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Fig. 5 a The mean dip estimates (dark gray) for each picture along with the correct dip angle for each picture (light gray) in Experiment 1.
Fifteen of the 16 pictures have mean dip estimates that are not significantly different from 90°.2 Figure 5b shows the frequency distribution of dip estimates across all pictures.
Fig. 8 The mean dip estimates (dark gray) along with the correct dip angle for each picture (light gray) for the Play-Doh models in Experiment 2. The mean number correct on the GBCT was 4.9 (SD 2.6) out of 14.
Fig. 11 a The mean dip estimates (dark gray) when viewing the top of the model only and correct dip angle for each model (light gray) in Experiment 3. b Frequency distribution of responses that fell within angle bins in Experiment 3. Are the visual inferences of dip angles based on memory for the object?
Fig. 14 a The mean dip estimates in Experiment 4. b Frequency distribution of responses that fell within angle bins in Experiment 4. These findings suggest that the prior observed in the previous experiments cannot be explained by participants' response biases.
Mean dip estimates for each picture are shown in Fig. 14a, and the distribution of responses is shown in Fig. 14b (collapsing across trials in which participants were required to make an estimate and the trials in which they chose to make one).
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Figure 7a shows the mean dip estimate and the correct dip angle for each picture.
The mean dip estimate and the correct answer are shown in Fig. 8.
Figure 11a shows the correct dip angle and the mean dip estimate given for each model when only the top was visible.
Figure 12 shows a scatterplot of the relationship between the proportion of trials in which participants held their original perception and the absolute difference between the correct answer and the mean dip estimate.
Dip estimates were given most often for the salmon cross-section (97%% of the trials) and least often for the papaya cross-section (37 %). Figure 5a shows the true dip angle (how the highlighted region actually extended into the object) and participants' mean dip estimate for each picture.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com