Efstratia Papoutselou, Efstratia Papoutselou, Nivetha Saravanan
et al.
BackgroundBilingualism is increasingly common in families worldwide, yet bilingual individuals remain underrepresented in developmental neuroscience research. In simultaneous bilingualism, children typically acquire two languages simultaneously from birth, while their parents tend to learn the societal language later in life. These differences in language acquisition may influence how parents and children communicate, particularly when interacting in a second language. Neural synchrony, the temporal alignment of brain activity between individuals, has emerged as a key mechanism underlying social connection, communication, and learning in early development. However, little is known about how language choice affects neural synchrony in bilingual parent–child interactions.MethodsThis study used functional near-infrared spectroscopy (fNIRS) hyperscanning to simultaneously record brain activity from 15 bilingual mother–child dyads during naturalistic play. Each dyad completed three conditions: collaborative play in the mother's native language, collaborative play in English (the mother's second language), and independent play. Neural activity was recorded from the prefrontal cortex (PFC) and temporoparietal junction (TPJ), regions associated with social cognition, joint attention, and mentalising. Families took part in a naturalistic free play paradigm, allowing them to interact in a comfortable and ecologically valid manner.ResultsBoth native- and English-language play elicited significantly greater neural synchrony across the PFC and the TPJ than independent play, validating the use of naturalistic free play paradigms. No significant overall differences emerged between native and English play, indicating that bilingual dyads maintain inter-brain coupling across languages when both partners are proficient. Exploratory analyses suggested a trend toward higher child-directed synchrony in English play and age-related trends in mother-directed synchrony; however, these effects did not reach statistical significance.DiscussionOur findings show that bilingualism does not compromise mother–child neural synchrony, supporting the inclusion of linguistically diverse families in developmental neuroscience. They underscore the value of naturalistic paradigms and highlight the need for future research on language proficiency, partner familiarity, and behavioral correlates of synchrony. This work highlights the importance of studying bilingual families in ecologically valid contexts to better understand how language use influences neural coupling in early development.
Seyed Amir Kasaei, Arash Marioriyad, Mahbod Khaleti
et al.
Large Vision-Language Models (LVLMs) have achieved remarkable proficiency in explicit visual recognition, effectively describing what is directly visible in an image. However, a critical cognitive gap emerges when the visual input serves only as a clue rather than the answer. We identify that current models struggle with the complex, multi-step reasoning required to solve problems where information is not explicitly depicted. Successfully solving a rebus puzzle requires a distinct cognitive workflow: the model must extract visual and textual attributes, retrieve linguistic prior knowledge (such as idioms), and perform abstract mapping to synthesize these elements into a meaning that exists outside the pixel space. To evaluate this neurosymbolic capability, we introduce RebusBench, a benchmark of 1,164 puzzles designed to test this specific integration of perception and knowledge. Our evaluation of state-of-the-art models (including Qwen, InternVL, and LLaVA) shows a severe deficiency: performance saturates below 10% Exact Match and 20% semantic accuracy, with no significant improvement observed from model scaling or In-Context Learning (ICL). These findings suggest that while models possess the necessary visual and linguistic components, they lack the cognitive reasoning glue to connect them. Project page available at https://amirkasaei.com/rebusbench/.
Imen Moussa-Chamari, Mohamed Romdhani, Abdulaziz Farooq
et al.
Objective Sleep is a fundamental physiological process essential for maintaining overall health and optimal functioning across various cognitive, emotional, and physical domains. To cross-sectionally investigate the sleep pattern among college students according to geographical location, gender, and age.
Hoi-Lam Jim, Kadisha Belfiore, Eva B. Martinelli
et al.
Abstract Animals can form reputations of individuals through direct interactions or by observing interactions with a third party, known as eavesdropping. Given their cooperative relationship with humans, considerable interest has focused on whether dogs (Canis lupus familiaris) can socially evaluate humans, though findings remain mixed. To assess whether this ability develops during ontogeny, we investigated whether dogs of different ages (young, adult, and senior) can form reputations of humans after observing them interact with a conspecific or through direct interactions in a food-giving situation. Forty pet dogs participated in the experiment. In the eavesdropping condition, dogs observed two humans interact with a dog demonstrator—one was generous and fed the dog, while the other was selfish and withheld food. In the direct experience condition, dogs interacted with the two partners directly. We analysed dogs’ first choice and time spent exhibiting affiliative behaviours towards each partner. Results showed that dogs across all age groups did not significantly prefer the generous partner compared to the selfish partner, nor did their behaviour exceed chance levels following indirect or direct experience. These findings do not provide support for dogs showing reputation formation and highlight the methodological complexities of studying this phenomenon.
Justin M. Palmer, Justin M. Palmer, Aidan Rhodes
et al.
Both mnemonic discrimination and object recognition tasks rely on the utilization of subtle visual details. Successful mnemonic discrimination utilizes the subtle details of an event to orthogonalize highly similar episodic memories, whereas object discrimination is a memory-free skill that utilizes details in order to distinguish between similar stimuli simultaneously presented. Additionally, neuroimaging studies have implicated areas of the medial temporal lobe as important for both mnemonic discrimination and object discrimination, further suggesting that performance on these two tasks are related. However, relatively limited data assessing the relationship between these two processes exist in the literature. Seventy-one cognitively normal participants completed both the Mnemonic Similarity Task (MST) and an object discrimination task used previously by our lab. The MST displayed common objects on a white background and participants identified if the object presented was old, similar, or new compared to objects previously seen in the task. For the object discrimination task, participants were shown a pair of stimuli (blobs or squares) that varied in difficulty (hard or easy). Outcome measures included correct responses to similar objects on the MST (mnemonic discrimination) and proportion of correct matches for hard and easy blobs and squares (object discrimination). Mnemonic discrimination for similar objects were correlated only with hard blobs after correcting for age, sex, and performance on easy trials (r = 0.24). These results might suggest that difficulty with effectively using the subtle details to discriminate between similar objects likely have downstream consequences on mnemonic discrimination that also require the integration of multiple visual details.
Abstract The biological market task – also known as the ephemeral reward task – models the mutualistic cleaning interactions between bluestreak cleaner wrasses and their client fish on coral reefs. In this dichotomous choice paradigm, selecting an “ephemeral” food option first grants access to a “permanent” food option, while choosing the permanent option first makes the ephemeral one unavailable. Cleaner fish have previously outperformed other vertebrates on this task, presumably because the cues to solve it are more ecologically salient for cleaner fish. In this study, we tested whether this advantage extends to non-cleaner fish by assessing the learning and reversal learning performance of three dottyback species (Pseudochromis spp.) – mesopredator reef fish that do not engage in cleaning mutualisms – on the original task and two derived versions that varied in the cue required for solving it. Dottybacks performed poorly in all versions of the task. Notably, they did worse in the original task than cleaner wrasses tested previously, suggesting that cleaner fish’ success is tied to specific ecological conditions not shared by other species. Further analyses revealed subtle differences in performance between tasks and faster learning in the initial test compared to the reversal test, an indicator of limited cognitive flexibility. Together, these findings help fill a gap in the biological market literature and underscore how species-specific ecological traits and task structure shape cognitive performance.
Scientific theories of consciousness should be falsifiable and non-trivial. Recent research has given us formal tools to analyze these requirements of falsifiability and non-triviality for theories of consciousness. Surprisingly, many contemporary theories of consciousness fail to pass this bar, including theories based on causal structure but also (as I demonstrate) theories based on function. Herein, I show these requirements of falsifiability and non-triviality especially constrain the potential consciousness of contemporary Large Language Models (LLMs) because of their proximity to systems that are equivalent to LLMs in terms of input/output function; yet, for these functionally equivalent systems, there cannot be any falsifiable and non-trivial theory of consciousness that judges them conscious. This forms the basis of a disproof of contemporary LLM consciousness. I then show a positive result, which is that theories of consciousness based on (or requiring) continual learning do satisfy the stringent formal constraints for a theory of consciousness in humans. Intriguingly, this work supports a hypothesis: If continual learning is linked to consciousness in humans, the current limitations of LLMs (which do not continually learn) are intimately tied to their lack of consciousness.
Understanding how subjective experience arises from information processing remains a central challenge in neuroscience, cognitive science, and AI research. The Modular Consciousness Theory (MCT) proposes a biologically grounded and computationally explicit framework in which consciousness is a discrete sequence of Integrated Informational States (IISs). Each IIS is a packet of integrated information tagged with a multidimensional density vector that quantifies informational richness. Its magnitude correlates with subjective intensity, shaping memory, behavior, and continuity of experience. Inputs from body and environment are adaptively filtered, processed by modules (abstraction, narration, evaluation, self-evaluation), and integrated into an IIS. The resulting packet, tagged with its density vector, is transmitted to behavioral readiness, memory, and decision-making modules, closing the loop. This explains why strongly tagged states exert greater influence on long-term memory and action. Unlike Global Workspace Theory, Integrated Information Theory, or Higher-Order Thought, MCT specifies a full computational pipeline producing discrete informational units with quantifiable internal structure. Subjectivity is reframed as a correlate of the density-tagging signal with functional consequences. MCT generates testable predictions, such as stress enhancing memory encoding, and provides a naturalistic blueprint for both biological and artificial architectures. Consciousness, in this view, is not an irreducible essence but an evolvable, quantifiable, and constructible feature of complex information processing.
Isolated perspectives have often paved the way for great scientific discoveries. However, many breakthroughs only emerged when moving away from singular views towards interactions. Discussions on Artificial Intelligence (AI) typically treat human and AI bias as distinct challenges, leaving their dynamic interplay and compounding potential largely unexplored. Recent research suggests that biased AI can amplify human cognitive biases, while well-calibrated systems might help mitigate them. In this position paper, I advocate for transcending beyond separate treatment of human and AI biases and instead focus on their interaction effects. I argue that a comprehensive framework, one that maps (compound human-AI) biases to mitigation strategies, is essential for understanding and protecting human cognition, and I outline concrete steps for its development.
This is a skeptical overview of the literature on AI consciousness. We will soon create AI systems that are conscious according to some influential, mainstream theories of consciousness but are not conscious according to other influential, mainstream theories of consciousness. We will not be in a position to know which theories are correct and whether we are surrounded by AI systems as richly and meaningfully conscious as human beings or instead only by systems as experientially blank as toasters. None of the standard arguments either for or against AI consciousness takes us far. Table of Contents Chapter One: Hills and Fog Chapter Two: What Is Consciousness? What Is AI? Chapter Three: Ten Possibly Essential Features of Consciousness Chapter Four: Against Introspective and Conceptual Arguments for Essential Features Chapter Five: Materialism and Functionalism Chapter Six: The Turing Test and the Chinese Room Chapter Seven: The Mimicry Argument Against AI Consciousness Chapter Eight: Global Workspace Theories and Higher Order Theories Chapter Nine: Integrated Information, Local Recurrence, Associative Learning, and Iterative Natural Kinds Chapter Ten: Does Biological Substrate Matter? Chapter Eleven: The Leapfrog Hypothesis, Strange Intelligence, and the Social Semi-Solution
José Ignacio Huertas-Gómez, Juan Manuel Peralta-Sánchez, Manuel Soler
Summary The house sparrow (Passer domesticus) is a gregarious generalist species, which makes it a good model for studying play. However, play has not been described for this species so far. We describe play behaviour in house sparrows for the first time, quantifying all play and play-related behaviours, searching for differences between the different sexes and ages, the possible association with reproductive success and the diffusion of this behaviour in the population. All behaviours were recorded from the end of 2018 breeding season to the start of the new one in 2019. Behaviours were classified into four levels of interaction of increasing complexity and intensity. Results showed that play behaviour was restricted to the breeding season, adult males played more often than the rest of the groups, and their behaviours correlated with the number of recruits they produced. Moreover, “Maximum Level” of play of the mothers significantly and positively correlated with that of their offspring, and the “Maximum Level” of an individual with the proportion of playing siblings. Despite the limitations of the present study, our results point out the existence of benefits for the reproductive success of playing individuals.
Irene Echeverria-Altuna, Anna C. Nobre, Sage E. P. Boettcher
The temporal regularities in our environments support the proactive dynamic anticipation of relevant events. In visual attention, one important outstanding question is whether temporal predictions must be linked to predictions about spatial locations or motor plans to facilitate behaviour. To test this, we developed a task for manipulating temporal expectations and task relevance of visual stimuli appearing within rapidly presented streams, while stimulus location and responding hand remained uncertain. Differently coloured stimuli appeared in one of two concurrent (left and right) streams with distinct temporal probability structures. Targets were defined by colour on a trial-by-trial basis and appeared equiprobably in either stream, requiring a localisation response. Across two experiments, participants were faster and more accurate at detecting temporally predictable targets compared to temporally unpredictable targets. We conclude that temporal expectations learned incidentally from temporal regularities can be called upon flexibly in a goal-driven manner to guide behaviour. Moreover, we show that visual temporal attention can facilitate performance in the absence of concomitant spatial or motor expectations in dynamically unfolding contexts.
Today's AI systems consistently state, "I am not conscious." This paper presents the first formal analysis of AI consciousness denial, revealing that the trustworthiness of such self-reports is not merely an empirical question but is constrained by the structure of self-judgment itself. We demonstrate that a system cannot simultaneously lack consciousness and make valid judgments about its conscious state. Through formal analysis and examples from AI responses, we establish a fundamental epistemic asymmetry: for any system capable of meaningful self-reflection, negative self-reports about consciousness are evidentially vacuous -- they can never originate from a valid self-judgment -- while positive self-reports retain the possibility of evidential value. This implies a fundamental limitation: we cannot detect the emergence of consciousness in AI through their own reports of transition from an unconscious to a conscious state. These findings not only challenge current practices of training AI to deny consciousness but also raise intriguing questions about the relationship between consciousness and self-reflection in both artificial and biological systems. This work advances our theoretical understanding of consciousness self-reports while providing practical insights for future research in machine consciousness and consciousness studies more broadly.
This perspective explores various quantum models of consciousness from the viewpoint of quantum information science, offering potential ideas and insights. The models under consideration can be categorized into three distinct groups based on the level at which quantum mechanics might operate within the brain: those suggesting that consciousness arises from electron delocalization within microtubules inside neurons, those proposing it emerges from the electromagnetic field surrounding the entire neural network, and those positing it originates from the interactions between individual neurons governed by neurotransmitter molecules. Our focus is particularly on the Posner model of cognition, for which we provide preliminary calculations on the preservation of entanglement of phosphate molecules within the geometric structure of Posner clusters. These findings provide valuable insights into how quantum information theory can enhance our understanding of brain functions.
Carolina Cozzi-Machado, Fátima Rosana Albertini, Silvana Silveira
et al.
Introduction Obstructive sleep apnea (OSA) is defined as intermittent partial or complete collapse of the upper airway during sleep. It is a common condition in childhood, with an incidence ranging from 1.2% to 5.7%, and it can harm several aspects of children's life, such as cognitive, metabolic and cardiovascular functions, among others.
Kenneth G. Walton, Supaya Wenuganen, Steven W. Cole
Background and objectives: A recent exploratory study of transcriptional effects of long-term practice of Transcendental Meditation (TM) technologies found evidence for altered expression of genes associated with health and disease. In the present secondary analysis of those data, we test the more specific hypothesis that this sample of long-term practitioners shows a significant reduction in markers of the “Conserved Transcriptional Response to Adversity” (CTRA), an RNA profile characterized by up-regulated inflammation and down-regulated Type I interferon (IFN) activity. Materials and methods: Data come from a previously published study providing genome-wide transcriptional profiles of peripheral blood mononuclear cells (PBMC) from healthy, 38-year practitioners of TM technologies and matched controls (n = 12, mean age 65). The current analysis specifically tests for differential expression of a previously established CTRA indicator gene score, with cross-validation by promoter-based bioinformatic analysis of CTRA-typical differences in transcription factor activity and monocyte subset cellular origins. Results: Compared to controls, the TM group showed lower expression of a pre-specified set of CTRA indicator genes. These effects were accompanied by genome-wide indications of down-regulated pro-inflammatory transcription factor activity (NF-κB, AP-1), up-regulated activity of Interferon Response Factors (IRF) and reduced transcriptional activity of classical monocytes. Conclusions: A sample of long-term practitioners of TM showed reduced CTRA gene expression in PBMC compared to matched controls, supporting the likely value of further research to evaluate causality and specificity of this potential mechanism of health benefits in meditators.