## Chart: Paper Folding Accuracy vs. Number of Training Samples ### Overview The image is a scatter plot showing the accuracy of two modeling types (Visual WM and Verbal WM) on a paper folding task, plotted against the number of training samples. The difficulty of the task (Normal and Hard) is also indicated. ### Components/Axes * **Title:** Paper Folding * **X-axis:** Num. of Training Samples, ranging from 0 to 2500. Axis markers are present at 0, 500, 1000, 1500, 2000, and 2500. * **Y-axis:** Accuracy, ranging from 20 to 90. Axis markers are present at 20, 30, 40, 50, 60, 70, 80, and 90. * **Legend (top-left):** * **Modeling:** * Visual WM (blue line) * Verbal WM (pink line) * **Difficulty (top-right):** * Normal (gray circle) * Hard (gray triangle) ### Detailed Analysis * **Visual WM (Normal Difficulty):** The blue line with circle markers represents the accuracy of the Visual WM model on the normal difficulty task. The line slopes upward. * At 500 training samples, the accuracy is approximately 52%. * At 1000 training samples, the accuracy is approximately 65%. * At 2300 training samples, the accuracy is approximately 72%. * **Visual WM (Hard Difficulty):** The blue line with triangle markers represents the accuracy of the Visual WM model on the hard difficulty task. The line slopes upward. * At 500 training samples, the accuracy is approximately 28%. * At 1000 training samples, the accuracy is approximately 35%. * At 2300 training samples, the accuracy is approximately 39%. * **Verbal WM (Normal Difficulty):** The pink circle represents the accuracy of the Verbal WM model on the normal difficulty task. * At 2300 training samples, the accuracy is approximately 54%. * **Verbal WM (Hard Difficulty):** The pink triangle represents the accuracy of the Verbal WM model on the hard difficulty task. * At 2300 training samples, the accuracy is approximately 27%. ### Key Observations * The accuracy of the Visual WM model increases with the number of training samples for both normal and hard difficulty tasks. * The Visual WM model performs better on the normal difficulty task compared to the hard difficulty task. * The Verbal WM model has a single data point at 2300 training samples for both normal and hard difficulty tasks. * The Visual WM model generally outperforms the Verbal WM model, especially at lower training sample sizes. ### Interpretation The data suggests that the Visual WM model is more effective than the Verbal WM model for the paper folding task, particularly when the number of training samples is limited. Increasing the number of training samples improves the accuracy of the Visual WM model. The difficulty of the task significantly impacts the accuracy of both models, with the normal difficulty task resulting in higher accuracy. The single data point for the Verbal WM model limits the ability to analyze its performance trend with varying training samples. The Visual WM model's performance increase with more training data indicates that it benefits from increased exposure to the task.

\n ## Line Chart: Paper Folding ### Overview This line chart depicts the relationship between the number of training samples and accuracy for different modeling approaches and difficulty levels in a paper folding task. The chart displays three lines representing "Visual WM", "Verbal WM", and "Modeling", along with data points for "Normal" and "Hard" difficulty levels. ### Components/Axes * **Title:** "Paper Folding" (centered at the top) * **X-axis:** "Num. of Training Samples" (ranging from 0 to 2500, with markers at 0, 500, 1000, 1500, 2000, and 2500) * **Y-axis:** "Accuracy" (ranging from 20 to 90, with markers at 20, 30, 40, 50, 60, 70, 80, and 90) * **Legend:** Located in the top-right corner. * "Modeling" - Blue line * "Visual WM" - Light Blue line * "Verbal WM" - Red line * "Difficulty" - Grey circle ("Normal"), Red triangle ("Hard") ### Detailed Analysis **Modeling (Blue Line):** The blue line representing "Modeling" shows an upward trend. * At 500 training samples, accuracy is approximately 28%. * At 1000 training samples, accuracy is approximately 35%. * At 2000 training samples, accuracy is approximately 40%. * At 2500 training samples, accuracy is approximately 40%. **Visual WM (Light Blue Line):** The light blue line representing "Visual WM" also shows an upward trend, with a steeper initial slope. * At 500 training samples, accuracy is approximately 52%. * At 1000 training samples, accuracy is approximately 65%. * At 2000 training samples, accuracy is approximately 72%. * At 2500 training samples, accuracy is approximately 72%. **Verbal WM (Red Line):** The red line representing "Verbal WM" shows a relatively flat trend. * At 500 training samples, accuracy is approximately 24%. * At 2500 training samples, accuracy is approximately 28%. **Difficulty - Normal (Grey Circle):** * At 1000 training samples, accuracy is approximately 35%. * At 2000 training samples, accuracy is approximately 72%. **Difficulty - Hard (Red Triangle):** * At 2500 training samples, accuracy is approximately 28%. ### Key Observations * "Visual WM" consistently outperforms "Verbal WM" across all training sample sizes. * "Modeling" shows a moderate improvement in accuracy with increasing training samples, but plateaus after 2000 samples. * "Visual WM" shows the most significant improvement in accuracy with increasing training samples. * The "Hard" difficulty level results in significantly lower accuracy compared to the "Normal" difficulty level, especially at 2500 training samples. * The "Verbal WM" accuracy remains relatively low and stable regardless of the number of training samples. ### Interpretation The data suggests that "Visual WM" is the most effective modeling approach for the paper folding task, particularly as the number of training samples increases. The significant difference in accuracy between "Normal" and "Hard" difficulty levels indicates that the task complexity has a substantial impact on performance. The plateauing of the "Modeling" line suggests that there may be a limit to the benefits of increasing training samples for this approach. The consistently low accuracy of "Verbal WM" suggests that this approach is not well-suited for this task. The chart demonstrates the importance of both model selection and task difficulty in achieving high accuracy in the paper folding task. The data could be used to inform the selection of appropriate modeling techniques and to adjust task complexity to optimize performance.

## Line Chart with Scatter Points: Paper Folding ### Overview The image displays a line chart titled "Paper Folding," plotting model accuracy against the number of training samples. It compares two modeling approaches ("Visual WM" and "Verbal WM") across two difficulty levels ("Normal" and "Hard"). The "Normal" difficulty is represented by lines connecting data points, while the "Hard" difficulty is represented by isolated scatter points at the highest training sample count. ### Components/Axes * **Title:** "Paper Folding" (centered at the top). * **X-Axis:** Labeled "Num. of Training Samples." The scale runs from 0 to 2500, with major tick marks at 0, 500, 1000, 1500, 2000, and 2500. * **Y-Axis:** Labeled "Accuracy." The scale runs from 20 to 90, with major tick marks at 20, 30, 40, 50, 60, 70, 80, and 90. * **Legend (Top-Left Corner):** * **Modeling:** * Blue line with circle markers: "Visual WM" * Red line with triangle markers: "Verbal WM" * **Difficulty:** * Circle marker: "Normal" * Triangle marker: "Hard" ### Detailed Analysis **Data Series & Trends:** 1. **Visual WM - Normal Difficulty (Blue Line with Circles):** * **Trend:** Shows a strong, consistent upward trend. Accuracy increases significantly as the number of training samples grows. * **Data Points (Approximate):** * At 500 samples: ~52% accuracy * At 1000 samples: ~65% accuracy * At 2500 samples: ~72% accuracy 2. **Verbal WM - Normal Difficulty (Red Line with Triangles):** * **Trend:** Shows a moderate, steady upward trend. Accuracy improves with more training samples, but at a slower rate and from a lower baseline than Visual WM. * **Data Points (Approximate):** * At 500 samples: ~29% accuracy * At 1000 samples: ~34% accuracy * At 2500 samples: ~39% accuracy 3. **Hard Difficulty Data Points (Scatter Points at 2500 Samples):** * **Visual WM - Hard (Blue Circle):** Positioned at approximately 54% accuracy. * **Verbal WM - Hard (Red Triangle):** Positioned at approximately 27% accuracy. ### Key Observations 1. **Performance Gap:** There is a substantial and consistent performance gap between the "Visual WM" and "Verbal WM" models across all training sample sizes for the "Normal" difficulty. Visual WM is significantly more accurate. 2. **Impact of Difficulty:** For both modeling approaches, performance on the "Hard" difficulty (at 2500 samples) is markedly lower than on the "Normal" difficulty at the same sample size. * Visual WM drops from ~72% (Normal) to ~54% (Hard). * Verbal WM drops from ~39% (Normal) to ~27% (Hard). 3. **Scaling Behavior:** Both models show improved accuracy with more training data, suggesting they benefit from increased sample size. The slope of improvement appears steeper for Visual WM. 4. **Relative Difficulty Impact:** The absolute drop in accuracy when moving from Normal to Hard difficulty is larger for the Visual WM model (~18 percentage points) than for the Verbal WM model (~12 percentage points), though Verbal WM starts from a much lower baseline. ### Interpretation The data suggests that for the "Paper Folding" task, a modeling approach based on **Visual Working Memory (WM) is substantially more effective** than one based on Verbal Working Memory, achieving nearly double the accuracy with sufficient training data. The consistent upward trends indicate that **both models are learning and generalizing** from the provided training samples, with no clear sign of performance plateauing within the tested range (up to 2500 samples). This implies that providing even more data could yield further improvements. The significant performance degradation on the "Hard" difficulty level for both models demonstrates that the task's complexity scales with difficulty. The fact that the **Verbal WM model performs very poorly on the Hard task** (27% accuracy) suggests its representational strategy may be particularly ill-suited for handling the increased complexity, possibly involving spatial reasoning or mental manipulation that is more naturally encoded visually. In summary, the chart provides strong evidence that **visual representations are superior to verbal ones for this specific paper folding task**, and that task difficulty critically modulates performance for both representational types. The results could inform the design of AI systems for spatial reasoning tasks, favoring visual encoding mechanisms.

## Line Chart: Paper Folding Accuracy vs. Training Samples ### Overview The chart illustrates the relationship between accuracy and the number of training samples for different modeling approaches and difficulty levels in a paper folding task. Accuracy is plotted on the y-axis (0–90), and training samples are on the x-axis (0–2500). Four data series are represented: Modeling (blue line), Verbal WM (red line), Normal (gray line), and Hard (triangle markers). ### Components/Axes - **Y-axis**: Accuracy (0–90, increments of 10). - **X-axis**: Number of Training Samples (0–2500, increments of 500). - **Legend**: Located in the top-right corner. Colors and markers: - Blue line: Modeling - Red line: Verbal WM - Gray line: Normal - Triangle markers: Hard ### Detailed Analysis 1. **Modeling (Blue Line)**: - Starts at ~50 accuracy with 500 samples. - Increases steadily to ~70 accuracy at 2500 samples. - Slope: Positive, linear trend. 2. **Verbal WM (Red Line)**: - Starts at ~30 accuracy with 500 samples. - Rises to ~40 accuracy at 2500 samples. - Slope: Positive but less steep than Modeling. 3. **Normal (Gray Line)**: - Flat line at ~30 accuracy across all sample counts. - No variation observed. 4. **Hard (Triangle Markers)**: - Flat line at ~25 accuracy across all sample counts. - No variation observed. ### Key Observations - **Modeling** shows the strongest improvement with increased training samples. - **Verbal WM** demonstrates moderate improvement but lags behind Modeling. - **Normal** and **Hard** difficulty levels remain static, suggesting no dependency on training data volume. ### Interpretation The data indicates that **Modeling** benefits most from additional training samples, achieving a 20-point accuracy gain (50→70). **Verbal WM** also improves but at a slower rate (30→40), implying potential limitations in its learning mechanism. The **Normal** and **Hard** difficulty levels show no improvement, suggesting they may represent fixed constraints (e.g., inherent task difficulty) rather than variables influenced by training. This could imply that certain aspects of the paper folding task (e.g., physical constraints) are unresponsive to iterative learning, while others (e.g., pattern recognition) are highly trainable. The divergence between Modeling and Verbal WM highlights the importance of task-specific modeling strategies.