Human Mood Research Articles

Age, adrenal steroids, and cognitive functioning in captive chimpanzees (Pan troglodytes).

Dehydroepiandrosterone-sulfate is the most abundant circulating androgen in humans and other catarrhines. It is involved in several biological functions, such as testosterone production, glucocorticoid antagonist actions, neurogenesis and neuroplasticty. Although the role of dehydroepiandrosterone-sulfate (DHEAS) in cognition remains elusive, the DHEAS/cortisol ratio has been positively associated with a slower cognitive age-decline and improved mood in humans. Whether this relationship is found in nonhuman primates remains unknown. We measured DHEAS and cortisol levels in serum of 107 adult chimpanzees to investigate the relationship between DHEAS levels and age. A subset of 21 chimpanzees was used to test the potential associations between DHEAS, cortisol, and DHEAS/cortisol ratio in cognitive function, taking into account age, sex, and their interactions. We tested for cognitive function using the primate cognitive test battery (PCTB) and principal component analyses to categorize cognition into three components: spatial relationship tasks, tool use and social communication tasks, and auditory-visual sensory perception tasks. DHEAS levels, but not the DHEAS/cortisol ratio, declined with age in chimpanzees. Our analyses for spatial relationships tasks revealed a significant, positive correlation with the DHEAS/cortisol ratio. Tool use and social communication had a negative relationship with age. Our data show that the DHEAS/cortisol ratio, but not DHEAS individually, is a promising predictor of spatial cognition in chimpanzees.

ERIL: An Algorithm for Emotion Recognition from Indian Languages Using Machine Learning

It is critical for a computer to understand the speaker’s mood during a human–machine conversation. Until now, we’ve only used neutral phrases or utterances to train robots. A person’s mood affects their performance. Machines have a hard time deciphering human mood from voice because humans can make fourteen distinct sounds in a second. For a machine to comprehend human behavior, it must first comprehend the human ear’s acoustic skills. Linear Prediction Coefficients (LPC) and Mel Frequency Cepstral Coefficients (MFCC) can simulate the human auditory system. Emotion Recognition from Indian Languages (ERIL) extracts emotions like fear, anger, surprise, sadness, happiness, and neutral. ERIL first pre-processes the voice signal, extracts selective MFCC, LPC, pitch, and voice quality features, then classifies the speech using Catboost. We tested ERIL on different benchmark classifiers to choose Catboost. ERIL is a multilingual emotion classifier, it is independent of any language. We checked it on Hindi, Gujarati, Marathi, Punjabi, Bangla, Tamil, Oriya, Kannada, Assamese, and Telugu. We recorded a speech dataset of various emotions in these languages. The accuracy of distinct emotions is 95.05 percent on average. The languages have a combined average of 95.05082 percent.

Sex-specific behavioral, neurobiological, and cardiovascular responses to chronic social stress in mice.

Psychosocial stress promotes and links mood and cardiovascular disorders in a sex-specific manner. However, findings in animal models are equivocal, in some cases opposing human dimorphisms. We examined central nervous system (CNS), behavioral, endocrine, cardiac, and hepatic outcomes in male or female C57Bl/6 mice subjected to chronic social stress (56 days of social isolation, with intermittent social confrontation encounters twice daily throughout the final 20 days). Females exhibited distinct physiological and behavioral changes, including relative weight loss, and increases in coronary resistance, hepatic inflammation, and thigmotaxic behavior in the open field. Males evidence reductions in coronary resistance and cardiac ischemic tolerance, with increased circulating and hippocampal monoamine levels and emerging anhedonia. Shared CNS gene responses include reduced hippocampal Maoa and increased Htr1b expression, while unique responses include repression of hypothalamic Ntrk1 and upregulation of cortical Nrf2 and Htr1b in females; and repression of hippocampal Drd1 and hypothalamic Gabra1 and Oprm in males. Declining cardiac stress resistance in males was associated with repression of cardiac leptin levels and metabolic, mitochondrial biogenesis, and anti-inflammatory gene expression. These integrated data reveal distinct biological responses to social stress in males and females, and collectively evidence greater biological disruption or allostatic load in females (consistent with propensities to stress-related mood and cardiovascular disorders in humans). Distinct stress biology, and molecular to organ responses, emphasize the importance of sex-specific mechanisms and potential approaches to stress-dependent disease.

ACh signaling modulates activity of the GABAergic signaling network in the basolateral amygdala and behavior in stress-relevant paradigms.

The balance between excitatory and inhibitory (E/I) signaling is important for maintaining homeostatic function in the brain. Indeed, dysregulation of inhibitory GABA interneurons in the amygdala has been implicated in human mood disorders. We hypothesized that acetylcholine (ACh) signaling in the basolateral amygdala (BLA) might alter E/I balance resulting in changes in stress-sensitive behaviors. We therefore measured ACh release as well as activity of calmodulin-dependent protein kinase II (CAMKII)-, parvalbumin (PV)-, somatostatin (SOM)- and vasoactive intestinal protein (VIP)-expressing neurons in the BLA of awake, behaving male mice. ACh levels and activity of both excitatory and inhibitory BLA neurons increased when animals were actively coping, and decreased during passive coping, in the light-dark box, tail suspension and social defeat. Changes in neuronal activity preceded behavioral state transitions, suggesting that BLA activity may drive the shift in coping strategy. In contrast to exposure to escapable stressors, prolonging ACh signaling with a cholinesterase antagonist changed the balance of activity among BLA cell types, significantly increasing activity of VIP neurons and decreasing activity of SOM cells, with little effect on CaMKII or PV neurons. Knockdown of α7 or β2-containing nAChR subtypes in PV and SOM, but not CaMKII or VIP, BLA neurons altered behavioral responses to stressors, suggesting that ACh signaling through nAChRs on GABA neuron subtypes contributes to stress-induced changes in behavior. These studies show that ACh modulates the GABAergic signaling network in the BLA, shifting the balance between SOM, PV, VIP and CaMKII neurons, which are normally activated coordinately during active coping in response to stress. Thus, prolonging ACh signaling, as occurs in response to chronic stress, may contribute to maladaptive behaviors by shifting the balance of inhibitory signaling in the BLA.

Effects of Binaural Beats on Mood and Cognition

Our brain constantly processes various acoustic stimulus, these stimuli consciously and subconsciously affecting us in everyday life. One of the most common acoustic stimuli that has been consciously used in many settings is music and its effect has been demonstrated and explained over the years with various sci entific research. In recent years, new acoustic stimuli, binaural beats started gaining a place in our lives. People started using these auditory signals to relax, sleep and even meditate. This study took a closer look at the effects of binaural beats on human mood states, emotions and cognition. Changes in the brain wave activity under theta, alpha and beta binaural beats were investigated using electroencephalogram (EEG). Experiment is conducted as fully between design where volunteers only listened to one of the three binaural beat conditions: Theta binaural beats (7 Hz), alpha binaural beats (10 Hz), and beta binaural beats (16 Hz). EEG and Mood questionnaire(PANAS-S) data is collected, and EEG data is analyzed in the frequency domain. Changes in relative power spectral density (PSD) of theta, alpha, and beta bands are calculated to draw meaningful conclusions.

Implementing a Real-time Emotion Detection System using Convolutional Neural Network

Abstract: Humans express their emotions through their facial expressions. They are crucial in interpersonal communication. Every human mood can be documented using this project's real-time emotion recognition system. In this project, various models generated using machine learning and deep learning algorithms are used. To identify human expressions in real time, application software is created that makes use of a few powerful Python packages. This project makes use of a number of libraries, including Keras, OpenCV, and Matplotlib. Training those emotions can detect various human emotions. This real-time emotion detection can be used across multiple platforms.

Eyes wide open: sleep disturbance, not melatonin, is the real issue to address for suicide risk.

Eyes wide open: sleep disturbance, not melatonin, is the real issue to address for suicide risk.

Human mood and cognitive function after different extreme cold exposure

Mood and cognitive function can change after cold exposure. Recovering from extreme cold is important, especially for people who work outdoors. This study was conducted to investigate cognitive function in the recovery period after being exposed to different temperatures of cold environments. 12 subjects underwent different cold exposures at −5 °C, −10 °C, and −15 °C in a climate chamber. Cognitive function was measured with the Neurobehavioral Core Test Battery (NCTB) and Stroop color-word test after 40 min of recovery at a room of 24 °C. The results showed that 40 min of recovery after cold exposure could not eliminate the differences between different degrees of extremely cold exposure in the mood effects (confusion-bewilderment). A decrease in ambient temperature could reduce visual perception and memory functions. Manual dexterity decreased by 13.6% in −10 °C compared with that in 24 °C due to colder ambient temperatures. At different degrees of cold exposure, selective attention decreased significantly as the ambient temperature dropped. The results from this study could be used to better organize work and rest periods according to task characteristics and duration of cold exposure.

Effects of wheel-running on anxiety and depression-relevant behaviours in the MCAO mouse model of stroke: moderation of brain-derived neurotrophic factor and serotonin receptor gene expression

Stroke continues to be a major cause of mortality globally. Post-stroke treatment is complicated by the heterogenous nature of pathology and the emergence of secondary psychological symptoms are an additional challenge to the recovery process. Poststroke depression (PSD) is a common co-morbidity and is a major impediment to recovery. While selective serotonin reuptake inhibitors (SSRIs) have proven to be clinically efficacious in treating PSD, the pathogenic processes that underlie the manifestation of depressive mood post-stroke remains unclear. Furthermore, the use of SSRIs is associated with risks of intracerebral haemorrhage, so alternative treatment options need to be continuously explored. Exercise has been demonstrated to be beneficial for improving mood in humans and preclinical models of neurological conditions. Little is known of the mood-related benefits of physical exercise post-stroke. Using the middle cerebral artery occlusion (MCAO) mouse model of cerebral ischaemia, we investigated whether behavioural deficits emerge post-MCAO and could be rescued by voluntary wheel-running. We report that MCAO induced hypo-locomotion and anhedonia-related behaviours, with some improvements conferred by wheel-running. Serotonin transporter gene expression was increased in the MCAO hippocampus and frontal cortex, but this increase remained despite wheel-running. Wheel-running associated up-regulation of BDNF gene expression was unaffected in MCAO mice, reflecting conservation of key neuroplasticity molecular pathways. Taken together, our results highlight the need for further research into serotonergic modulation of the affective symptoms of stroke.

Citizen Sensing with Soil, and the Intimate Alterity of Narrative Distance

New forms of ecological citizenship are emerging. As people wake up to an ecologically damaged world, while simultaneously experiencing the unsteady ground from which to imagine such a world, they start to take up low-cost technology to speculatively sense our surrounding ecologies. This article brings together narrative theory and environmental humanities while close-reading such a citizen sensing practice, that of Sounding Soil (2017—now). At stake in this sensing project is the elemental alterity of soil ecology that helps us to focalize a clear narrative distance between human voice and non-human mood. Going outside the analytical contours of normative environmental discourse and the ecocritical tradition, this article argues for the critical importance of narrative distance in sensing an ecology, because it subverts a logic of rendering the elemental as commensurable. How we pursue citizen sensing practices, therefore, is always premised on embodied, immersive, and discursively syncretic modes of speculative meaning-making: a sometimes uncomfortable but always critically improvisational engagement with elemental emergence. The dialectical tension between intimate sensing and narrative distance in Sounding Soil is no paradoxical story: it instead formalizes planetary narratives in which the ecology of soil materializes as a figure of alterity, not reducible to human voice.

Positive mood induction through music: The significance of listener age and musical timbre

This experimental research aimed to verify whether the variables of listener age and musical timbre play significant roles in the process of the impact of music on human mood state in the context of the positive mood induction procedure using music. The stimulus material was in the form of two timbrally different versions of the same musical piece. Two participant groups in their early and late adulthood (ages: 18–30 and 60–89) participated in the research. In each of the older and the younger groups, two further subgroups were presented with timbrally different versions of the same music. Two-way analysis of variance (ANOVA) with the intergroup scheme was used in the analysis of the research results. The analysis suggested statistically significant main effect of listener age, but no statistically significant main effect of timbre or interaction effect of listener age and timbre on mood state after listening to music. The average mood state after listening to music was more positive in the group of older adults than in the group of young adults (regardless of the timbral variant of music).

Large-scale societal dynamics are reflected in human mood and brain

The stock market is a bellwether of socio-economic changes that may directly affect individual well-being. Using large-scale UK-biobank data generated over 14 years, we applied specification curve analysis to rigorously identify significant associations between the local stock market index (FTSE100) and 479,791 UK residents’ mood, as well as their alcohol intake and blood pressure adjusting the results for a large number of potential confounders, including age, sex, linear and non-linear effects of time, research site, other stock market indexes. Furthermore, we found similar associations between FTSE100 and volumetric measures of affective brain regions in a subsample (n = 39,755; measurements performed over 5.5 years), which were particularly strong around phase transitions characterized by maximum volatility in the market. The main findings did not depend on applied effect-size estimation criteria (linear methods or mutual information criterion) and were replicated in two independent US-based studies (Parkinson’s Progression Markers Initiative; n = 424; performed over 2.5 years and MyConnectome; n = 1; 81 measurements over 1.5 years). Our results suggest that phase transitions in the society, indexed by stock market, exhibit close relationships with human mood, health and the affective brain from an individual to population level.

Speech Emotion Based Sentiment Recognition using Deep Neural Networks

The capacity to comprehend and communicate with others via language is one of the most valuable human abilities. We are well-trained in our experience reading awareness of different emotions since they play a vital part in communication. Contrary to popular belief, emotion recognition is a challenging task for computers or robots due to the subjective nature of human mood. This research proposes a framework for acknowledging the passionate sections of conversation, independent of the semantic content, via the recognition of discourse feelings. To categorize the emotional content of audio files, this article employs deep learning techniques such as convolutional neural networks (CNNs) and long short-term memories (LSTMs). In order to make sound information as helpful as possible for future use, models using Mel-frequency cepstral coefficients (MFCCs) were created. It was tested using RAVDESS and TESS datasets and found that the CNN had a 97.1% accuracy rate.

Effects of micro-high barometric pressure exposure and change in air quality on the human mood and autonomic nervous activity

Effects of micro-high barometric pressure exposure and change in air quality on the human mood and autonomic nervous activity

Drowsiness Detection using EEG signals and Machine Learning Algorithms

Drowsiness is described as a state of reduced consciousness and vigilance accompanied by a desire or want to sleep. Driver tiredness is frequently detected using wearable sensors that track vehicle movement and camera-based systems that track driver behavior. Many alternative EEG-based drowsiness detection systems are developed due to the potential of electroencephalogram (EEG) signals to observe human mood and the ease with which they may be obtained. This paper applies Deep learning architecture like Convolutional Neural networks (CNN) and algorithms for the classification of EEG data for Drowsiness Detection. The key measures of video-based approaches include the detection of physical features; nevertheless, problems such as brightness limitations and practical challenges such as driver attention limits its usefulness. The main measure of video-based methods is the degree of closure of the eyelids; however, its success is limited by constraints like as brightness restrictions and practical challenges such as driver distraction. We have extracted statistical features and trained using various classifiers like Logistic Regression, Naïve Bayes, SVM, and K Nearest Neighbours and compared the accuracy using a deep learning CNN model. Results demonstrate that CNN achieved an accuracy of 94.75% by delegating feature extraction on itself. Upon comparing existing state–of–the–art drowsiness detection systems, the testing results reveal a higher detection capability. The results show that the the suggested method can be used to develop a reliable EEG-based driving drowsiness detection system.

Context-Sensitive Personalities and Behaviors for Robots

This paper proposes Context-Sensitive Behaviors for Robots (CSBR), a method for generating diverse behaviors for robots in indoor environments based on five personality traits. This method is based on a novel model developed in this work that reacts to a synthetic genome that defines the personality of the robot. The model functions return different answers and reactions, depending on a given spoken request. The responses of the robot included spoken answers, facial animations, gestures, and actions. The novelty of this method lies in its capacity to adapt the behavior of the robot according to the context of the request. Moreover, the model is scalable since its functions not only return spoken answers but also physical responses, such as opening a gripper, saying hello with gestures, or animating a face that represents an emotion according to the context. Changes in the parameters of the synthetic genome produce different behaviors. By defining different synthetic genomes, robots can adapt to different people's moods. In this work, we introduce two scenarios for human–robot interaction in two domestic environments (house and office) through spoken requests from a human user. We implemented our method in Care-O-Bot 4 and defined three synthetic genomes to produce three behaviors: friendly, detached, and hostile. In the considered scenarios, we asked the robot the same set of requests for every synthetic genome. Not only did Care-O-Bot 4 answer according to its personality, but it also proved that our method produces different behaviors. For these scenarios, we assume that the given request includes its connotation. Since our method has characteristics influenced by context, we show that the robot's behavior changed according to the human mood and the environment.

The uniqueness of the art of music and its role in human life

Music often reflects the human mood in itself Against this background, for example, the one who drives the steering wheel is the best music, it is electronic music, and the women of recommend that scientists listen to classical music, after all, exactly such music serves to educate the future musical tastes of the child who will come into the world. They also say that music has a greater impact on the health and organism of creative individuals, often they are either inspired by music, or the music makes them bored on the contrary. Music as a background is often not felt to us. In shopping centers, restaurants, clothing stores, it is really very profitable, because it creates its own atmosphere. Often, depending on the type of music, we can determine the prestige of the institution. In this article, thoughts and comments are made about the peculiarities of the art of music and its importance in human life.

Acute cognitive performance and mood effects of coffee berry and apple extracts: A randomised, double blind, placebo controlled crossover study in healthy humans

ABSTRACT Background: Polyphenols from coffee berry (chlorogenic acid) and apple (flavanol) have been shown to improve mood and increase cerebral blood flow in healthy humans. These effects may underpin the cognitive effects of polyphenols seen previously. Objective: The aim of the present paper was to extend previous research by investigating the effects of coffee berry at high and low doses when combined with apple extract on cognitive performance and mood. Design: This randomised, double-blind, placebo controlled, crossover trial included 46 healthy males and females,18–49 years of age (mean age 23 years),consuming: 1100 mg coffee berry extract, 1100 mg coffee berry extract plus 275 mg apple extract, 100 mg coffee berry extract plus 275 mg apple extract or placebo on 4 separate occasions, completing cognitive and mood assessments pre-dose and then again at 1-, 3- and 6 hrs post-dose. Results: Analysis revealed a consistent pattern of alerting effects following 1100 mg coffee berry extract. Limited effects on cognitive function were observed. Specifically, faster peg and ball performance (executive function) was observed following 1100 mg coffee berry plus apple extract and accuracy on the Rapid Visual Information Processing (RVIP) task increased on the third of four repetitions following 1100 mg coffee berry alone. Interestingly, more false alarms on RVIP were observed following the same intervention. Conclusions: In line with previous findings, 1100 mg coffee berry engendered increased arousal. The absence of effects on mood when an apple extract was added, and the potential for the low dose of caffeine within the coffee berry to act synergistically with polyphenols, raise interesting future avenues of research. Abbreviations: Cognitive demand battery (CDB), Profile Of Mood States (POMS), Visual Analogue Scale (VAS), Rapid Visual Information Processing (RVIP)

Not All Moods Are Created Equal! Exploring Human Emotional States in Social Media

Emotional states of individuals, also known as moods, are central to the expression of thoughts, ideas and opinions, and in turn impact attitudes and behavior. As social media tools are increasingly used by individuals to broadcast their day-to-day happenings, or to report on an external event of interest, understanding the rich ‘landscape’ of moods will help us better interpret and make sense of the behavior of millions of individuals. Motivated by literature in psychology, we study a popular representation of human mood landscape, known as the ‘circumplex model’ that characterizes affective experience through two dimensions: valence and activation. We identify more than 200 moods frequent on Twitter, through mechanical turk studies and psychology literature sources, and report on four aspects of mood expression: the relationship between (1) moods and usage levels, including linguistic diversity of shared content (2) moods and the social ties individuals form, (3) moods and amount of network activity of individuals, and (4) moods and participatory patterns of individuals such as link sharing and conversational engagement. Our results provide at-scale naturalistic assessments and extensions of existing conceptualizations of human mood in social media contexts.

Light affects behavioral despair involving the clock gene Period 1.

Light at night has strong effects on physiology and behavior of mammals. It affects mood in humans, which is exploited as light therapy, and has been shown to reset the circadian clock in the suprachiasmatic nuclei (SCN). This resetting is paramount to align physiological and biochemical timing to the environmental light-dark cycle. Here we provide evidence that light at zeitgeber time (ZT) 22 affects mood-related behaviors also in mice by activating the clock gene Period1 (Per1) in the lateral habenula (LHb), a brain region known to modulate mood-related behaviors. We show that complete deletion of Per1 in mice led to depressive-like behavior and loss of the beneficial effects of light on this behavior. In contrast, specific deletion of Per1 in the region of the LHb did not affect mood-related behavior, but suppressed the beneficial effects of light. RNA sequence analysis in the mesolimbic dopaminergic system revealed profound changes of gene expression after a light pulse at ZT22. In the nucleus accumbens (NAc), sensory perception of smell and G-protein coupled receptor signaling were affected the most. Interestingly, most of these genes were not affected in Per1 knock-out animals, indicating that induction of Per1 by light serves as a filter for light-mediated gene expression in the brain. Taken together we show that light affects mood-related behavior in mice at least in part via induction of Per1 in the LHb with consequences on mood-related behavior and signaling mechanisms in the mesolimbic dopaminergic system.