Human Cognition Research Articles

Exploring perceptions, cognitive factors, and motivations: A study on green structures on residential rooftops

Urban greenhouse and green roof cultivation propose a nature-based solution to current socio-ecological challenges in urban ecosystems, as it offers several socio-ecological solutions and benefits. The interdisciplinary techniques for scientific explorations of cognition and perception towards urban green structures could not get the attention of the researchers across varying scientific disciplines, especially in the developing world. Addressing the personal factors involved in human cognition and its subsequent impact on perceptions will lay the foundation for the studies of the field. The study included questions about socio-demographic characteristics and socio-environmental motivations, as well as a set of images depicting potential future of green structures. The study was accomplished in Tabriz city of Iran with 375 participants. The findings showed that individuals prioritize certain types of rooftop green structures based on their personal characteristics and motivations. Additionally, cognitive differences were observed among individuals based on age, gender, and marital status, leading to perceptual differences towards green structures. Socio-environmental motivations activate perceptual responses within individuals, influencing their preferences for specific types of rooftop greenhouses. Understanding the response of individuals towards green structure types is crucial for effective planning and design. This knowledge can enhance the overall success and satisfaction of rooftop green structures projects, leading to greater wellness and a positive impact on the environment.

Advancing machine learning for identifying cardiovascular disease via granular computing

<p>Machine learning in cardiovascular disease has broad applications in healthcare, automatically identifying hidden patterns in vast data without human intervention. Early-stage cardiovascular illness can benefit from machine learning models in drug selection. The integration of granular computing, specifically z-numbers, with machine learning algorithms, is suggested for cardiovascular disease identification. Granular computing enables handling unpredictable and imprecise situations, akin to human cognitive abilities. Machine learning algorithms such as Naïve Bayes, K-Nearest Neighbor (KNN), Random Forest, and Gradient Boosting are commonly used in constructing these models. Experimental findings indicate that incorporating granular computing into machine learning models enhances the ability to represent uncertainty and improves accuracy in cardiovascular disease detection.</p>

Distinguishing Intuitive and Deliberative Reasoning Regarding Different Aspects of Science Classroom Discourse

Purpose The current study aimed to identify the distances between a science teacher's intuitive reasoning and researchers’ systematic analysis of various aspects of science classroom discourse. An expert science teacher, given the pseudonym Jake, engaged in self-reflective processes regarding different aspects of classroom discussions to assess the variations and similarities between the teacher's intuitive estimations and the researchers’ data-based reasoning. Design/Approach/Methods Data collection involved in-depth interviews and video recording. Six aspects of science classroom discourse were considered to elucidate the distances: structure, interactional pattern, verbal sequence, typology, cognitive demand, and communicative approaches. Findings The quantitative distances between Jake's estimations and systematic observations of the six aspects of science classroom discourse varied across different aspects of the observed science lesson. While no consistent pattern was observed in the distances, there were increasing and decreasing gaps between the different aspects of science classroom discourse. Originality/Value Considering the contemporary arguments on human cognition and the professional development of science teachers, potential reasons for the differences were explored. Educational recommendations were provided, particularly regarding supporting science teachers’ journey toward becoming reflective inquirers or researcher-teachers.

On the Unexpected Abilities of Large Language Models

Large Language Models (LLMs) are capable of displaying a wide range of abilities that are not directly connected with the task for which they are trained: predicting the next words of human-written texts. In this article, I review recent research investigating the cognitive abilities developed by LLMs and their relation to human cognition. I discuss the nature of the indirect process that leads to the acquisition of these cognitive abilities, their relation to other indirect processes, and the implications for the acquisition of integrated abilities. Moreover, I propose the factors that enable the development of abilities that are related only very indirectly to the proximal objective of the training task. Finally, I discuss whether the full set of capabilities that LLMs could possibly develop is predictable.

Understanding Human Cognition Through Computational Modeling.

One important goal of cognitive science is to understand the mind in terms of its representational and computational capacities, where computational modeling plays an essential role in providing theoretical explanations and predictions of human behavior and mental phenomena. In my research, I have been using computational modeling, together with behavioral experiments and cognitive neuroscience methods, to investigate the information processing mechanisms underlying learning and visual cognition in terms of perceptual representation and attention strategy. In perceptual representation, I have used neural network models to understand how the split architecture in the human visual system influences visual cognition, and to examine perceptual representation development as the results of expertise. In attention strategy, I have developed the Eye Movement analysis with Hidden Markov Models method for quantifying eye movement pattern and consistency using both spatial and temporal information, which has led to novel findings across disciplines not discoverable using traditional methods. By integrating it with deep neural networks (DNN), I have developed DNN+HMM to account for eye movement strategy learning in human visual cognition. The understanding of the human mind through computational modeling also facilitates research on artificial intelligence's (AI) comparability with human cognition, which can in turn help explainable AI systems infer humans' belief on AI's operations and provide human-centered explanations to enhance human-AI interaction and mutual understanding. Together, these demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems.

Tools and methods to study and replicate experiments addressing human social cognition in interactive scenarios.

In the last decade, scientists investigating human social cognition have started bringing traditional laboratory paradigms more "into the wild" to examine how socio-cognitive mechanisms of the human brain work in real-life settings. As this implies transferring 2D observational paradigms to 3D interactive environments, there is a risk of compromising experimental control. In this context, we propose a methodological approach which uses humanoid robots as proxies of social interaction partners and embeds them in experimental protocols that adapt classical paradigms of cognitive psychology to interactive scenarios. This allows for a relatively high degree of "naturalness" of interaction and excellent experimental control at the same time. Here, we present two case studies where our methods and tools were applied and replicated across two different laboratories, namely the Italian Institute of Technology in Genova (Italy) and the Agency for Science, Technology and Research in Singapore. In the first case study, we present a replication of an interactive version of a gaze-cueing paradigm reported in Kompatsiari et al. (J Exp Psychol Gen 151(1):121-136, 2022). The second case study presents a replication of a "shared experience" paradigm reported in Marchesi et al. (Technol Mind Behav 3(3):11, 2022). As both studies replicate results across labs and different cultures, we argue that our methods allow for reliable and replicable setups, even though the protocols are complex and involve social interaction. We conclude that our approach can be of benefit to the research field of social cognition and grant higher replicability, for example, in cross-cultural comparisons of social cognition mechanisms.

Events in context—The HED framework for the study of brain, experience and behavior

The brain is a complex dynamic system whose current state is inextricably coupled to awareness of past, current, and anticipated future threats and opportunities that continually affect awareness and behavioral goals and decisions. Brain activity is driven on multiple time scales by an ever-evolving flow of sensory, proprioceptive, and idiothetic experience. Neuroimaging experiments seek to isolate and focus on some aspect of these complex dynamics to better understand how human experience, cognition, behavior, and health are supported by brain activity. Here we consider an event-related data modeling approach that seeks to parse experience and behavior into a set of time-delimited events. We distinguish between event processes themselves, that unfold through time, and event markers that record the experiment timeline latencies of event onset, offset, and any other event phase transitions. Precise descriptions of experiment events (sensory, motor, or other) allow participant experience and behavior to be interpreted in the context either of the event itself or of all or any experiment events. We discuss how events in neuroimaging experiments have been, are currently, and should best be identified and represented with emphasis on the importance of modeling both events and event context for meaningful interpretation of relationships between brain dynamics, experience, and behavior. We show how text annotation of time series neuroimaging data using the system of Hierarchical Event Descriptors (HED; https://www.hedtags.org) can more adequately model the roles of both events and their ever-evolving context than current data annotation practice and can thereby facilitate data analysis, meta-analysis, and mega-analysis. Finally, we discuss ways in which the HED system must continue to expand to serve the evolving needs of neuroimaging research.

Long-term ayahuasca use is associated with preserved global cognitive function and improved memory: a cross-sectional study with ritual users.

Although several studies have been conducted to elucidate the relationship between psychedelic consumption and cognition, few have focused on understanding the long-term use influence of these substances on these variables, especially in ritualistic contexts. To verify the influence of ritualistic ayahuasca consumption on the cognition of experienced ayahuasca religious users (> 20years) and beginners (< 3years), which participated in rituals of the Centro Luz Divina (CLD), a Santo Daime church in Brazil. Observational, descriptive, and cross-sectional study was carried out in which 48 people participated divided into three groups: (a) experienced ayahuasca users (n = 16), (b) beginner ayahuasca users (n = 16) and (c) control group (n = 16). All groups were matched by sex, age, and education and contained 8 women and 8 men. Cognition was assessed with the WASI (intelligence quotient), Digit Span (verbal working memory), Corsi Block-Tapping Task (visuospatial-related and working memory), Rey-Osterrieth Complex Figure test (visual perception, immediate memory), and Wisconsin Card Sorting and Five Digit Test (executive functions). Groups were homogenous in terms of sociodemographic characteristics, with participants presenting average intellectual performance. There was no evidence of cognitive decline amongst ayahuasca users. The experienced group showed higher scores compared to the less experienced group in the Digit Span and Corsi Block-Tapping tasks, which assess working verbal and visuospatial memories respectively. We confirmed the botanical identities of Psychotria viridis and Banisteriopsis caapi and the presence of the alkaloids both in the plants and in the brew. Short and long-term ayahuasca consumption does not seem to alter human cognition, while long-term use seems to be associated with improvements in aspects of working memory when compared with short-term use.

Концепции поэтики и симуляции смыслов: философский дискурс о киноискусстве

Cinematic art possesses a unique capacity to engender alternative realms, fabricate spatial-temporal realities, and thereby engender the poetics of meaning. The existing modern research in philosophy, history, semiotics, cinematic reality, phenomenology, and narrative structure within cinematic art furnishes an extensive foundation for profound exploration into the quandaries of meaning and the logical construction of filmic language. The concepts of poetics and the simulation of meanings within cinematic art are intriguing to trace and analyze from diverse philosophical standpoints and perceptions. The art of cinema is based on and appeals to early forms of thinking, semantic image, ideal forms, representations of the world and art. The theories and worldview of ancient thinkers seem to have antedated the articulation of the authentic concept of cinema. Phenomenological research aims to fill the gaps in the system of human judgment, contemplation, logic, cognition and reason. Through the prism of philosophy, we glean a finer comprehension of how cinematic art shapes our perception of reality through replicas of natural world perception, portraying human existence within a simulacrum, which itself constitutes its own reality. As further investigations into these philosophical domains are pursued, a deeper comprehension of the complex relationship between cinema, perception, and the intricacies of human experience is developed.

Hippocampal sharp-wave ripples correlate with periods of naturally occurring self-generated thoughts in humans

Core features of human cognition highlight the importance of the capacity to focus on information distinct from events in the here and now, such as mind wandering. However, the brain mechanisms that underpin these self-generated states remain unclear. An emerging hypothesis is that self-generated states depend on the process of memory replay, which is linked to sharp-wave ripples (SWRs), which are transient high-frequency oscillations originating in the hippocampus. Local field potentials were recorded from the hippocampus of 10 patients with epilepsy for up to 15 days, and experience sampling was used to describe their association with ongoing thought patterns. The SWR rates were higher during extended periods of time when participants’ ongoing thoughts were more vivid, less desirable, had more imaginable properties, and exhibited fewer correlations with an external task. These data suggest a role for SWR in the patterns of ongoing thoughts that humans experience in daily life.

Predictive language processing: integrating comprehension and production, and what atypical populations can tell us

Predictive processing, a crucial aspect of human cognition, is also relevant for language comprehension. In everyday situations, we exploit various sources of information to anticipate and therefore facilitate processing of upcoming linguistic input. In the literature, there are a variety of models that aim at accounting for such ability. One group of models propose a strict relationship between prediction and language production mechanisms. In this review, we first introduce very briefly the concept of predictive processing during language comprehension. Secondly, we focus on models that attribute a prominent role to language production and sensorimotor processing in language prediction (“prediction-by-production” models). Contextually, we provide a summary of studies that investigated the role of speech production and auditory perception on language comprehension/prediction tasks in healthy, typical participants. Then, we provide an overview of the limited existing literature on specific atypical/clinical populations that may represent suitable testing ground for such models–i.e., populations with impaired speech production and auditory perception mechanisms. Ultimately, we suggest a more widely and in-depth testing of prediction-by-production accounts, and the involvement of atypical populations both for model testing and as targets for possible novel speech/language treatment approaches.

Development of White Cabbage, Coffee, and Red Onion Extracts as Natural Phosphodiesterase-4B (PDE4B) Inhibitors for Cognitive Dysfunction: In Vitro and In Silico Studies.

Human cognition fundamentally depends on memory. Alzheimer's disease exhibits a strong correlation with a decline in this factor. Phosphodiesterase-4 B (PDE4B) plays a crucial role in neurodegenerative disorders, and its inhibition is one of the promising approaches for memory enhancement. This study aimed to identify secondary metabolites in white cabbage, coffee, and red onion extracts and identify their molecular interaction with PDE4B by in silico and in vitro experiments. Crushed white cabbage and red onion were macerated separately with ethanol to yield respective extracts, and ground coffee was boiled with water to produce aqueous extract. Thin layer chromatography (TLC)-densitometry was used to examine the phytochemicals present in white cabbage, coffee, and red onion extracts. Molecular docking studies were performed to know the interaction of test compounds with PDE4B. TLC-densitometry analysis showed that chlorogenic acid and quercetin were detected as major compounds in coffee and red onion extracts, respectively. In silico studies revealed that alpha-tocopherol (binding free energy (∆Gbind) = -38.00 kcal/mol) has the strongest interaction with PDE4B whereas chlorogenic acid (∆Gbind = -21.50 kcal/mol) and quercetin (∆Gbind = -17.25 kcal/mol) exhibited moderate interaction. In vitro assay showed that the combination extracts (cabbage, coffee, and red onion) had a stronger activity (half-maximal inhibitory concentration (IC50) = 0.12 ± 0.03 µM) than combination standards (sinigrin, chlorogenic acid, and quercetin) (IC50 = 0.17 ± 0.03 µM) and rolipram (IC50 = 0.15 ± 0.008 µM). Thus, the combination extracts are a promising cognitive enhancer by blocking PDE4B activity.

Human-like Learning in Temporally Structured Environments

Natural environments have correlations at a wide range of timescales. Human cognition is tuned to this temporal structure, as seen by power laws of learning and memory, and by spacing effects whereby the intervals between repeated training data affect how long knowledge is retained. Machine learning is instead dominated by batch iid training or else relatively simple nonstationarity assumptions such as random walks or discrete task sequences. The main contributions of our work are: (1) We develop a Bayesian model formalizing the brain's inductive bias for temporal structure and show our model accounts for key features of human learning and memory. (2) We translate the model into a new gradient-based optimization technique for neural networks that endows them with human-like temporal inductive bias and improves their performance in realistic nonstationary tasks. Our technical approach is founded on Bayesian inference over 1/f noise, a statistical signature of many natural environments with long-range, power law correlations. We derive a new closed-form solution to this problem by treating the state of the environment as a sum of processes on different timescales and applying an extended Kalman filter to learn all timescales jointly. We then derive a variational approximation of this model for training neural networks, which can be used as a drop-in replacement for standard optimizers in arbitrary architectures. Our optimizer decomposes each weight in the network as a sum of subweights with different learning and decay rates and tracks their joint uncertainty. Thus knowledge becomes distributed across timescales, enabling rapid adaptation to task changes while retaining long-term knowledge and avoiding catastrophic interference. Simulations show improved performance in environments with realistic multiscale nonstationarity. Finally, we present simulations showing our model gives essentially parameter-free fits of learning, forgetting, and spacing effects in human data. We then explore the analogue of human spacing effects in a deep net trained in a structured environment where tasks recur at different rates and compare the model's behavioral properties to those of people.

Time, valence, and imagination: a comparative study of thoughts in restricted and unrestricted mind wandering.

William James' "stream of thought" is a key component of human cognition. Such thoughts arise in both restricted and unrestricted contexts, either with or without the presence of a secondary task. This study examines the similarities and differences in thoughts produced in these two contexts, which we call restricted and unrestricted mind wandering. Participants performed a mindfulness task representing restricted mind wandering and an unrestricted thought task where they spontaneously explored thoughts, reporting them as they arose. Participants then self-rated their thoughts based on valence, temporal orientation (past/present/future), and reality orientation (imaginary vs.real). Participants' emotional states were also evaluated using the Emotion Recall Task (ERT) and the PANAS questionnaire. Unrestricted mind wandering generated more thoughts, which were more positive and future-oriented than those in restricted mind wandering. Additionally, participants' thought valence correlated with their PANAS and ERT scores. Approximately 1 out of 4 thoughts in both restricted and unrestricted mind wandering were imaginary, with increased future orientation linked to more imaginative thought. Despite the statistical differences separating restricted and unrestricted thought, effect sizes were predominantly small, indicating that the thoughts arise during these two types of mind wandering are largely of the same kind.

Learning to learn: Single session acquisition of new rules by freely-moving mice

Abstract Learning from examples and adapting to new circumstances are fundamental attributes of human cognition. However, it is unclear what conditions allow for fast and successful learning, especially in non-human subjects. To determine how rapidly freely-moving mice can learn a new discrimination criterion (DC), we designed a two-alternative forced-choice visual discrimination paradigm in which the DCs governing the task can change between sessions. We find that experienced animals can learn a new DC after being exposed to only five training and three testing trials. The propensity for single session learning improves over time and is accurately predicted based on animal experience and criterion difficulty. After establishing procedural learning of a paradigm, mice continuously improve their performance in new circumstances. Thus, mice learn to learn.

The extimate core of understanding: absolute metaphors, psychosis and large language models

AbstractThis paper delves into the striking parallels between the linguistic patterns of Large Language Models (LLMs) and the concepts of psychosis in Lacanian psychoanalysis. Lacanian theory, with its focus on the formal and logical underpinnings of psychosis, provides a compelling lens to juxtapose human cognition and AI mechanisms. LLMs, such as GPT-4, appear to replicate the intricate metaphorical and metonymical frameworks inherent in human language. Although grounded in mathematical logic and probabilistic analysis, the outputs of LLMs echo the nuanced linguistic associations found in metaphor and metonymy, suggesting a mirroring of human linguistic structures. A pivotal point in this discourse is the exploration of “absolute metaphors”—core gaps in reasoning discernible in both AI models and human thought processes and central to the Lacanian conceptualization of psychosis. Despite the traditional divide between AI research and continental philosophy, this analysis embarks on an innovative journey, utilizing Lacanian philosophy to unravel the logic of AI, using concepts established in the continental discourse on logic, rather than the analytical tradition.

Simultaneous MEG and EEG source imaging of electrophysiological activity in response to acute transcranial photobiomodulation

IntroductionTranscranial photobiomodulation (tPBM) is a non-invasive neuromodulation technique that improves human cognition. The effects of tPBM of the right forehead on neurophysiological activity have been previously investigated using EEG in sensor space. However, the spatial resolution of these studies is limited. Magnetoencephalography (MEG) is known to facilitate a higher spatial resolution of brain source images. This study aimed to image post-tPBM effects in brain space based on both MEG and EEG measurements across the entire human brain.MethodsMEG and EEG scans were concurrently acquired for 6 min before and after 8-min of tPBM delivered using a 1,064-nm laser on the right forehead of 25 healthy participants. Group-level changes in both the MEG and EEG power spectral density with respect to the baseline (pre-tPBM) were quantified and averaged within each frequency band in the sensor space. Constrained modeling was used to generate MEG and EEG source images of post-tPBM, followed by cluster-based permutation analysis for family wise error correction (p &amp;lt; 0.05).ResultsThe 8-min tPBM enabled significant increases in alpha (8–12 Hz) and beta (13–30 Hz) powers across multiple cortical regions, as confirmed by MEG and EEG source images. Moreover, tPBM-enhanced oscillations in the beta band were located not only near the stimulation site but also in remote cerebral regions, including the frontal, parietal, and occipital regions, particularly on the ipsilateral side.DiscussionMEG and EEG results shown in this study demonstrated that tPBM modulates neurophysiological activity locally and in distant cortical areas. The EEG topographies reported in this study were consistent with previous observations. This study is the first to present MEG and EEG evidence of the electrophysiological effects of tPBM in the brain space, supporting the potential utility of tPBM in treating neurological diseases through the modulation of brain oscillations.

No evidence for cross-paradigm transfer of abstract task knowledge in adults and school-aged children.

Cognitive control is a hallmark of human cognition. A large number of studies have focused on the plasticity of cognitive control and examined how repeated task experience leads to the improvement of cognitive control in novel task environments. However, it has been demonstrated that training-induced changes are very selective and that transfer occurs within one task paradigm but not across different task paradigms. The current study tested the possibility that cross-paradigm transfer would occur if a common cognitive control strategy is employed across different task paradigms. Specifically, we examined whether prior experience of using reactive control in one task paradigm (i.e., either the cued task-switching paradigm or the AX-CPT) makes adults (N = 137) and 9- to 10-year-olds (N = 126) respond in a reactive way in a subsequent condition of another task paradigm in which proactive control could have been engaged. Bayesian generalized mixed-effects modelsrevealed clear evidence of an absence of cross-paradigm transfer of reactive control in both adults and school-aged children. Based on these findings, we discuss to what extent learned control could be transferred across different task contexts and the task-specificity of proactive/reactive control strategies.

Association between radiation dose, thyroid hormone, and IQ levels in children exposed to radiation in utero after the Chernobyl accident.

Few studies have explored the effects of n utero radiation exposure on human health and cognition and none have taken into account thyroid hormone levels (T3), which have shown to affect cognitive performance. We investigated mechanisms of possible radiation effects on IQ in two cohorts of 250 persons each: exposed n utero after the Chernobyl accident: a 'higher exposure group (HEG)', whose mothers resided in more heavily contaminated territories at the time of the Chernobyl accident, and a 'lesser exposure group (LEG)' whose mothers resided in less contaminated areas. The dataset included information on estimated prenatal thyroid radiation dose, gestation week at the time of the accident (ATA); thyroid hormones: T3 (triiodothyronine) and T4 (thyroxine) levels measured at age 11-12 years and general IQ measured at three time points: t1: 6-7 years old; t2: 11-12 years old and t3: 15-16 years old. Descriptive and inference analyses were used to explore the dynamic of changes through time and the associations between key variables at the three time points. Estimated radiation doses to the thyroid gland were substantially higher in the HEG than in the LEG (mean 391 vs 25 mGy respectively). Significant differences in thyroid hormones levels were observed between the two groups, with lower values in T3 (higher in T4) in the LEG. At t1, the general IQ, as well as verbal and non-verbal IQ scores, were lower in the HEG than in the LEG. In the HEG, analyses adjusting simultaneously for radiation dose, gestational week ATA and T3 levels suggest that all three variables are associated with IQ, with the latter being highest among those exposed later during gestation and decreasing with increasing level of dose and of T3. No significant association was observed between IQ and T4 levels. No effect of exposure on IQ was seen in the LEG. Further investigation of this hypothesis will be important to understand the relation between n utero exposure radiation dose to thyroid, thyroid hormone levels and IQ, taking into account effects of potential confounding factors (physiological stress, maternal anxiety related evacuation).

Reconsidering Self and Identity According to Mullah Sadr

The current study is based on a hypothesisThe Islamic philosopher Sadr al-Din al-Shirazi (1572-1641 AD), known as Mulla Sadr, reconsidered all previous Islamic philosophical discussion related to the concepts of self and identity. SoThe focus has been placed throughout the paragraphs of this articleA study on presenting Mulla Sadra’s views related to the self and identity, as well as his theory of knowledge and his vision about the formation of human identity through the accumulation of experience and the cognitive unity between the subject and the object, which he presented on the basis of the approach with the philosophical vision of the relationship between matter and image, which is philosophically traditional and known since Aristotle, and which was common and prevalent among people. Muslim philosophers in general. The beginning came in this studyWith fast and intense presentation. The legacy of the School of Transcendent Wisdom includes the most important stages of Mulla Sadra’s spiritual and intellectual development and all the historical, social, and political circumstances that accompanied the emergence of his star as a philosopher, then moving on to present his most important philosophical ideas about the human self, knowledge, and reason, and his own arguments about the immateriality of the soul. This study also addressed his most important ideas about the theory of knowledge and the independence of human cognitive abilities, as well as the influence of experience and its role in forming an identity for the mind, a variable and unstable identity that is formed gradually through accumulation and ultimately contributes to defining a special qualitative identity that emerges clearly through disparate patterns of thinking, which represents... The culmination of Mulla Sadra's reconsideration, which distinguished him from all the Muslim philosophers before him, including Ibn Sina and his theses about the self and self-awareness.