Language and emotions: emotional Sapir-Whorf hypothesis

Foreign-language effects in cross-cultural behavioral research: Evidence from the Tanzanian Hadza

Behavioral research in traditional subsistence populations is often conducted in a non-native language. Recent studies show that non-native language-use systematically influences behavior, including in widely used methodologies. However, such studies are largely conducted in rich, industrialized societies, using at least one European language. This study expands sample diversity. We presented four standard tasks-a "dictator" game, two sacrificial dilemmas, a wager task, and five Likert-risk tolerance measures-to 129 Hadza participants. We randomly varied study languages-Hadzane and Kiswahili-between participants. We report a moderate impact of study language on wager decisions, alongside a substantial effect on dilemma decisions and responses to Likert-assessments of risk. As expected, non-native languages fostered utilitarian choices in sacrificial dilemmas. Unlike previous studies, non-native-language-use decreased risk preference in wager and Likert-tasks. We consider alternative explanatory mechanisms to account for this reversal, including linguistic relativity and cultural context. Given the strength of the effects reported here, we recommend, where possible, that future cross-cultural research should be conducted in participants' first language.

The Power Is in the Word-Do Laypeople Interpret Descriptors of Dog Emotional States Correctly?

A basic understanding of dog behaviour and emotion is relevant not only for professionals, such as veterinary personnel or dog trainers, but also for dog owners and for people with little contact with dogs. Information about dog behaviour and emotions is mostly conveyed verbally. This study explores whether definitions of dog behaviour and emotion are understood in such a way that they can be allocated to a descriptor (i.e., a label such as "fearful"), even by people with low background knowledge. If people can match descriptors to definitions, this suggests that the definitions are distinct enough and elicit mental representations of behaviour that can fit the label. Good agreement on the definitions is a prerequisite for the validity of the descriptors used; however, no study to date has tested this. A sample of 236 adults was asked to match descriptors of Qualitative Behaviour Assessment (QBA) for veterinary and shelter situations to their correct definitions, e.g., the descriptor "fearful" to its definition "dog may try to flee, hide or freeze; ears back". Matching was substantially above chance; nonetheless, the mean proportion of correct responses was only 50% (SD ± 16.6%) for the veterinary QBA set and 33% (SD ± 14.3%) for the shelter QBA set. Performance in the matching task was positively correlated with measures of experience with dogs. Taken together, the results suggest that descriptor-definition pairs used to describe dog behaviour need to be clearly defined to avoid misinterpretations when teaching laypeople how to interpret canine behaviour.

Divergent Conceptualization of Embodied Emotions in the English and Chinese Languages

Traditional cognitive linguistic theories acknowledge that human emotions are embodied, yet they fail to distinguish the dimensions that reflect the direction of neural signaling between the brain and body. Differences exist across languages and cultures in whether embodied emotions are conceptualized as afferent (feelings from the body) or efferent (enacted through the body). This important distinction has been neglected in academic discourse, arguably as a consequence of the 'lexical approach', and the dominance within the affective psychology of the cognitive and semantic models that overlook the role of interoception as an essential component of affective experience. Empirical and theoretical advances in human neuroscience are driving a reappraisal of the relationships between the mind, brain and body, with particular relevance to emotions. Allostatic (predictive) control of the internal bodily states is considered fundamental to the experience of emotions enacted through interoceptive sensory feelings and through the evoked physiological and physical actions mediated through efferent neural pathways. Embodied emotion concepts encompass these categorized outcomes of bidirectional brain-body interactions yet can be differentiated further into afferent or interoceptive and efferent or autonomic processes. Between languages, a comparison of emotion words indicates the dominance of afferent or interoceptive processes in how embodied emotions are conceptualized in Chinese, while efferent or autonomic processes feature more commonly in English. Correspondingly, in linguistic expressions of emotion, Chinese-speaking people are biased toward being more receptive, reflective, and adaptive, whereas native English speakers may tend to be more reactive, proactive, and interactive. Arguably, these distinct conceptual models of emotions may shape the perceived divergent values and 'national character' of Chinese- and English-speaking cultures.

Impact of cultural-shift on multimodal sentiment analysis

Human communication is not limited to verbal speech but is infinitely more complex, involving many non-verbal cues such as facial emotions and body language. This paper aims to quantitatively show the impact of non-verbal cues, with primary focus on facial emotions, on the results of multi-modal sentiment analysis. The paper works with a dataset of Spanish video reviews. The audio is available as Spanish text and is translated to English while visual features are extracted from the videos. Multiple classification models are made to analyze the sentiments at each modal stage i.e. for the Spanish and English textual datasets as well as the datasets obtained upon coalescing the English and Spanish textual data with the corresponding visual cues. The results show that the analysis of Spanish textual features combined with the visual features outperforms its English counterpart with the highest accuracy difference, thereby indicating an inherent correlation between the Spanish visual cues and Spanish text which is lost upon translation to English text.

Limits of a Second Language: Native and Second Languages in Management Team Communication

Cultural differences in speech acts are common challenges in management involving Chinese and Western managers. Comparing four groups – Native-speaking Chinese, English-speaking Chinese, Chinese-speaking Westerners, and non-Chinese- speaking Westerners, we assessed the effects of language and ethnicity on the ability to predict communication obstacles in a management team scenario. Bilingual subjects were less likely to be influenced by ethnic biases. Still, bilinguals were not more likely to adjust their metacognitions about communication toward those of the native speakers. The study creates a link between management, cognition and linguistics, as well as having consequences for the study of metacognition in cross-cultural management.

Visual Attention and Lexical Involvement in L1 and L2 Word Processing: Emotional Stroop Effect

Given the fact that the process of engaging and interacting with the text is not only the function of a reader but also the text itself, the current study attempts to examine the role of the type of the word in the attentional engagement. More specifically, the present investigation aims to verify the interplay of sensorimotor information, emotions, and the linguistic information in the word processing. In so doing, for the scale validation, a sample of 220 Iranian English as a Foreign Language (EFL) learners from different language institutes were requested to complete the newly designed Persian and English lexical involvement scales. The results of Structural Equation Modeling (SEM) supported the factor structure and the reliability of the measures. Moreover, using Emotional Stroop task in the experimental set up, the results revealed that there is a strong positive relationship between lexical involvement and visual attentional engagement in L1(Persian). The association was conversely negative in L2 (English). Finally, the statistical analysis indicates that the lexical stimuli differ regarding their magnitude of lexical involvement in L1 and L2.

Language evolution: examining the link between cross-modality and aggression through the lens of disorders

We demonstrate how two linguistic phenomena, figurative language (implicating cross-modality) and derogatory language (implicating aggression), both demand a precise degree of (dis)inhibition in the same cortico-subcortical brain circuits, in particular cortico-striatal networks, whose connectivity has been significantly enhanced in recent evolution. We examine four cognitive disorders/conditions that exhibit abnormal patterns of (dis)inhibition in these networks: schizophrenia (SZ), autism spectrum disorder (ASD), synaesthesia and Tourette's syndrome (TS), with the goal of understanding why the two phenomena altered reactive aggression and altered cross-modality cluster together in these disorders. Our proposal is that enhanced cross-modality (necessary to support language, in particular metaphoricity) was a result, partly a side-effect, of self-domestication (SD). SD targeted the taming of reactive aggression, but reactive impulses are controlled by the same cortico-subcortical networks that are implicated in cross-modality. We further add that this biological process of SD did not act alone, but was engaged in an intense feedback loop with the cultural emergence of early forms of language/grammar, whose high degree of raw metaphoricity and verbal aggression also contributed to increased brain connectivity and cortical control. Consequently, in conjunction with linguistic expressions serving as approximations/'fossils' of the earliest stages of language, these cognitive disorders/conditions serve as confident proxies of brain changes in language evolution, helping us reconstruct certain crucial aspects of early prehistoric languages and cognition, as well as shed new light on the nature of the disorders. This article is part of the theme issue 'Reconstructing prehistoric languages'.

Insight Into the Hispanic Paradox: The Language Hypothesis

Hispanics have a lower burden of heart disease than would be predicted from their risk factors. Explanations for this phenomenon, the Hispanic paradox, focus on specific characteristics of the culture that affect stress appraisal and accumulation, including social connections. Features of culture evolve in the context of language, which influences the way emotions are appraised and expressed. The Spanish language, a unifying component defining Hispanic cultures, has unique features that may promote emotional expression, expand the emotional concepts implicated in the construction of emotion, and influence the appraisal of stress. Under chronic stress conditions, sustained responses can become maladaptive, leading to disease. Features of the Spanish language allow its speakers a wide range of emotion schemas by virtue of its emotion lexicon, the ability to easily minimize or exaggerate expressions, and ease in considering hypothetical situations with the use of the subjunctive. The hypothesis here proposes that the Spanish language is directly and indirectly (via culture) responsible for mitigating some of the effects of acute stress responses in Hispanics and, therefore, limits stress accumulation and is partly responsible for the Hispanic paradox.

Dynamic academic networking concept and its links with English language skills and research productivity-non-Anglophone context

Although the Science of Team Science or SciTS has already provided substantial evidence for research collaboration positive links to scientific productivity, much less is known about such links with broadly defined academic networking, especially with regard to the dilemma about forms of academic networking that may help individual scholars in handling risks and dynamics inherent in academic connections. This study uses cross-disciplinary theoretical insights to conceptualize “dynamic academic networking” as a distinct collaboration-related phenomenon that is theoretically linked with research productivity on the one hand, and with English language skills on the other, especially in the context of non-Anglophone academic systems. The study combines survey-based data and Scopus-based data to test two main hypothesized connections while controlling for the potential effects of other factors, e.g. home faculty research connections and faculty-industry professional connections. The research results provide support for the structural model which is also interpreted in terms of dynamic networking being valid concept in relation to further development of SciTS.

Learning interaction dynamics with an interactive LSTM for conversational sentiment analysis

Conversational sentiment analysis is an emerging, yet challenging subtask of the sentiment analysis problem. It aims to discover the affective state and sentimental change in each person in a conversation based on their opinions. There exists a wealth of interaction information that affects speaker sentiment in conversations. However, existing sentiment analysis approaches are insufficient in dealing with this subtask due to two primary reasons: the lack of benchmark conversational sentiment datasets and the inability to model interactions between individuals. To address these issues, in this paper, we first present a new conversational dataset that we created and made publicly available, named ScenarioSA, to support the development of conversational sentiment analysis models. Then, we investigate how interaction dynamics are associated with conversations and study the multidimensional nature of interactions, which is understandability, credibility and influence. Finally, we propose an interactive long short-term memory (LSTM) network for conversational sentiment analysis to model interactions between speakers in a conversation by (1) adding a confidence gate before each LSTM hidden unit to estimate the credibility of the previous speakers and (2) combining the output gate with the learned influence scores to incorporate the influences of the previous speakers. Extensive experiments are conducted on ScenarioSA and IEMOCAP, and the results show that our model outperforms a wide range of strong baselines and achieves competitive results with the state-of-art approaches.

Bittersweet: The Neuroscience of Ambivalent Affect

Ambivalent affective states, such as bittersweetness, nostalgia, and longing, which are felt as having both positive and negative aspects, are an important component of human experience but have received little attention. The most influential theoretical frameworks in affective neuroscience focus largely on univalenced states. As a result, it is not known whether ambivalent affect corresponds to a simultaneously positive and negative valenced state or whether it results from a rapid vacillation between positive and negative states. Here we hypothesize that ambivalent affect involves both mechanisms, that is, rapid vacillation and simultaneity of positive and negative affect, albeit at different neurobiological levels. Rapidly vacillating univalent emotions could give rise to an ambivalent feeling, a mechanism that depends on brainstem nuclei that facilitate rapid action programs of emotional behavior while inhibiting opposing behaviors. This reciprocal inhibition prevents organisms from simultaneously implementing responses to conflicting emotions but also allows for rapid switching between emotions triggered by counterfactual thinking and rapid reappraisal of situations. We propose that as these transitions occur and respective interoceptive information reaches the insular cortex, further processing of this "emotional moment" would allow separate emotional events to be experienced as one "mixed" and integrated feeling.

Introduction to the special issue on physics of mind

In recent years, both fields of physics and psychology have made important scientific advances. The emergence of new instruments gave rise to a data-driven neuroscience allowing us to learn about the state of the brain supporting known mental functions and conversely. In parallel, the appearance of new mathematics allowed the development of computational models describing fundamental brain functions and implementing them in technological applications. While emphasizing the methodology of physics, the special issue aims to bring together these trends in both the experimental and theoretical sciences in order to explain some of the most basic mental processes such as perception, cognition, emotion, consciousness, and learning. In this editorial, we define unsolved problems for brain and psychological sciences, discuss possible means toward their respective solutions, and outline some collaborative initiatives aiming toward these goals. The following problems are defined in gradual order of difficulty: what are the universal properties of human behavior across conditions and cultures? What have each culture learned over historical times and why should specific elements of knowledge be accumulated over cultural evolution? Can computational psychiatry help predict, understand, and cure mental disorders? What is the function of art and cultural artifacts such as music, fiction, or poetry for the cognitive system? How to explain the relation between first-person subjective experience and third-person objective physiological data? What neural mechanisms operate on which mental content at the highest levels of organization of the hierarchical brain? How do abstract ideas emerge from sensory-motor contingencies and what are the conditions for the birth of a new concept? Could symmetry play a role in psychogenesis and support the emergence of new hierarchical layers in cognition? How can we start addressing the question of meaning scientifically, and what does it entail for the physical sciences?

Physics of mind: Experimental confirmations of theoretical predictions

What is common among Newtonian mechanics, statistical physics, thermodynamics, quantum physics, the theory of relativity, astrophysics and the theory of superstrings? All these areas of physics have in common a methodology, which is discussed in the first few lines of the review. Is a physics of the mind possible? Is it possible to describe how a mind adapts in real time to changes in the physical world through a theory based on a few basic laws? From perception and elementary cognition to emotions and abstract ideas allowing high-level cognition and executive functioning, at nearly all levels of study, the mind shows variability and uncertainties. Is it possible to turn psychology and neuroscience into so-called "hard" sciences? This review discusses several established first principles for the description of mind and their mathematical formulations. A mathematical model of mind is derived from these principles. This model includes mechanisms of instincts, emotions, behavior, cognition, concepts, language, intuitions, and imagination. We clarify fundamental notions such as the opposition between the conscious and the unconscious, the knowledge instinct and aesthetic emotions, as well as humans' universal abilities for symbols and meaning. In particular, the review discusses in length evolutionary and cognitive functions of aesthetic emotions and musical emotions. Several theoretical predictions are derived from the model, some of which have been experimentally confirmed. These empirical results are summarized and we introduce new theoretical developments. Several unsolved theoretical problems are proposed, as well as new experimental challenges for future research.

Physics of the Mind

Is it possible to turn psychology into “hard science”? Physics of the mind follows the fundamental methodology of physics in all areas where physics have been developed. What is common among Newtonian mechanics, statistical physics, quantum physics, thermodynamics, theory of relativity, astrophysics… and a theory of superstrings? The common among all areas of physics is a methodology of physics discussed in the first few lines of the paper. Is physics of the mind possible? Is it possible to describe the mind based on the few first principles as physics does? The mind with its variabilities and uncertainties, the mind from perception and elementary cognition to emotions and abstract ideas, to high cognition. Is it possible to turn psychology and neuroscience into “hard” sciences? The paper discusses established first principles of the mind, their mathematical formulations, and a mathematical model of the mind derived from these first principles, mechanisms of concepts, emotions, instincts, behavior, language, cognition, intuitions, conscious and unconscious, abilities for symbols, functions of the beautiful and musical emotions in cognition and evolution. Some of the theoretical predictions have been experimentally confirmed. This research won national and international awards. In addition to summarizing existing results the paper describes new development theoretical and experimental. The paper discusses unsolved theoretical problems as well as experimental challenges for future research.

Case report: Is verbal cognitive performance in bilingual neuropsychiatric patients test-language dependent?

Bilingualism (BL) is increasing around the world. Although BL has been shown to have a broad impact-both positive and negative-on language and cognitive functioning, cognitive models and standards are mainly based on monolinguals. If we take cognitive performance of monolinguals as a standard, then the performance of bilinguals might not be accurately estimated. The assessment of cognitive functions is an important part of both the diagnostic process and further treatment in neurological and neuropsychiatric patients. In order to identify the presence or absence of cognitive deficit in bilingual patients, it will be important to determine the positive and/or negative impact of BL properties on measured cognitive performance. However, research of the impact of BL on cognitive performance in neuropsychiatric patients is limited. This article aims to compare the influence of the language (dominant-L1, second-L2) used for assessment of verbal cognitive performance in two cases of bilingual neuropsychiatric patients (English/Czech). Despite the fact that the two cases have different diagnoses, similarities in working memory and verbal learning profiles for L1 and L2 were present in both patients. We expected L1 to have higher performance in all measures when compared with L2. This assumption was partially confirmed. As expected, verbal working memory performance was better when assessed in L1. In contrast, verbal learning showed the same or better performance in L2 when compared with L1. Verbal fluency and immediate recall results were comparable in both languages. In conclusion, the language of administration partially influenced verbal performance of bilingual patients. Whether the language itself influenced low performance in a given language or it was a result of a deficit requires further research. According to our results, we suggest that an assessment in both languages needs to be a component of reasonable cognitive assessment of bilingual patients.

Brain. Conscious and unconscious mechanisms of cognition, emotions, and language

Conscious and unconscious brain mechanisms, including cognition, emotions and language are considered in this review. The fundamental mechanisms of cognition include interactions between bottom-up and top-down signals. The modeling of these interactions since the 1960s is briefly reviewed, analyzing the ubiquitous difficulty: incomputable combinatorial complexity (CC). Fundamental reasons for CC are related to the Gödel's difficulties of logic, a most fundamental mathematical result of the 20th century. Many scientists still "believed" in logic because, as the review discusses, logic is related to consciousness; non-logical processes in the brain are unconscious. CC difficulty is overcome in the brain by processes "from vague-unconscious to crisp-conscious" (representations, plans, models, concepts). These processes are modeled by dynamic logic, evolving from vague and unconscious representations toward crisp and conscious thoughts. We discuss experimental proofs and relate dynamic logic to simulators of the perceptual symbol system. "From vague to crisp" explains interactions between cognition and language. Language is mostly conscious, whereas cognition is only rarely so; this clarifies much about the mind that might seem mysterious. All of the above involve emotions of a special kind, aesthetic emotions related to knowledge and to cognitive dissonances. Cognition-language-emotional mechanisms operate throughout the hierarchy of the mind and create all higher mental abilities. The review discusses cognitive functions of the beautiful, sublime, music.

Emotions of "higher" cognition

The target article by Lindquist et al. considers discrete emotions. This commentary argues that these are but a minor part of human emotional abilities, unifying us with animals. Uniquely human emotions are aesthetic emotions related to the need for the knowledge of "high" cognition, including emotions of the beautiful, cognitive dissonances, and musical emotions. This commentary touches on their cognitive functions and origins.

Language and cognition influence on evolution of cultures

Evolution of cultures is influenced by languages. To understand this influence the paper analyzes how language and cognition interact in thinking. Is language just used for communication of completed thoughts, or is it fundamental for thinking? We review a hypothesis that language and cognition are two separate but closely interacting mechanisms, and identify each of them. Language accumulates cultural wisdom; cognition develops mental representations modeling surrounding world and adapts cultural knowledge to concrete circumstances of life. Language is acquired from surrounding language ‘ready-made’ and therefore can be acquired early in life. Cognition can not be acquired directly from experience; language is a necessary intermediary, a “teacher.” This model is consistent with recent neuroimaging data about cognition, remaining unnoticed by other theories. The proposed theory explains a number of properties of language and cognition, which previously seemed mysterious. It suggests mechanisms by which language grammars influence emotionality of languages and directs cultural evolution. This theory may explain specifics of English and Arabic cultures. We review theoretical and experimental evidence and discuss future directions

Language and cognition interaction neural mechanisms

How language and cognition interact in thinking? Is language just used for communication of completed thoughts, or is it fundamental for thinking? Existing approaches have not led to a computational theory. We develop a hypothesis that language and cognition are two separate but closely interacting mechanisms. Language accumulates cultural wisdom; cognition develops mental representations modeling surrounding world and adapts cultural knowledge to concrete circumstances of life. Language is acquired from surrounding language “ready-made” and therefore can be acquired early in life. This early acquisition of language in childhood encompasses the entire hierarchy from sounds to words, to phrases, and to highest concepts existing in culture. Cognition is developed from experience. Yet cognition cannot be acquired from experience alone; language is a necessary intermediary, a “teacher.” A mathematical model is developed; it overcomes previous difficulties and leads to a computational theory. This model is consistent with Arbib's “language prewired brain” built on top of mirror neuron system. It models recent neuroimaging data about cognition, remaining unnoticed by other theories. A number of properties of language and cognition are explained, which previously seemed mysterious, including influence of language grammar on cultural evolution, which may explain specifics of English and Arabic cultures.

Neurally and mathematically motivated architecture for language and thought

Neural structures of interaction between thinking and language are unknown. This paper suggests a possible architecture motivated by neural and mathematical considerations. A mathematical requirement of computability imposes significant constraints on possible architectures consistent with brain neural structure and with a wealth of psychological knowledge. How language interacts with cognition. Do we think with words, or is thinking independent from language with words being just labels for decisions? Why is language learned by the age of 5 or 7, but acquisition of knowledge represented by learning to use this language knowledge takes a lifetime? This paper discusses hierarchical aspects of language and thought and argues that high level abstract thinking is impossible without language. We discuss a mathematical technique that can model the joint language-thought architecture, while overcoming previously encountered difficulties of computability. This architecture explains a contradiction between human ability for rational thoughtful decisions and irrationality of human thinking revealed by Tversky and Kahneman; a crucial role in this contradiction might be played by language. The proposed model resolves long-standing issues: how the brain learns correct words-object associations; why animals do not talk and think like people. We propose the role played by language emotionality in its interaction with thought. We relate the mathematical model to Humboldt's "firmness" of languages; and discuss possible influence of language grammar on its emotionality. Psychological and brain imaging experiments related to the proposed model are discussed. Future theoretical and experimental research is outlined.

Emotion in the Pursuit of Understanding

Theories of cognitive processes, such as decision making and creative problem solving, for a long time neglected the contributions of emotion or affect in favor of analysis based on use of deliberative rules to optimize performance. Since the 1990s, emotion has increasingly been incorporated into theories of these cognitive processes. Some theorists have in fact posited a “dual-systems approach” to understanding decision making and high-level cognition. One system is fast, emotional, and intuitive, while the other is slow, rational, and deliberative. However, one’s understanding of the relevant brain regions indicate that emotional and rational processes are deeply intertwined, with each exerting major influences on the functioning of the other. Also presented in this paper are neural network modeling principles that may capture the interrelationships of emotion and cognition. The authors also review evidence that humans, and possibly other mammals, possess a “knowledge instinct,” which acts as a drive to make sense of the environment. This drive typically incorporates a strong affective component in the form of aesthetic fulfillment or dissatisfaction.

Uncovering the Hidden Issues in E-Government Adoption in a Least Developed Country

Bangladesh, in common with many least developed countries, has been struggling to find a workable strategy to adopt information and communication technology (ICT) and e-government in its public sector organizations. There has been no satisfactory progress in this area despite a number of high-level initiatives. As a result, the country is failing to keep pace in e-government advances compared with other developing countries. This paper reports a study that involved focus groups and interviews with key stakeholders in Bangladesh. A process model is developed to show the interrelationships amongst the major barriers in the adoption of ICT in Bangladesh public sector. A lack of knowledge and entrenched attitudes and mindsets are seen as the key underlying contributors to the lack of progress. The paper introduces important directions for the formulation of long-term strategies for the successful adoption of ICT in the Bangladesh public sector and provides a basis for further theoretical development.

Neural mechanisms of the mind, Aristotle, Zadeh, and fMRI

Processes in the mind: perception, cognition, concepts, instincts, emotions, and higher cognitive abilities for abstract thinking, beautiful music are considered here within a neural modeling fields (NMFs) paradigm. Its fundamental mathematical mechanism is a process "from vague-fuzzy to crisp," called dynamic logic (DL). This paper discusses why this paradigm is necessary mathematically, and relates it to a psychological description of the mind. Surprisingly, the process from "vague to crisp" corresponds to Aristotelian understanding of mental functioning. Recent functional magnetic resonance imaging (fMRI) measurements confirmed this process in neural mechanisms of perception.

Cognitively Inspired Neural Network for Recognition of Situations

The authors present a cognitively inspired mathematical learning framework called Neural Modeling Fields (NMF). They apply it to learning and recognition of situations composed of objects. NMF successfully overcomes the combinatorial complexity of associating subsets of objects with situations and demonstrates fast and reliable convergence. The implications of the current results for building multi-layered intelligent systems are also discussed.

Musical emotions: functions, origins, evolution

Theories of music origins and the role of musical emotions in the mind are reviewed. Most existing theories contradict each other, and cannot explain mechanisms or roles of musical emotions in workings of the mind, nor evolutionary reasons for music origins. Music seems to be an enigma. Nevertheless, a synthesis of cognitive science and mathematical models of the mind has been proposed describing a fundamental role of music in the functioning and evolution of the mind, consciousness, and cultures. The review considers ancient theories of music as well as contemporary theories advanced by leading authors in this field. It addresses one hypothesis that promises to unify the field and proposes a theory of musical origin based on a fundamental role of music in cognition and evolution of consciousness and culture. We consider a split in the vocalizations of proto-humans into two types: one less emotional and more concretely-semantic, evolving into language, and the other preserving emotional connections along with semantic ambiguity, evolving into music. The proposed hypothesis departs from other theories in considering specific mechanisms of the mind-brain, which required the evolution of music parallel with the evolution of cultures and languages. Arguments are reviewed that the evolution of language toward becoming the semantically powerful tool of today required emancipation from emotional encumbrances. The opposite, no less powerful mechanisms required a compensatory evolution of music toward more differentiated and refined emotionality. The need for refined music in the process of cultural evolution is grounded in fundamental mechanisms of the mind. This is why today's human mind and cultures cannot exist without today's music. The reviewed hypothesis gives a basis for future analysis of why different evolutionary paths of languages were paralleled by different evolutionary paths of music. Approaches toward experimental verification of this hypothesis in psychological and neuroimaging research are reviewed.

Cross-situational learning of object-word mapping using Neural Modeling Fields

The issue of how children learn the meaning of words is fundamental to developmental psychology. The recent attempts to develop or evolve efficient communication protocols among interacting robots or virtual agents have brought that issue to a central place in more applied research fields, such as computational linguistics and neural networks, as well. An attractive approach to learning an object-word mapping is the so-called cross-situational learning. This learning scenario is based on the intuitive notion that a learner can determine the meaning of a word by finding something in common across all observed uses of that word. Here we show how the deterministic Neural Modeling Fields (NMF) categorization mechanism can be used by the learner as an efficient algorithm to infer the correct object-word mapping. To achieve that we first reduce the original on-line learning problem to a batch learning problem where the inputs to the NMF mechanism are all possible object-word associations that could be inferred from the cross-situational learning scenario. Since many of those associations are incorrect, they are considered as clutter or noise and discarded automatically by a clutter detector model included in our NMF implementation. With these two key ingredients--batch learning and clutter detection--the NMF mechanism was capable to infer perfectly the correct object-word mapping.