Max Planck Institute for Human Development

Large Language Models (LLMs) increasingly shape how people access, produce, and reason with information. These models do not merely generate language, they mirror the data, discourse, and cognitive patterns on which they are trained. As a result, they often reflect and amplify existing social and cognitive biases, and even stereotypical representations, influencing what information is surfaced, how perspectives are represented, and which voices are privileged or silenced. Understanding these reflections requires going beyond technical bias detection toward examining how bias emerges in LLM outputs, how users perceive and react to it, and how design choices can reinforce or mitigate biased interactions. REFLECT offers a human-centered exploration of bias in LLMs, connecting perspectives from computer science, human–computer interaction, and cognitive psychology. REFLECT examines multiple ways in which bias manifests in LLMs (from selection effects to acquiescence and stereotypical associations) and discusses what these manifestations reveal about the interaction between human data, model training, and generative processes, also exploring interaction and design strategies that can help make such reflections visible and open to critical interpretation. Designed as a half-day session, REFLECT provides a concise yet reflective introduction to understanding bias as an emergent property of LLMs. By the end, participants will be equipped with conceptual and practical tools to identify and interpret how LLMs reflect biases, fostering more transparent, accountable, and trustworthy human–AI interactions.

Animacy is central to HRI, as it is critical for perceptions of intentionality and influences acceptance, interaction quality, and trust. In order to better understand the underlying mechanisms of the sense of animacy and its role in HRI, we conducted an experiment (n=13) using a social robotic object that is ambiguous with respect to its perceived animacy. We examined how brain activity related to a subjective animacy rating of the robot by participants after watching it perform simple gestures. Using functional imaging, we found that the ventral occipitotemporal (VOT) cortex, a region known to distinguish animate from inanimate entities, also responds to robots along this same organizational gradient. In an exploratory analysis, activity in the left VOT was associated with the individual differences in participants’ subjective animacy rating of the robot. This extends established principles of animacy perception to non-humanoid robots and suggests that individual differences in brain responses may shape how robots are experienced. We discuss implications for HRI, including guiding social cue design, informing gesture alignment with robot roles, and raising ethical considerations around attachment and trust.

The influence of architectural design on well-being is increasingly recognised, underscoring the need to understand how humans evaluate their surroundings. This study examines the impact of low-level visual features and high-level architectural elements in images of house facades generated by a generative adversarial network (GAN). We introduce a novel method to create controlled image datasets using the style mixing component of StyleGAN2-ADA, enabling the combination of specific architectural features with styles representing low-level image properties, such as brightness or colour. Our dataset consists of 900 images encompassing five high-level features (house size, garage door visibility, number of windows, entrance door visibility, and roof shape) and 16 low-level visual features. These GAN-generated images were rated in an online experiment by 303 participants on six dimensions: facelikeness, hominess, relaxation, invitingness, safety, and price. Linear mixed-effects models identified significant predictors, including the number of floors as a high-level feature and green pixel percentage, saturation (SD), brightness (SD), and contrast as low-level features. This indicated that houses with two floors and images with an overall higher amount of green pixel, as well as diversity of saturation and brightness and a lower contrast were rated as, e.g., more inviting and safer. The analysis showed that high- and low-level features explained up to 54% of the variance in house price ratings, with high-level features accounting for the larger share and highlighting their relevance for house facade evaluations. This work advances the use of GANs and offers a method to modify visual elements that shape architectural evaluations.

Foraging complexity and competitive social challenges are considered key drivers of human cognition. Yet, the decision-making mechanisms that underlie social foraging in the real world remain unknown. Integrating high-precision Global Positioning System (GPS) tracking and video footage from large-scale foraging competitions with cognitive-computational modeling and agent-based simulations, we show how foragers integrate personal, social, and ecological information to guide spatial search and patch-leaving decisions. We show how the social context emerges as a key driver of foraging dynamics. Foragers adaptively rely on social information to locate resources when unsuccessful and extend giving-up times in the presence of others, which results in increased area-restricted search at high social densities. These findings demonstrate the importance of sociality for human foraging decisions and provide a template for harnessing high-resolution tracking data to study real-world cognition.

Which factors determine whether information temporarily held in working memory (WM) can later be remembered from long-term memory (LTM)? Previous work has shown that retrieving ("testing") memories from LTM can benefit their future LTM recall. Here, we examined the extent to which a benefit for subsequent LTM may also occur after retrieval from WM, depending on whether the WM contents were retrieved from a prioritized or deprioritized state. In three experiments (n = 383 participants), we combined variants of a visual WM paradigm with a subsequent surprise LTM recall test. We found a LTM benefit of WM testing both for prioritized and deprioritized WM contents, which, interestingly, was stronger for the deprioritized information. This pattern showed similarly across experiments with different priority manipulations. Subsequent LTM benefits generally occurred after WM testing with a recall-like test format (continuous report), but not after simple WM comparisons against a probe. The surprisingly larger LTM benefit for deprioritized WM contents may reflect enhanced encoding of the participants' own subjective WM report - as opposed to the originally presented sample information - into LTM.

Participants were randomly assigned to slow-placed breathing or a breathing controlIndividualized protocols determined breathing pacesTen weeks of daily slow-paced breathing practice reduced plasma Aβ42 levels.

The frontier of artificial intelligence (AI) is constantly moving, raising fears and concerns whenever AI is deployed in a new occupation. Some of these fears are legitimate and should be addressed by AI developers-but others may result from psychological barriers, suppressing the uptake of a beneficial technology. Here, we show that country-level variations across occupations can be predicted by a psychological model at the individual level. Individual fears of AI in a given occupation are associated with the mismatch between psychological traits people deem necessary for an occupation and perceived potential of AI to possess these traits. Country-level variations can then be predicted by the joint cultural variations in psychological requirements and AI potential. We validated this preregistered prediction for six occupations (doctors, judges, managers, care workers, religious workers, and journalists) on a representative sample of 500 participants from each of 20 countries (total N = 10,000). Our findings may help develop best practices for designing and communicating about AI in a principled yet culturally sensitive way, avoiding one-size-fits-all approaches centered on Western values and perceptions. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

ABSTRACTListening to music is a ubiquitous human activity, but little is known about its functional cerebral correlates. We investigated the dynamics of fMRI‐based brain activation patterns associated with two musical compositions and examined whether these patterns are modulated by the degree of musical expertise. Specifically, 24 aspiring professionals and 17 amateur musicians listened to a baroque composition by J. S. Bach and an early modern piece by A. Webern. Using measures of dynamic and static functional connectivity and graph theory, we identified two distinct brain states: one characterized by higher modularity (greater segregation), and the other by higher global efficiency (greater integration). Participants spent more time in the segregated state while listening to Bach, and more frequently shifted to the integrated state during Webern's piece. An anticorrelation was observed between segregation and music complexity as measured by permutation entropy, indicating that music with higher complexity elicited more integrated brain states. Individuals with greater musical expertise demonstrated higher global efficiency during the Webern piece and engaged more frontal, temporal, and parietal regions as functional hubs. These findings suggest that musical form and expertise jointly shape the brain's functional organization during naturalistic music listening.

There is an urgent need for targeted, evidence-based interventions to build resilience to misinformation among social media's most avid users: adolescents. Research on misinformation susceptibility is mostly focused on adults. However, adolescents encounter different types of (mis)information and undergo rapid social, emotional and cognitive changes. These changes can increase vulnerability to misinformation through social influence, emotional manipulation and cognitive biases, while also offering unique opportunities for resilience. Taking a developmental perspective, we outline how adolescents' susceptibility to misinformation differs from that of adults, propose a research agenda to systematically study these processes and introduce a Bayesian framework of belief updating tailored to social media contexts. Finally, we highlight how these insights inform age-appropriate interventions to promote resilience. This Perspective underscores the vital role that social sciences have in understanding and combating the harmful influence of misinformation on youth's beliefs and behaviours, while leveraging their strengths.