Ultra rapid object categorization: effects of level, animacy and context

Abstract

It is widely agreed that in object categorization bottom-up and top-down influences interact. How top-down processes affect categorization has been primarily investigated in isolation, with only one higher level process at a time being manipulated. Here, we investigate the combination of different top-down influences (by varying the level of category, the animacy and the background of the object) and their effect on rapid object categorization. Subjects participated in a two-alternative forced choice rapid categorization task, while we measured accuracy and reaction times. Subjects had to categorize objects on the superordinate, basic or subordinate level. Objects belonged to the category animal or vehicle and each object was presented on a gray, congruent (upright) or incongruent (inverted) background. The results show that each top-down manipulation impacts object categorization and that they interact strongly. The best categorization was achieved on the superordinate level, providing no advantage for basic level in rapid categorization. Categorization between vehicles was faster than between animals on the basic level and vice versa on the subordinate level. Objects in homogenous gray background (context) yielded better overall performance than objects embedded in complex scenes, an effect most prominent on the subordinate level. An inverted background had no negative effect on object categorization compared to upright scenes. These results show how different top-down manipulations, such as category level, category type and background information, are related. We discuss the implications of top-down interactions on the interpretation of categorization results.

Figure 1. Illustration of stimuli and experimental design.

(A) Two example objects on different context conditions. (B) Trial sequence. (C) Schematic of tested categories on each level of categorization. The arrow indicates pairs of categories that were tested against each other.

Figure 2. Influence of “level” on ultra-rapid object categorization.

For each level (superordinate, basic and subordinate) the mean performance (A) and reaction times (B) are shown. Error bars represent the normalized 95% confidence intervals of the mean (Cousineau-Morey approach [47], [48]). *p<.05 **p≤.01 ***p≤.001.

Figure 3. Influence of “animacy” on ultra-rapid object categorization.

Performance (A) and reaction times (B) for animal and vehicle category are shown. Error bars represent the normalized 95% confidence intervals of the mean (Cousineau-Morey approach [47], [48]). *p<.05, **p≤.01, ***p≤.001.

Figure 4. Effect of context on object categorization.

The responses (percentage correct (A) and reaction times (B)) are shown for different context conditions. Objects were embedded in either a gray, upright or inverted context. Error bars represent the normalized 95% confidence intervals of the mean (Cousineau-Morey approach [47], [48]). *p<.05, **p≤.01 ***, p≤.001.

Figure 5. Interaction of level of categorization and different categories (animal and vehicle).

Performance (A) and reaction times (B) for animal and vehicle category on each level of categorization are shown. Error bars represent the normalized 95% confidence intervals of the mean (Cousineau-Morey approach [47], [48]). *p<.05, **p≤.01, ***p≤.001.

Figure 6. Interaction of level and context.

Higher accuracy was obtained for the gray context condition at the subordinate level. Error bars represent the normalized 95% confidence intervals of the mean (Cousineau-Morey approach [47], [48]). *p<.05, **p≤.01, ***p≤.001.

Figure 7. Interaction of animacy and context.

Reaction times for animal and vehicle categories in all three context conditions are shown. The bar graphs represent the calculated difference between animal and vehicle condition. Error bars represent the normalized 95% confidence intervals of the mean (Cousineau-Morey approach [47], [48]). *p<.05, **p≤.01, ***p≤.001.