Data from: Short-latency preference for faces in the primate superior colliculus

# Data from: Short-latency preference for faces in the primate superior colliculus [https://doi.org/10.5061/dryad.b5mkkwhjw](https://doi.org/10.5061/dryad.b5mkkwhjw) README for all datasets provided for manuscript: * Short-latency preference for faces in the primate superior colliculus * Gongchen Yu\* , Leor N. Katz\* , Christian Quaia, Adam Messinger, Richard J. Krauzlis * These authors contributed equally Correspondence: [e-mail], [e-mail] Data are provided in .mat files. The provided m file PlotFigures.m has been created to load the datafiles and plot. The folder functionforplot contains all the functions that are needed for PlotFigure.m Resultant plots match those in the paper. Readme for each figure is within the PlotFigures.m function. We also add them here: ##### ##### Figure 1C Inside this mat: 'foraveragecategorypsth\_individualcategory' - 1*5 cell corresponds to 5object categories: 'face', 'body', 'hand', 'fruit&vegetable', 'humanmade'. In each cell, it is a 222*1001 matrix. Each row of the matrix represents the time course of normalized firing rate for one neuron, each column represents the normalized firing rate for a time bin (bin width 20ms, sliding step 1ms). 'psth\_bin' - center of the bins, aligned on object onset ##### Figure 1D Inside this mat: 'p\_forplot' - 222*1001 matrix, each row represents the time course ofANOVA (with the factor of object category) p value for one neuron, eachcolumn represents the p value for each time bin (bin width 20ms, slidingstep 1ms).'preference\_forplot' - 222*1001 matrix, each row represents the object category (1, 2, 3, 4, 5 correspond to 'face', 'body', 'hand', 'fruit&vegetable', 'human made') evoking the highest response for one neuron, each column represents this object preference for each time bin (bin width 20ms, sliding step 1ms). 'psth\_bin' - center of the bins, aligned on object onset ##### Figure 1E Inside this mat: 'foraveragecategorypsth\_meansubtraction\_individualcategory' - 1*5 cell corresponds to 5object categories: 'face', 'body', 'hand', 'fruit&vegetable', 'humanmade'. In each cell, it is a 113*1001 matrix. Each row of the matrix represents the time course of mean subtracted normalized firing rate for one object selective neuron, each column represents the normalized firing rate for a time bin (bin width 20ms, sliding step 1ms). 'psth\_bin' - center of the bins, aligned on object onset ##### Figure 1F Inside this mat: 'Activity\_matrix\_sorted\_visualonly' - 80*150 matrix, each row representsthe mean subtracted normalized firing rate for each visual-only neuron, eachcolumn represents the mean subtracted response to each of the 150 objectexamplars (1 to 30 face, 31 to 60 body, 61 to 90 hand, 91 to 120 fruit &vegetable, 121 to 150 human made).'Activity\_matrix\_sorted\_visualmotor' - 31*150 matrix, each row represents the mean subtracted normalized firing rate for each visual-motor neuron, each column represents the mean subtracted response to each of the 150 object examplars (1 to 30 face, 31 to 60 body, 61 to 90 hand, 91 to 120 fruit & vegetable, 121 to 150 human made). ##### Figure 1G Inside this mat: 'Activity\_matrix' - 113*150 matrix, each row representsthe mean subtracted normalized firing rate for each visual-only neuron, eachcolumn represents the mean subtracted response to each of the 150 objectexamplars (1 to 30 face, 31 to 60 body, 61 to 90 hand, 91 to 120 fruit &vegetable, 121 to 150 human made).'Activity\_mean' - 1*150 matrix, the mean across all the neurons (rows of 'Activity\_matrix') to each of the 150 object examplars (1 to 30 face, 31 to 60 body, 61 to 90 hand, 91 to 120 fruit & vegetable, 121 to 150 human made). 'relative\_salience' - 1\*150 matrix, the relative salience of the 150 object examplars (1 to 30 face, 31 to 60 body, 61 to 90 hand, 91 to 120 fruit & vegetable, 121 to 150 human made). ##### Figure 2A Inside this mat: 'individual\_classificationaccuracy' - 10\*81 matrix, time course of classification accuracy for 10 different classifiers (each row represents one classifier), each column represents the classification accuracy in each time bin (binsize 40ms, sliding in 5ms steps) These 10 different classifiers are: * Row 1: Face vs Body * Row 2: Face vs Hand * Row 3: Face vs Fruit * Row 4: Face vs Human made * Row 5: Body vs Fruit * Row 6: Body vs Human made * Row 7: Hand vs Fruit * Row 8: Hand vs Human made * Row 9: Body vs Hand * Row10: Fruit vs Human made 'timebin' - center of the bins, aligned on object onset ##### Figure 2B Inside this mat: 'confusion\_matrix\_early' - 4*4 matrix, classification accuracy confusion matrix for early window (40 to 80ms after object onset)'confusion\_matrix\_late' - 4*4 matrix, classification accuracy confusion matrix for late window (90 to 130ms after object onset) Both confusion matrice have the same row and column structure, it is listed below: column1 column2 column3 column4row1 face vs humanmade face vs fruit face vs hand face vs body row2 body vs humanmade body vs fruit body vs hand nan row3 hand vs humanmade hand vs fruit nan nan row4 fruit vs humanmade nan nan nan ##### Figure 2C Inside this mat: 'facenonface\_classificationaccuracy' - 1*81 matrix, time course of classificationaccuracy for the 'face vs nonface' classifier, each value represent thedata in each time bin (binsize 40ms, sliding in 5ms steps)'animateinanimate\_classificationaccuracy' - 1*81 matrix, time course of classification accuracy for the 'animate vs inanimate' classifier, each value represent the data in each time bin (binsize 40ms, sliding in 5ms steps) 'timebin' - center of the bins, aligned on object onset ##### Figure 2D Inside this mat: Mean, upper and lower bound of 95 confidence intervals for monkey vs human face classification accuracy for early window (40 to 80ms after object onset) 'earlymonkeyhumanface\_performance\_mean' 'earlymonkeyhumanface\_performance\_lowerCI' 'earlymonkeyhumanface\_performance\_upperCI' Mean, upper and lower bound of 95 confidence intervals for monkey vs human face classification accuracy for late window (90 to 130ms after object onset) 'latemonkeyhumanface\_performance\_mean' 'latemonkeyhumanface\_performance\_lowerCI' 'latemonkeyhumanface\_performance\_upperCI' Mean, upper and lower bound of 95 confidence intervals for upright vs inverted face classification accuracy for early window (40 to 80ms after object onset) 'earlyUDface\_performance\_mean' 'earlyUDface\_performance\_lowerCI' 'earlyUDface\_performance\_upperCI' Mean, upper and lower bound of 95 confidence intervals for upright vs inverted face classification accuracy for late window (90 to 130ms after object onset) 'lateUDface\_performance\_mean' 'lateUDface\_performance\_lowerCI' 'lateUDface\_performance\_upperCI' ##### Figure 3B Inside this mat: 'bData' - 1\*8 struct corresponding to 8 sessions of SC recording before and after LGN inactivation Inside 'bData': 'sessionName' - name of the recording session 'monkeyStr' - monkey subject name 'clr' - color for plot 'sacFailMap' - data for plotting saccade failure heat map figure 3B 'sacFailPercent' - data for ploTting saccade failure contra vs ipsi figure 3C ##### Figure 3C Inside this mat: 'bData' - 1\*8 struct corresponding to 8 sessions of SC recording before and after LGN inactivation Inside 'bData': 'sessionName' - name of the recording session 'monkeyStr' - monkey subject name 'clr' - color for plot 'sacFailMap' - data for plotting saccade failure heat map figure 3B 'sacFailPercent' - data for ploTting saccade failure contra vs ipsi figure 3C ##### Figure 3D First, inside this mat are all results before LGN inactivation: 'foraveragecategorypsth\_forplot\_beforeLGNinactivation' - 1*3 cell corresponds to 3object categories: 'face', 'hand', 'human made'. In each cell, it is a 114*1001 matrix. Each row of the matrix represents the time course of normalized firing rate for one neuron, each column represents the normalized firing rate for a time bin (bin width 20ms, sliding step 1ms). 'p\_forplot\_beforeLGNinactivation' - 114*1001 matrix, each row represents the time course ofANOVA (with the factor of object category) p value for one neuron, eachcolumn represents the p value for each time bin (bin width 20ms, slidingstep 1ms).'preference\_forplot\_beforeLGNinactivation' - 114*1001 matrix, each row represents the object category (1, 2, 3 correspond to 'face', 'hand', 'human made') evoking the highest response for one neuron, each column represents this object preference for each time bin (bin width 20ms, sliding step 1ms). 'psth\_bin' - center of the bins, aligned on object onset, same for both psth and ANOVA plot ##### Figure 3E First, inside this mat are all results after LGN inactivation: 'foraveragecategorypsth\_forplot\_afterLGNinactivation' - 1*3 cell corresponds to 3object categories: 'face', 'hand', 'human made'. In each cell, it is a 114*1001 matrix. Each row of the matrix represents the time course of normalized firing rate for one neuron, each column represents the normalized firing rate for a time bin (bin width 20ms, sliding step 1ms). 'p\_forplot\_afterLGNinactivation' - 114*1001 matrix, each row represents the time course ofANOVA (with the factor of object category) p value for one neuron, eachcolumn represents the p value for each time bin (bin width 20ms, slidingstep 1ms).'preference\_forplot\_afterLGNinactivation' - 114*1001 matrix, each row represents the object category (1, 2, 3 correspond to 'face', 'hand', 'human made') evoking the highest response for one neuron, each column represents this object preference for each time bin (bin width 20ms, sliding step 1ms). 'psth\_bin' - center of the bins, aligned on object onset, same for both psth and ANOVA plot ##### Figure 4A Inside this mat: 'confusion\_matrix\_V1model' - 4\*4 matrix, classification accuracy confusion matrix for the V1-based model Both confusion matrice have the same row and column structure, it is listed below: column1 column2 column3 column4row1 face vs humanmade face vs fruit face vs hand face vs body row2 body vs humanmade body vs fruit body vs hand nan row3 hand vs humanmade hand vs fruit nan nan row4 fruit vs humanmade nan nan nan ##### Figure 4B Inside this mat: Classification results of 10 different classifiers using V1-based model output, the structures are all 10*1 matrix:'result\_classification\_V1\_mean' - mean of the classification accuracy'result\_classification\_V1\_lowerCI' - lower bound of the 95 confidence interval of the classification accuracy'result\_classification\_V1\_upperCI' - upper bound of the 95 confidence interval of the classification accuracyClassification results of 10 different classifiers using SC early response (40 to 80ms after object onset), the structures are all 10*1 matrix: the structures are all 10\*1 matrix: 'result\_classification\_SCearly\_mean' - mean of the classification accuracy 'result\_classification\_SCearly\_lowerCI' - lower bound of the 95 confidence interval of the classification accuracy 'result\_classification\_SCearly\_upperCI' - upper bound of the 95 confidence interval of the classification accuracy These 10 classifers are listed by row: * Row 1: Face vs Body * Row 2: Face vs Hand * Row 3: Face vs Fruit * Row 4: Face vs Human made * Row 5: Body vs Hand * Row 6: Body vs Fruit * Row 7: Body vs Human made * Row 8: Hand vs Fruit * Row 9: Hand vs Human made * Row10: Fruit vs Human made ##### Figure 4C Inside this mat: Classification results of 10 different classifiers using V1-based model output, the structures are all 10*1 matrix:'result\_classification\_V1\_mean' - mean of the classification accuracy'result\_classification\_V1\_lowerCI' - lower bound of the 95 confidence interval of the classification accuracy'result\_classification\_V1\_upperCI' - upper bound of the 95 confidence interval of the classification accuracyClassification results of 10 different classifiers using SC late response (90 to 130ms after object onset), the structures are all 10*1 matrix: the structures are all 10\*1 matrix: 'result\_classification\_SClate\_mean' - mean of the classification accuracy 'result\_classification\_SClate\_lowerCI' - lower bound of the 95 confidence interval of the classification accuracy 'result\_classification\_SClate\_upperCI' - upper bound of the 95 confidence interval of the classification accuracy These 10 classifers are listed by row: * Row 1: Face vs Body * Row 2: Face vs Hand * Row 3: Face vs Fruit * Row 4: Face vs Human made * Row 5: Body vs Hand * Row 6: Body vs Fruit * Row 7: Body vs Human made * Row 8: Hand vs Fruit * Row 9: Hand vs Human made * Row10: Fruit vs Human made

# 数据来源：《灵长类上丘（superior colliculus, SC）对面孔的短潜伏期偏好》[https://doi.org/10.5061/dryad.b5mkkwhjw]
本手稿配套所有数据集的说明文档：
* 论文标题：Short-latency preference for faces in the primate superior colliculus
* 作者：龚晨宇*、Leor N. Katz*、Christian Quaia、Adam Messinger、Richard J. Krauzlis，其中*代表共同第一作者
* 通讯邮箱：[e-mail], [e-mail]

数据集以MATLAB数据文件（.mat）格式存储。配套提供的MATLAB脚本`PlotFigures.m`用于加载数据并生成绘图，`functionforplot`文件夹包含`PlotFigures.m`运行所需的全部依赖函数，生成的绘图与论文中的插图完全一致。各插图的说明文档已包含在`PlotFigures.m`脚本中，此处补充如下：

##### 图1C
该.mat文件包含变量`foraveragecategorypsth_individualcategory`：为1×5元胞数组，对应5类物体类别：面孔（face）、躯体（body）、手部（hand）、果蔬（fruit&vegetable）、人造物品（humanmade）。每个元胞内存储222×1001矩阵，矩阵每一行代表单个神经元的归一化放电率时程，每一列代表单个时间窗的归一化放电率（时间窗宽度20ms，滑动步长1ms）。变量`psth_bin`：以刺激出现时刻（onset）对齐的时间窗中心值。

##### 图1D
该.mat文件包含变量`p_forplot`：为222×1001矩阵，每一行代表单个神经元的方差分析（Analysis of Variance, ANOVA，以物体类别为因素）p值时程，每一列对应单个时间窗的p值（时间窗宽度20ms，滑动步长1ms）。变量`preference_forplot`：为222×1001矩阵，每一行代表单个神经元在各时间窗下诱发最强响应的物体类别（1、2、3、4、5分别对应面孔、躯体、手部、果蔬、人造物品），每一列代表该时间窗下的物体偏好得分。变量`psth_bin`：以刺激出现时刻对齐的时间窗中心值。

##### 图1E
该.mat文件包含变量`foraveragecategorypsth_meansubtraction_individualcategory`：为1×5元胞数组，对应5类物体类别：面孔、躯体、手部、果蔬、人造物品。每个元胞内存储113×1001矩阵，矩阵每一行代表单个物体选择性神经元的去均值归一化放电率时程，每一列代表单个时间窗的归一化放电率（时间窗宽度20ms，滑动步长1ms）。变量`psth_bin`：以刺激出现时刻对齐的时间窗中心值。

##### 图1F
该.mat文件包含两个变量：
1. `Activity_matrix_sorted_visualonly`：为80×150矩阵，每一行代表单个视觉响应神经元的去均值归一化放电率，每一列代表该神经元对150个物体样本的平均响应（1~30为面孔样本，31~60为躯体样本，61~90为手部样本，91~120为果蔬样本，121~150为人造物品样本）。
2. `Activity_matrix_sorted_visualmotor`：为31×150矩阵，每一行代表单个视觉-运动神经元的去均值归一化放电率，每一列代表该神经元对150个物体样本的平均响应（样本分组规则同上）。

##### 图1G
该.mat文件包含三个变量：
1. `Activity_matrix`：为113×150矩阵，每一行代表单个视觉响应神经元的去均值归一化放电率，每一列代表该神经元对150个物体样本的平均响应（样本分组规则同上）。
2. `Activity_mean`：为1×150矩阵，代表所有神经元对150个物体样本的平均响应（样本分组规则同上）。
3. `relative_salience`：为1×150矩阵，代表150个物体样本的相对显著性（样本分组规则同上）。

##### 图2A
该.mat文件包含变量`individual_classificationaccuracy`：为10×81矩阵，代表10种不同分类器的分类准确率时程，每一行对应一个分类器，每一列对应单个时间窗的分类准确率（时间窗宽度40ms，滑动步长5ms）。10种分类器分别为：
* 第1行：面孔 vs 躯体
* 第2行：面孔 vs 手部
* 第3行：面孔 vs 果蔬
* 第4行：面孔 vs 人造物品
* 第5行：躯体 vs 果蔬
* 第6行：躯体 vs 人造物品
* 第7行：手部 vs 果蔬
* 第8行：手部 vs 人造物品
* 第9行：躯体 vs 手部
* 第10行：果蔬 vs 人造物品
变量`timebin`：以刺激出现时刻对齐的时间窗中心值。

##### 图2B
该.mat文件包含两个变量：
1. `confusion_matrix_early`：4×4矩阵，代表早期时间窗（刺激出现后40~80ms）的分类准确率混淆矩阵。
2. `confusion_matrix_late`：4×4矩阵，代表晚期时间窗（刺激出现后90~130ms）的分类准确率混淆矩阵。
两个混淆矩阵的行、列结构完全一致，具体如下：
| 列1 | 列2 | 列3 | 列4 |
|-----|-----|-----|-----|
| 面孔 vs 人造物品 | 面孔 vs 果蔬 | 面孔 vs 手部 | 面孔 vs 躯体 |
| 躯体 vs 人造物品 | 躯体 vs 果蔬 | 躯体 vs 手部 | 无数据（nan） |
| 手部 vs 人造物品 | 手部 vs 果蔬 | 无数据（nan） | 无数据（nan） |
| 果蔬 vs 人造物品 | 无数据（nan） | 无数据（nan） | 无数据（nan） |

##### 图2C
该.mat文件包含两个变量：
1. `facenonface_classificationaccuracy`：为1×81矩阵，代表“面孔 vs 非面孔”分类器的分类准确率时程，每个值对应单个时间窗的分类准确率（时间窗宽度40ms，滑动步长5ms）。
2. `animateinanimate_classificationaccuracy`：为1×81矩阵，代表“有生命 vs 无生命”分类器的分类准确率时程，每个值对应单个时间窗的分类准确率（时间窗宽度40ms，滑动步长5ms）。
变量`timebin`：以刺激出现时刻对齐的时间窗中心值。

##### 图2D
该.mat文件包含以下变量，分别对应不同分类任务的95%置信区间均值、上下界：
1. 早期时间窗（刺激出现后40~80ms）猕猴面孔 vs 人类面孔分类任务：`earlymonkeyhumanface_performance_mean`（准确率均值）、`earlymonkeyhumanface_performance_lowerCI`（95%置信区间下界）、`earlymonkeyhumanface_performance_upperCI`（95%置信区间上界）。
2. 晚期时间窗（刺激出现后90~130ms）猕猴面孔 vs 人类面孔分类任务：`latemonkeyhumanface_performance_mean`、`latemonkeyhumanface_performance_lowerCI`、`latemonkeyhumanface_performance_upperCI`。
3. 早期时间窗正立面孔 vs 倒立面孔分类任务：`earlyUDface_performance_mean`、`earlyUDface_performance_lowerCI`、`earlyUDface_performance_upperCI`。
4. 晚期时间窗正立面孔 vs 倒立面孔分类任务：`lateUDface_performance_mean`、`lateUDface_performance_lowerCI`、`lateUDface_performance_upperCI`。

##### 图3B
该.mat文件包含变量`bData`：为1×8结构体数组，对应外侧膝状体（lateral geniculate nucleus, LGN）失活前后的8次上丘记录会话。`bData`结构体包含以下字段：
* `sessionName`：记录会话名称
* `monkeyStr`：实验猕猴名称
* `clr`：绘图所用颜色
* `sacFailMap`：用于绘制眼跳失败热图的数据（对应图3B）
* `sacFailPercent`：用于绘制眼跳失败率对侧 vs 同侧图的数据（对应图3C）

##### 图3C
该.mat文件包含变量`bData`：为1×8结构体数组，对应外侧膝状体失活前后的8次上丘记录会话。`bData`结构体包含以下字段：
* `sessionName`：记录会话名称
* `monkeyStr`：实验猕猴名称
* `clr`：绘图所用颜色
* `sacFailMap`：用于绘制眼跳失败热图的数据（对应图3B）
* `sacFailPercent`：用于绘制眼跳失败率对侧 vs 同侧图的数据（对应图3C）

##### 图3D
该.mat文件包含外侧膝状体失活后的全部实验结果：
变量`foraveragecategorypsth_forplot_beforeLGNinactivation`：为1×3元胞数组，对应3类物体类别：面孔、手部、人造物品。每个元胞内存储114×1001矩阵，矩阵每一行代表单个神经元的归一化放电率时程，每一列代表单个时间窗的归一化放电率（时间窗宽度20ms，滑动步长1ms）。
变量`p_forplot_beforeLGNinactivation`：为114×1001矩阵，每一行代表单个神经元的方差分析（以物体类别为因素）p值时程，每一列对应单个时间窗的p值（时间窗宽度20ms，滑动步长1ms）。
变量`preference_forplot_beforeLGNinactivation`：为114×1001矩阵，每一行代表单个神经元在各时间窗下诱发最强响应的物体类别（1、2、3分别对应面孔、手部、人造物品），每一列代表该时间窗下的物体偏好得分。
变量`psth_bin`：以刺激出现时刻对齐的时间窗中心值，适用于脉冲响应和方差分析绘图。

##### 图3E
该.mat文件包含外侧膝状体失活前的全部实验结果：
变量`foraveragecategorypsth_forplot_afterLGNinactivation`：为1×3元胞数组，对应3类物体类别：面孔、手部、人造物品。每个元胞内存储114×1001矩阵，矩阵每一行代表单个神经元的归一化放电率时程，每一列代表单个时间窗的归一化放电率（时间窗宽度20ms，滑动步长1ms）。
变量`p_forplot_afterLGNinactivation`：为114×1001矩阵，每一行代表单个神经元的方差分析（以物体类别为因素）p值时程，每一列对应单个时间窗的p值（时间窗宽度20ms，滑动步长1ms）。
变量`preference_forplot_afterLGNinactivation`：为114×1001矩阵，每一行代表单个神经元在各时间窗下诱发最强响应的物体类别（1、2、3分别对应面孔、手部、人造物品），每一列代表该时间窗下的物体偏好得分。
变量`psth_bin`：以刺激出现时刻对齐的时间窗中心值，适用于脉冲响应和方差分析绘图。

##### 图4A
该.mat文件包含变量`confusion_matrix_V1model`：为4×4矩阵，代表基于初级视觉皮层（V1）的模型的分类准确率混淆矩阵。两个混淆矩阵的行、列结构完全一致，具体如下：
| 列1 | 列2 | 列3 | 列4 |
|-----|-----|-----|-----|
| 面孔 vs 人造物品 | 面孔 vs 果蔬 | 面孔 vs 手部 | 面孔 vs 躯体 |
| 躯体 vs 人造物品 | 躯体 vs 果蔬 | 躯体 vs 手部 | 无数据（nan） |
| 手部 vs 人造物品 | 手部 vs 果蔬 | 无数据（nan） | 无数据（nan） |
| 果蔬 vs 人造物品 | 无数据（nan） | 无数据（nan） | 无数据（nan） |

##### 图4B
该.mat文件包含两类分类结果数据，均为10×1矩阵结构：
1. 基于V1模型输出的10种分类器分类结果：
* `result_classification_V1_mean`：分类准确率均值
* `result_classification_V1_lowerCI`：分类准确率95%置信区间下界
* `result_classification_V1_upperCI`：分类准确率95%置信区间上界
2. 基于上丘早期响应（刺激出现后40~80ms）的10种分类器分类结果：
* `result_classification_SCearly_mean`：分类准确率均值
* `result_classification_SCearly_lowerCI`：分类准确率95%置信区间下界
* `result_classification_SCearly_upperCI`：分类准确率95%置信区间上界
10种分类器按行顺序如下：
* 第1行：面孔 vs 躯体
* 第2行：面孔 vs 手部
* 第3行：面孔 vs 果蔬
* 第4行：面孔 vs 人造物品
* 第5行：躯体 vs 手部
* 第6行：躯体 vs 果蔬
* 第7行：躯体 vs 人造物品
* 第8行：手部 vs 果蔬
* 第9行：手部 vs 人造物品
* 第10行：果蔬 vs 人造物品

##### 图4C
该.mat文件包含两类分类结果数据，均为10×1矩阵结构：
1. 基于V1模型输出的10种分类器分类结果：
* `result_classification_V1_mean`：分类准确率均值
* `result_classification_V1_lowerCI`：分类准确率95%置信区间下界
* `result_classification_V1_upperCI`：分类准确率95%置信区间上界
2. 基于上丘晚期响应（刺激出现后90~130ms）的10种分类器分类结果：
* `result_classification_SClate_mean`：分类准确率均值
* `result_classification_SClate_lowerCI`：分类准确率95%置信区间下界
* `result_classification_SClate_upperCI`：分类准确率95%置信区间上界
10种分类器按行顺序如下：
* 第1行：面孔 vs 躯体
* 第2行：面孔 vs 手部
* 第3行：面孔 vs 果蔬
* 第4行：面孔 vs 人造物品
* 第5行：躯体 vs 手部
* 第6行：躯体 vs 果蔬
* 第7行：躯体 vs 人造物品
* 第8行：手部 vs 果蔬
* 第9行：手部 vs 人造物品
* 第10行：果蔬 vs 人造物品