Chapter 1 Fundamental Reflections on Minds and Machines
Sangeetha Menon, Saurabh Todariya, and Tilak Agerwala
We live in a complex world, and the complexity exists not just in degree but in diversity that is pluralistically rich and spectral. The diversity in gender, ethnicity, culture, economic status, cultural practices, add to what Michael Polanyi described as “tacit knowledge” (Polanyi, 1966) which emphasizes the building of knowledge as a process to include and integrate the personal unknown. The increasing challenges with adopting the absolutist or dualist methods in distributing outcomes, sharing challenges, and managing productivity urge social scientists, philosophers, psychologists, technologists, and science leaders to see beyond the polemics of interdisciplinary and multi-disciplinary perspectives. Can we address the complexity in natural sciences and social sciences with the same conceptual frameworks? Can there be combined human-natural-machine systems that can move towards resilient adaptation? A common argument is that “incommensurability and unification constrain the interdisciplinary dialogue, whereas pluralism drawing on core social scientific concepts would better facilitate integrated sustainability research” (Olsson et al., 2015). Perhaps the major challenge in panarchy is to address the diverse trajectories that will be taken by different natural and societal systems, and the possibility of their unification leading to a homogeneity that does not capture varied resilience narratives. It has come to a scenario today where we have to dialogue on social practices and leadership strategies that encourage and empower diversity by adopting the
S. Menon (B)
NIAS Consciousness Studies Programme, National Institute of Advanced Studies, Bangalore, India
e-mail: sangeetha.menon@nias.res.in
S. Todariya
Human Sciences Research Group, International Institute of Information Technology, Hyderabad, India
T. Agerwala
Seidenberg School of Computer and Information Systems, Pace University, New York, USA
National Institute of Advanced Studies, Bangalore, India
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024
S. Menon et al. (eds.), AI, Consciousness and The New Humanism, https://doi.org/10.1007/978-981-97-0503-0_1
transdisciplinary approach of including the dissimilar and making multiple actors part of the process towards better reach of scientific and technological knowledge. Such an approach and attempt is expected to raise further concerns for addressing humanism and its changing phases.
The burgeoning possibilities in AI, stem cell research, and cognitive sciences encourage a vision that grapples with the unseen in terms of multiple questions on ethics, equal justice, and preservation of self-identity. Artificial narrow intelligence (ANI), based on statistical learning, is the AI in our world today. ANI can perform a narrow set of tasks (such as playing chess and checking the weather) and is used extensively in natural language processing, image, speech recognition, and decisionmaking, but is nowhere close to having human-like intelligence. ANI systems are evolving very rapidly, as evidenced by ChatGP, a powerful conversational agent that uses a state-of-the-art language-generation model developed by OpenAI to generate human-like text, have natural and engaging conversations with users, and generate seemingly new, realistic content that is coherent and follows grammatical and structural rules, but its text can be incorrect. Based on statistical learning, ChatGPT does not understand the world, does not think, is incapable of logical reasoning, and, like other ANI systems, raises ethical issues of bias, fairness, safety, and security.
The four discourses that are pertinent to the “science and society” rubric today are: sustainability, artificial intelligence, climate change, and indigenous medicine. The focus on these four discourses and practices is pertinent for the Indian society and science establishments and is representative of the massive changes affecting the economic, ecological, social, psychological, and evolutionary systems in an unprecedented manner. One of the major challenges is to bring in different actors into the field of reflective engagement and take a participatory approach with the phenomenology of examining their and others experiences towards understanding sustainability and creating sustainable development goals.
The Internet, smart gadgets, and smarter algorithms are fast changing the needs and comforts the humans seek in order to exist and thrive in the new digital world that could facilitate equal access to information and services. Beneath the smoothbed of the pleasures offered by AI are the multiple questions of identity, ethics, and consciousness which are important towards responding to the sustainability questions of well-being and species sustenance. One of the central questions in AI is of “collective intelligence” and “generative AI”, and how knowledge is constructed, evolved, developed, and used in the new world that is dependent on AI-based utilities and applications. It is important to consider how AI intervenes with social sciences, natural sciences, technologies, security and surveillance, law and ethics, philosophy and psychology, so as to present possibilities for greater access and wider distribution across the populations. Equally important is to consider the anthropomorphising of AI and what are the tacit ways in which we have enlivened machines and algorithms with human life, emotions, and aspirations. Can a machine think, believe, aspire, and be purposeful as a human? What is the place in the machine world, for hope, meaning, and transformative enlightenment that inspires human existence? How are the minds of machines different from that of humans?
In recent years, scholars, scientists, technology pioneers, and philosophers have raised concern on the possible threat of artificial intelligence superseding human species. Such speculation provides the context and the need for us to step back and ask a few fundamental questions: what is intelligence? How is intelligence understood in various disciplines like computer sciences, philosophy, psychology, and the arts? What does it mean for an entity to be an agent and perform an act? What is the self? What is consciousness? Is it possible for a machine to think and act the way humans do? What is the place of aesthetic, creative, and profound experiences in deciding and influencing “intelligence”, behaviour, and value systems? What is “intelligence”? What is “experience”? Is intelligence the power to compute, to be logical, to make rational decisions, to perform, to be successful, and to learn from “experiences”? Experience might sound an anathema in the discussion on “intelligence” since it brings the classical debates on subject versus data, subjectivity versus objectivity, and also functional success versus ethics. Can a machine have experience? Is human intelligence possible without the richness presented by the frailties and intensity of personal experiences? Can one have self-consciousness without having an experience or being an “experiencer” and the agent of action?
In the context of advancing AI developments in science, technologies, and generation of big data, it is pertinent that human existence, ethics, aspirations, and transformative values are placed within the context of collective well-being and co-existence. Such a rich context is possible only if there is a presentation of multi-disciplinary engagements along with the questions on the present and future of AI and the insights that will ensue through fundamental reflections. The set of 19 chapters in this book contextualizes perspectives from the fields of computer science, information theory, neuroscience and brain imaging, social sciences, health sciences, psychiatry, and philosophy to engage with the frontier questions concerning artificial intelligence and human experience, with implications for cultural and social lives. The volume will also highlight the place of a new humanism while we attempt to respond to questions on the final frontiers of human existence and machine intelligence.
The book presents 19 chapters that provide diverse perspectives and fundamental reflections on minds and machines, in the context of the recent developments in AI. Georg Northoff focuses on how a machine can augment humans rather than do what they do and extend this beyond AGI-style tasks to enhancing peculiarly personal human capacities, such as well-being and morality. He discusses these capacities with the help of notions such as “environment-agent nexus” and adaptive architectures from the brain sciences. Northoff targets the functionality of the environment-agent nexus, specifically its potential augmentation by a machine, namely by IAA. He argues that for an artificial agent to assist in the regulation of such a delicate interplay, great sensitivity and adaptivity will be required and propose the modelling of such environment-agent nexus on the basis of the lessons learned from the brain, and to this end IAA requires artificial agents to endow greater environmental attunement than current AI systems.
Sujas Bhardwaj, Kaustuv Kanti Ganguli, and Shantala Hegde discuss music perception, cognition, and production research by examining neural correlates of
musical components to a better understanding of the interplay of multiple neural pathways that are both unique and shared among other higher neurocognitive processes. Sujas et al. believe that artificial intelligence (AI) and machine learning (ML) models that are data-driven approaches can investigate whether our current understanding of the neural substrates of musical behaviour can be translated to teach machines to perceive, decode, and produce music akin to humans and how AI algorithms can extract features from human-music interaction. The intent of the authors is to train ML models on such features to help in information retrieval to look at the brain’s natural music processing, recognizing the patterns concealed within it, deciphering its deeper meaning, and, most significantly, mimicking human musical engagements.
Nithin Nagaraj investigates “whether machines can think causally” and debates on whether AI systems and algorithms such as deep learning (DL), machine learning (ML), and artificial neural networks (ANN) though are efficient in finding patterns in data by means of heavy computation and sophisticated information processing via probabilistic and statistical inference and have an inherent ability for true causal reasoning and judgement. Two other questions that are discussed in this chapter are: what are the specific factors that make causal thinking so difficult for machines to learn, and is it possible to design an imitation game for causal intelligence machines (a causal Turing Test)?
Tilak Agerwala presents a case for ethics and multi-disciplinarity in the context of AI. He argues that autonomous and intelligent systems and services that use narrow artificial intelligence technologies are far from having human-like intelligence, though at the same time AISSN systems can have unanticipated and harmful impacts. This chapter highlights the ethical challenges of AISSNs using three diverse and pervasive examples: Internet of Things, conversational AI, and semi-autonomous vehicles. The author contends that AISSNs will be the norm for the foreseeable future and that artificial general intelligence will not develop anytime soon, and depending on the problem domain, multi-disciplinary teams of computer scientists and engineers, sociologists, economists, ethicists, linguists, and cultural anthropologists will be required to implement humanistic design processes.
Swami Bodhananda extends the implications for ethics in machines with the help of insights from key concepts in Yoga and Vedanta and examines whether a machine can ever follow ethical principles and make decisions that are still favourable to the human species. According to the author Advaita-yoga drishti envisages such a prospect for humans and suggests that the yogi is identified with the entire panpsychic realm and the universal self, which is nothing but a holistic, cosmic integrated information network. A conjecture that is proposed in this chapter is that a “moral machine” need not have consciousness to function morally, for the well-being of living beings, and need not be conscious of, or subjectively feel (qualia) such actions. The author argues that similar to human beings, “moral machines” can be fallible, and it will be a matter of learning by trial and error over time and receiving feedback in a continuous manner, that better efficiency is achieved. He also proposes that Advaita prepares humans for the evolutionary possibility of human obsolescence and the ever-present consciousness, and the willingness to self-sacrifice, if need be,
for higher knowledge and greater possibilities of manifestations is the ultimate value that Advaita teaches.
Robert Geraci, in his chapter, examines singularity beyond the Silicon Valley and proposes the transmission of AI values in a global context, where global communities can and should consider how to reformulate transcendent dreams of artificial intelligence (AI) that arose in the USA. He believes that the goals of AI superintelligence and evolution from human to posthuman machines seek hegemonic dominion over our perception of AI. Geraci suggests that Indian culture has its own resources for contemplating cosmic change. He adds in his chapter that whether and how we receive Silicon Valley’s enthusiastic desire for technological transcendence impacts our deployment of AI; to maximize global equity, we must build our machines— and our understanding of them—with attention to values and ethics from outside Silicon Valley. The circulation of Apocalyptic AI into India creates new possibilities for future technological deployment, and it is to be watched whether Indians will introduce local values, such as svaraj, dharma, or ahimsa, or new philosophies, such as Advaitan approaches to human cognition.
Avik Sarkar, Poorva Singh, and Mayuri Varkey in their chapter examine healthcare artificial intelligence in India and ethical aspects and believe that several nations globally, including India, lack trained healthcare professionals to take good care of the population, and emerging technologies like artificial intelligence (AI) can help provide healthcare services to the predominantly underserved population. They further explore the use of AI in addressing public health and pandemics. Avik et al. examine the current situation, AI applications globally, followed by those in India while analysing the scenarios. They suggest that using innovative AI tools helps enhance healthcare professionals’ productivity by relieving them of mundane, repetitive administrative-oriented activities and provides an overview of the various areas where AI is used to help healthcare professionals and, thus, help patients, and concludes with a discussion on the factors in the adoption of AI in India along with suggestions for increasing the adoption in the healthcare sector.
The chapter by Parag Kulkarni and LM Patnaik examines human learning and machine learning from the perspective of creativity and formulates creative learning models to build abilities in machines to deliver ingenious solutions. This chapter tries to unfold different facets of human learning and machine learning with this unexplored element of surprising creativity. It further tries to formulate creative learning models to build abilities in machines to deliver ingenious solutions. According to Kulkarni et al., creative intelligence is about combination and transformation and carries an element of surprise and differentiation, even in high entropy and uncertain states. The chapter further discusses questions such as: is it possible to learn this ability to surprise or if creativity is just an outcome of an accident or an offshoot of routine work, and if then how can machines learn to produce these surprises? Madhurima Das focuses on “learning agility” in the context of the AI landscape and its inherent impact on organizational design, functions, processes, and behaviour. According to Das, it is imperative to adopt a mindset that is open, aware, inquisitive, reflective, empathetic, innovative, resilient, and risk taking towards developing the ability and willingness to learn from earlier experiences and apply that learning to
perform better in newer situations. This chapter explores the impact of AI (automation and digitization) on core organizational components, the need for an agile ecosystem to respond to the digital transformations at the organizational level, and the role of the individual as they move from self-awareness to self-immersion.
Urvakhsh Meherwan Mehta, Kiran Bagali, and Sriharshasai Kommanapalli in their chapter carefully examine the promises, pitfalls, and solutions of implementing machine learning in mental health. This study focuses on the rapidly growing applications of machine learning techniques to model and predict human behaviour in a clinical setting, where mental disorders continue to remain an enigma and most discoveries, therapeutic or neurobiological, stem from serendipity. The authors critically review the applied aspects of artificial intelligence and machine learning in decoding important clinical outcomes in psychiatry. They propose and examine predicting the onset of psychotic disorders to classifying mental disorders and long-range applications, along with the veridicality and implementation of the results in the real world. Mehta et al. finally highlight the promises, challenges, and potential solutions of implementing these operations to better model mental disorders.
Malnutrition crisis among children in India continues to be an alarming issue, and with rapidly evolving technology, access to proper nutrients for every child is a possibility. Bita Afsharinia, Naveen B. R., and Anjula Gurtoo discuss the AIbased technological interventions for tackling child malnutrition and probe the nutritional factors leading to impaired growth in children and suggests context-specific interventions. The study by Afsharinia et al. suggest that new artificial intelligence (AI) applications such as Anaemia Control Management (ACM) software will assist in anaemia management, in routine clinical practice, of a child and that the AIbased virtual assistant application involving Momby within the Anganwadi/ICDS Programme could improve access to health services and information for mothers who have struggled to access important pre-and postnatal care. The chapter presents the findings and conclusions that it is imperative to strategize and implement AI as advanced approaches to tackle malnutrition arising from nutritional deficiency and health issues.
Prakash Panneerselvam presents Autonomous Weapon System (AWS) and the debates on legal-ethical consideration and meaningful human control challenges in the military environment. According to the author in the last five years, Autonomous Weapon System (AWS) has generated intense debate globally over the potential benefit and potential problems associated with these systems, and military planners understand that AWS can perform the most difficult and complex tasks, with or without human interference, and, therefore, can significantly reduce military casualties and save costs. While several prominent public intellectuals including influential figures like Elon Musk and Apple cofounder Steve Wozniak called for banning of “offensive autonomous weapons beyond meaningful human control”, the militaries believe that the AWS can perform better without human control and follow legal and ethical rules better than soldiers. Panneerselvam looks into the emergence of AWS, its future potential, and how it will impact future war scenarios, by examining the debates over the ethical-legal use of AWS and the viewpoints of military planners.
Prasanth Balakrishnan Nair with field experience as a commanding officer and fighter pilot, in his chapter, elaborates artificial intelligence and war and attempts to understand their convergence and the resulting complexities in the military decisionmaking process. Nair believes that it is important to understand what constitutes the emergence of AI-based decision support systems and how nations can optimally exploit their inevitable emergence. According to the author, it is equally important to understand the associated risks involved and the plausible mitigating strategies, and the inherent question of ethics and morality cannot be divorced from these strategies, especially when it involves decision-making processes that can involve disproportionate and indiscriminate casualties of both man and material. The chapter attempts a holistic understanding of the crucial manned-unmanned teaming and how responsible nations can assimilate and operationalize this into their joint warfighting doctrines. The study proposes a “whole of nation approach” towards AI, since the database that decides the optimal AI-based solution requires access to metadata that would be required to have an “Uncertainty Quantification (UQ)” associated with it so that these can be weighed in by the AI DSS when making decision or providing solutions to the military decision-makers.
Sarita Tamang and Ravindra Mahilal Singh examine the theme of converging intelligence modelled on human decision-making and the decision theory in computational systems. The argument presented in this chapter is that the decision theory in AI where Scenario thinking, “the ability to predict the future”, is a key attribute of intelligent behaviour in machines as well as humans. The authors believe that based on the converging approach to intelligence in artificial systems and human reasoning, we can examine closely whether AI holds any insight for human reasoning and whether human actions can be simulated through decision-making models in AI.
Crockett surveys elements of non-human cognition to explore the ways to think across the boundary that is usually asserted between living and machinic intelligence, mainly drawing on the work of Catherine Malabou and N. Katherine Hayles. The author examines how a biological model of neuroplasticity is conjoined to a machinic conception of artificial intelligence and argues that the science of epigenetics applies to both living organisms and machines. Crockett reviews the work of Hayles and presents her demonstration on how cognition operates beyond consciousness in both organic and machinic terms. The author concludes that we need to follow Malabou and Hayles to creatively imagine new alliances, new connections, and new ways to resist corporate capitalism and above all think and enact our organic and inorganic autonomisms differently by adapting the philosophy of Hayles and Haraway to help us understand cognition and kin across multiple networks. He believes that machines, animals, plants, fungi, bacteria, electrons, quantum fields, and people: each and all of these express entanglements of profound multiplicity.
Saurabh Todariya explores in his chapter the phenomenology of embeddedness in Heidegger and contrasts it with AI. Todariya reviews the claims of AI by making the phenomenological inquiry into the nature of human existence and inquire whether the metanarrative of AI would encompass the various dimensions of human intelligence. In the process Todariya attempts to show that the claims of AI are dependent on the notion of computational intelligence which Heidegger calls as the “present-at-hand”
that refers to those skills which require explicit, procedural, and logical reasoning. The author discusses the concept of “practical knowledge” of Hubert Dreyfus or “embodied cognition” which requires the mastering of the practical skills through the kinaesthetic embodied efforts like swimming, dancing, mountain climbing, etc. The chapter discusses the argument that phenomenology does not interpret the world as the object to be calculated but as the affordances provided by our embodied capacities. Since our experience of the world is based on our embodied capacities to realize the certain kind of possibilities in the world, Todariya concludes that the practical, embedded, phenomenological agency makes human intelligence as situated, while the discourse on AI ignores claims of situated, phenomenological understanding of the intelligence. According to Todariya, the situated and contextual understanding of intelligence poses what is called as the “Frame Problem” in AI and could be properly understood and addressed through the phenomenological understanding of the body.
Ashwin Jayanti attempts an investigation of the “Ontology of AI vis-à-vis Technical Artefacts”, in his chapter. He argues that most of the philosophical discourse has focused on the analysis and clarification of the epistemological claims of intelligence within AI and on the moral implications of AI, and that philosophical critiques of the plausibility of artificial intelligence do not have much to say about the realworld repercussions of introducing AI systems. Jayanti argues that most of the moral misgivings about AI have to do with conceiving them as autonomous agents beyond the control of human actors, and in this study, he examines such assumptions by investigating into the ontology of AI systems vis-à-vis ordinary (non-AI) technical artefacts to see wherein lies the distinction between the two. He further reviews how contemporary ontologies of technical artefacts apply to AI and holds the position that clarifying the ontology of AI is crucial to understand their normative and moral significance and the implications therefrom.
The concluding chapter in this volume by Sangeetha Menon discusses the concept of self in AI and with focus on the “LaMDA”, the large language model of Open AI, and concludes that the focus has to be on the “person” that enlivens the self, than a cognitive or functional architecture. She argues that the debate on the possible presence of sentience in a large language model (LLM) chatbot such as LaMDA inspires to examine the notions and theories of self, its construction, and reconstruction in the digital space as a result of interaction. The question whether the concept of sentience can be correlated with a digital self without a place for personhood undermines the place of sapience and such/their/other high-order capabilities. Menon believes that the concepts of sentience, self, personhood, and consciousness require discrete reflections and theorisations. The theoretical concerns surrounding the discussion on LaMDA and the positioning of AI ethics are mired by the confounding of personhood with the self, and consciousness with sentience. The ability to contemplate and responsibly act using knowledge, experience, understanding, discernment, common sense, self-reflection, and insight gives one the higher-order ability of sapience. Without a person who learns, observes, reflects, and transforms, Sangeetha Menon argues that the self has no meaning except for being an abstract framework notion that can never be touched. She concludes that the metaphysical nature of consciousness cuts across theories of causality and invites a method that is practised and reflected
towards the discovery of life-changing purposes and the interconnectedness of beings and the many worlds.
This edited volume is an attempt to bring in a broader scenario to contextualize the reflections on minds and machines, in the context of the developments and interventions of AI, and present perspectives from eighteen chapters that jointly argue for considering the new humanism that might include ethical, metaphysical, clinical, and health considerations for a sustainable future of the human species and well-being that touches as many lives as possible. While the general concerns and the larger scenario in the background of which the chapters are presented in this volume are acknowledged by the editors and the authors, we wish to make a disclaimer that the specific claims or ideas mentioned in the individual chapters belong to the authors, and we believe that they can be open to further debates.
References
Olsson, L., Jerneck, A., Thoren, H., Persson, J., & O’Byrne, D. (2015). Why resilience is unappealing to social science: Theoretical and empirical investigations of the scientific use of resilience. Science Advances, 1(4). https://doi.org/10.1126/sciadv.1400217 Polanyi, M. (1966). The Tacit Dimension. London: Routledge & Kegan Paul.
An Open Dialogue Between Neuromusicology and Computational Modelling Methods
Sujas Bhardwaj, Kaustuv Kanti Ganguli, and Shantala Hegde
Abstract Music perception, cognition, and production research have progressed significantly from examining neural correlates of musical components to a better understanding of the interplay of multiple neural pathways that are both unique and shared among other higher neurocognitive processes. The interactions between the neural connections to perceive an abstract entity like music and how musicians make music are an area to be explored in greater depth. With the abstract nature of music and cultural differences, carrying out research studies using ecologically valid stimuli is becoming imperative. Artificial intelligence (AI) and machine learning (ML) models are data-driven approaches that can investigate whether our current understanding of the neural substrates of musical behaviour can be translated to teach machines to perceive, decode, and produce music akin to humans. AI algorithms can extract features from human-music interaction. Training ML models on such features can help in information retrieval to look at the brain’s natural music processing, recognizing the patterns concealed within it, deciphering its deeper meaning, and, most significantly, mimicking human musical engagements. The question remains how these models can be generalized for knowledge representation of human musical behaviour and what would be applications in a more ecologically valid manner.
S. Bhardwaj
Department of Neurology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru 560029, India
S. Bhardwaj · S. Hegde (B)
Music Cognition Lab, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru 560029, India
e-mail: shegde@nimhans.ac.in
K. K. Ganguli
New York University, Abu Dhabi, UAE
College of Interdisciplinary Studies, Zayed University, Abu Dhabi, UAE
S. Hegde
Department of Clinical Psychology, Clinical Neuropsychology and Cognitive Neuroscience Center, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru 560029, India
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024
S. Menon et al. (eds.), AI, Consciousness and The New Humanism, https://doi.org/10.1007/978-981-97-0503-0_2
Keywords Neuromusicology · Computational modelling · Music AI · Machine learning
1 Introduction
How the human brain perceives and produces music has been a very intriguing question in the field of cognitive neuroscience and brain sciences. Often treated as a special kind of language, music as a biological phenomenon has been studied from a multi-disciplinary perspective. A new branch called neuromusicology has come into existence, and the knowledge base of this discipline lies in the understanding of the neural basis of music perception, cognition, and production from a cognitive neuroscience perspective. Cognitive neuroscientists in the field of neuromusicology observe the physiological functions of the brain as they occur in real time during task-related or spontaneous brain activity. Researchers employ a variety of methods, including electroencephalography (EEG), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and many more. These bioelectric signals and neuroimaging data reveal how the brain deals with musical information in real time and which specific brain regions are engaged in music processing and are able to enjoy music (Neuhaus, 2017). In other words, the key player in neuromusicology is brain activity.
1.1 Neural Basis of Music Perception and Cognition
Music is one of the many inherent human qualities and is considered the language of the soul or the art of feeling. Despite being omnipresent in our lives, music is only found in human beings. The relationship between music and the brain is intricate, encompassing several neuronal circuits involved in sensory perception, higher neurocognitive functions, motor functions, language functions, and social cognition (Bhardwaj & Hegde, 2022; Peretz, 2006; Sihvonen et al., 2017). Existing studies have uncovered the neurocomputational basis of music processing, influencing sensory components like pitch and rhythm, alongside the analysis of intricate structures such as melody and harmony. The inferior and medial pre-frontal cortex, pre-motor cortex, and superior temporal gyri contribute to the processing of higherorder melodic patterns (Janata et al., 2002a, 2002b; Patel, 2003). Pre-frontal regions like the dorsoprefrontal cortex, the cingulate cortex, and the inferior parietal areas are where attention to the playing music is mostly distributed (Janata et al., 2002a, 2002b; Zatorre et al., 1994). The hippocampus, in addition to the medial temporal and parietal regions of the brain in charge of episodic memory, regulates familiarity connected to the musical experience (Janata, 2009; Platel et al., 2003). The network of deep limbic and paralimbic centres, amygdala, hippocampus, cingulate cortex, and orbitofrontal cortex regulate emotional engagement with music. The brain’s reward
system depends on this network as well (Blood & Zatorre, 2001; Koelsch, 2010; Salimpoor et al., 2011). The cerebellum, basal ganglia, motor, and somatosensory cortices are all involved in the perception of rhythm in music as well as physical movement in response to beats (Grahn & Rowe, 2009; Zatorre et al., 2007). These discoveries have also had an impact on AI and ML systems, allowing machines to learn and write like humans. There is a plethora of evidence (e.g., Rampinini et al., 2023), but more research is needed to give a comprehensive picture of how music understanding and creative mechanisms function in the brain. We present a fresh theoretical perspective on the brain’s unsupervised learning mechanism concerning the neurocomputational structures involved in music processing, also acknowledging the limitations imposed by both computational and neurobiological factors.
1.1.1Musicians—The Role Models in Knowing More About the Brain
The literature shows that musicians are excellent research subjects for studies on rhythm perception and music comprehension. Musicians categorize musical structures very efficiently, hence, they are role models for machines to learn from and design artificial neural networks for learning music. Researchers have established in previous studies that non-musicians are also musical because of the innate ability humans have for musical understanding and perception (Koelsch et al., 2000). Numerous control groups, both musicians and non-musicians, have been utilized by researchers to better understand how the brain interprets music and the effects of musical training. When compared to non-musicians, musicians have a stronger awareness of uncertainty (Hansen & Pearce, 2014; Paraskevopoulos et al., 2012). Musical training has an impact on how the brain processes musical syntax (Goldman et al., 2018; Przysinda et al., 2017). Numerous studies have examined western classical musical instruction, but much fewer have examined non-classical modes of music teaching. The inventiveness of the musicians and the variations in their levels of learning interest can be discovered by analysing the higher and lower-order statistical learning models. The data compression, computational speed (errors, time, necessary synapses, storage space, etc.), the capability of improvisation in music comprehension models, and the wow factor can all be determined by the implementation of these models in machines in future (Schmahmann, 2004; Schmidhuber, 2006). The ideal musical machine system will behave like a musician, be able to compose music, improvise, or perform with other musicians, or at the least, be able to talk about music like musicians do. Music is ephemeral, anepistemic, autoanaphoric, enchanting, and culturally as important as language thus cognitive science of music becomes important to decode the music’s architecture (Wiggins, 2020). The information theory approach can be useful for enabling machines to interact and learn while taking part in live musical performances, listening to pre-recorded improvisations, or both. Hierarchical structure, abstraction, and reference are the three most important cognitively valid relationships of human perception and memory afford of musicians that the human-like artificial musician has to pick up during interaction to learn from the musical representation.
1.1.2Can
Machines Learn/Produce Music?
We currently reside in the era of artificial intelligence (AI), and the most significant revolution of our time is machine learning (ML). The emergence of electronic musical instruments, coupled with robust computers, has opened avenues for exploring instruments capable of making intelligent adaptations within the musical context. This is a step in the direction of the new humanism, and we want the machines to understand how people perceive and relate to music.
The ability of a machine to mimic intelligent human behaviour is known as AI. Machine creativity has advanced significantly since its inception in the 1950s, especially in the field of music composition. The creation of music is influenced by the interdependence of ML, machine creativity, pattern recognition, and learning mechanisms. Linear regression (modelling relationships), supervised ML algorithms (decision trees), unsupervised ML algorithms, logistic regression models, random forests, dimensionality reduction algorithms, support vector machine, K-means clustering, gradient boosting, and Markov models are some popular methods used in ML.
Will computers ever be able to learn music? Can computers understand what it is about music that evokes emotion or enjoyment in listeners? Why does each person’s interpretation of music differ? How are different musical emotions distinguishable? When we give it some thought, we realize that computers only regard music as data. Data-driven modelling techniques exploit repetitive patterns from (un)structured data. Sophisticated AI can even discover long-term hidden correlations which are beyond the computational and memory capacity of an average human brain. But whether these trends bear any physical significance in conceptual comprehension remains unanswered. This raises the debate between computational and human musical interpretive judgement. Thus, adding knowledge constraints to empirical ML models can improve their ability to simulate the cognitive and neurological components of language and music processing. We can imbue machines with artificial reasoning or perspectives that may enable machines to create music appropriately. Artificial intelligence has made it feasible to develop intelligent machines that could work together on musical composition, performance, and digital sound processing to produce meaningful melodies. The semantic and syntactic facets of musical “meaning” can be revealed by reflecting on “memory” for musical attributes. It is possible to gain insights on how to comprehend the abstractions of learning, affect, consciousness, and wisdom through a controlled multi-disciplinary inquiry. ML algorithms have demonstrated efficacy across diverse domains, spanning independent music creation, linguistic analysis, optimizing search engine performance, and refining social network content. Grounded in probabilistic methodologies driven by incoming data, these algorithms autonomously generate predictions, eschewing the need for explicit instructions. Classifiable into distinct categories like supervised, unsupervised, and reinforcement learning, each variant imparts unique learning capabilities to computational systems, mirroring facets of human cognitive processes. This parallelism facilitates the creation of machine-generated models intelligible to human interpreters, providing transparency into the mechanisms of learning and prediction. Consequently, this congruence serves as a catalyst for the
development of AI models inspired by the intricacies of the human brain, envisaging a harmonious co-existence of human and computational entities within societal frameworks. For instance, the integration of statistical learning theory within the ML paradigm furnishes neuroscientific inquiry with cogent concepts, facilitating a nuanced understanding of implicit learning mechanisms intrinsic to the human brain (Perruchet & Pacton, 2006). Implicit learning, an inherent characteristic of neural processing, encompasses “unsupervised learning,” characterized by an absence of explicit instructions, learning intentions, or conscious awareness of acquired knowledge (Norris & Ortega, 2000). The purported function of the cerebral statistical learning mechanism extends to assimilating diverse acoustic insights, spanning disciplines such as musicality and linguistic comprehension. Interdisciplinary investigations propose that acoustical insights acquired through statistical learning find storage in various memory compartments, with mechanisms for data transfer operative between cortical and subcortical regions. This knowledge manifests through multi-faceted processing modes, delineated into semantic-episodic, short-long-term, and implicit-explicit (procedural-declarative) categorizations.
2 Brain and Statistics
The auditory cortex is the recipient of external auditory stimuli through an ascending neural pathway that encompasses the cochlea, brainstem, superior and olivary complex, midbrain’s inferior colliculus, and the medial geniculate body within the thalamus (Daikoku, 2021; Koelsch, 2011; Pickles, 2013). The cognitive processing of auditory information involves two essential dimensions: temporal and spatial. Temporal information stems from the discrete time intervals of neuronal spiking in the auditory nerve, whereas spatial information is derived from the tonotopic organization of the cochlea. This intricate neural orchestration contributes to the cognitive aspects of auditory perception (Moore, 2013). The auditory pathway includes significant ascending and descending projections. The auditory cortex sends more descending than ascending projections to nuclei like the dorsal nucleus of the inferior colliculus (Huffman & Henson, 1990; Zatorre & McGill, 2005).
The brain possesses computational capabilities to simulate probability distributions related to our surroundings, enabling the anticipation of future states and optimizing both perception and action to address environmental uncertainty (Pickering & Clark, 2014). Sensory predictive coding (Friston, 2010) can be utilized to evaluate prediction error or a discrepancy between sensory information and a prediction (Kiebel et al., 2008; Rao & Ballard, 1999). The ascending processing of auditory information involves key contributions from the auditory brainstem and thalamus, primary auditory cortex, auditory association cortex, pre-motor cortex, and frontal cortex (Daikoku, 2021; Friston et al., 2016; Tishby & Polani, 2011). As a result, a wide range of cognitive functions such as prediction, action, planning, and learning are incorporated into the processing of auditory data.
16S.Bhardwajetal.
The brain encodes probability distributions within sequential information (Harrison et al., 2006), through the unsupervised and implicit process of statistical learning (Cleeremans et al., 1998) allowing the brain to assess the uncertainty (Hasson, 2017). The brain selects the best course of action to accomplish a particular objective based on internal statistical models that forecast possible future situations (Monroy et al., 2017, 2019). The interaction of statistical learning can lead to the cultural creation (Feher et al., 2017) fostering musical originality (Daikoku, 2018a). Therefore, statistical learning is a crucial skill for the developing brain that supports both the production and perception of music. The probability distribution from a music corpus is closely related to the pitch prediction of innovative melodies (Pearce et al., 2010a, 2010b). Additionally, the brain efficiently interprets chord sequences through the correlation between the predictability of individual chords and the unpredictability of the global harmonic phrase (Tillmann et al., 2000). Probability and uncertainty encoding do not operate independently but instead interact with one another. Musicians are better at perceiving uncertainty in a tune than nonmusicians (Hansen & Pearce, 2014). Sustained musical training reduces uncertainty, optimizing the brain’s probabilistic music model of music for the generalization of musical structure, musical proficiency, and prediction processing effectiveness.
3 Computational Musicology
Empirical informatics research in music follows two main methodologies: datadriven, focusing on big data and statistical modelling, and knowledge-driven, emphasizing musicological principles and potential human judgments. Both methods are widely accepted within their respective domains, each with its merits and drawbacks. Combining them could be beneficial, but trade-off exists. The alignment of standard music information retrieval (MIR) approaches with human judgments is a complex question, considering the nuanced nature of ‘human judgment’ or how humans ‘think’. Cognitive musicology explores these complexities, delving into music perception, cognition, and emotions. As part of music psychology, it uses computer modelling to understand music-related knowledge representation rooted in artificial intelligence, aiming to model processes in the representation, storage, perception, performance, and generation of musical knowledge. The inquiry into how human memory represents musical attributes prompts a comparison between human and machine intelligence. While ML and AI aim to align with human perception, a significant distinction exists in the bottom-up approach of feature modelling for machines and the top-down operation of human perception (Suomala & Kauttonen, 2022). Achieving alignment with human cognition requires interdisciplinary research, extending beyond individual efforts. An informed design strategy within a specific domain can make proposing a cognitively-based computational model plausible. Simultaneously, estimating human judgement, involving subjective experiments poses inherent challenges. Solutions, such as psychoacoustic, behavioural studies, and cognitive experiments, offer insights, but generalization
remains difficult within the highly controlled framework of precise experimental methods.
In melodic analysis, a crucial concept is ‘modelling.’ When examining how a mathematical model aligns with melodic movement, it is essential to focus on human cognition-relevant melodic segments. Exploring how melodies are encoded in human memory and identifying key anchors in melodic segments are central considerations for musicians and listeners. Drawing inspiration from speech production and perception experiments, questions arise about the interdependence of these two systems. Other pertinent inquiries involve distinctions between short-term and long-term memory, the role of working memory in music training, production, and performance, and the timescale of psychoacoustic relevance in musical events (Ganguli, 2013; Ganguli & Rao, 2017; Ganguli et al., 2022).
4 Perceptual Attributes of Musical Dimensions
Rhythm, melody, harmony, timbre, dynamics, texture, and form are seven elements of music which can be useful in distinguishing different styles, eras, compositions, regions, and musical pieces from one another (Gomez & Gerken, 2000; Kuhl, 2004; Ong et al., 2017).
• Rhythm
The pattern of music in time is known as rhythm. It consists of the beat, tempo, and metre together. The term ‘rhythm’ especially refers to how musical notes are arranged (either compressed or extended) over a steady beat. The music’s pulse, or how quickly or slowly it moves along, is called the beat (the pulse one taps their foot to while listening to a song). The beat’s tempo is its speed. The most straightforward method for determining tempo is to use a metronome, an analogue or digital device that keeps track of the beats per minute (bpm). Within a song, beats are organized into discrete patterns by the metre.
• Melody
The coherent progression of tones is known as a melody. A melody is made up of discrete sounds called pitches or notes. When one hums, sings, or plays a tune, they are making a succession of pitches. A scale is a set of notes used to create music; the melodies it produces can be recognizable, predictable, or unpredictable. Major scales are typically thought to sound pleasant, while minor scales are thought to sound sad, frightened, or angry. The directions of the melodies, which can move up, down, or remain flat are represented by melodic contours.
• Harmony
It is the interaction between pitches as they are played simultaneously; harmony requires the simultaneous playing of many notes. Intervals (two notes played simultaneously and the space between them), chords (three or more notes played simultaneously), and triads are all examples of harmony (most classical and popular music
18S.Bhardwajetal.
uses triadic harmony, using three-note chords). The interactions of all the intervals inside a chord produce the musical mood. Harmony is a mixture of pitches that can be either pleasant or unpleasant depending on how they sound together. It aids in creating an atmosphere and a narrative through music.
• Timbre
It is the characteristic of sound that makes it easier to distinguish between identical melodies made using various means. Timbre can be referred to as the tone colour. Timbre results from the instrument’s material, articulation, sustained pitch, etc. The instrument’s material options include plastic, animal skin, metal, wood, vocal cords, etc. The timbre of an instrument also depends on whether it is hollow or solid, thin or thick, large or little, etc. Based on the initial sound, the softness or hardness of the articulation, and the force with which the instrument is struck to make sound, articulation or attack is determined. A factor in timbre is also the sound’s richness and intensity.
• Dynamics
It has to do with the loudness or softness of the music. Volume scale options include very loud, loud, medium loud, soft, and very soft. The gradual or a slow amplification of the volume, or occasionally a combination of both, can be used to illustrate the variability of dynamics.
• Texture
The sonic arrangement resulting from the interplay of musical voices is termed “texture.” Monophony is characterized by the presence of a single musical line playing at a given moment. This can manifest as a solo performance by a lone musician or in unison, where multiple performers execute the same musical line. Heterophony shares similarities with unison, but with the distinction that one voice may exhibit slight variations compared to the others. In instances where two or more voices are amalgamated, one vocal serves as the melody, and the other voices serve as the supporting cast. Polyphony is the simultaneous independent movement of two or more voices. Polyphony can convey a sense of conflict or harmony and thus can create textural differences.
• Form
The musical roadmap or form is the shape of the musical composition as determined by new and repeated segments. Binary (A B), Ternary (A B A), Song Form (A B A B), Modified Song Form (A A B A), Strophic (AAAAAA), Rondo (A B A C A D A), or Theme and Variation (A A’ A” A’” A’” A””) are all examples of forms. When a portion repeats but differs significantly from its initial format, variation in the theme is labelled with the prime symbol.
4.1 Gisting, Chunking, and Grouping (Transitional Probability Aspect) of the Musical Information
It reflects perceptually important melodic elements that would aid in a more accurate representation of the melody line in human memory. Learning increases the quantity and quality of synaptic connections and builds stronger, faster, and more precise neural networks in the brain. Less is sometimes better. Making and internalizing brief, condensed informational units is the first step in learning. People in organized groups employ brief musical excerpts. The music can be swiftly and successfully learned by grouping together specific musical passages. Chunking involves learning music with laser-like accuracy by focusing on minuscule portions of it.
The first study on human statistical learning capacity in lexical acquisition was done in infants (Saffran et al., 1996). They were exposed to voice sequences for four minutes that randomly concatenated three-syllable pseudo-words before they learned to distinguish them from non-words. The results indicated that neonates possess the ability to acquire words through statistical learning of nearby transitional likelihood, even in the absence of cues like pauses or intonation that typically convey word boundaries. Subsequent research has demonstrated the significance of statistical learning based on nearby transitional probability in the acquisition of sequential structures in musical elements, including pitches, timbre, and chord sequences (Daikoku et al., 2016; Kim et al., 2011; Koelsch et al., 2016; Saffran et al., 1999, 2005). The early phases of learning a native language and music have traditionally been thought to be represented by the statistical learning that takes place during lexical acquisition. Various statistical or probabilistic learning techniques may help to partially explain how higher-level structures like syntactic processing are learned. ML models such as n-gram or nth-order Markov models have been extensively employed in natural language processing and autonomous music composition (Brent, 1999; Raphael & Stoddard, 2004). It is still unclear whether the human brain uses the similar inherent computational mechanism for lexical and syntactic structure acquisition or relies on independent systems.
4.2 Syntax and Grammar of the Musical Information
The arrangement of statistically chunked words and the interaction between nonadjacent reliance and adjacent dependency in syntax are critical issues. However, in many experimental paradigms, the generation of musical expectation between neighbouring events and the construction of hierarchically ordered musical structures have frequently been misunderstood.
In music, analogous hierarchical structures are discernible. For hierarchical structures in music, several researchers have developed computational and generative models, such as the Generative Theory of Tonal Music (Lerdahl & Jackendoff, 1983) and the Generative Syntax Model (Rohrmeir, 2011). To develop advanced
statistical learning models closely mirroring those utilized in natural language and music processing, it proves beneficial to take into account factors such as categorization (Jones & Mewhort, 2007), non-adjacent (non-local) transitional probabilities (Frost & Monaghan, 2016; Pena et al., 2002), and higher-order transitional probabilities (Daikoku, 2018a).
4.3 Memory of Music
Exploring memory of music could potentially lead us towards the identification of a fundamental structure or pattern for a melodic phrase and its improvisations. Statistical learning derived from episodic experiences represents a method for partially acquiring semantic memory (Altmann, 2017). In this context, a single episodic experience is statistically abstracted to yield semantic knowledge that encapsulates the common statistical features across encountered information (Sloutsky, 2010). This implies that statistical accumulation across various episodes constitutes a portion of the statistical learning that underlies chunk formation and word acquisition. The integration of semantic memory to produce novel episodic memory by statistical learning, however, appears to be occurring concurrently in an opposite statistical learning process (Altmann, 2017).
Recent neurophysiological experiments have shown that even after brief exposure (5–10 min), the statistical learning effect is still evident in neuronal response (Daikoku et al., 2014; Francois et al., 2017). However, very few studies have looked into how long statistical knowledge can last. Due to their implicit nature in memory, statistical learning and artificial language learning have been hypothesized to have some characteristics (Guillemin & Tillmann, 2021). Implicit memory can last for up to two years, in accordance with studies on artificial grammar learning (Allen & Reber, 1980). Given the shared attributes between artificial language learning and implicit memory acquired through statistical learning, it is conceivable that such memory possesses both short-term and long-term properties (Kim et al., 2009). Memory consolidation has been shown to convert implicit memory into explicit memory (Fischer et al., 2006).
Memory consolidation, action and production, and social communication are other ways to be able to memorize the chunk of information for longer duration. Useful information needs to be stored and the less useful information should be discarded from brain to use memory efficiently. The brain’s ability to discern precise sequence statistics is related to adaptability of the motor corticostriatal circuits, while the skill to anticipate probable outcomes is related to modulation in the motivational and executive corticostriatal circuits (Daikoku, 2021). The brain exhibits adaptability in its decision-making strategies, employing distinct neural circuits in response to fluctuations in environmental statistics. Prediction errors often escalate during interpersonal interactions. However, this error can be mitigated by executing one’s own actions in response to those of others.
4.5 Mathematical Modelling and Statistical Learning
In order to reduce prediction error, the brain can compute sequential transitional probability, understand entropy distributions, and predict probable states utilizing statistical models (Daikoku, 2021). The neurobiology of predictive coding and statistical learning can be evaluated by calculating entropy from the probability distribution (Harrison et al., 2006). Uncertainty of an outcome is known as conditional entropy (Friston, 2010). Curiosity is a kind of a motivation towards novelty-seeking behaviour and to resolve uncertainty (Kagan, 1972; Schwartenbeck et al., 2013). Mutual information is a measure of dependency between two variables in the brain, which reduces the uncertainty of events (Harrison et al., 2006).
Experimental approaches such as computational modelling are useful to comprehend the learning mechanisms of brain (Daikoku, 2018b, 2019; Pearce & Wiggins, 2012; Rohrmeier & Rebuschat, 2012; Wiggins, 2020). Computational modelling represents the pertinent neural mechanism in the sensory cortices, integrating statistical variations (Daikoku, 2019; Roux & Uhlhaas, 2014; Turk-Browne et al., 2009). Simple recurrent network (SRN) is one such example of neural network. It learns the patterns of co-occurrence through error-driven learning and by giving weights to the predictions. Many neural network and deep learning approaches have been used in order to model the human multi-dimensional semantic memory, abstraction of episodic memory and language (Hochreiter & Schmidhuber, 1997; Landauer & Dumais, 1997; Lund & Burgess, 1996).
The chunking hypothesis has proven valuable for developers and researchers seeking to model the information dynamics of cognitive processes (Wiggins & Sanjekdar, 2019) as well as in the context of music (Pearce & Wiggins, 2012). In this framework, learning is rooted in the extraction, storage, and combination of information chunks. Markov decision process (MDP) is a well-known reinforcement learning model that extends the simple perspective policy by including active processes like choices and rewards (Friston et al., 2014, 2015; Pezzulo et al., 2015; Schwartenbeck et al., 2013). The Markov model can be interpreted in various ways. Using the variable order notion for accurate modelling of the statistical learning of musical sequences including distributional characteristics of the music, Information Dynamics of Music (IDyOM) is such programme. On the other hand, Information Dynamics of the Thinking (IDyOT) learns using the statistical mechanics of learning music and languages (Winkler & Czigler, 2012).
The learning impact dynamically influences the temporal aspect of the statistical learning model. When considering a substantial music corpus, the probability distribution plays a crucial role in anticipating pitch in novel melodies (Pearce et al., 2010a, 2010b). In accordance with the predictive coding hypothesis, machine learning models used in neurophysiological experiments (Daikoku, 2018b; Pearce & Wiggins, 2012; Pearce et al., 2010a, 2010b; Stufflebeam et al., 2009) consistently showed heightened neural activity for stimuli with high information content (i.e., low probability) compared to those with low information content (i.e., high probability).
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Musca vomitoria, 86.
Muscicapa grisola, II 163.
Muscicapa luctuosa, II 163.
Muscicapa ruticilla, het broeden van in onvolwassen gevederte, II 201.
Musculus acromio-basilaris, 71.
Musculus ischio-pubicus, 70.
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M u s k u s o s , horens van den , II 232.
M u s k u s r a t , beschermende gelijkenis van een op een aardkluit, II 286.
Musophagae, kleuren en nestbouw van de , II 163; beide seksen van even schitterend, II 169
M u s c h , huis , strijdlustigheid van de mannelijke , II 39; een — die leert zingen als een kneutje, II 52, 327; kleuren van de , II 187; onvolwassen gevederte van de , II 179.
M u s c h , wit gekroonde, jongen van de , II 204.
M u s s c h e n , het vinden van nieuwe gezellen door , II 101; seksen en jongen der , II 200; het leeren zingen der , II 327.
M u s s c h e n , huis- en ring , II 163
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M u t s a a p , 270
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M u z i k a l e maten, waarneming der door dieren, II 326; vermogens van den mensch, II 324 v v
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N.
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N a a l d v i s s c h e n , broedzak der mannetjes van de , 285, II 19.
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N a c h t r e i g e r , geluid van den — , II 49.
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N a g e l s , rood of purper verven der in een deel van Afrika, 332.
N a p e l s , geringer overmaat der mannelijke sekse bij onwettige dan bij wettige geboorten te , 478.
N a r w a l , tanden van den , II 227, 235.
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N a t h u s i u s , von, over de ontwikkeling van secundaire seksueele kenmerken, 453
N a t u u r k e u s , zie N a t u u r l i j k e Te e l t k e u s
N a t u u r l i j k e en seksueele teeltkeus vergeleken, 457.
N a t u u r l i j k e t e e l t k e u s , de invloed der — op de vroege voorouders van den mensch, 78; invloed der op den mensch, 93, 95; beperking van het beginsel der , 94; invloed der op sociale dieren, 96; de heer Wallace over de beperking der door den invloed der verstandelijke vermogens van den mensch, 238; invloed der op den vooruitgang in de Vereenigde Staten, 255.
N a a k t k i e u w i g e weekdieren, zie Nudibranchia.
N a u l e t t e , grootte der hoektanden in de kaak van la , 70.
N a v o l g i n g , zie N a b o o t s i n g .
N a i j v e r der zangvogels, II 50.
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Necrophorus, gesjirp van , 564, 567
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N e t e l d i e r e n , zie Coelenterata.
N e t v l e u g e l i g e I n s e k t e n , zie Neuroptera.
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Neurothemis, dimorphisme bij , 550.
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N e u s a a p , 270
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N i e r , 60.
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N i e u w e W e r e l d , Apen der , zie Platyrrhinae
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N o m m e r k a p e l , 581.
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N u n e m a y a , baarden van de inboorlingen van , II 317.
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O.
Ocelli, gemis der bij de wijfjes der Mutillen, 531.
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Oecanthus nivalis, kleurverschil bij de seksen van , 548.
O e r n i e r e n , zie Corpora Wolffiana.
Oidemia, II 221.
O l i f a n t , 277; onbehaardheid van den , 89; langzame voortplanting van den , 77; bedrog gepleegd door den vrouwelijken — , 116; veelwijvige gewoonten van den Indischen , 446; strijdlustigheid van den mannelijken , II 226; tanden van den , II 227, 228, 234, 243; wijze van vechten van den Indischen , II 242; geur van den mannelijken , II 270; witte of grijze paarden door den aangevallen, II 284; getrouwheid van den — , 187
O l i v i e r , over geluiden voortgebracht door Pimelia striata, 569.
Omaloplia brunnea, gesjirp van — , 566.
O m b e r v i s s c h e n , geluid der , II 20, zie Umbrina.
O m z i c h t i g h e i d , door dieren verkregen, 128.
O n d e r - s o o r t , 340
O n g e h u w d e s t a a t , zie C e l i b a a t
Onitis furcifer, uitsteeksels aan de dijen der voorpooten van de mannetjes, en op den kop en op het borststuk der wijfjes van , 560
O n m a t i g h e i d , geen ondeugd bij de wilden, 205; verwoestende werking der — , 250
O n r u s t e n , 487.
Onthophagus, 556.
Onthophagus rangifer, seksueele verschillen van , 557; variabiliteit der horens van , 557.
O n t m a n n i n g , invloed der op de horens van dieren, II 233.
O n t s t e k i n g der ingewanden bij Cebus Azarae, 13.
O n t w i k k e l i n g , embryonale van den mensch, 15, 17; correlatieve , II 124; de mensch alleen vatbaar voor trapsgewijze , 128.
O n v o l w a s s e n gevederte der vogels, II 175, 178
O n v r u c h t b a a r h e i d , aanleg tot van éénige dochters, 248; bij kruising een kenmerk van afzonderlijke soorten, 329 [458]
O n w e l v o e g e l i j k h e i d , afkeer van een moderne deugd, 206
O n w e t t i g e , verhouding der seksen bij en wettige kinderen, 477
O o i e v a a r , zwarte — , seksueele verschillen in de longpijpen van den — , II 57; roode snavel van den — , II 212.
O o i e v a a r s , II 211, 214; seksueel verschil in de kleur der oogen bij de , II 123.
O o g , vernieling van het , 60; verandering van stelling van het , 88; schuinheid van het oog door de Chineezen en Japaneezen als schoonheid beschouwd, II 337
O o g e n , verschil in de kleur der — bij de seksen der vogels, II 123; gesteelde bij het mannetje van Chloëon, 531.
O o g h a r e n , uittrekken der door de Indianen van Paraguay, II 340.
O o g l e d e n , zwart kleuren der in één deel van Afrika, 332.
O o g l i d , derde , zie Membrana nictitans.
O o g v l e k k e n , vormingswijze en veranderlijkheid der op het gevederte van vogels, II 126; op de vleugels van vlinders, 585, 591, II 127.
O o r , beweging van het , 21; de schelp voor den mensch van geen nut, 21; rudimentaire punten van het — bij den mensch, 22
O p g e r i c h t e gang van den mensch, 82, 83
Ophidia, seksueele verschillen van — , II 26
Ophidium, geluid van — , II 20.
O p l e t t e n d h e i d , toonen van — bij de dieren, 121.
O p o s s u m s , verspreiding der — in Amerika, 333.
O p r e c h t h e i d , niet zeldzaam tusschen leden van den zelfden stam, 284; door sommige stammen zeer hoog geschat, 209.
O p v o e d i n g , invloed der op de ongelijkheid in geestvermogens bij de seksen van den mensch, II 323.
O r a n g , oor van den foetus van den , 23.
O r a n g - o e t a n , II 318; Bischoff over de overeenkomst van de hersenen van den met die van den mensch, 13;
leeftijd waarop de volwassen is, 15; ooren van den , 21; wormvormig aanhangsel van den , 28; platte nesten van den — , 113; schrik van een op het zien van een schildpad, 120; gebruik van een stok als hefboom door een , 130; gebruik van werktuigen door een , 131; gebruik der bladeren van den Pandanus door den om zich des nachts te bedekken, 132; handen van den , 81; gemis van tepelvormige uitsteeksels bij den — , 84; richting van het haar op de armen van den , 271; de een der meest afwijkende vormen, 274; veronderstelde ontwikkeling van den , 343; stem van den , II 268; eenwijvige levenswijze van den , II 355; baard van den mannelijken , II 274
O r a n j e a p p e l e n , het behandelen der — door de apen, 81
O r b i g n y , A d’, over den invloed van droogte en vocht op de kleur der huid, 363; over de Yura-Cara’s, II 339.
Orchestia Darwinii, dimorphisme der mannetjes van , 521,
Orchestia Tucuratinga, ledematen van , 519, 525.
Oreas canna, kleuren van , II 279.
Oreas Derbianus, kleuren van , II 279, 288.
O r g a n e n , grijp , 437; gebruik der voor een ander dan het oorspronkelijke doel, II 328.
O r g a n i s c h e reeks, de definitie van von Baer van vooruitgang, of hoogere ontwikkeling in de , 287
Oriolus, soorten van , die in onvolwassen gevederte broeien, II 202
Oriolus melanocephalus, kleur der seksen bij , II 170
Ornithoptera Croesus, 488; magellanus, kleuren van , 612
Ornithorhynchus, 277; spoor van het [459]mannetje, II 228; een overgang tot de reptielen, 280
Orocetes erythrogastra, jongen van , II 206.
O r r o n y , hol van , 29.
Orsodacna atra, kleurverschil bij de seksen van , 556.
Orthoptera, 541; gedaanteverwisseling van , 469; gehoorwerktuig van sjirpende , 542; kleuren van , 548; rudimentaire sjirporganen der wijfjes van , 546; beschouwing over het sjirpen van de en Homoptera, 547; verhouding der seksen bij de — , 494.
Ortygornis gularis, strijdlustigheid van het mannetje van , II 42.
Oryctes, sjirpen van , 566; seksueele verschillen in de sjirporganen van , 568.
Oryx leucoryx, gebruik der horens van , II 237, 241.
Osphranter rufus, seksueel verschil in kleur van , 277.
O t a h e i t e , bewoners van , 259; samendrukking van den neus door de bewoners van , II 343.
Otaria jubata, manen van het mannetje van , 251.
Otaria nigrescens, verschil in kleur bij de seksen van , II 278.
Otis bengalensis, liefdevertooningen van het mannetje van , II 63.
Otis tarda, veelwijverij van , 448; keelzak bij het mannetje van , II 56
O u d e r l i j k e liefde, gedeeltelijk het gevolg van natuurlijke teeltkeus, 189; bij zeesterren, spinnen en oorwormen, 190
O u d e r s , invloed van den leeftijd der op de sekse der kinderen, 479
O v e r d r i j v i n g van natuurlijke kenmerken door den mensch, II 343
O v e r e e n k o m s t , kleine punten van tusschen den mensch en de apen, 269; seksueele — , 456
O v e r e e n k o m s t i g e verandering in het gevederte der vogels, II 70
O v e r g a n g e n , trapsgewijze — van secundaire seksueele kenmerken, II 129
O v e r m a a t , grootere — van vrouwelijke geboorten bij natuurlijke kinderen, oorzaken daarvan, 479.
O v e r p l a n t e n van mannelijke kenmerken op vrouwelijke vogels, II 184.
O v e r p l a n t i n g , gelijke van tot versiering dienende kenmerken op beide seksen bij de zoogdieren, II 286.
O v e r t a l l i g e vingers, bij den mensch, 66; meer voorkomende bij mannen dan bij vrouwen, 453; erfelijkheid van , 463; vroege ontwikkeling der , 469
Ovibos moschatus, horens van , II 232
Ovis cycloceros, wijze van vechten van — , II 235, 241
O w e n , Prof , over de Corpora Wolffiana, 17; over den grooten toon bij den mensch, 17; over de membrana nictitans en den plica semilunaris, 24; over de ontwikkeling van de achterste maaltanden bij verschillende menschenrassen, 27; over de lengte van den blinden darm bij de Koala, 28; over de wervels van het koekoeksbeen, 30; over tot het voortplantingsstelsel behoorende rudimentaire deelen, 31; over abnormale toestanden van de baarmoeder bij den mensch, 67; over het aantal vingers bij Ichthyopterygia, 66; over zeedraken, 66;
over de hoektanden bij den mensch, 69; over het loopen van den chimpanzee en den orang, 81; over de tepelvormige uitsteeksels bij de hoogere apen, 84; over de behaardheid der olifanten in hoogere streken, 89; over de staartwervels der apen, 91; klassificatie der Zoogdieren, 266; over het haar der apen, 271; over de verwantschap der Ichthyosauriërs met de visschen, 280; over monogamie en polygamie bij de antilopen, 446; over de horens bij Antilocapra Americana, 466; over den muskaatachtigen geur der krokodillen gedurende den paartijd, II 26; over [460]de riekende stof afscheidende klieren der slangen, II 27; over den dugong, II 227; over den cachelot en Ornithorhynchus, II 228; over de horens van het edelhert, II 238; over de tanden van de Camelidae, II 242; over de tanden van den mammouth, II 243; over de horens van den Ierschen reuzeneland, II 244; over de stem van de giraffe, het stekelvarken en het hert, II 266; over den keelzak van den gorilla en den orang, II 268; over de riekende stoffen afscheidende klieren der Zoogdieren, II 270, 271; over den invloed der ontmanning op de stemorganen van den mensch, II 324; over de stem van Hylobates agilis, II 326; over Amerikaansche eenwijvige apen, II 356
Oxynotus, verschil tusschen de wijfjes van twee soorten van — , II 183
P a a r d , verdwijning van het fossiele — in Zuid-Amerika, 360; veelwijverij van het , 446; hondstanden van het mannelijke , II 227; winterkleed van het , II 287.
P a a r d e n , het droomen der , 123; snelle vermeerdering der in Zuid-Amerika, 77; vermindering der hondstanden bij de , 85; — van de Falklandseilanden en de Pampas, 347; getalsverhouding der seksen van , 443, 444; lichtere kleur der in Siberië gedurende den winter, 460;
seksueele voorkeur bij , II 255; voorkeur der om te paren met andere van de zelfde kleur, II 284; getalsverhouding van mannelijke en vrouwelijke geboorte bij de , 480; de — vroeger gestreept, II 293
P a a r t i j d , seksueele kenmerken die zich bij vogels vertoonen in den — , II 77
Pachydermata, 446.
P a d d e n , II 22; behandeling der eieren door sommige mannelijke , 286; de mannetjes eerder ter voortplanting gereed dan de wijfjes, 440.
P a g e ’s, zie Theclae.
P a g e t , over abnormale ontwikkeling van haren bij den mensch, 26; over de dikte der huid aan de voetzolen der kinderen, 61.
Palaemon, over de knijpers van , 520
P a l a e o l i t h i s c h e periode, 259
Palaeornis, seksueele kleurverschillen bij , 215
Palaeornis javanicus, kleur van den snavel van , II 171
Palaeornis rosea, jongen van , II 180
P a l a e s t i n a , verhouding der seksen bij den vink in , 485; musch van — , II 200
Palamedea cornuta, vleugelsporen van — , II 45
P a l l a s , over de volmaaktheid der zinnen bij de Mongolen, 62; over het niet bestaan van verband tusschen het klimaat en de kleur der huid, 362; over de veelwijverij van Antilope saïga, 446; over de lichtere kleur van de paarden en het hoornvee in Siberië gedurende den winter, 460; over de tanden van het muskusdier, II 242, 243; over de riekende stof afscheidende klieren bij de Zoogdieren, II 271; over de riekende stof afscheidende klieren van het muskusdier, II 271;
over verandering van de kleur der zoogdieren in den winter, II 287; over het ideaal van vrouwelijke schoonheid in Noordelijk China, II 337
Palmaris accessorius, verscheidenheden van den , 53
P a m p a s , paarden uit de , 347
P a n g e n e s i s , hypothese van de , 459, 462
Panniculus carnosus, 19
P a n s c h , over de hersenen der apen, 396
P a n t e r k a t , seksueele verschillen in kleur bij de , II 278
P a p e g a a i , raketvormige vederen [461]in den staart van een — , II 70; voorbeeld van welwillendheid in een — , II 104.
P a p e g a a i e n , nabootsend vermogen der , 120; de opmerkingen van den heer Buxton omtrent , 185; verandering van kleur bij de , 94; geschreeuw der , II 58; het leven bij drietallen van , II 402; kleuren der , II 209; seksueel verschil in kleur bij — , II 215; kleuren en nestbouw der , II 163, 166, 167; onvolwassen gevederte der , II 178; muzikale vermogens der , II 328.
Papilio, seksueele kleurverschillen bij soorten van , 578; verhouding der seksen bij Noord-Amerikaansche soorten van , 488; kleur van de vleugels bij soorten van , 584
Papilio Ascanius, 578
Papilio Sesostris en Childrenae, veranderlijkheid van , 590
Papilio Turnus, 488
Papilionidae, variabiliteit der , 590
P a p o e a ’s, contrast in inborst tusschen de en de Maleiers, 331; scheidingslijn tusschen de — en de Maleiers, 333;
baarden der , II 317; haar der , II 333
Paradisea apoda, naakte plek achter in den staart van ; II 64; gevederte van , II 70; verschil tusschen de wijfjes van — en Paradisea papuana, II 183
Paradisea papuana, voor het wijfje pronkende, II 73.
Paradisea rubra, II 71.
P a r a d i j s v o g e l s , II 97, 172; volgens Lesson de veelwijvig, 448; het ratelen der met de schachten hunner vederen, II 58; raketvormige vederen bij , II 70; seksueele kleurverschillen bij de , II 71; draadachtige vederen bij , II 70, 71; vertooning van het gevederte door de mannetjes der — , II 84
P a r a g u a y , uittrekken der oogharen en wenkbrauwen door de Indianen van — , II 340.
P a r a s i e t e n , op menschen en dieren, 14; een bewijs van soortelijk verschil, 333; samenhang van de kleur van de huid met het beveiligd zijn voor , 363.
P a r e l h o e n d e r s , met één wijfje levende, 449; somtijds veelwijvig, 449; vlekken op de vederen der , II 128
Parinae, seksueel kleurverschil bij de , II 166
P a r k , M u n g o, over een negerin die haar kinderen leerde de waarheid te beminnen, 205; zijn behandeling door een negerin, 204, II 320; over het denkbeeld der negers, betreffende het voorkomen der blanken, II 339.
P a r k i e t , variatie in de kleur der dijen van een mannelijken Australischen , II 121.
P a r t h e n o g e n e s i s , bij de Tenthredinae, 493; bij Cynipidae, 493;
bij Crustacea, 495.
Parus coeruleus, II 166.
Passer, seksen en jongen van , II 200
Passer brachydactylus, II 200
Passer domesticus, II 163, 200
Passer montanus, II 163, 200
P a t a g o n i ë r s , zelfopoffering van , 196; huwelijken der , II 365
P a t r i j s , eenwijvig, 448; verhouding der seksen bij de — , 464; vrouwelijke , II 184.
P a t r i j z e n , het leven bij drietallen van , II 102; kleine vluchten mannelijke in de lente, II 102; het onderscheiden van personen door , II 105.
„P a t r i j z e n d a n s e n ” , II 63.
P a t t e r s o n , Bisschop, over de Melanesiërs, 353.
P a t t e r s o n , de heer, over de Agrionidae, 550.
P a u l i s t a ’s, in Brazilië, 338
P a u w , veelwijvig, 448; seksueele kenmerken van den , 468; strijdlustigheid van het mannetje van den — , II 43; rammelen van den met de schachten der vederen, II 58; verlengde staartvederen van den , II 69, 93; pronken van [462]den , II 82; oogvlekken van den , II 126, 130; lastigheid van den langen staart van den voor het wijfje, II 149, 158, 159; voortdurende toeneming in schoonheid van het gevederte van den , II 203
P a u w , Indische, zie Pavo cristatus
P a u w , Javaansche, zie Pavo muticus.
P a u w - l i p v i s c h , zie Labrus Pavo.
P a u w , zwartvleugel-, zie Pavo nigripennis
P a u w e n , voorkeur der vrouwelijke voor sommige mannetjes, II 115; de eerste stappen tot de paring bij de door de wijfjes gedaan, II 116
Pavo cristatus, 468, II 130
Pavo muticus, 468, II 130; sporen van het wijfje van — , II 45, 157
Pavo nigripennis, II 115.
P a y a g u a s Indianen, dunne beenen en dikke armen der — , 60.
P a y a n , de heer, over de verhouding der seksen bij schapen, 482.
P a r e l m o e r - k a p e l , Engelsche, zie Argynnis aglaia.
Pediculi, van tamme dieren en menschen, 334.
Pedionomus torquatus, seksen van , II 190.
P e e l , J., over kruising van schapen, II 232.
Pelecanus erythrorhynchus, hoornachtige kam op den snavel van den mannelijken gedurende den paartijd, II 77.
Pelecanus onocrotalus, voorjaarsvederen van , II 81.
P e l e l é , II 334.
P e l i k a a n , een blinde , door zijn makkers gevoed, 186; een jonge door oude vogels geleid, 186; strijdlustigheid van den mannelijken , II 41
P e l i k a n e n , gezamenlijk visschen der , 184
P e l l e n , Hamburger , 459, 471
Pelobius hermanni, gesjirp van , 565, 567
P e l s , witheid van den der pooldieren in den winter, 460.
P e l s d r a g e n d e , vooruitgang in scherpzinnigheid der dieren, 129.
Penelope nigra, geluid door het mannetje van voortgebracht, II 65
P e n n a n t , over de gevechten der zeehonden, II 226; over de gevechten der klapmuts, II 270
P e n s e e l z w i j n , Afrikaansch, tanden en knobbels van het , II 250
Penthe, haarkussens aan de sprieten van het mannetje van , 533
P e r i o d e , betrekking tusschen de der veranderlijkheid en de seksueele teeltkeus, 472.
Perisoreus canadensis, jongen van — , II 198.
Peritrichia, verschil in kleur bij de seksen van een soort van , 556.
Pernis cristata, II 121.
P e r r i e r , Dr., over grijporganen van mannelijke ingewandswormen, 437.
P e r r i e r , over de seksueele teeltkeus volgens Darwin, 553.
P e r s o n n a t , M., over Bombyx Yama-Maju, 489.
P e r u a n e n , de beschaving der niet uit vreemde bron, 259.
P e r z e n , het bloed der , veredeld door kruising met Georgiërs en Circassiërs, II 351.
P e t e r s e l i e v l i n d e r s , 577, 581.
Petronia, II 200.
Petrocincla cyanea, jongen van , II 206.
P f e i f f e r , Ida, over Javaansche denkbeelden van schoonheid, II 339.
P h a l a n g e r , zwarte verscheidenheden van den vosachtigen , II 283.
Phacochoerus aethiopicus, tanden en kussens van , II 249
