There are many different notions of information in logic, epistemology, psychology, biology and cognitive science, which are employed differently in each discipline, often with little overlap. Since our interest here is in biological processes and organisms, we develop a taxonomy of functional information that extends the standard cue/signal distinction. Three general, main claims are advanced here. This new taxonomy can be useful in describing learning and communication. It avoids some problems that the natural/non-natural information distinction faces. Functional information is produced (...) through exploration and stabilisation processes. (shrink)
This article provides a conceptual account of causal understanding by connecting current psychological research on time and causality with philosophical debates on the causal asymmetry. I argue that causal relations are viewed as asymmetric because they are understood in temporal terms. I investigate evidence from causal learning and reasoning in both children and adults: causal perception, the temporal priority principle, and the use of temporal cues for causal inference. While this account does not suffice for correct inferences of causal structure, (...) I show it to serve as a preliminary understanding of causal concepts as asymmetric, that later incorporates other types of evidence. This approach supplies causal models with an asymmetric concept of causation that underlies hypotheses about causal structure, as I will illustrate from the framework of the knowledge-based causal induction model. I further argue for an integrating perspective, showing how the understanding of causes as preceding their effects underlies both psychological models and philosophical debates over time and the causal asymmetry, particularly regarding problem cases such as simultaneous causation or backwards causation, and the conceptual connection between causation and action. (shrink)
This paper examines the possibility of an objective evaluation of emotions occurring within the learning process and methods for embedding such an evaluation in advanced learning systems. The main conceptual understandings of emotion in learning and teaching are systematized, with an emphasis on the process philosophy approach. Different models of emotion are considered and the possible generalization of Whitehead’s approach to the role of emotion in education is examined. Special attention is given to significant developments in artificial intelligence in identifying (...) the entire spectrum of emotions and their quantitative estimation as sensor-based variables in data-driven technology. This emotional identification is also explored with respect to data acquisition, processing and classification in computer-based systems for educational purposes. The correlation between emotion and performance outcome in learning is studied to inform an interdisciplinary approach which can improve the learning process. As a result, a complex system for emotion measurement and management is proposed. This can be of interest for the further development of intelligent autonomous tutors. (shrink)
First impressions suggest the following contrast between perception and memory: perception generates new beliefs and reasons, justification, or evidence for those beliefs; memory preserves old beliefs and reasons, justification, or evidence for those beliefs. In this paper, I argue that reflection on perceptual learning gives us reason to adopt an alternative picture on which perception plays both generative and preservative epistemic roles.
The family of cognitive models sometimes referred to as the “Learning Pyramid” enjoys a considerable level of authority within several areas of educational studies, despite that nobody knows how they originated or whether they were supported by any empirical evidence. This article investigates the early history of these models. Through comprehensive searches in digital libraries, we have found that versions of the Learning Pyramids have been part of educational debates and practices for more than 160 years. These findings demonstrate that (...) the models did not originate from empirical research. We also argue that the contemporary Learning Pyramids, despite their continued modifications and modernizations, have failed to keep up with the developments of cognitive psychology. The conception of memory implied by the Learning Pyramids deviates significantly from the standard picture of human memory. (shrink)
Over 35 years ago, Meltzoff and Moore (1977) published their famous article ‘Imitation of facial and manual gestures by human neonates’. Their central conclusion, that neonates can imitate, was and continues to be controversial. Here we focus on an often neglected aspect of this debate, namely on neonatal spontaneous behaviors themselves. We present a case study of a paradigmatic orofacial ‘gesture’, namely tongue protrusion and retraction (TP/R). Against the background of new research on mammalian aerodigestive development, we ask: How does (...) the human aerodigestive system develop and what role does TP/R play in the neonate’s emerging system of aerodigestion? We show that mammalian aerodigestion develops in two phases: (1) from the onset of isolated orofacial movements in utero to the post-natal mastery of suckling at 4 months after birth, and; (2) thereafter, from preparation to the mastery of mastication and deglutition of solid foods. Like other orofacial stereotypies, TP/R emerges in the first phase and vanishes prior to the second. Based upon recent advances in activity-driven early neural development, we suggest a sequence of three developmental events in which TP/R might participate: the acquisition of tongue control, the integration of the central pattern generator for TP/R with other aerodigestive CPGs, and the formation of connections within the cortical maps of S1 and M1. If correct, orofacial stereotypies are crucial to the maturation of aerodigestion in the neonatal period but also unlikely to co-occur with imitative behavior. (shrink)
Organizational learning can be described as a transfer of individuals’ cognitive mental models to shared mental models. Employees, seeking the same colleagues for advice, are structurally equivalent, and the aim of the paper is to study if the concept can act as a conduit for organizational learning. It is argued that the mimicking of colleagues’ advice seeking structures will induce structural equivalence and transfer the accuracy of individuals’ cognitive mental models to shared mental models. Taking a dyadic level of analysis (...) authors revisit a classical case and present novel data analyses.The empirical results indicate that the mimicking of advice seeking structures can alter cognitive accuracy. It is discussed the findings’ implications for organization learning theory and practice, addressed the study’s limitations, and suggested avenues for future research. (shrink)
I consider three aspects in which machine learning and philosophy of science can illuminate each other: methodology, inductive simplicity and theoretical terms. I examine the relations between the two subjects and conclude by claiming these relations to be very close.
This paper undertakes a theoretical investigation of the 'learning' aspect of science as opposed to the 'knowledge' aspect. The practical background of the paper is in agricultural systems research – an area of science that can be characterised as 'systemic' because it is involved in the development of its own subject area, agriculture. And the practical purpose of the theoretical investigation is to contribute to a more adequate understanding of science in such areas, which can form a basis for developing (...) and evaluating systemic research methods, and for determining appropriate criteria of scientific quality. Two main perspectives on science as a learning process are explored: research as the learning process of a cognitive system, and science as a social, communicational system. A simple model of a cognitive system is suggested, which integrates both semiotic and cybernetic aspects, as well as a model of self-reflective learning in research, which entails moving from an inside 'actor' stance to an outside 'observer' stance, and back. This leads to a view of scientific knowledge as inherently contextual and to the suggestion of reflexive objectivity and relevance as two related key criteria of good science. (shrink)
J. J. OʼDonnell is one those scholars whose learning is assumed rather than displayed. As a result, his brief approach to the long-terms effects of the computer revolution onreading and higher education feels like a bracing, sophisticated exchange of ideas. Like conversation, O'Donnellʼs thesis is not terribly unified or orderly. He often makessidetracks from his focus on high technology and literacy into explaining such interestingthings as how we choose our cultural ancestry instead of merely evolving out of it, the errors (...) of current education, and perhaps more than you ever wanted to know aboutother avatars of the word such as St. Jerome, St. Augustine, and Cassiodorus. Greatcover too. (shrink)
Rather than attempting to characterize a relation of confirmation between evidence and theory, epistemology might better consider which methods of forming conjectures from evidence, or of altering beliefs in the light of evidence, are most reliable for getting to the truth. A logical framework for such a study was constructed in the early 1960s by E. Mark Gold and Hilary Putnam. This essay describes some of the results that have been obtained in that framework and their significance for philosophy of (...) science, artificial intelligence, and for normative epistemology when truth is relative. (shrink)
Formal learning theory is an approach to the study of inductive inference that has been developed by computer scientists. In this paper, I discuss the relevance of formal learning theory to such standard topics in the philosophy of science as underdetermination, realism, scientific progress, methodology, bounded rationality, the problem of induction, the logic of discovery, the theory of knowledge, the philosophy of artificial intelligence, and the philosophy of psychology.
While orthodox (Neyman-Pearson) statistical tests enjoy widespread use in science, the philosophical controversy over their appropriateness for obtaining scientific knowledge remains unresolved. I shall suggest an explanation and a resolution of this controversy. The source of the controversy, I argue, is that orthodox tests are typically interpreted as rules for making optimal decisions as to how to behave--where optimality is measured by the frequency of errors the test would commit in a long series of trials. Most philosophers of statistics, however, (...) view the task of statistical methods as providing appropriate measures of the evidential-strength that data affords hypotheses. Since tests appropriate for the behavioral-decision task fail to provide measures of evidential-strength, philosophers of statistics claim the use of orthodox tests in science is misleading and unjustified. What critics of orthodox tests overlook, I argue, is that the primary function of statistical tests in science is neither to decide how to behave nor to assign measures of evidential strength to hypotheses. Rather, tests provide a tool for using incomplete data to learn about the process that generated it. This they do, I show, by providing a standard for distinguishing differences (between observed and hypothesized results) due to accidental or trivial errors from those due to systematic or substantively important discrepancies. I propose a reinterpretation of a commonly used orthodox test to make this learning model of tests explicit. (shrink)