Summary of Occlusion Advantage Results

In document An attentional theory of continuity editing (Page 156-162)

Chapter 4: Cuing a Cut

4.3 Experiment 1: Results

4.3.1 General statistics

4.3.3.2 Summary of Occlusion Advantage Results

When subjects are repeatedly presented the same Exit condition they should be able to adapt to the Exit condition and prepare their saccades before the cut occurs. This analysis of the four Exit groups (0%, 50%, 100%, and Random) has shown that only the 100% and Random Exit group show any decrease in correct response rate

156 (associated with a saccade) prior to the cut. 100% Exit subjects also show the shortest period of decreased correct response rate (82-125ms) indicating that by preparing the saccade before the cut they ensure a quicker recovery after the cut. The 50% Exit group also show quick recovery (125ms) but saccades in this group appear to be made in response to the cut.

When performance is compared between the beginning (condition 1) and end (condition 9) of the experimental session the effect of learning can be seen. All Exit groups (except 50%) show an increase in correct response rates at cue position 1 between conditions 1 and 9 but this is only significant for 100% Exit. The 50% Exit group shows a later significant increase at cue position 3.

In terms of reaction times, only 100% shows significantly faster reaction times in condition 9 compared to 1 even though most Exit groups show a trend in that direction. For 50% Exit subjects appear to be successfully learning the Exit condition as the reaction times are very long and variable in condition 1 but by condition 9 they are quick and consistent. However, this difference is not significant. By comparison, 100% Exit already shows consistent reaction times in condition 1 and by condition 9 the only slow reaction time, cue position 1, has disappeared.

In combination, these results seem to suggest that, in general, 100% Exit produces the clearest signs of saccade preparation before the cut and quickest recovery after the cut. 100% Exit also shows an increase in correct response rates and decrease in reaction times over the course of the experiment. However, as performance for 100% Exit is already very good this improvement is minor. By comparison, 50% Exit shows a significant shortening of the period of decreased accuracy and a trend towards faster reaction times at the end of the experiment. This level of improvement is more pronounced than for 100% Exit as performance under 50% Exit was initially mush worse. Therefore, whilst learning does occur under 100% Exit, the effect of learning is more pronounced with 50% Exit. These results both support the occlusion advantage hypothesis and suggest that the advantage of occlusion may also occur when occlusion is incomplete (e.g. 50%).

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4.3.4

Pursuit initiation hypothesis

Viewers expect the focal-object to move back onto the screen from a fully-occluded position after the cut.

This hypothesis predicts that viewers will expect the focal-object to be fully occluded by the screen edge in the frame after the cut (i.e. have 0% Entry) and direct their attention to that position. This will result in more accurate responses to the cues presented at the cue positions immediately after the cut (1, 3, 4, 5, and 7). Performance under experimental conditions 2 (0% Entry), 4 (50%), 6 (100%), and 8 (Random) was compared.

The first evidence of any advantage for 0% Entry should be seen in the correct response rate across all subjects and cue positions. However, all Entry conditions produce very similar correct response rates (0% Entry= 81.72%, 50%= 81.97%, 100%= is 83.08%, and Random= 81.97%) with no significant difference between them (Greenhouse-Gesisser: F=.389, df=2.624, p=.735).

Figure 4-18: Mean Correct response rate (y-axis: %) at each cue position (x-axis: frames relative to cut) compared across 0% (blue), 50% (green), 100% (red), and Random (black) Entry

158 Figure 4-18 shows the correct response data at each cue position split across the four Entry conditions. Most cue positions show no difference between Entry conditions except for positions 4 and 5. At cue position 4 there is a main effect of Entry (significant repeated-measures ANOVA with Greenhouse-Geisser (GG) correction: F=4.403, df=2.768, p<.01) and this can be attributed to 100% Entry having a significantly higher correct response rate compared to all other Entry conditions (0% Entry: mean difference= 20.119, p<.01; 50% Entry: mean diff.= 11.548, p<.05, one- tailed; Random Entry: mean diff.= 11.190, p<.05).

At cue position 5 there is no main effect of Entry (GG: F=2.184, df=2.824, p=.103) but 100% Entry is significantly higher than 0% (mean diff. = -12.31, p<.05) and Random Entry (mean diff. = -5.0, p<.05, one-tailed). These results indicate that 100% Entry leads to the best performance after the cut. Performance under 0% Entry (blue line in Figure 4-18) was predicted by the Pursuit Initiation hypothesis to produce the best performance but it actually takes the longest to return to the pre-cut level of performance.

Analysis for the occlusion advantage hypothesis (see 4.3.3) has shown that there is a significant difference between correct response rates for the Exit groups. This difference may be confounding the effect of Entry. Therefore, the data will be split across the four Exit subject groups to see if there are any Exit-specific Entry effects.

159 Figure 4-19: Mean correct response rates (y-axis: %) at each cue position (x-axis: frames relative to cut) split across the four Entry conditions, 0% (blue), 50% (green), 100% (red), Random (black). The four graphs indicate different Exit groups: 0% (top left), 50% (top right), 100% (bottom left), and Random Exit (bottom right).

When data is split across Exit groups (as in Figure 4-19) different effects emerge. If we consider the best presentation condition to be that which results in the shortest period of decreased correct response rates surrounding the cut we can identify the best Entry conditions for each Exit. For 0% Exit the best Entry condition appears to be 100% as this returns to the pre-cut level of correct responses quicker (by cue position 4) than any other Entry condition (0%, 50%, and Random do not recover until position 7). Performing a repeated-measures ANOVA between the Entry conditions at each cue position shows that 100% Entry produces a statistically higher correct response rate at cue positions 4 and 5 compared with 0% Entry (position 4: mean diff = -33.33, p<.05, one-tailed; position 5: mean diff.=-16.667 p<.05) and Random Entry (position 4: mean diff.=33.333, p<.05). This indicates that for the 0% Exit subject group, 100% Entry produces the best performance.

For the 50% Exit group (top right, Figure 4-19) it is hard to see which Entry condition is the best. It looks as if 100% Entry may return to pre-cut levels earliest (by cue position 5) but Random also seems to return at this point and 50% Entry’s level of correct responses are not far off baseline. Comparing the correct response rate at each cue position to baseline (correct response rate at cue position -4) indicates that 100%, and Random Entry both return to baseline by cue position 4 (100%: t=1.188, df=6, p=.280.; Random: t=1.441, df=6, p=.200). 50% Entry returns

160 by cue position 5 (t=1.698, df=6, p=.140). By comparison, 0% Entry only shows a significant deviation from baseline at cue position 1 (t=4.599, df=6, p<.01) but this is probably due to the low mean and large variance at cue position -4 (mean=82.86%, s.d.=13.80). Comparing the correct response rates for each Entry condition across all cue positions shows that the only significant difference between Entry conditions is at cue position 0 where 0% Entry is significantly lower than all other Entry conditions (50%: mean diff.=-17.143, p<.05; 100%: mean diff.=-8.571, p<.05, one- tailed; and Random: mean diff.=-17.143, p<.05). This difference at cue position 0 indicates a pre-cut attention withdrawal for 0% Entry. However, this does not result in quicker recovery after the cut. The absence of any post-cut significant differences indicates that there is no single Entry condition that is clearly the best for 50% Exit, although 100% and Random do recover more quickly than the other conditions. Given the analysis of the occlusion expectation and advantage hypotheses (see sections 4.3.2 and 4.3.3), there appears to be a consensus that 100% Exit is the best condition for saccade preparation prior to the cut and shows quick recovery afterwards. If we now look to see if one Entry condition produces the best performance within 100% Exit, we find that there is little difference between the Entry conditions as they all recover very quickly (Figure 4-19, bottom left graph). All Entry conditions show a significant decrease in correct response rates at cue position 1 but all also show a recovery back to baseline by position 3 (0%: t=.000, df=5, p=1.0; 50%: t=1.168, df=5, p=.296; 100%: t=.000, df=5, p=1.00; Random: t=1.464, df=5, p=.203). The only interesting differences between Entry conditions occur at cue positions 0 and 4. At cue position 0, 50% Entry shows a significant decrease in accuracy compared with the baseline79 (t=2.739, df=5, p<.05) but between-Entry conditions this decrease only proves to be significant from Random Entry (mean diff.=-23.333, p<.05). However, the fact that 50% Entry is significantly different to its baseline does suggest that some degree of saccade preparation is occurring before the cut.

161 The other interesting effect is a sudden and isolated decrease in correct response rate at cue position 4 for 0% Entry (4<-4, t=2.712, df=5, p<.05). A similar decrease at position 4 can be seen for 0% and Random Entry under 0% Exit and 0%, 50%, and Random Entry under 50% Exit (see Figure 4-19). Cue position 4 with 0% Entry places the focal-object half on and half off the screen with the reaction time cue presented directly on top of the screen edge. This decrease in the subject’s ability to correctly identify the RT cue may be due to the screen edge obscuring the cue in someway. An interpretation of this “edge-effect” will be left until the Discussion section.

Ignoring the “edge-effect”, there does not seem to be any one Entry condition which produces better performance than the rest (Figure 4-19, bottom left graph). 50% Entry may show the only signs of predictive saccading but 0%, 100% and Random Entry all result in a shorter period of attentional withdrawal (83ms, cue position 1 to 3). These results are inconclusive.

Looking at the correct response rates for Random Exit it is hard to see one Entry condition that creates the best performance. Paired t-tests between each cue position and -4 (“baseline”) shows that all Entry conditions recover back to baseline by cue position 4 with the only difference being that 50%, 100%, and Random Entry all also show a significant decrease in correct response rate at cue position 0 (50%: t=2.236, df=5, p<.05, one-tailed; 100%: t=2.236, df=5, p<.05, one-tailed; Random: t=2.390 df=5, p<.05, one-tailed). Comparing the correct response rates for Entry condition at these cue positions shows that the only significant difference is that Random Entry is significantly lower than 0% at cue position 0 (mean diff.=16.667 p<.05). This clear sign of pre-cut attention withdrawal seems to suggest that for a cut with Random Exit, Random Entry is expected.

In document An attentional theory of continuity editing (Page 156-162)