The ability to appropriately sequence a list of discrete items is an important facet in performing routine cognitive tasks and may play a significant role in the acquisition of early communication skills. Though the serial learning abilities of some species, such as chimpanzees and rhesus macaques are well documented, there is virtually no information on the extent of these skills with gorillas. In this study, a young female western lowland gorilla has demonstrated the ability to learn a list of (...) seven Arabic numerals by selecting them on a monitor behind an infrared touchframe. As list length increased, Rollie required fewer trials to reach the criterion, suggesting an acquisition of skills associated with serial learning. Rollie was most likely to correctly select the first item in the sequence and least likely to select the penultimate item in the sequence. Her performance suggests serial learning abilities in the range of other non-verbal species and provides preliminary evidence that supports the assertion that gorillas can succeed at this form of serial learning task. (shrink)

In popular discussions, the nuances of AI are often abridged as “the algorithm”, as the specific arrangements of machine learning, deep learning and automated decision-making on social media platforms are typically shrouded in proprietary secrecy punctuated by press releases and transparency initiatives. What is clear, however, is that AI embedded on social media functions to recommend content, personalize ads, aggregate news stories, and moderate problematic material. It is also increasingly apparent that individuals are concerned with the uses, implications, (...) and fairness of algorithmic systems. Perhaps in response to concerns about “the algorithm” by individuals and governments, social media platforms utilize transparency initiatives and official statements, in part, to deflect official regulation. In the following paper, I draw from transparency initiatives and statements from representatives of Facebook and TikTok as case studies of how AI is embedded in these platforms, with attention to the promotion of AI content moderation as a solution to the circulation of problematic material and misinformation. This examination considers the complexity of embedded AI as a material-discursive apparatus, predicated on discursive techniques—what is seeable, sayable, knowable in a given time period—as well as the material arrangements—algorithms, datasets, users, platforms, infrastructures, moderators, etc. As such, the use of AI as part of the immensely popular platforms Facebook and TikTok demonstrates that AI does not exist in isolation, instead functioning as human–machine ensemble reliant on strategies of acceptance via discursive techniques and the changing material arrangements of everyday embeddedness. (shrink)

Jerry Fodor’s arguments against the possibility of concept learning, and the responses that have been offered in defense of the coherence of concept learning, have both by and large assumed that concept learning is a descriptive process. I offer an alternative, ostensive approach to concept learning and explain how descriptive concept learning can be explained as a version of ostensive concept learning. I argue that an ostensive view of concept learning offers an empirically (...) plausible and philosophically adequate account of concept learning that is explanatorily superior to traditional descriptive views. (shrink)

Sociability is essential for modern robots to increase their acceptability in human environments. Traditional techniques use manually engineered utility functions inspired by observing pedestrian behaviors to achieve social navigation. However, social aspects of navigation are diverse, changing across different types of environments, societies, and population densities, making it unrealistic to use hand-crafted techniques in each domain. This paper presents a data-driven navigation architecture that uses state-of-the-art neural architectures, namely Conditional Neural Processes, to learn global and local controllers of the mobile (...) robot from observations. Additionally, we leverage a state-of-the-art, deep prediction mechanism to detect situations not similar to the trained ones, where reactive controllers step in to ensure safe navigation. Our results demonstrate that the proposed framework can successfully carry out navigation tasks regarding social norms in the data. Further, we showed that our system produces fewer personal-zone violations, causing less discomfort. (shrink)

A critical challenge in robot learning from demonstration is the ability to map the behavior of the trainer onto a robot’s existing repertoire of basic/primitive capabilities. In part, this problem is due to the fact that the observed behavior of the teacher may consist of a combination of the robot’s individual primitives. The problem becomes more complex when the task involves temporal sequences of goals. We introduce an autonomous control architecture that allows for learning of hierarchical task (...) representations, in which: every goal is achieved through a linear superposition of robot primitives and sequencing across goals is achieved through arbitration. We treat learning of the appropriate superposition as a state estimation problem over the space of possible linear fusion weights, inferred through a particle filter. We validate our approach in both simulated and real world environments with a Pioneer 3DX mobile robot. (shrink)

Exploiting the skills of others enables individuals to reduce the risks and costs of resource innovation. Social corvids are known to possess sophisticated social and physical cognitive abilities. However, their capacity for imitative learning and its inter-individual transmission pattern remains mostly unexamined. Here we demonstrate the large-billed crows' ability to learn problem-solving techniques by observation and the dominance-dependent pattern in which this technique is transmitted. Crows were allowed to observe one of two box-opening behaviours performed by a dominant or (...) subordinate demonstrator and then tested regarding action and technique. The observers successfully opened the box on their first attempts by using non-matching actions but matching techniques to those observed, suggesting emulation. In the subsequent test sessions, dominant observers (i.e. those dominant to the bird acting as demonstrator) consistently used the learned technique, whereas subordinates (i.e. those subordinate to the bird acting as demonstrator) learned alternative techniques by explorative trial and error. Our findings demonstrate crows' capacity to learn by observing behaviours and the effect of dominance on transmission patterns of behavioural skills. Keywords: social learning; imitation; emulation; affordance; culture; innovation. (shrink)

To what degree can language be acquired from linguistic input alone? This question has vexed scholars for millennia and is still a major focus of debate in the cognitive science of language. The complexity of human language has hampered progress because studies of language–especially those involving computational modeling–have only been able to deal with small fragments of our linguistic skills. We suggest that the most recent generation of Large Language Models (LLMs) might finally provide the computational tools to determine empirically (...) how much of the human language ability can be acquired from linguistic experience. LLMs are sophisticated deep learning architectures trained on vast amounts of natural language data, enabling them to perform an impressive range of linguistic tasks. We argue that, despite their clear semantic and pragmatic limitations, LLMs have already demonstrated that human-like grammatical language can be acquired without the need for a built-in grammar. Thus, while there is still much to learn about how humans acquire and use language, LLMs provide full-fledged computational models for cognitive scientists to empirically evaluate just how far statistical learning might take us in explaining the full complexity of human language. (shrink)

Previous research suggests that implicit sequence learning is superior for believers in the paranormal and individuals with increased cerebral dopamine. Thirty-five healthy participants performed feedback-guided anticipations of four arrow directions. A 100-trial random sequence preceded two 100-trial biased sequences in which visual targets on trial t tended to be displaced 90° clockwise or counter-clockwise from those on t − 1. ISL was defined as a positive change during the course of the biased run in the difference between pro-bias and (...) counter-bias responses. It was hypothesized that this difference would be greater for believers in the paranormal than for skeptics, for those who received dopamine than for those who received placebo, and for believers who received dopamine than for the other groups. None of the hypotheses were supported by the data. It is suggested that a simple binary guessing task with a focus on prediction accuracy during early trials should be considered for future explorations. (shrink)

Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in which (...) they are presumptuous. After elaborating this moral concern, I explore the possibility that carefully procuring the training data for image recognition systems could ensure that the systems avoid the problem. The lesson of this paper extends beyond just the particular case of image recognition systems and the challenge of responsibly identifying a person’s intentions. Reflection on this particular case demonstrates the importance (as well as the difficulty) of evaluating machine learning systems and their training data from the standpoint of moral considerations that are not encompassed by ordinary assessments of predictive accuracy. (shrink)

(I) MATHEMATICAL LECTURES. LECTURE I. Of the Name and general Division of the Mathematical Sciences. BEING about to treat upon the Mathematical Sciences, ...

Explores the resources for contemporary ethics found in the work of the Cheng brothers, canonical neo-Confucian philosophers. Yong Huang presents a new way of doing comparative philosophy as he demonstrates the resources for contemporary ethics offered by the Cheng brothers, Cheng Hao (1032–1085) and Cheng Yi (1033–1107), canonical neo-Confucian philosophers. Huang departs from the standard method of Chinese/Western comparison, which tends to interest those already interested in Chinese philosophy. While Western-oriented scholars may be excited to learn about Chinese philosophers who (...) have said things similar to what they or their favored philosophers have to say, they hardly find anything philosophically new from such comparative work. Instead of comparing and contrasting philosophers, each chapter of this book discusses a significant topic in Western moral philosophy, examines the representative views on this topic in the Western tradition, identifies their respective difficulties, and discusses how the Cheng brothers have better things to say on the subject. Topics discussed include why one should be moral, how weakness of will is not possible, whether virtue ethics is self-centered, in what sense the political is also personal, how a moral theory can be of an antitheoretical nature, and whether moral metaphysics is still possible in this postmodern and postmetaphysical age. “This book presents the philosophical ideas of the Cheng brothers intelligently, convincingly, and powerfully. It is among the best books ever written on the Cheng brothers, including works in the Chinese language.” — Kam-por Yu, coeditor of Taking Confucian Ethics Seriously: Contemporary Theories and Applications. (shrink)

Philosophers have recently focused on critical, epistemological challenges that arise from the opacity of deep neural networks. One might conclude from this literature that doing good science with opaque models is exceptionally challenging, if not impossible. Yet, this is hard to square with the recent boom in optimism for AI in science alongside a flood of recent scientific breakthroughs driven by AI methods. In this paper, I argue that the disconnect between philosophical pessimism and scientific optimism is driven by a (...) failure to examine how AI is actually used in science. I show that, in order to understand the epistemic justification for AI-powered breakthroughs, philosophers must examine the role played by deep learning as part of a wider process of discovery. The philosophical distinction between the 'context of discovery' and the 'context of justification' is helpful in this regard. I demonstrate the importance of attending to this distinction with two cases drawn from the scientific literature, and show that epistemic opacity need not diminish AI's capacity to lead scientists to significant and justifiable breakthroughs. (shrink)

This paper argues, based on Lewis’ claim that communication is a coordination game (Lewis in Minnesota studies in the philosophy of science, University of Minnesota Press, Minneapolis, pp 3–35, 1975), that we can account for the communicative function of demonstratives without assuming that they semantically refer. The appeal of such a game theoretical version of the case for non-referentialism is that the communicative role of demonstratives can be accounted for without entering the cul de sac of trying to construct conventions (...) of ever-increasing complexity. Instead communication via demonstratives is explained with reference to the general, non-domain specific ability of human beings to solve games of coordination. Furthermore, there is empirical support for such a view. Judgments concerning demonstrative reference have been shown to be sensitive to judgments concerning common ground (Clark et al. in J Verb Learn Verb Behav 22:245–258, 1983), which is exactly what the non-referentialist account would predict. The game theoretical account also allows for an intuitively plausible, non-referentialist treatment of Speaks’ ‘trumping argument’ (Speaks in Philos Stud 174:709–734, 2017), as well as the Carnap/Agnew puzzle (Kaplan in Syntax Semant 9:221–43, 1970). (shrink)

Banded geckos (Coleonyx variegatus) were trained, at the rate of five daily trials, on eight intradimensional spatial shifts to a criterion of 80% correct per reversal. In contrast to several previously reported failures to obtain reversal learning, Coleonyx demonstrated significant improvement on both errors and trials to criterion. Their reversal performance compares favorably with that of birds and mammals and a common index of reversal ability, the mean total error, did not differentiate between taxa.

This book present interdisciplinary research in the social sciences and humanities by connecting seemingly disparate sources through a sensitivity to endangered human values. It links reflections on the contemporary relationship between art and technology in a post-modern context, seeing art in terms of crossing boundaries and exploring virtuality. It deals with the consequences of economics colonising other disciplines, in terms of the processes by which the social becomes the economic. Using Jantsch''s evolutionary paradigm, the concept of self-transcendence is seen as (...) crucial for the understanding of human beings and their social systems. Incorporating recent thinking from the natural sciences, the learning process can be conceived as the life and activity of all complex systems, including those once conceived as organisms, machines, cultures or economies. Without the societal embrace of scientific and technological development no collective or individual meaning can be assigned to the production of new knowledge. The book seeks to recognise the point where a collective learning process becomes the heart of productivity, and where the shift from the hegemony of material labour to immaterial labour becomes fundamental. The author brings new understandings of art, the social, and technology together, based on the idea that history is not a story told in separate physical, social and spiritual spaces and that the most fundamental problem of today is how to find some shared meaning in a fractured world. The author analyses, at a global level, the process of the co-production of scientific and social order, of culture and technology, of life sciences and economic and political regimes. It rightly identifies the rise of the role of knowledge and the move of capital into life sciences as a new stage in the history of capitalism: what we can qualify as cognitive capitalism or biocapitalism. In this new era of capitalism, what is being manufactured and sold are not just tangible and non-tangible goods, whose increasing importance, as the author shows, poses unsurmountable theoretical problems to the theoretical apparatus of economic science. The increasing mercantilisation of the world appears at the same time as a bio-power, i.e. a set of instruments creating and controlling different forms of life, forms of communication, standards of socialisation, education, the individual and collective imaginary, etc. More fundamentally, the encounter of life sciences and the information technology integrates and subjects the most essential mechanisms of biological and social reproduction to the logic of capital valorization. To understand the complexity of these changes and the ethical and philosophical questions that the development of technology and sciences poses to the future of mankind one must break through the disciplinary barriers delineating different disciplines in social sciences and those separating social sciences from natural sciences. Professor Matko Mestrovic manages to tackle this challenge not only because of his impressive and masterful knowledge of different disciplines in the social and natural sciences, but he does it also owing to his capacity for theoretical elaboration that allows him to lay the foundations of a new transdisciplinary paradigm. This is why his work can raise the awareness of the general public on two issues: on a global and profound vision of the challenges posed by the new millennium; and on the need for a radical theoretical innovation bringing into question the disciplinary certitudes in develop a social science able to better understand the movement and the ambiguity of history. - Carlo Vercellone, Universite de Paris 1, Pantheon-Sorbonne Mestrovic provides a unique insight into the often forgotten relation between economics on the one hand and arts and culture on the other, demonstrating that these adomains function as a total social facticity and not as separate, entirely independent elements thereof. In doing so he is dispelling the illusions about the disciplinary self-containdness of individual forms of knowledge and is relying on those paradigms of contemporary scientific thought whose aepistemiological programs are based on close cooperation and aopening up and not on the persuasion about one''s own positions and dispositions. - Prof. dr. Rade Kalanj, Redovni profesor na Odjelu za sociologiju. (shrink)

In recent years, the family of algorithms collected under the term ``deep learning'' has revolutionized artificial intelligence, enabling machines to reach human-like performances in many complex cognitive tasks. Although deep learning models are grounded in the connectionist paradigm, their recent advances were basically developed with engineering goals in mind. Despite of their applied focus, deep learning models eventually seem fruitful for cognitive purposes. This can be thought as a kind of biological exaptation, where a physiological structure becomes (...) applicable for a function different from that for which it was selected. In this paper, it will be argued that it is time for cognitive science to seriously come to terms with deep learning, and we try to spell out the reasons why this is the case. First, the path of the evolution of deep learning from the connectionist project is traced, demonstrating the remarkable continuity, and the differences as well. Then, it will be considered how deep learning models can be useful for many cognitive topics, especially those where it has achieved performance comparable to humans, from perception to language. It will be maintained that deep learning poses questions that cognitive sciences should try to answer. One of such questions is the reasons why deep convolutional models that are disembodied, inactive, unaware of context, and static, are by far the closest to the patterns of activation in the brain visual system. (shrink)

This paper presents an implemented computational model of word acquisition which learns directly from raw multimodal sensory input. Set in an information theoretic framework, the model acquires a lexicon by finding and statistically modeling consistent cross‐modal structure. The model has been implemented in a system using novel speech processing, computer vision, and machine learning algorithms. In evaluations the model successfully performed speech segmentation, word discovery and visual categorization from spontaneous infant‐directed speech paired with video images of single objects. These (...) results demonstrate the possibility of using state‐of‐the‐art techniques from sensory pattern recognition and machine learning to implement cognitive models which can process raw sensor data without the need for human transcription or labeling. (shrink)

Those with knowledge about scientific instruments come from many different fields. Prominent among them are (1) collectors and dealers, (2) curators, (3) historians, (4) instrument makers, (5) philosophers, and (6) scientists (the order is alphabetical, not value-laden). The annual symposium of the Scientific Instrument Commission often brings members of each of these groups together, and they learn from one another. What follows are brief reflections on the activities of each group when its members consider instruments.

Drawing on recent work in developmental and comparative psychology, this paper argues that demonstratives function to coordinate the interlocutors' joint focus of attention, which is one of the most basic functions of human communication. The communicative importance of demonstratives is reflected in a number of properties that together characterize them as a particular word class: In contrast to other closed-class expressions, demonstratives are universal, they are generally so old that their roots cannot be traced back to other linguistic items, they (...) are among the earliest words that children learn, and they are closely tied to a particular gesture. Moreover, demonstratives play an important role in the organization of discourse and the diachronic evolution of grammar, which arguably is also motivated by their communicative function to establish joint attention. (shrink)

From a theoretical perspective, most discussions of statistical learning have focused on the possible “statistical” properties that are the object of learning. Much less attention has been given to defining what “learning” is in the context of “statistical learning.” One major difficulty is that SL research has been monitoring participants’ performance in laboratory settings with a strikingly narrow set of tasks, where learning is typically assessed offline, through a set of two-alternative-forced-choice questions, which follow a (...) brief visual or auditory familiarization stream. Is that all there is to characterizing SL abilities? Here we adopt a novel perspective for investigating the processing of regularities in the visual modality. By tracking online performance in a self-paced SL paradigm, we focus on the trajectory of learning. In a set of three experiments we show that this paradigm provides a reliable and valid signature of SL performance, and it offers important insights for understanding how statistical regularities are perceived and assimilated in the visual modality. This demonstrates the promise of integrating different operational measures to our theory of SL. (shrink)

Learning from Chinese Philosophies engages Confucian and Daoist philosophies in creative interplay, developing a theory of interdependent selfhood in the two philosophical traditions. Karyn Lai draws on the unique insights of the two philosophies to address contemporary debates on ethics, community and government. Issues discussed include questions on selfhood, attachment, moral development, government, culture and tradition, and feminist queries regarding biases and dualism in ethics. Throughout the book, Lai demonstrates that Chinese philosophies embody novel and insightful ideas for addressing (...) contemporary issues and problems. (shrink)

The position advanced in this paper is that the bedrock of emotional feelings is contained within the evolved emotional action apparatus of mammalian brains. This dual-aspect monism approach to brain–mind functions, which asserts that emotional feelings may reflect the neurodynamics of brain systems that generate instinctual emotional behaviors, saves us from various conceptual conundrums. In coarse form, primary process affective consciousness seems to be fundamentally an unconditional “gift of nature” rather than an acquired skill, even though those systems facilitate (...) skill acquisition via various felt reinforcements. Affective consciousness, being a comparatively intrinsic function of the brain, shared homologously by all mammalian species, should be the easiest variant of consciousness to study in animals. This is not to deny that some secondary processes cannot be evaluated in animals with sufficiently clever behavioral learning procedures, as with place-preference procedures and the analysis of changes in learned behaviors after one has induced re-valuation of incentives. Rather, the claim is that a direct neuroscientific study of primary process emotional/affective states is best achieved through the study of the intrinsic , albeit experientially refined, emotional action tendencies of other animals. In this view, core emotional feelings may reflect the neurodynamic attractor landscapes of a variety of extended trans-diencephalic, limbic emotional action systems—including SEEKING, FEAR, RAGE, LUST, CARE, PANIC, and PLAY. Through a study of these brain systems, the neural infrastructure of human and animal affective consciousness may be revealed. Emotional feelings are instantiated in large-scale neurodynamics that can be most effectively monitored via the ethological analysis of emotional action tendencies and the accompanying brain neurochemical/electrical changes. The intrinsic coherence of such emotional responses is demonstrated by the fact that they can be provoked by electrical and chemical stimulation of specific brain zones—effects that are affectively laden. For substantive progress in this emerging research arena, animal brain researchers need to discuss affective brain functions more openly. Secondary awareness processes, because of their more conditional, contextually situated nature, are more difficult to understand in any neuroscientific detail. In other words, the information-processing brain functions, critical for cognitive consciousness, are harder to study in other animals than the more homologous emotional/motivational affective state functions of the brain. (shrink)

Higher education has become more student-centered as the Bologna process assigns students more time to study and research. Online teaching has been needed during the pandemic, which can be challenging regarding didactic and assessment. This paper analyzes project-based learning (PBL) as a form of teaching and assessing students in a bioethics course on reproductive ethics. The team project was the final assessment of the Faculty of Bioethics core curriculum course, "Bioethics, Technology and Procreation,” offered to two student groups in (...) the 2019–2020 school year. The analysis of the results of PBL is descriptive qualitative with semi-quantitative data from student feedback. Forty students were presented with a detailed methodology of team projects and were encouraged to form teams of 3 to 5. They need to develop a team project with creativity and pastoral sensibility that will communicate the content assimilated in class to a selected target audience, with adequate means to measure the impact of their project on their target. Thirty-eight students formed ten teams and presented ten projects. Each team had 2–4 encounters with the professor and 6–10 encounters among team members. Six of the ten projects were categorized as didactic, three mediatic, one didactic-mediatic and 0 artistic. The grades of this final assignment ranged from 7 to 10, with an average of 8.7 out of 10. The survey feedback demonstrated high satisfaction, as students discovered new values in teamwork and pastoral applications. However, this innovation can be more time-consuming for the professor and the students, requiring more significant time management, teamwork and communication competency. (shrink)

This Special Issue addresses a range of educational issues linked to main themes from our 2019 book The Emergence of Complexity: Rethinking Education as a Social Science. This book elaborated two major theses that raise fundamental questions for philosophy of education. First, that learning by groups is typically a distinctive kind of learning that is not reducible to learning by individuals. Second, that a degree of holism, as against a focus on individuals, is essential for achieving a (...) convincing understanding of this distinctive type of group learning. These two theses are of direct interest to philosophy of education since they challenges the resources of received theories of learning. Yet this kind of group learning characterises the vast bulk of human learning situations that occur outside of formal education systems. In this Special Issue introductory article we, firstly, introduce some basic principles of complexity theory, and demonstrate that, together with the concept of a ‘co-present group’ (between 2 and about 12 individuals), these ideas offer develop novel understandings of the distinctive learning that occurs within such groups. Secondly, we outline and illustrate with further examples the main features of co-present groups. Thirdly, we consolidate our conceptual work by showing five ways in which our account of group learning serves to refurbish the concept of ‘learning’. (shrink)