Time course of predictability
Looking to the target object increased over the three seconds before contact. Specifically, for both studies, there was a main effect of time bin (Study 1, χ2 = 2183.1, df = 5, p < 0.001; study 2, χ2 = 1858, df = 5, p < 0.001), suggesting that looks to target objects increased as object contact approached. Figure 3 displays the amount of time participants spent looking at target objects within each of the six 500-ms bins during the three seconds before the actor contacted the target object.
Time course of predictability around event boundaries
To investigate the time course of predictability around event boundaries, mixed-effects models were tested with time bin and boundary type (within events, fine boundary, coarse boundary, both fine, and coarse boundaries) as fixed effects and item, movie, and subject as random effects. For both studies, a model with an interaction between time bin and boundary type fit the data significantly better than a model with only the main effects (Study 1, AIC = 259,606 vs AIC = 259,602, χ2 = 33.9, df = 15, p = 0.004; Study 2, AIC = 245,626 vs AIC = 245,630, χ2 = 25.4, df = 15, p = 0.04). For both studies, there was a significant main effect of bin (Study 1, F = 233.72, df = 5, p < 0.001; Study 2, F = 211.75, df = 5, p < 0.001), and a significant interaction between time bin and boundary type (Study 1, F = 2.26, df = 15, p = 0.004; Study 2, F = 1.69, df = 15, p = 0.04). The form of the interaction is illustrated in Figs. 4 and 5: for objects contacted in the middles of events participants looked to the object relatively early, whereas for objects contacted near event boundaries they tended to look more just before object contact. The main effect of boundary type was not significant (Study 1, F = 0.95, df = 3, p = 0.42; Study 2, F = 1.38, df = 3, p = 0.25).
To determine whether boundary types differed significantly from one another, three nested models were tested: a null model containing a binary variable coding whether there was an event boundary present or not, a model with this binary variable and a variable coding for the effect of fine boundaries, and a model adding a variable coding for the effect of coarse boundaries. All three models also included interaction terms coding for the interaction of time point and boundary type. None of these models were significantly different from one another (Study 1, largest χ2 = 12.86, df = 12, p = 0.38; Study 2, largest χ2 = 13.41, df = 12, p = 0.34).Footnote 1 Therefore, all three boundary conditions (fine, coarse, and both fine and coarse) were collapsed into a single boundary variable, as depicted in Fig. 4. For both studies, a model with an interaction between time bin and boundary type fit the data better than a model with only the main effects (Study 1, AIC = 259,605 vs 259,591, χ2 = 23.7, df = 5, p < 0.001; Study 2, AIC = 245,624 vs 245,620, χ2 = 14.6, df = 5, p = 0.01). There was a significant main effect of time bin (Study 1, F = 422.05, df = 5, p < 0.001; Study 2, F = 370.82, df = 5, p < 0.001), and again there was a significant interaction between time bin and boundary type for both studies (Study 1, F = 4.75, df = 5, p < 0.001; Study 2, F = 2.92, df = 5, p = 0.01). The main effect of boundary was again not significant (Study 1, F = 0.13, df = 1, p = 0.72; Study 2, F = 1.50, df = 1, p = 0.22).
To assess which, if any, individual time points had significant differences between the boundary and within-event conditions, we fitted mixed-effects models testing the difference for each time point. None of these were significant (Study 1, largest F = 3.03, p = 0.08; Study 2, largest F = 1.04, p = 0.31).
Follow-up analyses found that the size of interest areas differed significantly between boundary and within-event conditions for Study 1, but not for Study 2 (Study 1, Within = 5470.9 pixels, sd = 2631.5, Boundary = 5731.5 pixels, sd = 2552.3, t = − 7.1, p < 0.001; Study 2, Within = 3877.5 pixels, sd =2882.8, Boundary = 3864.7 pixels, sd = 2632.6, t = 0.31, p = 0.75). Therefore, to ensure that interest area size did not drive the event boundary effects, mixed-effects models that controlled for the size of each interest area were tested. The same pattern of results as reported above was found for both studies. There were main effects of interest area size (Study 1, F = 17.56, df = 1, p < 0.001; Study 2, F = 27.17, df = 1, p < 0.001) and of time point (F = 422.05, df = 5, p < 0.001; Study 2, F = 370.82, df = 5, p < 0.001), and there was a significant interaction of time point and boundary type (Study 1, F = 4.75, df = 5, p < 0.001; Study 2, F = 2.92, df = 5, p = 0.01). The main effect of boundary was not significant for either study (Study 1, F = 0.10, df = 1, p = 0.76; Study 2, F = 1.55, df = 1, p = 0.21).
In sum, both experiments showed an interaction between time and boundary condition, such that around event boundaries compared to within events, participants looked less at the target objects early and looked more during the 500 ms before object contact. There was no overall reduction in looking to the target object. In other words, event boundaries were associated with a shift in looking such that looks to the target location occurred closer to the point at which the object would be contacted. There was no evidence that coarse and fine boundaries differed from each other, and in neither experiment could the effect be statistically localized to any individual time points. In addition, these effects held even after controlling for the size of the interest areas.