James William Tanaka

As AI-generated faces become indistinguishable from real ones, deepfake technology poses escalating threats to information integrity and security. While algorithms can detect deepfakes, they suffer from opacity and critical vulnerabilities—and training humans to identify specific visual artifacts has proven largely ineffective. Here, we introduce a fundamentally different approach that harnesses people’s global impressions of faces. Building on findings that AI and human faces evoke systematically different perceptual impressions (Miller et al., 2023), we trained participants to attend to these distinguishing qualities without explicit instruction on how to use them. Using a rigorous pre–post design with untrained test faces, we demonstrate that all participants ( N = 45) improved, with mean accuracy nearly doubling. High performers achieved near-perfect detection, and participants developed metacognitive insight, showing appropriate confidence calibration only after training. A test–retest control study ruled out practice effects as an explanation for training gains, and an online replication demonstrates the scalability of our approach. As the technology advances, targeting systematic biases that are inherent to generative AI, and which manifest in global features, may offer a more durable defense than approaches reliant on image artifacts alone.

Although faces and bodies provide essential cues to one’s identity, it is not well understood how the observer encodes and processes the two sources

Texture perception plays a vital role in various fields, from computer vision to geology, influencing object recognition, image segmentation, and rock classification. Despite advances in convolutional neural networks (CNNs), their effectiveness in texture-based classification tasks, particularly in rock classification, still needs exploration. This paper addresses this gap by evaluating different CNN architectures using diverse publicly available texture datasets and custom datasets tailored for rock classification. We investigated the performance of 38 distinct models pre-trained on the ImageNet dataset, employing both transfer learning and fine-tuning techniques. The study highlights the efficacy of transfer learning in texture classification tasks and offers valuable perspectives on the performance of different networks on different datasets. We observe that while CNNs trained on datasets like ImageNet prioritize texture-based features, they face challenges in nuanced texture-to-texture classification tasks. Our findings underscore the need for further research to enhance CNNs’ capabilities in texture analysis, particularly in the context of rock classification. Through this exploration, we contribute insights into the suitability of CNN architectures for rock texture classification, fostering advancements in both computer vision and geology.

When we see a person approaching us on the street, can we attend to their face independently of the body and conversely, can we attend to their body, independently of their face? Or, is face and body information combined to form a whole person percept? To test these questions, we constructed a whole-person composite stimulus by aligning or misaligning the face from one person with the body from another. Pairs of aligned or misaligned face-body composites were presented sequentially to participants in a “same/different” task and discrimination difficulty was parametrically manipulated via a morphing procedure. In Experiment 1, participants were asked to judge the identity of the face and ignore the body. In Experiment 2, participants were asked to judge the identity of the body and ignore the face. In both experiments, we found a congruency effect where “same/different” discriminations to the cued portion (Experiment 1: face; Experiment 2: body) were influenced by information in the to-be-ignored uncued portion. The magnitude of the congruency effect increased as the discrimination decisions became more difficult. Critically, spatial alignment between the face and body did not affect the congruency effect; indicating that the integration of face and body information occurs at the decisional stage of processing rather than at the perceptual holistic encoding stage. Together, our results indicate that it is difficult to selectively attend to just the face or the body in person perception.

Saccadic choice tasks use eye movements as a response method, typically in a task where observers are asked to saccade as quickly as possible to an image of a prespecified target category. Using this approach, face-selective saccades have been observed within 100 ms poststimulus. When taking into account oculomotor processing, this suggests that faces can be detected in as little as 70 to 80 ms. It has therefore been suggested that face detection must occur during the initial feedforward sweep, since this latency leaves little time for feedback processing. In the current experiment, we tested this hypothesis using backward masking—a technique shown to primarily disrupt feedback processing while leaving feedforward activation relatively intact. Based on minimum saccadic reaction time, we found that face detection benefited from ultra-fast, accurate saccades within 110 to 160 ms and that these eye movements are obtainable even under extreme masking conditions that limit perceptual awareness. However, masking did significantly increase the median SRT for faces. In the manual responses, we found remarkable detection accuracy for faces and houses, even when participants indicated having no visual experience of the test images. These results provide evidence for the view that the saccadic bias to faces is initiated by coarse information used to categorize faces in the feedforward sweep but that, in most cases, additional processing is required to quickly reach the threshold for saccade initiation.

Humans can readily and effortlessly learn new faces encountered in the social environment. As a face transitions from unfamiliar to familiar, the ability to generalize across different images of the same person increases substantially. Fast periodic visual stimulation and EEG (FPVS-EEG) was used to isolate identity-specific responses that generalize across different images of the same person from low-level visual processing and face-general processes that aren’t identity-specific. We observed these signals emerge and increase in magnitude as a group of strangers became lab mates (N=9). The neural response to an unfamiliar identity that remained unfamiliar did not change. Comparison of the response to the newly familiarised face to a highly overlearned face (the own-face) showed that this identity-specific signal was modulated by level of familiarity. The study presents the first examination of identity-specific processing changes as they occur in situ from normal, everyday face experience.

While motion information is important for the early stages of vision, it also contributes to later stages of object recognition. For example, human observers can detect the presence of a human, judge its actions, and judge its gender and identity simply based on motion cues conveyed in a point-light display. Here we examined whether object expertise enhances the observer's sensitivity to its characteristic movement. Bird experts and novices were shown point-light displays of upright and inverted birds in flight, or upright and inverted human walkers, and asked to discriminate them from spatially scrambled point-light displays of the same stimuli. While the spatially scrambled stimuli retained the local motion of each dot of the moving objects, it disrupted the global percept of the object in motion. To estimate a detection threshold in each object domain, we systematically varied the number of noise dots in which the stimuli were embedded using an adaptive staircase approach. Contrary to our predictions, the experts did not show disproportionately higher sensitivity to bird motion, and both groups showed no inversion cost. However, consistent with previous work showing a robust inversion effect for human motion, both groups were more sensitive to upright human walkers than their inverted counterparts. Thus, the result suggests that real-world experience in the bird domain has little to no influence on the sensitivity to bird motion and that birds do not show the typical inversion effect seen with humans and other terrestrial movement.