Neuroscience Data Research Articles

Unnecessary reliance on multilevel modelling to analyse nested data in neuroscience: When a traditional summary-statistics approach suffices.

Nested data structures create statistical dependence that influences the effective sample size and statistical power of a study. Several methods are available for dealing with nested data, including the summary-statistics approach and multilevel modelling (MLM). Recent publications have heralded MLM as the best method for analysing nested data, claiming benefits in power over summary-statistics approaches (e.g., the t-test). However, when cluster size is equal, these approaches are mathematically equivalent. We conducted statistical simulations demonstrating equivalence of MLM and summary-statistics approaches for analysing nested data and provide supportive cases for the utility of the conventional summary-statistics approach in nested experiments. Using statistical simulations, we demonstrate that losses in power in the summary-statistics approach discussed in the previous literature are unsubstantiated. We also show that MLM sometimes suffers from frequent singular fit errors, especially when intraclass correlation is low. There are indeed many situations in which MLM is more appropriate and desirable, but researchers should be aware of the possibility that simpler analysis (i.e., summary-statistics approach) does an equally good or even better job in some situations.

ЭНАКТИВИСТСКАЯ КРИТИКА НЕЙРОБИОЛОГИЧЕСКОЙ ТЕОРИИ СОЗНАНИЯ

The article considers the main arguments against the neurobiological theory of consciousness from the point of view of the enactivist approach within the philosophy of mind. The neurobiological theory of consciousness, which reduces consciousness to neural activity, is currently the dominant approach to the mind-body problem. The neurobiological theory emerged as a result of advances in research on the phenomena of consciousness and through the development of technologies for visualizing the internal processes of mind. However, at the very heart of this theory, there is a number of logical contradictions. The non-reductive enactivist approach to consciousness, introduced in this article, contributes to the existing argumentation against the reduction of consciousness to neural processes with remonstrations that take into account the modern neuroscientific data. The article analyzes the argumentation of the sensorimotor enactivism developed by A. Noe and offers the account of the teleosemantic approach to the concept of information provided by R. Cao. The key problems of the neurobiological theory of consciousness are highlighted, and the objections emerging within the framework of the enactivist approach are analyzed. Since the main concepts on which the neural theory is based are the concepts of neural substrate, cognition as representation, and information as a unit of cognition, the author of the article presents three key enactivist ideas that oppose them. First, the enactivist concept of cognition as action allows us to consider the first-person experience as a mode of action, and not as a state of the brain substrate. Second, the article deals with the “explanatory externalism” argument proposed by Noe, who refutes the image of cognition as a representation in the brain. Finally, in order to critically revise the concept of information as a unit of cognition, the author analyzes Cao’s idea, which represents a teleosemantic approach, but is in line with the general enactivist argumentation. Cao shows that the application of the concept “information” to neural processes is problematic: no naturalized information is found in the brain as a physical substrate. A critical revision of beliefs associated with the neural theory of consciousness leads us to recognize that there are not enough grounds for reducing consciousness to processes that take place in the brain. That is why Noe calls expectations that the visualization of processes taking place in the brain with the help of the modern equipment will be able to depict the experience of consciousness the “new phrenology”, thus indicating the naive character of neural reduction. The article concludes that natural science methods are insufficient for the study of consciousness.

Toward A Neuroscientifically Informed “Reasonable Person” Test

Legal tests invoking the viewpoint of a so-called reasonable person play an important role in many domains of the law, ranging from intellectual property to free speech. In such cases, a central question involves determining how a hypothetical individual with “an ordinary or average level of care, prudence, or knowledge” would respond. Despite the seemingly commonsensical nature of these tests, their judicial application can be controversial due to concerns about subjectivity and vulnerability to explicit or implicit biases. Here we take a step toward addressing these concerns by using neuroscientific tools to observe, without the use of self-report, the nature of mental representations central to a set of disputes invoking the reasonable person. Specifically, using an fMRI-based measure, repetition suppression, to generate a neural index of subjective visual similarity, we sought to inform the application of the reasonable person test to a class of intellectual property law that evaluates whether a trademark is so similar to another as to generate consumer confusion. We show that, by leveraging well-established neuroscientific knowledge about visual processing, it is possible to construct a parsimonious neural index of subjective similarity using signals from object-sensitive brain regions identified a priori. Moreover, this neural index, aggregated across multiple participants, is sufficiently precise to detect instances of experimenter-induced bias in behavioral reports. Together these findings shed light on the potential evidentiary value of neuroscientific data to inform questions involving the reasonable person and suggest a novel domain for the use of neuroscience in law.

The Ontology Of Biomedical Rationality: A System Of Scientific Foundations And Reflection (Aspects Of Material And Informational Reality Paradigm)

In the context of general philosophical knowledge, a comprehensive reference concept of biomedical rationality has been formulated for the first time in the present article. The concept is close in meaning to digitalization, but differs from a methodological perspective. In the system of scientific foundations, it is presented as a core notion in the characterization of human ontos and as notion reflecting the multidisciplinary ideas about the values of the existing technological order. The phenomenon of reflection is considered as a gnoseological basis, an explanatory principle, a condition for a renewed style of rational thinking in the modern scientific paradigm. From the connective tissue individuality, the causality of processes in the living and the foundation of biorationality as the intersection of reality are modeled. The evaluative and parametric analysis of the phenomenon based on the generalization of new neuroscience data shows the theoretical and methodological prospects of this understanding within the framework of the substrate and attributive approaches. The operational nature of the concept of reflection and its components - vector, efficiency, intensity - are introduced into the system of personality traits as performing a predictive role on the way to truth, including medical truth. The involvement of the patient's personality as a component of the 5-PSM model of medicine as the most developing sector of science means the vision of reflection as an instrumental-significant resource in the objectification of information. In the context of post-non-classical science, deployed to the paradigm of the integrity of information-material reality, reflection in the medical worldview provides the focus of attention on the essence of the disease and the patient's personality in diagnostics.

Chapter 3 - Hypothalamic stress systems in mood disorders

Stress system dysfunction is a typical characteristic of acute depression and other mood disorders. The exact pattern of factors predisposing for stress-related mental disorders is yet to be unraveled. However, corticosteroid receptor function plays an important role for appropriate or dysfunctional neuroendocrine responses to stress exposure and hence in resilience or risk for the development and course of both, depression and anxiety disorders. Solid neuroscience data strongly support that both neuropeptides, corticotropin-releasing hormone (CRH) and vasopressin (AVP), are central in coordinating humoral and behavioral adaptation to stress. Other neuropeptides, including oxytocin, neuropeptide S, neuropeptide Y, and orexin, are also considered important contributors. Attempts to turn neuropeptide biology into treatments for stress-related disorders need to consider that neuropeptide receptors are specific drug targets for certain patient populations rather than universal targets for all patients, like biogenic amine systems. That is why most negative clinical trials testing neuropeptide receptor antagonists have been in fact failed trials by design, because no companion tests were used to identify which patients with depression are most likely to benefit from a specific neuropeptide receptor-targeting drug treatment. Therefore, the most important future research task is discovery and development of appropriate companion tests that will allow the successful transfer of the precious treasure of neuropeptide system-targeting drugs into clinics.

Mild to moderate levels of anxiety in a healthy population cohort are related to face recognition with negative emotions and white matter integrity

AbstractBackgroundThe face recognition tasks were applied to test the healthy population cohort with mild to moderate levels of anxiety and they underwent MRI scans to evaluate their brains' white matter integrity.MethodMAP‐MRI analysis applied to estimate diffusion indexes Generalised Fractional Anisotropy (GFA) of segmented white matter tracts in a healthy (N=174) population‐based sample (mean MMSE 28.39) from the Cambridge Centre for Ageing and Neuroscience (Cam‐CAN) data including normal adults ages 18‐88 to examine microstructural properties of specific association fiber tracts involved in face recognition processing. Half sample (n=88) had anxiety symptoms ( Hospital Anxiety Scale 9.7) and half sample (n=88) were matched with age and sex and without anxiety symptoms. Two kinds of behavioral face tests to white matter measures including three pairs of association tracts, namely inferior fronto‐occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF) and uncinated fasciculus (UF). The two kinds of behavioral face tests applied were Benton Test of Facial Recognition which assesses the ability to match pictures of unfamiliar faces with variation of head orientation and lighting, and face recognition test of emotion assessing recognition of positive emotion (happiness), negative emotions (angry, disgust, fear, and sad) and surprise emotion. Paired sample t test were applied to compare the two groups' HAS, behavioral tests, and GFA of white matter tracts.ResultPaired t test results showed hospital Anxiety Scale, mean reaction time (RT) of face recognition test in negative emotion, GFA of left IFOF and left UF were significantly different in two groups. Pearson correlation analysis showed mean RT of face recognition test in negative emotion was significantly negative correlated with GFA of left UF.ConclusionHealthy life‐span population cohort with mild to moderate anxiety symptoms spent longer time recognizing faces with negative emotions and white matter integrity of the specific tract UF reflected this characteristic. There was brain‐behavior relationship.

Graded Empathy: A Neuro-Phenomenological Hypothesis.

The neuroscience of empathy has enormously expanded in the past two decades, thereby making instrumental progress for the understanding of neural substrates involved in affective and cognitive aspects of empathy. Yet, these conclusions have relied on ultrasimplified tasks resulting in the affective/cognitive dichotomy that was often modeled and overemphasized in pathological, developmental, and genetic studies of empathy. As such, the affective/cognitive model of empathy could not straightforwardly accommodate and explain the recent surge of neuroscientific data obtained from studies employing naturalistic approaches and intergroup conditions. Inspired by phenomenological philosophy, this article paves the way for a new scientific perspective on empathy that breaks thorough the affective/cognitive dichotomy. This neuro-phenomenological account leans on phenomenological analyses and can straightforwardly explain recent neuroscience data. It emphasizes the dynamic, subjective, and piecemeal features of empathic experiences and unpicks the graded nature of empathy. The graded empathy hypothesis postulates that attending to others' expressions always facilitates empathy, but the parametric modulation in the levels of the empathic experience varies as a function of one's social interest (e.g., via intergroup or inter-personal cues) in the observed other. Drawing on multiple resources that integrate neuroscience with phenomenology, we describe the potential of this graded framework in an era of real-life experimentation. By wearing lenses of neuro-phenomenology, this original perspective can change the way empathy is considered.

An expanded model for perceptual visual single object recognition system using expectation priming following neuroscientific evidence

Under numerous circumstances, humans recognize visual objects in their environment with remarkable response times and accuracy. Existing artificial visual object recognition systems have not yet surpassed human vision, especially in its universality of application. We argue that modeling the recognition process in an exclusive feedforward manner hinders those systems’ performance. To bridge that performance gap between them and human vision, we present a brief review of neuroscientific data, which suggests that considering an agent’s internal influences (from cognitive systems that peripherally interact with visual-perceptual processes) recognition can be improved. Then, we propose a model for visual object recognition which uses these systems’ information, such as affection, for generating expectation to prime the object recognition system, thus reducing its execution times. Later, an implementation of the model is described. Finally, we present and discuss an experiment and its results.

Incorporating Neuroscience Data into Agent-Based Simulation Models of Buyer Behavior

Purpose: The article aims to analyze the possibility of using various cognitive neuroscience techniques when building the agent model of buyer behavior and propose an experimental procedure for obtaining qualitative data based on the triangulation of methods. Design/Methodology/Approach: The proposed approach combines agent-based simulation with cognitive neuroscience techniques at the stage of designing the characteristics and behavior rules of agents-consumers. Findings: The consumer’s purchasing behavior is determined by the compilation of the influence of environmental factors and marketing stimuli as well as by his personality traits. Due to the necessity to consider all these elements when mapping the consumer-agent characteristics and decision rules, traditional methods of data collection may not be sufficient. In such a situation, cognitive neuroscience techniques can become a source of additional information, allowing to take into account the influence of emotions or cognitive abilities on one’s decisions. To make it possible, it is necessary to conduct experiments with the use of neuroscience research tools (e.g., EEG, GSR, HR etc.) aimed at detecting emotional and cognitive states during exposure to an advertisement of a specific product. The neurophysiological data collected during the experiments allow for a more accurate estimation of the qualitative parameters describing consumer behavior rules. Practical Implications: The proposed concept allows for a more accurate representation of agents-consumers’ features and decision rules. Consequently, the agent-based model more reliably reflects reality, and thus the results obtained during model simulation are more valuable and can be the basis for formulating marketing plans. Originality/Value: The proposed approach enriches the methodology of data collection and estimation of qualitative parameters in building agent models of buyer behavior.

State-Dependent Cortical Unit Activity Reflects Dynamic Brain State Transitions in Anesthesia

Understanding the effects of anesthesia on cortical neuronal spiking and information transfer could help illuminate the neuronal basis of the conscious state. Recent investigations suggest that the brain state identified by local field potential spectrum is not stationary but changes spontaneously at a fixed level of anesthetic concentration. How cortical unit activity changes with dynamically transitioning brain states under anesthesia is unclear. Extracellular unit activity was measured with 64-channel silicon microelectrode arrays in cortical layers 5/6 of the primary visual cortex of chronically instrumented, freely moving male rats (n = 7) during stepwise reduction of the anesthetic desflurane (6%, 4%, 2%, and 0%). Unsupervised machine learning applied to multiunit spike patterns revealed five distinct brain states. A novel desynchronized brain state with increased spike rate variability, sample entropy, and EMG activity occurred in 6% desflurane with 40.0% frequency. The other four brain states reflected graded levels of anesthesia. As anesthesia deepened the spike rate of neurons decreased regardless of their spike rate profile at baseline conscious state. Actively firing neurons with wide-spiking pattern showed increased bursting activity along with increased spike timing variability, unit-to-population correlation, and unit-to-unit transfer entropy, despite the overall decrease in transfer entropy. The narrow-spiking neurons showed similar changes but to a lesser degree. These results suggest that (1) anesthetic effect on spike rate is distinct from sleep, (2) synchronously fragmented spiking pattern is a signature of anesthetic-induced unconsciousness, and (3) the paradoxical, desynchronized brain state in deep anesthesia contends the generally presumed monotonic, dose-dependent anesthetic effect on the brain.SIGNIFICANCE STATEMENT Recent studies suggest that spontaneous changes in brain state occur under anesthesia. However, the spiking behavior of cortical neurons associated with such state changes has not been investigated. We found that local brain states defined by multiunit activity had a nonunitary relationship with the current anesthetic level. A paradoxical brain state displaying asynchronous firing pattern and high EMG activity was found unexpectedly in deep anesthesia. In contrast, the synchronous fragmentation of neuronal spiking appeared to be a robust signature of the state of anesthesia. The findings challenge the assumption of monotonic, anesthetic dose-dependent behavior of cortical neuron populations. They enhance the interpretation of neuroscientific data obtained under anesthesia and the understanding of the neuronal basis of anesthetic-induced state of unconsciousness.

From Neuroscience to Law: Bridging the Gap.

Since our moral and legal judgments are focused on our decisions and actions, one would expect information about the neural underpinnings of human decision-making and action-production to have a significant bearing on those judgments. However, despite the wealth of empirical data, and the public attention it has attracted in the past few decades, the results of neuroscientific research have had relatively little influence on legal practice. It is here argued that this is due, at least partly, to the discussion on the relationship of the neurosciences and law mixing up a number of separate issues that have different relevance on our moral and legal judgments. The approach here is hierarchical; more and less feasible ways in which neuroscientific data could inform such judgments are separated from each other. The neurosciences and other physical views on human behavior and decision-making do have the potential to have an impact on our legal reasoning. However, this happens in various different ways, and too often appeal to any neural data is assumed to be automatically relevant to shaping our moral and legal judgments. Our physicalist intuitions easily favor neural-level explanations to mental-level ones. But even if you were to subscribe to some reductionist variant of physicalism, it would not follow that all neural data should be automatically relevant to our moral and legal reasoning. However, the neurosciences can give us indirect evidence for reductive physicalism, which can then lead us to challenge the very idea of free will. Such a development can, ultimately, also have repercussions on law and legal practice.

Parameter clustering in Bayesian functional principal component analysis of neuroscientific data.

The extraordinary advancements in neuroscientific technology for brain recordings over the last decades have led to increasingly complex spatiotemporal data sets. To reduce oversimplifications, new models have been developed to be able to identify meaningful patterns and new insights within a highly demanding data environment. To this extent, we propose a new model called parameter clustering functional principal component analysis (PCl-fPCA) that merges ideas from functional data analysis and Bayesian nonparametrics to obtain a flexible and computationally feasible signal reconstruction and exploration of spatiotemporal neuroscientific data. In particular, we use a Dirichlet process Gaussian mixture model to cluster functional principal component scores within the standard Bayesian functional PCA framework. This approach captures the spatial dependence structure among smoothed time series (curves) and its interaction with the time domain without imposing a prior spatial structure on the data. Moreover, by moving the mixture from data to functional principal component scores, we obtain a more general clustering procedure, thus allowing a higher level of intricate insight and understanding of the data. We present results from a simulation study showing improvements in curve and correlation reconstruction compared with different Bayesian and frequentist fPCA models and we apply our method to functional magnetic resonance imaging and electroencephalogram data analyses providing a rich exploration of the spatiotemporal dependence in brain time series.

Crowdsourcing neuroscience: Inter-brain coupling during face-to-face interactions outside the laboratory

When we feel connected or engaged during social behavior, are our brains in fact “in sync” in a formal, quantifiable sense? Most studies addressing this question use highly controlled tasks with homogenous subject pools. In an effort to take a more naturalistic approach, we collaborated with art institutions to crowdsource neuroscience data: Over the course of 5 years, we collected electroencephalogram (EEG) data from thousands of museum and festival visitors who volunteered to engage in a 10-min face-to-face interaction. Pairs of participants with various levels of familiarity sat inside the Mutual Wave Machine—an artistic neurofeedback installation that translates real-time correlations of each pair's EEG activity into light patterns. Because such inter-participant EEG correlations are prone to noise contamination, in subsequent offline analyses we computed inter-brain coupling using Imaginary Coherence and Projected Power Correlations, two synchrony metrics that are largely immune to instantaneous, noise-driven correlations. When applying these methods to two subsets of recorded data with the most consistent protocols, we found that pairs’ trait empathy, social closeness, engagement, and social behavior (joint action and eye contact) consistently predicted the extent to which their brain activity became synchronized, most prominently in low alpha (~7–10 Hz) and beta (~20–22 Hz) oscillations. These findings support an account where shared engagement and joint action drive coupled neural activity and behavior during dynamic, naturalistic social interactions. To our knowledge, this work constitutes a first demonstration that an interdisciplinary, real-world, crowdsourcing neuroscience approach may provide a promising method to collect large, rich datasets pertaining to real-life face-to-face interactions. Additionally, it is a demonstration of how the general public can participate and engage in the scientific process outside of the laboratory. Institutions such as museums, galleries, or any other organization where the public actively engages out of self-motivation, can help facilitate this type of citizen science research, and support the collection of large datasets under scientifically controlled experimental conditions. To further enhance the public interest for the out-of-the-lab experimental approach, the data and results of this study are disseminated through a website tailored to the general public (wp.nyu.edu/mutualwavemachine).

Collective almost synchronization-based model to extract and predict features of EEG signals

Understanding the brain is important in the fields of science, medicine, and engineering. A promising approach to better understand the brain is through computing models. These models were adjusted to reproduce data collected from the brain. One of the most commonly used types of data in neuroscience comes from electroencephalography (EEG), which records the tiny voltages generated when neurons in the brain are activated. In this study, we propose a model based on complex networks of weakly connected dynamical systems (Hindmarsh–Rose neurons or Kuramoto oscillators), set to operate in a dynamic regime recognized as Collective Almost Synchronization (CAS). Our model not only successfully reproduces EEG data from both healthy and epileptic EEG signals, but it also predicts EEG features, the Hurst exponent, and the power spectrum. The proposed model is able to forecast EEG signals 5.76 s in the future. The average forecasting error was 9.22%. The random Kuramoto model produced the outstanding result for forecasting seizure EEG with an error of 11.21%.

Reproducibility in the absence of selective reporting: Anillustration from large-scale brain asymmetry research.

The problem of poor reproducibility of scientific findings has received much attention over recent years, in a variety of fields including psychology and neuroscience. The problem has been partly attributed to publication bias and unwanted practices such as p‐hacking. Low statistical power in individual studies is also understood to be an important factor. In a recent multisite collaborative study, we mapped brain anatomical left–right asymmetries for regional measures of surface area and cortical thickness, in 99 MRI datasets from around the world, for a total of over 17,000 participants. In the present study, we revisited these hemispheric effects from the perspective of reproducibility. Within each dataset, we considered that an effect had been reproduced when it matched the meta‐analytic effect from the 98 other datasets, in terms of effect direction and significance threshold. In this sense, the results within each dataset were viewed as coming from separate studies in an “ideal publishing environment,” that is, free from selective reporting and p hacking. We found an average reproducibility rate of 63.2% (SD = 22.9%, min = 22.2%, max = 97.0%). As expected, reproducibility was higher for larger effects and in larger datasets. Reproducibility was not obviously related to the age of participants, scanner field strength, FreeSurfer software version, cortical regional measurement reliability, or regional size. These findings constitute an empirical illustration of reproducibility in the absence of publication bias or p hacking, when assessing realistic biological effects in heterogeneous neuroscience data, and given typically‐used sample sizes.

Az okoseszközök használata kognitív idegtudományi nézőpontból

A szerzők az okoseszközök használatát megalapozó kognitív tényezők változásának néhány jellemzőjét bemutatva a 21. század oktatási lehetőségeit és kihívásait elemzik. A tanulmány kitér a digitális eszközök használatának kezdeti szakaszaira, az okostelefonok alkalmazásának lehetséges előnyeire, akadályaira, valamint veszélyeire. A tanulás neurális alapfolyamatainak bemutatásából kiinduló elemzés bemutatja az okoseszközök használatát kísérő agyi változások szisztematikus kutatásának feltételeit, az összefüggések tudományos szigorúsággal feltárandó tényezőit. Az alapkészségek fejlődését és eltéréseit meghatározó agyi változások vizsgálatából származó adatok és az azokra épülő modellek megbízhatóságát a szerzők az olvasás és szövegértés, a korai matematika, valamint a zenei nevelés területén kiemelve számos példát mutatnak be arra vonatkozóan, miért kevés a megbízható idegtudományi adat az okoseszközökkel támogatott tanulásra vonatkozóan. Külön kitér a tanulmány a függőségekre, valamint az okoseszközök használatát megalapozó kognitív és motivációs tényezőkre. A tanulmány a virtuális valóság és a gamifikáció típusú oktatási módszerek területén feltárandó ismeretek bemutatásával hangsúlyozza, hogy az agyi hálózatok változásában mérhető hatást ezen a területen miért ismerjük kevésbé, mint a tanulás más faktoraival kapcsolatban.

Can’t Stop the Feeling: Influence of Incidental Emotions on Manager’s Willingness to Delegate to AI

Artificial Intelligence (AI) based technologies provide manifold opportunities to augment managerial decision-making. Managers can delegate decisions to the increasingly available and capable AI-enabled decision-making systems. This study looks into the managerial willingness to do so with a particular emphasis on the influence of emotions on the manager’s choice to hand over strategic decisions to AI. Prior research has shown that both cognitive and emotional aspects influence decision-making. Furthermore, we know that emotions induced by technology influence a system’s adoption and use. However, despite the acknowledged relevance of emotions, prior research has not yet emphasised the impact of incidental emotions – emotions triggered by events unrelated to the decision or the AI-enabled system use – on decision-making and delegation. Using an experimental research design enhanced by data collected through cognitive neuroscience methods, we analyse the impact of positive, incidental emotions and their states valence and arousal on a manager’s willingness to delegate a strategic decision to an AI-enabled system. The findings reveal that positive incidental emotions, as well as high values of valence, positively impact a managers willingness to delegate strategic decisions to an AI-enabled system. The paper concludes by discussing the study’s implications for technology adoption and managerial decision delegation by highlighting the relevance of emotional factors in managers’ adoption of AI and the additional value of neuroscientific data.

Sentir et percevoir. Du coma à l’éveil de coma

The evaluation of comatose patients in coma or of those with a consciousness disorder (unresponsive wakefulness syndrome, minimally conscious state) is an important issue in their management. Neuroscientific data show that multiple brain functions (such as sensation, perception, motor imaging, attention) are preserved in some of these patients. Several studies have also shown that the use of personalized stimulation, i.e. that refers to specific autobiographical content, improves the chances to observe behavioral, vegetative, cerebral or cognitive responses in patients in a coma or with a consciousness disorder. Among other significant stimuli, music is a very reliable tool, probably because of its links with multiple brain functions (motor, arousal, emotional, autobiographical, etc.). These results suggest that despite a dysfunction in or lack of consciousness, the memory traces associated with these patients’ life stories, experiences, and preferences can still be mobilized.

Neuromarketing, subliminal advertising, and hotel selection: An EEG study

This study aims to understand how hotel videos embedded with a smiling face emoji as a subliminal message affect consumers’ selection of hotels, with their brain activities measured and collected while they watched the videos. Data was collected from sixteen participants who completed two rounds of experiments. A chi-square test of homogeneity, paired sample t-test, and Bayes factor were performed to address the two proposed research questions. The results of this study reveal that participants’ selection of hotels would be significantly affected by the subliminal stimuli of a smiling face emoji. Meanwhile, neuroscientific data identifies significant differences between participants’ two (theta and beta) out of five bands of brainwaves while they were viewing hotel videos with and without the subliminal message. Suggestions for future studies and practical operations are also discussed.

Greater lifestyle engagement is associated with better age-adjusted cognitive abilities.

Previous evidence suggests that modifiable lifestyle factors, such as engagement in leisure activities, might slow the age-related decline of cognitive functions. Less is known, however, about which aspects of lifestyle might be particularly beneficial to healthy cognitive ageing, and whether they are associated with distinct cognitive domains (e.g. fluid and crystallized abilities) differentially. We investigated these questions in the cross-sectional Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data (N = 708, age 18–88), using data-driven exploratory structural equation modelling, confirmatory factor analyses, and age-residualized measures of cognitive differences across the lifespan. Specifically, we assessed the relative associations of the following five lifestyle factors on age-related differences of fluid and crystallized age-adjusted abilities: education/SES, physical health, mental health, social engagement, and intellectual engagement. We found that higher education, better physical and mental health, more social engagement and a greater degree of intellectual engagement were each individually correlated with better fluid and crystallized cognitive age-adjusted abilities. A joint path model of all lifestyle factors on crystallized and fluid abilities, which allowed a simultaneous assessment of the lifestyle domains, showed that physical health, social and intellectual engagement and education/SES explained unique, complementary variance, but mental health did not make significant contributions above and beyond the other four lifestyle factors and age. The total variance explained for fluid abilities was 14% and 16% for crystallized abilities. Our results are compatible with the hypothesis that intellectually and physically challenging as well as socially engaging activities are associated with better crystallized and fluid performance across the lifespan.