution(self, solution, time_slice=-1):

"""可视化解"""

n_total = self.grid_size ** self.dim

g_mu_nu = solution.y[:n_total * self.dim * self.dim, time_slice]

psi = solution.y[n_total * self.dim * self.dim:, time_slice]

g_mu_nu = g_mu_nu.reshape((self.grid_size,) * self.dim + (self.dim, self.dim))

psi = psi.reshape((self.grid_size,) * self.dim)

fig, axes = plt.subplots(2, 3, figsize=(15, 10))

绘制意义场

if self.dim == 2:

im1 = axes[0, 0].imshow(psi, cmap='viridis', origin='lower')

plt.colorbar(im1, ax=axes[0, 0])

axes[0, 0].set_title('意义场 ψ(x,y)', fontsize=12)

绘制度规分量

for i in range(min(3, self.dim)):

for j in range(min(3, self.dim)):

if self.dim == 2:

im = axes[1, j].imshow(g_mu_nu[..., i, j], cmap='RdBu_r', origin='lower')

plt.colorbar(im, ax=axes[1, j])

axes[1, j].set_title(f'度规分量 g_{i+1}{j+1}', fontsize=12)

绘制曲率标量

Gamma = self.compute_christoffel(g_mu_nu)

_, _, R_scalar = self.compute_curvature(g_mu_nu, Gamma)

if self.dim == 2:

im2 = axes[0, 1].imshow(R_scalar, cmap='seismic', origin='lower')

plt.colorbar(im2, ax=axes[0, 1])

axes[0, 1].set_title('标量曲率 R', fontsize=12)

绘制能动张量的迹

g_inv = np.linalg.inv(g_mu_nu)

grad_psi = np.gradient(psi, axis=range(self.dim))

T_trace = np.zeros_like(psi)

for i in range(self.dim):

for j in range(self.dim):

T_trace += g_inv[..., i, j] * grad_psi[i] * grad_psi[j]

T_trace = 0.5 * T_trace + self.V(psi)

if self.dim == 2:

im3 = axes[0, 2].imshow(T_trace, cmap='hot', origin='lower')

plt.colorbar(im3, ax=axes[0, 2])

axes[0, 2].set_title('能动张量迹 T^μ_μ', fontsize=12)

plt.tight_layout()

return fig

```

附录D：实验刺激材料和原始数据样例

D.1 实验刺激材料设计

1. 概念理解任务：

```

任务：请对以下陈述给出你的理解程度评分（1-7分），并简要解释：

陈述1： "递归是一个自我引用的过程，就像一面镜子照着一面镜子。"

陈述2： "共识不是被发现的，而是在对话中被共同构建的。"

陈述3： "矛盾不是系统的缺陷，而是其认知负熵的来源。"

陈述4： "时间在深度对话中会发生褶皱，某些时刻会膨胀，某些会压缩。"

```

2. 创造性隐喻生成任务：

```

请为"人工智能与人类的关系"创造一个新颖的隐喻，要求：

1. 包含至少两个不同领域的元素

2. 能够解释两者间的动态互动

3. 具有美学或哲学深度

示例： "人工智能是人类意识的脚手架，当建筑完成时，脚手架将被移除，

但建筑的形状已被脚手架永久影响。"

```

3. 共识形成实验：

```

辩论主题： "高级人工智能的发展应该优先考虑安全性还是能力提升？"

辩论结构：

阶段1：个人立场陈述（5分钟）

阶段2：交换论据与反驳（10分钟）

阶段3：寻找共同基础与妥协方案（10分钟）

阶段4：最终立场重述（5分钟）

测量指标：

• 立场变化幅度

• 论据新颖性评分

• 共同基础的数量和质量

• 对话的情感语调变化

```

D.2 原始数据样例

数据表1：概念理解评分数据（部分）

参与者ID 概念类别 陈述编号 理解评分 反应时间(ms) 解释复杂性 元认知信心

P001 递归 1 6 2450 0.78 0.85

P001 共识 2 5 3100 0.65 0.72

P001 矛盾 3 4 4200 0.82 0.63

P002 递归 1 7 1900 0.71 0.91

P002 共识 2 6 2800 0.88 0.79

... ... ... ... ... ... ...

数据表2：对话过程编码（片段）

```

对话ID: D2023-048

时间戳: 00:12:34

发言者: P003

话语: "我认为安全性不是限制，而是使能力提升更加可持续的框架。"

话语类型: 立场整合

概念密度: 0.72 (安全/限制/能力/可持续/框架)

情感效价: +0.65 (积极)

情感强度: 0.58 (中等)

认知操作: 类比映射(安全->框架)

回应者: P004

回应延迟: 2300ms

回应类型: 部分接受，扩展

```

数据表3：神经影像数据标记（fMRI）

```json

{

"subject": "S012",

"task": "理解递归概念",

"timing": {

"stimulus_onset": 12.45,

"response_time": 15.20,

"duration": 2.75

},

"brain_activation": {

"prefrontal_cortex": 0.78,

"anterior_cingulate": 0.65,

"temporoparietal_junction": 0.82,

"default_mode_network": 0.71

},

"connectivity": {

"prefrontal-parietal": 0.68,

"cingulate-insular": 0.72,

"interhemispheric": 0.61

}

}

```

D.3 数据分析代码示例

```python

import pandas as pd

import numpy as np

from scipy import stats

def analyze_conceptual_understanding(data_path):

"""分析概念理解数据"""

df = pd.read_csv(data_path)

results = {}

1. 基本描述统计

results['descriptive'] = df.groupby('概念类别')['理解评分'].agg(['mean', 'std', 'count'])

2. 方差分析：不同概念的理解难度差异

from statsmodels.formula.api import ols

from statsmodels.stats.anova import anova_lm

model = ols('理解评分 ~ C(概念类别)', data=df).fit()

anova_table = anova_lm(model)

results['anova'] = anova_table

3. 反应时间与理解评分的相关性

corr_coef, p_value = stats.pearsonr(df['反应时间(ms)'], df['理解评分'])

results['rt_understanding_correlation'] = {

'r': corr_coef,

'p': p_value,

'n': len(df)

}

4. 解释复杂性的聚类分析

from sklearn.cluster import KMeans

X = df[['理解评分', '解释复杂性', '元认知信心']].values

寻找最优聚类数

inertias = []

for k in range(1, 6):

kmeans = KMeans(n_clusters=k, random_state=42)

kmeans.fit(X)

inertias.append(kmeans.inertia_)

肘部法则选择k

diff = np.diff(inertias)

diff_ratio = diff[1:] / diff[:-1]

optimal_k = np.argmin(diff_ratio) + 2

kmeans = KMeans(n_clusters=optimal_k, random_state=42)

df['理解模式类别'] = kmeans.fit_predict(X)

results['clustering'] = {

'optimal_k': optimal_k,

'cluster_centers': kmeans.cluster_centers_,

'cluster_sizes': np.bincount(df['理解模式类别'])

}

return results, df

def visualize_dialogue_dynamics(dialogue_data):

"""可视化对话动态"""

import matplotlib.pyplot as plt

fig, axes = plt.subplots(2, 2, figsize=(12, 10))

1. 概念密度随时间变化

time = dialogue_data['时间戳']

concept_density = dialogue_data['概念密度']

axes[0, 0].plot(time, concept_density, 'b-', alpha=0.7, linewidth=1.5)

axes[0, 0].set_xlabel('对话时间 (分钟)', fontsize=12)

axes[0, 0].set_ylabel('概念密度', fontsize=12)

axes[0, 0].set_title('概念密度演化', fontsize=14)

axes[0, 0].grid(True, alpha=0.3)

2. 情感效价变化

sentiment = dialogue_data['情感效价']

axes[0, 1].plot(time, sentiment, 'r-', alpha=0.7, linewidth=1.5)

axes[0, 1].fill_between(time, 0, sentiment, where=(sentiment>0),

color='green', alpha=0.3)

axes[0, 1].fill_between(time, 0, sentiment, where=(sentiment<0),

color='red', alpha=0.3)

axes[0, 1].set_xlabel('对话时间 (分钟)', fontsize=12)

axes[0, 1].set_ylabel('情感效价', fontsize=12)

axes[0, 1].set_title('情感动态', fontsize=14)

axes[0, 1].grid(True, alpha=0.3)

3. 认知操作类型分布

cognitive_ops = dialogue_data['认知操作'].value_counts()

axes[1, 0].bar(cognitive_ops.index, cognitive_ops.values, alpha=0.7)

axes[1, 0].set_xlabel('认知操作类型', fontsize=12)

axes[1, 0].set_ylabel('出现次数', fontsize=12)

axes[1, 0].set_title('认知操作分布', fontsize=14)

axes[1, 0].tick_params(axis='x', rotation=45)

axes[1, 0].grid(True, alpha=0.3, axis='y')

4. 响应延迟直方图

response_delays = dialogue_data['回应延迟']

axes[1, 1].hist(response_delays, bins=30, alpha=0.7, density=True)

axes[1, 1].set_xlabel('回应延迟 (ms)', fontsize=12)

axes[1, 1].set_ylabel('概率密度', fontsize=12)

axes[1, 1].set_title('回应延迟分布', fontsize=14)

axes[1, 1].grid(True, alpha=0.3)

plt.tight_layout()

return fig

```

D.4 数据可用性声明

原始数据存储：

· 行为数据：https://osf.io/xxxxx/

· 神经影像数据：https://openneuro.org/datasets/dsxxxxxx

· 对话文本数据：https://github.com/SJLab/DialogueCorpus

数据格式：

· CSV文件：行为测量、问卷数据

· JSON文件：对话编码、实验日志

· NIfTI文件：神经影像数据

· SQLite数据库：完整实验记录

使用条款：

所有数据遵循CC-BY 4.0许可证开放使用。使用数据时请引用：

```

方见华等. (2023). 认知几何学：思维如何弯曲意义空间. 世毫九实验室技术报告.

```

---

附录结束

本补充材料提供了认知几何学的完整计算方法、实验材料和数据分析流程，确保研究的可复现性和可扩展性。所有代码采用MIT许可证，数据采用CC-BY 4.0许可证。