The comparison of performance did not show any difference between the processing of contextual human faces and animals when presented in an upright orientation. In our protocol, half of the stimuli were also presented upside down and the present section compares the processing of inverted contextual faces and inverted animals to investigate whether the similarity found with upright stimuli extends to inverted ones. As in the preceding section, the comparison is carried out on over 9,200 trials for each condition.

Mean accuracy was virtually identical for inverted faces (94.7%) and inverted animals (94.8%) (Figure 2B and Figure 3B). Accuracy showed the same biases than with upright stimuli, with a higher accuracy (97.9% vs. 93.9%; Wilcoxon test, z = -4.1, p < .0001) on inverted animal targets than on inverted contextual faces. Moreover, the higher accuracy on inverted distractors observed in the contextual face task (95.4%) when compared to the animal task (91.7%) was highly significant (Wilcoxon test, z = -3.9, p < .0001).

Figure 4 illustrates the higher number of errors performed on inverted distractors in the animal task both when compared to the set of upright stimuli in the animal task and when compared to the set of inverted distractors processed in the contextual face task. The figure also illustrates that, regardless of their orientation, neutral distractors induce a higher number of false alarms in the animal categorization task. Again this is true when compared to the other set of distractors in the animal task, or when compared to the performance on neutral distractors in the contextual face task.

In this section, we focus more specifically on the presence and the strength of the inversion effect as a function of the target category.

Inversion produced a very weak decrease of global accuracy (<2%) that was very similar for both animals and human faces (orientation effect: F = 37.1, p < .0001; no interaction between task and orientation factors) (Figure 2C and 2D and Figure 3C and 3D). The percentage of correct go-responses decreased significantly with inversion for both animals (98.7% vs. 97.9%, Wilcoxon test, z = -2.7, p = .006) and contextual faces (97.5% vs. 93.9%, z = -4.1, p < .0001). Statistically, this was shown by a main orientation effect (F = 27.6, p < .0001) that was stronger for faces (interaction between orientation and task factors: F = 19.7, p < .0001). In parallel with the slight decrease of global accuracy, inverted pictures were also categorized on average with significantly longer RT (Figure 2C and 2D and Figure 3C and 3D) than upright pictures (mean RT: F = 140.7, p < .0001; median RT: F = 72.9, p < .0001). This held true for both categories but with an inversion effect on speed that was reliably more pronounced for faces (+23 ms on both mean and median RT, both paired t test: p < .0001) than for animals (+13 ms on mean RT, p < .0001; +9 ms on median RT, p = .001). Although the global reaction time increase appears robust with both kinds of inverted targets at the level of mean and median RT, it is far from being as obvious when considering the minimal processing time. When determined on the overall data, no effect was seen regardless of the categorization task. At the individual level, however, there was a small inversion effect for both categories with a nonsignificant tendency to be more pronounced for faces (+24 ms, p < .0001) than for animals (+15 ms, p = .004). The time course of performance showed that the stimulus inversion did not simply shift the curve toward longer latencies but rather decreased the slope of the functions that originate at similar early latencies.

Overall, subjects were able to respond both very accurately and rapidly in the two tasks. This level of performance is impressive given the extreme variability of the photographs used in this experiment. It can be taken as the hallmark of the sophistication of the fast mechanisms implemented in the ventral pathway of the human brain (Riesenhuber & Poggio, 2000; Thorpe & Imbert, 1989; VanRullen et al., 1998; Wallis & Rolls, 1997). If this conclusion had already been reached from results of earlier studies, here we extend these findings by showing that (1) the fast coarse categorization of objects in natural scenes is very weakly affected by inversion; (2) contextual human faces cannot be processed faster or more efficiently than another relevant visual category such as animals; and (3) the inversion effect, although very weak in both tasks, is slightly more pronounced for faces.

The fact that animals are processed with the same speed and accuracy as contextual human faces when both types of targets are presented at different scales, in varied number and position in the image, argues against a hardwired face mechanism that would be more efficient than other non-face object mechanisms (Tarr & Cheng, 2003). Because it has been shown previously that animals could not be processed faster than another relevant, nonbiological category, such as means of transport (VanRullen & Thorpe, 2001a,2001b), contextual faces cannot be said to benefit from specific temporal advantages, at least in our task. We do not want to argue that this kind of rapid categorization process would apply to any object category; instead, it might depend on a certain level of expertise (that needs to be determined) beyond which the categorization of any behaviorally relevant object could rely on such fast processes.

Although we found evidence that inversion of natural scenes did produce reliable effects on performance, with responses delayed (13 ms vs. 23 ms for animal and faces) and accuracy impaired for inverted pictures (1% vs. 3.5% for animal and faces), it is important to note that these effects were both very weak (although slightly more pronounced for faces). With such temporal constraints, very little time would be available to implement a mental rotation mechanism during the time course of the categorization process. On the other hand, the speed of recognition of an object might depend on the rate of accumulation of activity from object selective neurons (Perrett et al., 1998; Ashbridge, Perrett, Oram, & Jellema, 2000). Neurons in higher-level occipito-temporal visual areas respond to complex stimuli such as animals and faces. At the level of neuronal populations, the strength of the population response is correlated to the number of activated neurons. Now, we can hold the very plausible assumption that the population response must reach a given constant threshold activation level (Hanes & Schall, 1996) in order for a behavioral response to be triggered. Through experience, more neurons, each one more selectively tuned, respond to animals, human faces, and body parts in the upright position compared to inverted positions. Groups of neurons responding to upright and inverted objects would start to respond at about the same latency but responses would accumulate more slowly in the case of inverted stimuli, leading to an increase in response latency. This hypothesis is supported by the time course of performance (Figure 3) that originated at similar latencies but increased with different slopes depending on whether the stimuli were presented upright or upside down. It follows that, on average, it takes slightly more time to reach the threshold for inverted stimuli, and therefore to categorize them.

If the processing of upright faces and animals followed the same behavioral temporal course, what is special in faces that led to differences in the processing of inverted stimuli? The inversion effect is usually taken as evidence that face processing relies preferentially on configural mechanisms distinct from part-based mechanisms thought to be more important in the processing of other objects (e.g., see review in Itier & Taylor, 2002; Rossion & Gauthier, 2002). When faces appear in their typical upright orientation, configural information is extracted. This extraction is disrupted by inversion, except for objects whose discrimination relies on characteristic features that are not affected by inversion. However, following Perrett's hypothesis, the fact that faces were more sensitive to inversion than animals can be explained by a face population selectivity more strictly linked to the canonical upright view through experience (see support for such a view in Rossion & Gauthier, 2002; Tarr & Gauthier, 2000). Accordingly, neurons would fire less efficiently in response to inverted than upright faces, leading to a smaller accumulation of activity for inverted faces compared to inverted animals (because the latter might be represented by a cell population less strictly tuned to the upright orientation). As a consequence, the stronger inversion effect for faces often explained by the specificity of face processing (Farah et al., 1995, 1998) can be alternatively explained by the rate of accumulation of selective neural activity.

However, it remains possible that different strategies or brain mechanisms were used in the two tasks. Inversion had different effects on each category: when looking for animals, subjects made a high number of incorrect responses on inverted distractors, whereas when looking for human faces, they tended to miss more inverted targets. This could be the consequence of a greater similarity between animals and distractors than between faces and distractors, and the use of more specific representations to perform the face task than the animal task. This hypothesis is supported by the fact that more errors on neutral distractors and on inverted distractors were performed during the animal task than during the face task.

Finally, animals were slightly more easily detected in natural scenes than faces, which might indicate that the two sets of images were not equated in difficulty and might potentially have masked a processing speed advantage in favor of faces. Furthermore, this discrepancy might also potentially explain the very weak inversion effect found for faces. To test these alternative explanations and further characterize the processing of faces in natural scenes, we designed a second experiment.

Subjects had no a priori information about the presence, the size, the position, or number of targets in an image, and to prevent learning, each image was seen only once in one orientation (upright or inverted), either as a target or as a non-target, by each subject.

In Experiment 2, despite the greater target/distractor similarity compared to Experiment 1, the use of close-up views led to excellent performances both in terms of accuracy and speed. ANOVA tests performed on the overall results showed no category effect on global accuracy (97.4% for both human and animal faces), target accuracy (99.3% for both) or distractor accuracy (95.5% for both). However, median RT were shorter in response to human faces (377 ms) than to animal faces (387 ms) (F = 4.6, p = .043), a main effect that was not significant for mean RT (humans: 389 ms; animals: 397 ms). The next two sections will present a detailed analysis of these global results using post hoc ANOVA, paired t tests, and Wilcoxon tests. The first section will compare the processing of upright human faces to the processing of upright animal faces. The second section will concentrate on inverted stimuli. The third section will present specifically the differences between upright and inverted stimuli on the processing of human faces and animal faces.

Mean accuracy was virtually identical for both kinds of upright pictures with 97.7% in the human face task versus 97.9% in the animal face task (Figure 6A and Figure 7A). Targets were better categorized than non-targets (99.5% vs. 96%, respectively, F = 37.7, p < .0001), with similar proportions of go-responses for upright humans (99.6%) and upright animals (99.5%). Contrary to Experiment 1, subjects tended, on average, to respond about 10-ms faster for human than for animal faces (Figure 6A and Figure 7A). This slight advantage reached significance for median RT (371 ms vs. 384 ms, paired t test: p =.031) but not for mean RT (382 ms vs. 392 ms, n.s.d.). This effect is relatively clear on the RT distribution for intermediate and long latency responses. On the other hand, although it is barely visible on the initial part of the RT distribution of Figure 6A or at the onset of the performance time course functions of Figure 7A, the 10-ms global advantage in favor of human compared to animal pictures was also observed with the minimal processing time computed on cumulated population data (260 ms vs. 270 ms, respectively). The same tendency in favor of human pictures was seen for individual minimal processing time in both tasks, but it did not reach significance (327 ms vs. 338 ms, n.s.d.).

No statistical difference could be seen between the accuracy scores computed for each task. Indeed, subjects again reached very similar performances (Figure 6B and Figure 7B) scoring 97.2% with inverted human faces and 96.9% with animal faces. Correct go responses were triggered in similar proportion in both tasks (99.0% vs. 99.2%).

The overall mean RT showed a 6-ms lag between human face (396 ms) and animal face processing (402 ms) that did not reach significance. This lag reached 8 ms when calculated on the overall median RT between human faces (median RT: 382 ms) and animal faces (median RT: 391 ms), an effect that did not reach significance either.

Inversion also slightly delayed RT (mean: +14 ms and +10 ms, F(1,23) = 58.3, p < .0001; median: +11 ms and +7 ms, F(1,23) = 34.7, p < .0001, for human and animal faces, respectively), an effect that was not significantly stronger for human than for animal pictures, as shown by an absence of interaction between task and orientation factors. However, the result concerning minimal RT calculated from the overall population data showed a difference between early processing of human and animal faces. There was no effect of orientation for animal faces (270 ms for upright and inverted stimuli), but the minimal RT was 20 ms shorter with upright faces (260 ms) than inverted faces (280 ms). This small differential effect between the two tasks can be seen in Figure 7 by comparing the initial part of the d’ curves in Figure 7C and 7D. The performance curve with inverted human faces is shifted toward longer latency with the same slope than for upright faces, whereas with animal faces, the earliest responses appear at the same latency, and only the slope of the performance curve is affected when inverting the animal faces. However, this result on the overall data set was not confirmed by the analysis of individual minimal reaction time showing the same inversion effect for human faces (+9 ms) and animal faces (+7 ms) (F = 16.5, p < .0001, no interaction with the category factor).

Experiment 2 tried to provide a more direct comparison of human face versus animal face processing in natural scenes by using more homogenous sets of images. Levels of difficulty in the two tasks were similar regarding target detection accuracy. Despite high feature similarities between targets, and despite our considerable effort to use confusing distractors sharing global features with close-ups of faces, subjects performed remarkably well in these two tasks, in which processing efficiency was virtually identical. The high accuracy level reached in this experiment might be explained by the fact that humans (and faces in particular) constitute a very special object class, automatically categorized and segregated by our visual system, hence producing no interference with other object categories. Indeed, as in Experiment 1, we found evidence that neutral distractors were associated with more errors in the animal task than in the human task, which might imply that there was a higher similarity between neutral distractors and animals than between neutral distractors and humans. However, does this mean that human faces would benefit from computational advantages that would make them easier or faster to detect? We found no clear evidence in favor of this hypothesis. In the present experiment, contrary to the first one, there was a tendency for human faces to be processed on average about 10-ms faster than animal faces, an advantage that was present for both upright and inverted orientations, but appeared only for upright stimuli when considering the earliest behavioral responses. Such a small but reliable effect might be explained at the neuronal population level by a larger number of neurons coding for human faces than for different animal faces, thus slightly reducing the time to threshold decision as previously postulated in the discussion of Experiment 1. Indeed, a 10-ms difference in processing speed does not fit with the involvement of a different mechanism for the processing of human faces compared to animal faces, but rather point to a quantitative difference in the processing of the two categories rather than to a qualitative difference. The time course of performance in the two tasks strengthen this interpretation. Thus, these results are best explained in a framework in which the ventral pathway is conceived as implementing a unitary mechanism processing all object categories (Tarr & Cheng, 2003). Under such a framework, the speed to categorical decision threshold would depend on the number of neurons tuned to a specific category. According to this working hypothesis, time to threshold would be not surprisingly shorter for an extensively represented category such as human faces compared to another object category such as animal faces. Following this idea, a delay as short as 10 ms can find an explanation at the level of a neuronal population more sensitive to a category than the other, rather than in the involvement of a totally different mechanism. This delay was rather small probably due to the task used in the two experiments used here. Indeed, a superordinate categorization task might rely on coarsely defined diagnostic features (Schyns, 1998; Ullman, Vidal-Naquet, & Sali, 2002). A processing time course similar to the one found here for animals and humans has also been reported for another category like means of transport (VanRullen & Thorpe, 2001a), which suggests that the same level of complexity might be reached in a large range of natural scene categorization tasks. The use of more demanding categorization tasks relying on more specific features might reveal more dramatically an existing bias at the neuronal population level between two categories. If subjects had been asked to realize a gender discrimination task with human and animal faces, the difference between the two categories would certainly have been much larger. However, even in this condition, the same simple mechanism of accumulation of evidence working at the level of a large neuronal population might be sufficient to explain the results. This kind of experiment will be important in the future to distinguish between different models of organization of the ventral pathway.

A complementary interpretation on the small difference in processing speed between human and animal faces lies in the smaller range of variability between different human faces compared to the large differences between faces of vertebrate animals (birds, monkeys, antelopes, reptiles, etc.). This seemed to be partly the case, given that more structure appeared in the “mean image” for humans than for animals (Figure 5B). It might be that reducing the number of different animal species would have allowed a more specific pre-setting of the neuronal population responding to animals, thus eliminating any differences at all between animal and human faces.

As in Experiment 1, another weak but consistent effect was seen with inversion in both tasks. Whereas the accuracy impairment appeared to be of similar magnitude for animal and human faces, the earliest response to inverted human faces could appear with a 20-ms delay when compared with upright human faces. This might be the hallmark of face configural processing, more disrupted by inversion than other object processing routines (Yin, 1969). However, as already developed in the discussion of the first experiment, a more simple explanation, emphasizing experience-induced bias at the neuronal population level, could constitute a viable alternative. According to this model, there is no need to call for the involvement of a mental rotation mechanism or a mechanism specifically dedicated to the processing of upright human faces. One might argue that models of object recognition relying on a time consuming normalization stage between sensory inputs and memory templates might explain the inversion effects in our two experiments (Tarr & Bülthoff, 1998; Ullman, 1996). However, although we found reliable inversion effects, the maximal increase in processing time was about 20 ms. Thus, if a normalization mechanism (e.g., mental rotation) had to be done at the neuronal level it would have to fit in this demanding 20-ms time window. Instead, it has been suggested that whatever the orientation, neuronal responses start to accumulate at the same latency at the population level (Perrett et al., 1998). Life experience, in which stimuli appear more often in the upright orientation, would bias the population selectivity so that more cells respond to upright than inverted stimuli (Ashbridge et al., 2000). As a consequence, neuronal responses would accumulate faster to reach the categorization threshold in the former rather than in the later case. By integrating both category and orientation biases in this simple mechanism, it is possible to explain the larger orientation effect on processing speed in the human than in animal face task. Our results support this view because we did find a robust inversion effect for animals. Again, this explanation directly supports models of object processing in which there are quantitative rather than qualitative differences between human faces and other object categories. From the point of view emphasized in the first section of this discussion, larger inversion effects for human faces might be found as task requirements become more demanding. Indeed, if the strength of the inversion effect was stronger for human faces than for animal faces in the superordinate categorization task used here, this difference was not extremely important, and might be related to task instructions. A more important disruption of human face processing compared to other objects is found when subjects are asked to perform a recognition task (Diamond & Carey, 1986; Yin, 1969). This effect might be explained by the use of more specific representations that are themselves more specifically tuned to the orientation in which they have been learned. In keeping with this hypothesis, it has been shown that non-face object categories can present the same inversion effect as faces in a recognition task if subjects are experts at distinguishing between individuals of these categories (Diamond & Carey, 1986; Gauthier & Tarr, 1997). It follows that an apparent dichotomy between face and non-face object processing, such as the strength of the inversion effect, is not necessarily the hallmark of an independent face system; alternatively, it could reflect one point along a continuum of dynamically changing computational strategies (Riesenhuber & Poggio, 2002; Tarr & Cheng, 2003; Tarr & Gauthier, 2000).

These two experiments showed that in the context of natural scenes, faces are categorized following a time course very similar to another biological object category such as animals. Because it has been demonstrated that a nonbiological object category such as vehicles could be processed as efficiently as the animal category (VanRullen & Thorpe, 2001a, 2001b), it might well be that every well known object category could be selected in a “glimpse” by a wave of processing in the ventral pathway (Riesenhuber & Poggio, 2000, 2002; VanRullen et al., 1998). Given the strong temporal constraints in these tasks, with selective responses appearing as early as 260 ms, such a fast coarse categorization process might rely on the activation of neurons selective to visual diagnostic properties by an essentially feed-forward flow of activation. Furthermore, the relatively weak inversion effects found in these experiments indicate that the representations activated to categorize a natural scene are relatively coarse, at least coarser than several high-level properties that have been found to be strongly affected by inversion (Tarr & Bülthoff, 1998). It might thus suggest that this kind of fast visual categorization of complex stimuli do not necessarily rely on similarly complex high-level representations, but might rather be achieved through the detection of diagnostic features of intermediate complexity (Ullman et al., 2002). Further experiments will be necessary to precisely determine the nature of these representations. This pattern of results is overall compatible with models that suggest the existence of a single object processing system whose performance is modulated by expertise, level of recognition, and information availability (Perrett et al., 1998; Schyns, 1998; Tarr & Cheng, 2003). The interplay between these different factors would determine the efficiency of the system, without requiring any face-specific module, or any mental rotation mechanism.