Cognitive Research-Principles and Implications最新文献

Braille is a tactile writing system that enables individuals to read through the sense of touch. Although letter recognition research in the visual modality has informed reading instruction debates, the processes underlying braille letter recognition have received comparatively less attention which has led to little input from researchers toward educators. In this study, we first quantified the formal properties of braille dots using measures of cue validity and entropy-based informativeness, and we tested whether the 26 letters of the braille alphabet were linearly separable in the six-dimensional binary space defined by dot presence. We then examined letter discriminability in fluent Spanish braille readers using a same-different task that included all possible letter combinations. From participants' accuracy and response time data, we constructed perceptual similarity matrices and applied hierarchical clustering to characterize the structure of braille letter similarity. The resulting clusters revealed a structured perceptual space that reflected both local dot features and global configurations. These results provide a characterization of the perceptual structure of the braille alphabet and show constraints on tactile letter recognition that extend beyond dot overlap, offering a benchmark to guide experimental control, instructional sequencing of letters, and computational models of tactile letter recognition.

A primary aim of human-AI teaming is to achieve better collaborative performance than either can achieve alone. Despite considerable efforts in this direction, issues such as overreliance of users on decision aids continue to be a challenge which prevent this. In this study, we evaluated the potential of non-concurrent advice presentation as a strategy to reduce overreliance in a face-matching task. We conducted three pre-registered experiments examining (a) on-demand binary advice, (b) on-demand similarity ratings, and (c) conditional advice (i.e. advice presented only if participants' initial unaided decision is different from the AI prediction), compared to concurrent advice. Across all experiments, we did not find significant differences in the overall performance of participants in the concurrent vs. experimental conditions. But, we found that participants followed AI advice more when they demanded it. Conversely, when they demanded similarity ratings, they followed advice less. Thus on-demand similarity ratings reduced overreliance on AI compared to concurrent similarity ratings presentation. However, overall, similarity ratings were not more helpful compared to basic advice. We also found that participants were less likely to follow AI advice when presented after their initial unaided decision contradicted the AI prediction and were more confident in rejecting incorrect advice, but not as confident when accepting correct advice. Overall, non-concurrent paradigms have potential to reduce overreliance, but at the cost of underreliance on correct advice.

While people are often experts in perceiving and categorizing faces into meaningful social categories (i.e., race), there are suboptimal scenarios such as mask use that may impair face processing. Here we examined how mask use may differentially impact own- and other-race face processing in social categorization, and the underlying neurocognitive mechanisms using simultaneous eye movement and EEG recording. We found that mask use made participants' face scanning patterns more eyes-focused and consistent, and reduced the differences in both eye movement pattern and early attention-related ERP component P1 between viewing own- and other-race faces. Moreover, mask use did not change how people categorize biracial morphed faces, or the advantage in categorization speed of other-race faces. These results suggest that when perceiving masked faces, information from the eye region may be sufficient for social categorization, and that race-based social categorizations can be impervious to mask use. Interestingly, we found that when viewing other-race faces, where people have less perceptual expertise, those who show more consistent face scanning patterns have more efficient processing of masked faces. These findings have important implications for cross-race face perception, especially when face perception condition becomes suboptimal. SIGNIFICANCE STATEMENT: As mask use has become a common practice in response to respiratory virus outbreaks, it has inadvertently altered both health practices and the complex dynamics of social interaction. In a world that values diversity and cross-racial interactions, understanding how masks influence our cognitive processes during cross-race face perception is not just timely but vital. Given this context, we examined the effect of mask use on race categorization, by systematically investigating eye movement behavior, and neural representations of own versus other-race faces, and how these mask-induced changes are associated with each other. By utilizing simultaneous eye movement and EEG recording, our study reveals that the eye region can significantly influence social categorization, suggesting that race-based categorizations persist even in the presence of masks. Interestingly, we found that for other-race faces with which people have less perceptual expertise, those who adjust to a more consistent face scanning pattern for masked faces have more efficient processing of masked faces. This highlights the importance of individuals' visual routine adaptability when the viewing condition is not optimal. Though the current research is called by the demand for COVID-19, our findings can be generalized to a broader context and enhance our understanding of human visual and social cognition.

Performing under pressure, particularly in multitasking environments, is a critical challenge in both everyday life and high-stakes professions. This study investigated the differential effects of monitoring and outcome pressure on time-sharing performance and allocation of visual attention. Using a within-subjects design, 30 participants completed a recently devised time-sharing task requiring prioritization under three different pressure conditions. We hypothesized that in a high-demand time-sharing environment, outcome pressure would impair task performance and visual sampling of subtasks more than monitoring pressure. To investigate, we recorded participants' task performance metrics and eye movements. However, our confirmatory analyses found no evidence supporting either hypothesis. In contrast, our additional exploratory analyses revealed that monitoring pressure, not outcome pressure, led to a statistically significant performance decrease. Notably, this effect occurred without changes in visual sampling. This unexpected finding reflects the high sensorimotor demands of the task, specifically the need for precise and rapid mouse movements, which may have been disrupted by the participants' heightened self-consciousness under monitoring pressure. Our findings contribute to the literature on the differential effects of monitoring and outcome pressure, with potential implications for high-stakes domains like military operations. In situations requiring fine motor control-such as piloting aircraft or operating drones-monitoring pressure may disrupt performance, even without altering attentional allocation. Similarly, everyday activities like driving under observation (e.g., driving tests) or performing in front of an audience may be affected. Understanding how pressure disrupts performance in such scenarios can inform training and support strategies to mitigate its impact.

Although gaze following is an important socio-interactive process, little is known about how this behavior is affected when multiple gaze cues are encountered in groups. Emerging research suggests that both visual consistency of cues and group size may play a role. For example, in groups of three, a minority of target-congruent gaze cues (or 1/3 faces looking at the target) have been found to facilitate target responses, whereas in groups of five, a majority of target-congruent gaze cues (or 3/5 faces looking at the target) were needed for the same effect. Here, in two preregistered experiments, we provide a high-powered conceptual replication of these past experiments and extend them to examine the possible uniqueness of responses to gaze using a comparison with arrows. We found that a minority of target-congruent gaze and arrow cues significantly facilitated target responses regardless of group size. Furthermore, we found that additional target-congruent cues, either gaze or arrows, led to further significant response facilitation. Thus, initially, responses were facilitated by a minority proportion of target-congruent cues with response times continuing to decrease with increasing numerosity of cues' spatial consistency toward the target. This suggests that humans may use both quorum-like and numerosity evaluation flexibly to guide responses in contexts presenting with multiple social or non-social cues.

As targets become rare in visual search tasks, the likelihood of missing them increases-a phenomenon known as the low-prevalence effect (LPE). This has important implications for real-world searches, but reducing the LPE has proven challenging. In Experiment 1, we used a low-prevalence T-among-Ls task and found that distributing "probe" trials-trials with known targets and post-response feedback-reduced the LPE. In Experiment 2, participants searched for two low-prevalence targets (T and O among Ls and Qs), and we varied how often each appeared in probe trials. The probe benefit scaled with the frequency of the matching target, suggesting limited generalizability to non-probed targets. Experiment 3 used eye tracking to examine whether probes affected quitting thresholds, decision criteria, or guidance. Results showed that probes biased top-down guidance toward features of frequently probed targets, without affecting the number of items inspected or the decision criterion. In Experiment 4, we tested whether feedback was necessary for the probe benefit. Findings suggest that probes improve rare-target search by altering perceived prevalence, not through feedback alone. Overall, probes may reduce the LPE by increasing perceived prevalence and thereby increasing search guidance, but only when probe targets closely match actual search targets.

Professions such as military, aviation, submarine operation, and emergency response require individuals to navigate complex environments characterized by limited information, stringent time constraints, and significant pressures. Effective decision making under pressure is crucial in safety-critical professions, yet measuring this expertise remains challenging. Inspired by the military context, this article introduces the virtual reality decision-making expertise (VR-DMX) environment, designed to evaluate decision-making expertise under time constraints within a virtual reality scenario. VR-DMX simulates an amusement arcade where users must decide how to allocate time across various games to maximize ticket earnings. Through two validation studies (N = 60 and N = 76), we examined two metrics: Total Tickets (measuring overall performance) and DMX score (isolating decision-making quality). Both metrics demonstrated symmetrical distributions without floor or ceiling effects, with coefficients of variation comparable to established individual difference measures (32.4-37.4% for Total Tickets; 20.8-27.6% for DMX score). The moderate correlation between metrics (meta-analysis r = 0.771, 95% CI [0.599, 0.943]) indicates they measure related but distinct constructs. Our findings indicate that VR-DMX effectively differentiates individual performance levels and captures a distinct decision-making component that is separate from general cognitive abilities. Comparing decision-making expertise between professionals in safety-critical fields with those without such experience would be a sensible next step to help validate the potential for selection and training applications. VR-DMX was designed to measure decision-making expertise in safety-critical contexts, and initial validation data demonstrating effective differentiation of individual performance levels suggest that continued development could fulfill this design intention for applications in selection, training, and performance prediction.

Deepfakes are synthetic media created by deep-generative methods to fake a person's audio-visual representation. Growing sophistication of deepfake technology poses significant challenges for both machine learning (ML) algorithms and humans. Here we used real and deepfake static face images (Study 1) and dynamic videos (Study 2) to (i) investigate sources of misclassification errors in machines, (ii) identify psychological mechanisms underlying detection performance in humans, and (iii) compare humans and machines in their classification decision accuracy and confidence. Study 1 found that machines achieved excellent performance in classifying real and deepfake images, with good accuracy in feature classification. Humans, in contrast, experienced challenges in distinguishing between real and deepfake static images. Their classification accuracy was at chance level, and this underperformance relative to machines was accompanied by a truth bias and low confidence for the detection of deepfake images. Using dynamic video stimuli, Study 2 found that performance of machines was near chance level, with poor feature classification. Further, machines showed greater lie bias and reduced decision confidence relative to humans who outperformed machines in the detection of video deepfakes. Finally, Study 2 revealed that higher analytical thinking, lower positive affect, and greater internet skills were associated with better video deepfake detection in humans. Combined, the findings across these two studies advance understanding of factors contributing to deepfake detection in both machines and humans; and can inform intervention toward tackling the growing threat from deepfakes by identifying areas of particular benefit from human-AI collaboration to optimize the detection of deepfakes.

There has been a steep rise in the use of decision aids enabled by artificial intelligence (AI) for facial identity verification. The use of such systems and the impact of their implementation on human decision-making is still not well understood. The current study aimed to explore factors associated with the design of the paradigm and of the presentation of AI-enabled predictions. Across three pre-registered experiments, we examined the impact of (a) stated AI accuracy, (b) mismatch frequency (i.e. proportion of match and mismatch pairs), and (c) advice type (binary only vs. binary + similarity rating) on performance in a one-to-one face matching task. Participants' performance generally improved when aided by AI compared to a baseline without decision support. The largest improvement was observed when no information on the AI's overall accuracy was provided. Further, the frequency of mismatches did not influence performance, but biased responses. Finally, similarity ratings marginally improved overall performance and increased users' certainty in their decisions, but did not help to dismiss inaccurate predictions. Additionally, two findings were consistent across all experiments. First, participants often failed to dismiss inaccurate AI predictions, resulting in significantly lower performance compared to accurate predictions. Second, on a group level, the human-AI team did not outperform the AI alone, though examination of individual performance showed that some participants were able to exceed the AI's accuracy. These findings contribute towards determining appropriate design formats for AI prediction in a human-in-the-loop system, so that the performance of the human-AI team can be maximised.