We argue why interpretability should have primacy alongside empiricism for several reasons: first, if machine learning models are beginning to render some of the high-risk healthcare decisions instead of clinicians, these models pose a novel medicolegal and ethical frontier that is incompletely addressed by current methods of appraising medical interventions like pharmacological therapies; second, a number of judicial precedents underpinning medical liability and negligence are compromised when ‘autonomous’ ML recommendations are considered to be en par with human instruction in (...) specific contexts; third, explainable algorithms may be more amenable to the ascertainment and minimisation of biases, with repercussions for racial equity as well as scientific reproducibility and generalisability. We conclude with some reasons for the ineludible importance of interpretability, such as the establishment of trust, in overcoming perhaps the most difficult challenge ML will face in a high-stakes environment like healthcare: professional and public acceptance. (shrink)

Within the ecological and enactive approaches in cognitive science, a tension exists in how the process of skill learning is understood. Skill learning can be understood in a narrow sense, as a process of bodily change over time, or in an extended sense, as a change in the structure of the animal–environment system. We propose to resolve this tension by rejecting the first understanding in favor of the second. We thus defend an extended approach to skill learning. (...) An extended understanding of skill learning views bodily changes as being embedded in a larger process of interaction between the organism and specific structures in the environment. Such an extended approach is committed to the claims that (1) the appropriate unit of analysis for understanding skill learning is not the body but the activity and (2) learning consists in the establishment and adaptive organization of enabling constraints on that activity. We focus on two example cases: maintaining upright posture and walking. In both cases, environmental structures play a constitutive role in the activity throughout learning, but the specific environmental structures that are involved in the activity change over time. At an early stage, the child makes use of an environmental “support”—for example, holding onto furniture to maintain upright posture. Later, once further constraints have been established, the child is able to let go of the furniture and remain upright. We argue that adopting an extended understanding of skill learning offers a promising strategy for unifying ecological and enactive approaches and can also potentially ground a radically embodied approach to higher cognition. (shrink)

Human language is a salient example of a neurocognitive system that is specialized to process complex dependencies between sensory events distributed in time, yet how this system evolved and specialized remains unclear. Artificial Grammar Learning (AGL) studies have generated a wealth of insights into how human adults and infants process different types of sequencing dependencies of varying complexity. The AGL paradigm has also been adopted to examine the sequence processing abilities of nonhuman animals. We critically evaluate this growing literature (...) in species ranging from mammals (primates and rats) to birds (pigeons, songbirds, and parrots) considering also cross‐species comparisons. The findings are contrasted with seminal studies in human infants that motivated the work in nonhuman animals. This synopsis identifies advances in knowledge and where uncertainty remains regarding the various strategies that nonhuman animals can adopt for processing sequencing dependencies. The paucity of evidence in the few species studied to date and the need for follow‐up experiments indicate that we do not yet understand the limits of animal sequence processing capacities and thereby the evolutionary pattern. This vibrant, yet still budding, field of research carries substantial promise for advancing knowledge on animal abilities, cognitive substrates, and language evolution. (shrink)

Will robots ever be able to learn like humans? To answer that question, one first needs to ask: what is learning? Hubert and Stuart Dreyfus had a point when they claimed that computers and robots would never be able to learn like humans because human learning, after an initial phase of rule-based learning, is uncertain, context sensitive and intuitive under contract F49620-C-0063 with the University of California) Berkeley, February 1980.. Washington, DC: Storming Media. https://www.stormingmedia.us/15/1554/A155480.html. Accessed 10 Oct (...) 2017, 1980). I would add that learning also builds on prior learning, and that from the outset, human learning is a socio-cultural materially grounded collective epistemology. This posthuman acknowledgement shifts the focus from the individual learner to learning within collective phenomena. Dreyfus and Dreyfus do not seem to emphasise the essentially social and cultural nature of the human condition. Learning theory, new materialism and postphenomenology have emphasised in different ways the materially based socio-cultural nature of human learning. They thereby point towards a ‘posthuman’ learning that is far from the machine-like or enhanced creature envisioned by singularists. Until robots are essentially social and ground their epistemologies in socio-cultural materiality, I suggest that human-like AI is not possible. (shrink)

This paper studies efficient learning with respect to mind changes. Our starting point is the idea that a learner that is efficient with respect to mind changes minimizes mind changes not only globally in the entire learning problem, but also locally in subproblems after receiving some evidence. Formalizing this idea leads to the notion of uniform mind change optimality. We characterize the structure of language classes that can be identified with at most α mind changes by some learner (...) (not necessarily effective): A language class L is identifiable with α mind changes iff the accumulation order of L is at most α. Accumulation order is a classic concept from point-set topology. To aid the construction of learning algorithms, we show that the characteristic property of uniformly mind change optimal learners is that they output conjectures (languages) with maximal accumulation order. We illustrate the theory by describing mind change optimal learners for various problems such as identifying linear subspaces and one-variable patterns. (shrink)

When making decisions, humans can observe many kinds of information about others' activities, but their effects on performance are not well understood. We investigated social learning strategies using a simple problem-solving task in which participants search a complex space, and each can view and imitate others' solutions. Results showed that participants combined multiple sources of information to guide learning, including payoffs of peers' solutions, popularity of solution elements among peers, similarity of peers' solutions to their own, and relative (...) payoffs from individual exploration. Furthermore, performance was positively associated with imitation rates at both the individual and group levels. When peers' payoffs were hidden, popularity and similarity biases reversed, participants searched more broadly and randomly, and both quality and equity of exploration suffered. We conclude that when peers' solutions can be effectively compared, imitation does not simply permit scrounging, but it can also facilitate propagation of good solutions for further cumulative exploration. (shrink)

In this concise book based on his Arne Ryde Lectures in 2002, Young suggests a conceptual framework for studying strategic learning and highlights theoretical developments in the area. He discusses the interactive learning problem; reinforcement and regret; equilibrium; conditional no-regret learning; prediction, postdiction, and calibration; fictitious play and its variants; Bayesian learning; and hypothesis testing. Young's framework emphasizes the amount of information required to implement different types of learning rules, criteria for evaluating their performance, and (...) alternative notions of equilibrium to which they converge. He also stresses the limits of what can be achieved: for a given type of game and a given amount of information, there may exist no learning procedure that satisfies certain reasonable criteria of performance and convergence. In short, Young has provided a valuable primer that delineates what we know, what we would like to know, and the limits of what we can know, when we try to learn about a system that is composed of other learners. (shrink)

The organization of middle schools and the practices of middle school teachers in Japan and the United States differ dramatically, Gerald K. LeTendre demonstrates in this compelling comparative study. Based on his long-term observations in Japanese and American schools and on analyses of curricula and classroom practices, the author describes what teachers, administrators, and counselors in each country believe about adolescent development. He explores how these beliefs are put into practice and how they affect adolescent development. In both nations, LeTendre (...) observes, school personnel are extremely concerned with volition: the developing willpower of young adolescents. But while both Americans and Japanese believe that nurturing a young person’s ability to use his or her will is crucial, they take very different approaches to dealing with expressions of will. LeTendre also finds conflicting expectations and theories about adolescent development within each system, and he investigates how these can lead to confusion and contradictory rules. (shrink)

An interpretation of memory from the perspective of logical learning theory is presented. In contrast to traditional associationistic theories of learning and memory, which rest on mediation modeling, LLT rests on a predication model. Predication draws on formal and final causation whereas mediation is limited to material and efficient causation. It is held in LLT that memory begins in predicate organization, where framing meanings are logically extended to targets. Passage of time is irrelevant in this meaning extension. The (...) effectiveness of memory depends on the cohesiveness or "tightness" of meaningful organization as framed by a relevant context. Well-organized contexts facilitate memory and poorly organized contexts are detrimental to memory. Possible reasons for good or poor organization in memory are discussed, and relevant research findings cited. The paper closes with a separate definition of memory as a content and as a process. (shrink)

In this paper I defend a teleological explanation of normativity, i. e., I argue that what an organism is supposed to do is determined by its etiological function. In particular, I present a teleological account of the normativity that arises in learning processes, and I defend it from some objections.