How faces (and cars) may become special

Are you for real? Decoding realistic AI-generated faces from neural activity

Can we trust our eyes? Until recently, we rarely had to question whether what we see is indeed what exists, but this is changing. Artificial neural networks can now generate realistic images that challenge our perception of what is real. This new reality can have significant implications for cybersecurity, counterfeiting, fake news, and border security. We investigated how the human brain encodes and interprets realistic artificially generated images using behaviour and brain imaging. We found that we could reliably decode AI generated faces using people's neural activity. However, while at a group level people performed near chance classifying real and realistic fakes, participants tended to interchange the labels, classifying real faces as realistic fakes and vice versa. Understanding this difference between brain and behavioural responses may be key in determining the 'real' in our new reality. Stimuli, code, and data for this study can be found at https://osf.io/n2z73/.

Word and Face Recognition Processing Based on Response Times and Ex-Gaussian Components

The face is a fundamental feature of our identity. In humans, the existence of specialized processing modules for faces is now widely accepted. However, identifying the processes involved for proper names is more problematic. The aim of the present study is to examine which of the two treatments is produced earlier and whether the social abilities are influent. We selected 100 university students divided into two groups: Spanish and USA students. They had to recognize famous faces or names by using a masked priming task. An analysis of variance about the reaction times (RT) was used to determine whether significant differences could be observed in word or face recognition and between the Spanish or USA group. Additionally, and to examine the role of outliers, the Gaussian distribution has been modified exponentially. Famous faces were recognized faster than names, and differences were observed between Spanish and North American participants, but not for unknown distracting faces. The current results suggest that response times to face processing might be faster than name recognition, which supports the idea of differences in processing nature.

Fast saccades towards faces are robust to orientation inversion and contrast negation

Eye movement studies show that humans can make very fast saccades towards faces in natural scenes, but the visual mechanisms behind this process remain unclear. Here we investigate whether fast saccades towards faces rely on mechanisms that are sensitive to the orientation or contrast of the face image. We present participants pairs of images each containing a face and a car in the left and right visual field or the reverse, and we ask them to saccade to faces or cars as targets in different blocks. We assign participants to one of three image conditions: normal images, orientation-inverted images, or contrast-negated images. We report three main results that hold regardless of image conditions. First, reliable saccades towards faces are fast - they can occur at 120-130 ms. Second, fast saccades towards faces are selective - they are more accurate and faster by about 60-70 ms than saccades towards cars. Third, saccades towards faces are reflexive - early saccades in the interval of 120-160 ms tend to go to faces, even when cars are the target. These findings suggest that the speed, selectivity, and reflexivity of saccades towards faces do not depend on the orientation or contrast of the face image. Our results accord with studies suggesting that fast saccades towards faces are mainly driven by low-level image properties, such as amplitude spectrum and spatial frequency.

To Google or Not: Differences on How Online Searches Predict Names and Faces

Word and face recognition are processes of interest for a large number of fields, including both clinical psychology and computer calculations. The research examined here aims to evaluate the role of an online frequency’s ability to predict both face and word recognition by examining the stability of these processes in a given amount of time. The study will further examine the differences between traditional theories and current contextual frequency approaches. Reaction times were recorded through both a logarithmic transformation and through a Bayesian approach. The Bayes factor notation was employed as an additional test to support the evidence provided by the data. Although differences between face and name recognition were found, the results suggest that latencies for both face and name recognition are stable for a period of six months and online news frequencies better predict reaction time for both classical frequentist analyses. These findings support the use of the contextual diversity approach.

Profiles on the Orientation Discrimination Processing of Human Faces

Face recognition is a crucial subject for public health, as socialization is one of the main characteristics for full citizenship. However, good recognizers would be distinguished, not only by the number of faces they discriminate but also by the number of rejected stimuli as unfamiliar. When it comes to face recognition, it is important to remember that position, to some extent, would not entail a high cognitive cost, unlike other processes in similar areas of the brain. The aim of this paper was to examine participant’s recognition profiles according to face position. For this reason, a recognition task was carried out by employing the Karolinska Directed Emotional Faces. Reaction times and accuracy were employed as dependent variables and a cluster analysis was carried out. A total of two profiles were identified in participants’ performance, which differ in position in terms of reaction times but not accuracy. The results can be described as follows: first, it is possible to identify performance profiles in visual recognition of faces that differ in position in terms of reaction times, not accuracy; secondly, results suggest a bias towards the left. At the applied level, this could be of interest with a view to conducting training programs in face recognition.

Can You Identify These Celebrities? A Network Analysis on Differences between Word and Face Recognition

Face recognition is located in the fusiform gyrus, which is also related to other tasks such word recognition. Although these two processes have several similarities, there are remarkable differences that include a vast range of approaches, which results from different groups of participants. This research aims to examine how the word-processing system processes faces at different moments and vice versa. Two experiments were carried out. Experiment 1 allowed us to examine the classical discrimination task, while Experiment 2 allowed us to examine very early moments of discrimination. In the first experiment, 20 Spanish University students volunteered to participate. Secondly, a sample of 60 participants from different nationalities volunteered to take part in Experiment 2. Furthermore, the role of sex and place of origin were considered in Experiment 1. No differences between men and women were found in Experiment 1, nor between conditions. However, Experiment 2 depicted shorter latencies for faces than word names, as well as a higher masked repetition priming effect for word identities and word names preceded by faces. Emerging methodologies in the field might help us to better understand the relationship among these two processes. For this reason, a network analysis approach was carried out, depicting sub-communities of nodes related to face or word name recognition, which were replicated across different groups of participants. Bootstrap inferences are proposed to account for variability in estimating the probabilities in the current samples. This supports that both processes are related to early moments of recognition, and rather than being independent, they might be bilaterally distributed with some expert specializations or preferences.

Gender and hometown population density interact to predict face recognition ability

Several studies have found that individuals from small hometowns show diminished face recognition ability as compared with individuals from larger hometowns. We further this line of research by relating six measures of face recognition ability to hometown density. We predicted that the three face recognition ability measures which included a learning component would relate to hometown density whereas the three measures which did not include such a learning component would not. Instead, we found that none of the six measures related to hometown density. Interestingly, we found interactions between gender and hometown population density on many of these measures and on a general index of face recognition, with females from small hometowns outperforming males from small hometowns but no such differences in the large hometown group. In a follow-up re-analysis of a previous study, we found a similar interaction in one of two face recognition ability measures. Together, these results reveal a pattern of gender differences modulated by hometown population density. If indeed experience with faces in one's hometown influences face recognition ability, understanding these effects may require more than a quantification of the environment. Men and women growing up in the same environment likely have different experiences, which likely modulates effects on visual abilities.

Individual differences in object recognition

There is substantial evidence for individual differences in personality and cognitive abilities, but we lack clear intuitions about individual differences in visual abilities. Previous work on this topic has typically compared performance with only 2 categories, each measured with only 1 task. This approach is insufficient for demonstration of domain-general effects. Most previous work has used familiar object categories, for which experience may vary between participants and categories, thereby reducing correlations that would stem from a common factor. In Study 1, we adopted a latent variable approach to test for the first time whether there is a domain-general object recognition ability, o. We assessed whether shared variance between latent factors representing performance for each of 5 novel object categories could be accounted for by a single higher-order factor. On average, 89% of the variance of lower-order factors denoting performance on novel object categories could be accounted for by a higher-order factor, providing strong evidence for o. Moreover, o also accounted for a moderate proportion of variance in tests of familiar object recognition. In Study 2, we assessed whether the strong association across categories in object recognition is due to third-variable influences. We find that o has weak to moderate associations with a host of cognitive, perceptual, and personality constructs and that a clear majority of the variance in and covariance between performance on different categories is independent of fluid intelligence. This work provides the first demonstration of a reliable, specific, and domain-general object recognition ability, and suggest a rich framework for future work in this area. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Orientation-invariance of individual differences in three face processing tasks

Numerous studies have reported impairments in perception and recognition, and, particularly, in part-integration of faces following picture-plane inversion. Whether these findings support the notion that inversion changes face processing qualitatively remains a topic of debate. To examine whether associations and dissociations of the human face processing ability depend on stimulus orientation, we measured face recognition with the Cambridge Face Memory Test (CFMT), along with experimental tests of face perception and selective attention to faces and non-face objects in a sample of 314 participants. Results showed strong inversion effects for all face-related tasks, and modest ones for non-face objects. Individual differences analysis revealed that the CFMT shared common variance with face perception and face-selective attention, however, independent of orientation. Regardless of whether predictor and criterion had same or different orientation, face recognition was best predicted by the same test battery. Principal component decomposition revealed a common factor for face recognition and face perception, a second common factor for face recognition and face-selective attention, and two unique factors. The patterns of factor loadings were nearly identical for upright and inverted presentation. These results indicate orientation-invariance of common variance in three domains of face processing. Since inversion impaired performance, but did not affect domain-related associations and dissociations, the findings suggest process-specific but orientation-general mechanisms. Specific limitations by constraints of individual differences analysis and test selection are discussed.