test_extracted_captions/chanFormulatingFixatingEffects2024.json

{"CAPTION FIG7.png": "'\\n\\n## References\\n\\nFig. 7: Example exploration graphs used for coding hill-climbing strategies: not \u201call hill-climbing\u201d (left) and \u201call hill-climbing\u201d (right), where each transition between participant moves is plotted on the x-axis, and the Euclidean distance between each move and its immediately preceding move is plotted on the y-axis. In the not \u201call hill-climbing\u201d example, the first 10 moves (the 0th-10th moves), where there are substantial variations in Dropdown move distances across the sequences, would be coded as non-hillclimbing (N), while the 10th-20th moves and the 20th-30th moves, where Dropdown move distances are consistently low, would be coded as hillclimbing (H). In the \u201call hill-climbing\u201d example, all first 30 moves would be coded as hillclimbing (H).\\n\\n'", "CAPTION FIG1.png": "'\\n\\n[MISSING_PAGE_POST]\\n\\n'", "CAPTION TAB2.png": "'\\n\\n\\\\begin{table}\\n\\\\begin{tabular}{l l'", "CAPTION FIG4.png": "'\\n\\n## References\\n\\n* [1] A. A. K. K. (1999) The mathematical theory of the mathematical theory'", "CAPTION FIG5.png": "'\\n\\n## References\\n\\nFig. 5: Maximum score at \\\\(n\\\\)-th move for each participant: left) HD; right) LD. We observe (1) cumulative disadvantages for the List condition, as well as (2) early advantages for the In-Context interface, especially with LD examples.\\n\\n'", "CAPTION TAB1.png": "'\\n\\n\\\\begin{table}\\n\\\\begin{tabular}{l l'", "CAPTION TAB3.png": "'\\n\\n\\\\begin{table}\\n\\\\begin{tabular}{l l'", "CAPTION FIG8.png": "'\\n\\n## References\\n\\nFig. 8: Raw proportion of participants with a predominantly hillclimbing strategy in the first 30 moves, across interface conditions (a) with both HD and LD examples (b). More \u201cList\u201d participants did hillclimbing in the first 30 moves with both high and low diverse example sets than \u201cIn-Context\u201d participants. More \u201cList\u201d participants did hillclimbing for the first 30 moves with LD examples than other combinations of the presentation and example sets.\\n\\n'", "CAPTION FIG6.png": "'\\n\\n## References\\n\\nFig. 6: Raw proportion of participants expressed \u201cnot using (examples)\u201d, \u201csimulation-based\u201d or \u201cmodel-based\u201d in their answer to \u201cHow did you use initial examples (the values of ten points given to you)?\u201d. Error bars are standard error of proportion. More participants, self-reported using a model-based strategy in the In-Context condition compared to other conditions.\\n\\n'", "CAPTION FIG3.png": "'\\n\\n## References\\n\\n* [1] A. A. K. K. (1999) The \\\\(\\\\alpha\\\\)-function of the \\\\(\\\\alpha'", "CAPTION FIG2.png": "'Figure 2: Screenshot of experimental interface, shown for the List condition: the 100x100 grid, which constituted the search environment for the task, was shown on the left panel: participants explored the space by clicking anywhere on the 100x100 grid. The 10 initial examples, moves remaining, the score of current move, the current max score and score legend were shown on the right panel. In the Dropdown condition, the dropdown menu as seen in Figure 3 was shown in the same position as the list of examples in the List condition. In the In-Context condition, examples were instead overlaid as points, with corresponding values, on the search grid, as shown in Figure 3.\\n\\n'"}