**5. Conclusions**

Previous research has established a role for color information in the early and late visual processes of object recognition. However, many of these studies did not control for the color diagnosticity status of the objects or investigated only high-color diagnostic objects (Davidoff, 1991; Davidoff, Walsh, & Wagemans, 1997; Gegenfurtner & Rieger, 2000; Goffaux et al., 2005; Lu et al., 2010; Wurm, Legge, Isenberg, & Luebker, 1993). For example, Davidoff (1991) proposed a model of object recognition where color contributes to object recognition in the later stages of the visual processing. In this model, the author proposed the existence of two separate representations, one for object structure and another for object function, termed *has-a* and *is-a* representations, respectively. Object color, according to this model, is part of the *has-a* properties, so that recognition of an object's color takes place after the initial visual representation has accessed the *has-a* color knowledge. The absence of color at the stored object structure was first questioned by Price and Humphreys (1989). Price and Humphreys (1989) argued that there are separated representations for color and shape, but that these representations are richly interconnected and that appropriated color objects activate color representations that in turn activate associated shape representations (Humphreys et al., 1994; Price and Humphreys, 1989). Actually, the data presented in this 84 Advances in Object Recognition Systems

The major outcome of these studies is that the influence of color on object recognition depends on object diagnosticity status. Tanaka and Presnell (1999) proposed that color information contributes to object recognition only when objects are color diagnostic (see also, Nagai & Yokosawa, 2003; Oliva & Schyns, 2000). However, recent studies have reported results that suggest that color contributes to the recognition of both color and noncolor diagnostic objects (Rossion & Pourtois, 2004; Uttl, Graf, & Santacruz, 2006). We have provided data that may clarify these apparently contradictory results. Our studies suggest that color information affects different levels of visual processing during the recognition of color and non-color diagnostic objects. For the recognition of non-color diagnostic objects, color information is an important cue for the initial image segmentation and visual input organization, making the selection of a structural description, stored in the long-term visual memory, easier and faster, thus resulting in faster object verification. Moreover, our results also show an absence of color effects for non-color diagnostic objects in the later stages of the visual process. However, for color diagnostic objects, we observed an additional role for color information. Beyond the facilitation that color information confers on the initial visual stages, our results showed a strong color effect in the later stages of object recognition. It appears that color affects the later stages of recognition of color diagnostic objects in two different ways. First, color information triggers the selection of the structural object description from long-term visual memory. When we see an object, color and shape are likely processed in a parallel fashion. Some studies suggest that the same neural circuits, in early visual cortical regions, process information about color, shape and luminance (Gegenfurtner, 2003). At some point, this information must be combined to achieve a unitary representation of the visual world. One possibility is that this information is combined during the selection of structural description, where color might act as a cue that limits the range of candidate structural descriptions. The results also suggest that the templates corresponding to color diagnostic objects are stored in our visual memory system in a typical color format. Second, color information contributes to the activation and retrieval of

Previous research has established a role for color information in the early and late visual processes of object recognition. However, many of these studies did not control for the color diagnosticity status of the objects or investigated only high-color diagnostic objects (Davidoff, 1991; Davidoff, Walsh, & Wagemans, 1997; Gegenfurtner & Rieger, 2000; Goffaux et al., 2005; Lu et al., 2010; Wurm, Legge, Isenberg, & Luebker, 1993). For example, Davidoff (1991) proposed a model of object recognition where color contributes to object recognition in the later stages of the visual processing. In this model, the author proposed the existence of two separate representations, one for object structure and another for object function, termed *has-a* and *is-a* representations, respectively. Object color, according to this model, is part of the *has-a* properties, so that recognition of an object's color takes place after the initial visual representation has accessed the *has-a* color knowledge. The absence of color at the stored object structure was first questioned by Price and Humphreys (1989). Price and Humphreys (1989) argued that there are separated representations for color and shape, but that these representations are richly interconnected and that appropriated color objects activate color representations that in turn activate associated shape representations (Humphreys et al., 1994; Price and Humphreys, 1989). Actually, the data presented in this

the semantic network associated with these objects.

**5. Conclusions** 

work shows that the role of color in object recognition dependents on the correlation between color and shape. When the correlation between color and shape is high, as it is in the case of the color diagnostic objects, color information is especially important at the semantic representation level, whereas when the correlation between color and shape is low, as it is in the case of the non-color diagnostic objects, color information improves object recognition only at the early stages of the visual processing. These results suggest that color improves object recognition in the early stages of the visual processing for all objects. However, because non-color diagnostic objects are not strongly associated with a color, no further color advantage is expected at the higher processing levels.

The results reviewed in this contribution advance our current understanding of the role of color information during object recognition and its relationship with the object's color diagnosticity status. Together our results showed that color modulates the recognition of color and non-color diagnostic objects at different levels of visual processing: for color diagnostic objects, color plays an important role at the semantic level; for non-color diagnostic objects, color plays a role at the pre-semantic recognition level.
