People often continue to rely on information even after it has been retracted–a phenomenon known as the continued-influence effect (CIE) of misinformation. This study investigated real-time indicators of misinformation susceptibility by recording the eye movements of 74 participants as they read pairs of newspaper-style articles containing critical information about the cause of an event that was either retracted or not. A post-reading questionnaire assessed memory for the passages and inferential judgements related to the retracted information. The roles of individual differences in language proficiency and working memory on the CIE were also tested. Questionnaire data replicated prior findings that repetition of the original information improved recall memory for the event. Eye-tracking data revealed that retractions were associated with increased processing effort during encoding of corrective information and reduced re-reading of non-causal details. Reminders of the original misinformation were linked to faster overall reading speeds. Higher reading proficiency predicted greater reductions in misinformation susceptibility, and both reading proficiency and verbal working memory capacity facilitated real-time processing of causal information. Finally, longer reading times and slower reading speeds were tentatively associated with reduced misinformation susceptibility but only when retractions were presented without explicit reminders. Together, these findings suggest that misinformation susceptibility reflects both individual differences in cognitive abilities and the effectiveness of reminder-based corrections.

When making decisions under uncertainty, we rely on the information we currently have. Presumably, the more information we have, the better our decisions turn out to be. What, though, drives our motivation to seek more information? The current research investigated people's confidence judgments in one's current information and their relationship to seeking more information when making diagnoses when presented with a sequence of medical symptoms. In Experiment 1, we explored people’s confidence judgments in relation to the accumulation of information they received. In Experiment 2, we tested whether a participant’s confidence levels would guide the decision to seek more information. The results showed that (1) people had the most overconfidence with a short period of exposure to medical knowledge (as opposed to a long period or none at all) and (2) that overconfidence was associated with choosing not to seek more information. In short, a "blip of knowledge" can lead to an illusion of knowing, which, in turn, could lead to a sub-optimal decision when it comes to medical diagnosing.Supplementary InformationThe online version contains supplementary material available at 10.1186/s41235-026-00717-x.

Cognitive offloading refers to the use of physical actions and the external environment to reduce cognitive demand. Offloading strategies such as creating external reminders instead of relying on internal memory are highly effective and play a key role in supporting real-world cognition. Previous work has shown that people have systematic biases in their offloading strategies, which are related to biased metacognitive evaluations of cognitive ability. While metacognitive interventions could potentially mitigate these biases, research investigating their effects has produced mixed results. Here, we examined the influence of a brief metacognitive intervention comprising just five trials during an initial practice session. After the intervention, participants performed a memory task where they decided between using internal memory (for maximum reward) or external reminders (for reduced reward), allowing us to determine the optimality of offloading strategies. Experiment 1 (N = 164) showed that making metacognitive predictions and subsequently receiving feedback led to improved metacognitive calibration and more optimal reminder-setting strategies. Experiment 2 (N = 416) replicated this pattern and found that making predictions alone was ineffective. These findings suggest that a metacognitive intervention combining prediction with feedback could potentially optimise cognitive offloading in everyday life.

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.

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.Supplementary InformationThe online version contains supplementary material available at 10.1186/s41235-025-00701-x.

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.

Multimedia learning environments require learners to process and integrate information across visual and auditory modalities, often under conditions of limited cognitive capacity. In this study, we examined how visual load (defined as the number of images accompanying audio narration) and individual differences in language proficiency, sustained attention, and working memory influence learning outcomes in international university students. In two experiments (N = 61, M = 21.2 years), we examined how different visual loads affected memory recall. In Experiment 1, participants viewed narrated slides that included varying numbers of images, specifically from 0 to 3 images, and then completed an immediate recall task. In Experiment 2, we compared recall performance for audio-only vs. audio-and-picture information across two visual load conditions (1 vs. 3 images). Results showed that increasing visual support enhanced the learning of audio-and-picture information but had no benefit for audio-only content. Additionally, lower English proficiency and reduced attention were associated with poorer recall, especially under higher visual load. These findings support cognitive load theory and highlight how individual cognitive and language abilities can limit effective multimedia learning. The implications of these findings are discussed in relation to the design of digital instructional materials tailored for diverse learner populations.

When navigating individuals form mental representations of their environment. Significant individual differences exist, particularly in the ability to develop survey knowledge—a map-like understanding of spatial relationships. While many studies have focused on navigation via predetermined routes, less is known about individual differences in learning environments from free exploration. This study investigated whether exploration behaviors, such as the tendency to revisit places or diffuse in the environment, are linked to mental representations and whether visuospatial factors, like visuospatial working memory and self-reported wayfinding inclinations (e.g., self-efficacy and pleasure in exploring, and spatial anxiety), contribute to these relationships. A sample of 234 participants (147 womencompleted a visuospatial working memory task and wayfinding questionnaires, then freely explored a virtual city in a CAVE environment. Revisiting (tendency to re-explore visited areas) and diffusion (extent of environmental coverage) were calculated. Their survey knowledge was assessed using a map drawing task. The results showed that exploration behaviors were related to individual differences: women exhibited less revisiting and diffusion than men; higher self-efficacy and pleasure in exploring were associated with reduced revisiting; and joystick familiarity favoured diffusion. Furthermore, exploration behaviors mediated the relationship between gender and self-efficacy and pleasure in exploring with map accuracy. Map drawing was also directly predicted by visuospatial working memory ability. These findings offer insights into the role of individual differences in exploration behaviors and their relationship to survey knowledge gained from free exploration, highlighting the importance of individual factors in how environments are experienced and processed.Significance statementSome people are better at understanding where landmarks are in a place and how they are connected, while others find it more difficult. This paper investigates whether the way we move through an environment (how we explore) relates to this ability, along with other personal factors. When exploring a city, some people move around a lot, while others stay closer to familiar areas. We found that this is related to how well people understand the layout of the place. Importantly, we also found that personal attitudes toward orientation matter: people who feel more confident and enjoy exploring tend to revisit places less. On the other hand, the ability to mentally handle visual and spatial information (visuospatial working memory) doesn’t change how we explore, but it does help us build a better understanding of the environment. Gender and familiarity with using a joystick also related to how people explored and what they learned. By studying how people move and how this relates to their individual characteristics, we can better understand how we learn about the environments.Supplementary InformationThe online version contains supplementary material available at 10.1186/s41235-025-00696-5.

The current survey measured students' knowledge, use, and perceived effectiveness of different learning strategies. Whereas a common assumption from previous research is that students forego using effective strategies because they lack awareness of them, the current results demonstrate that students are quite aware of certain strategies such as retrieval practice, spacing, and explaining, but are less aware of other strategies like pretesting and interleaving, and seem to overrate the effectiveness of highlighting and rereading notes. Even when students were aware of effective learning strategies, their primary reasons for not using them were the cognitive costs associated with them (time, effort, anxiety, and increased planning and preparation). Students reported teachers, friends, and self-discovery as the most common sources of learning about both effective and ineffective strategies, and published research as the least common source. We discuss the implications of these results for designing interventions aimed at improving students' self-regulated learning decisions.

To face ecological disasters, one of the major emergencies of modern times, fostering pro-environmental behaviors appears crucial. While research explored various behavioral drivers, few studies addressed the conceptual representation of ecological events at different ages. Our preregistered study fills this gap, examining how different generations conceptualize ecology, considering evidence suggesting a link between climate change abstractness and willingness to act pro-environmentally. Older (> 65 yo) and younger (18–35 yo) individuals performed multiple tasks targeting conceptual processing (categorization), semantic organization (rating), and conceptual representation (feature generation) of ecological (e.g., deforestation) compared to concrete (e.g., spoon) and abstract (e.g., affirmation) concepts. In the categorization task, participants responded to critical ecological, concrete, and abstract words but refrained from responding to animal words. The rating task involved evaluating critical words on semantic aspects (e.g., ness, Familiarity), and the feature generation task listing properties true for each concept. Innovatively, participants were tested in three possible settings (indoor, natural outdoor, urbanized outdoor) to test whether exposure to nature impacts categorization.Results showed that independent from the setting, ecological concepts were similar to abstract concepts, especially in semantic organization and conceptual representation, or more abstractly characterized than them, especially in conceptual processing. Age differences were minimal, but older adults, who showed more positive attitudes toward ecology and nature, characterized ecological concepts more concretely than younger adults.Theoretically, the multifarious characterization of ecological phenomena supports the flexibility of our conceptual system and points to the necessity of overcoming the classical concrete-abstract dichotomy and studying concepts in a more “situated” manner. From a societal point of view, the age-dependent abstractness of ecological concepts might inform policies on how to improve climate change campaigns, tailoring them to different age groups.Supplementary InformationThe online version contains supplementary material available at 10.1186/s41235-025-00689-4.