The assumption that people possess a repertoire of strategies to solve the inference problems they face has been made repeatedly. The experimental findings of two previous studies on strategy selection are reexamined from a learning perspective, which argues that people learn to select strategies for making probabilistic inferences. This learning process is modeled with the strategy selection learning (SSL) theory, which assumes that people develop subjective expectancies for the strategies they have. They select strategies proportional to their expectancies, which are updated on the basis of experience. For the study by Newell, Weston, and Shanks (2003) it can be shown that people did not anticipate the success of a strategy from the beginning of the experiment. Instead, the behavior observed at the end of the experiment was the result of a learning process that can be described by the SSL theory. For the second study, by Bröder and Schiffer (2006), the SSL theory is able to provide an explanation for why participants only slowly adapted to new environments in a dynamic inference situation. The reanalysis of the previous studies illustrates the importance of learning for probabilistic inferences.

Evaluative studies of inductive inferences have been pursued extensively with mathematical rigor in many disciplines, such as statistics, econometrics, computer science, and formal epistemology. Attempts have been made in those disciplines to justify many different kinds of inductive inferences, to varying extents. But somehow those disciplines have said almost nothing to justify a most familiar kind of induction, an example of which is this: “We’ve seen this many ravens and they all are black, so all ravens are black.” This is enumerative induction in its full strength. For it does not settle with a weaker conclusion (such as “the ravens observed in the future will all be black”); nor does it proceed with any additional premise (such as the statistical IID assumption). The goal of this paper is to take some initial steps toward a justification for the full version of enumerative induction, against counterinduction, and against the skeptical policy. The idea is to explore various epistemic ideals, mathematically defined as different modes of convergence to the truth, and look for one that is weak enough to be achievable and strong enough to justify a norm that governs both the long run and the short run. So the proposal is learning-theoretic in essence, but a Bayesian version is developed as well.

Recently, a connection has been established between two branches of computability theory, namely between algorithmic randomness and algorithmic learning theory. Learning-theoretical characterizations of several notions of randomness were discovered. We study such characterizations based on the asymptotic density of positive answers. In particular, this note provides a new learning-theoretic definition of weak 2-randomness, solving the problem posed by (Zaffora Blando, Rev. Symb. Log. 2019). The note also highlights the close connection between these characterizations and the problem of convergence on random sequences.

This paper focuses on the process of language acquisition in childhood. Departing from accounts—such as that of Jean Piaget, which considers cognitive development as the main condition of language acquisition—Taylor shows how deeply our linguistic capacity is rooted in a prior socio-affective realm of social spaces or communion. Beyond Taylor, the question arises as to whether one can identify different normative consequences for pedagogical practices, as well as for the status of childhood in social theory. The implications of Taylor’s language theory for the relation between human and world will be suggested by connecting the intrinsic dimensions of linguistic communication to the theory of resonance.

We consider the arithmetic complexity of index sets of uniformly computably enumerable families learnable under different learning criteria. We determine the exact complexity of these sets for the standard notions of finite learning, learning in the limit, behaviorally correct learning and anomalous learning in the limit. In proving the ${\rm{\Sigma }}_5^0$-completeness result for behaviorally correct learning we prove a result of independent interest; if a uniformly computably enumerable family is not learnable, then for any computable learner there is a ${\rm{\Delta }}_2^0$ enumeration witnessing failure.

The purpose of the present study was to investigate the effect of the horse's laterality on the symmetry of rein tension in right-handed riders. Eleven right-handed riders rode both a right-lateralized (RL) and a left-lateralized (LL) horse. Rein tension was measured during three circles of walk, trot and canter and four walk–halt transitions in each direction. Tensions were recorded continuously using a rein tension meter. The LL horse was ridden with significantly stronger mean tension in the left rein than in the right rein (1.5 vs. 1.4 kg; P = 0.0352). Significantly more tension was applied to the outside rein in a clockwise (1.4 vs. 1.2 kg; P = 0.0202), but not in a counterclockwise, direction (1.3 vs. 1.2 kg; P = 0.49). Less minimum tension (0.06 vs. 0.29 kg) and greater maximum (6.4 vs. 3.9 kg) and range of tension (6.3 vs. 3.6 kg) occurred in the left rein of the RL horse (P < 0.0001) and the right rein of the LL horse (0.37 vs. 0.08 kg, 4.8 vs. 7.4 kg, 4.3 vs. 7.3 kg respectively; P < 0.0001). The results of the present study indicate that the different utilization of both reins is likely to be influenced by the laterality of both horse and rider. These findings may have important implications for equine training, since consistency of reinforcement is an important factor for equine learning success.

The general process view of learning, which guided research into learning for the first half of this century, has come under attack in recent years from several quarters. One form of criticism has come from proponents of the so-called biological boundaries approach to learning. These theorists have presented a variety of data showing that supposedly general laws of learning may in fact be limited in their applicability to different species and learning tasks, and they argue that the limitations are drawn by the nature of each species' adaptation to the particular requirements of its natural environment. The biological boundaries approach has served an important critical function in the move away from general process learning theory, but it is limited in its ability to provide an alternative to the general process approach. In particular, the biological boundaries approach lacks generality, it is in some respects subservient to the general process tradition, and its ecological content is in too many cases limited to ex post facto adaptive explanations of learning skills. A contrasting, ecological approach to learning, which can provide a true alternative to general process theory, is presented. The ecological approach begins by providing an ecological task description for naturally occurring instances of learning; this step answers the question: What does this animal learn to do? The next step is an analysis of the means by which learning occurs in the course of development, answering the question: How does the animal learn to do this? On the basis of such analyses, local principles of adaptation are formulated to account for the learning abilities of individual species. More global principles are sought by generalization among these local principles and may form the basis for a general ecological theory of learning.

This critical appraisal of contemporary interpretations in the area of infantile attachment begins with an outline of the principal features of the Bowlby-Ainsworth ethological theory, the instrumental/operant learning theory of Gewirtz, and Hoffman's classical conditioning model. Some attention is also given to Cairns's contiguity learning analysis and the Hoffman-Solomon opponentprocess model Discussion of these theories is followed by a review of representative data from infants at four phyletic levels (precocial birds, dogs, monkeys, and human beings), with an emphasis on three aspects of social bonding: (a) the formation and persistence of social ties in the infant under conditions of maltreatment, (b) the role of the attachment object in the adjustment of the infant to the broader environment (the so-called secure base effect), and (c) the infant's reaction to involuntary separation from the attachment object.

An attempt is made to judge how well each of the interpretations accounts for all or part of the data, with the conclusion that current theories do not accord completely with documented attachment phenomena. The following criticisms are highlighted: Ethological theory emphasizes that infants' behavior systems have been shaped by the ordinarily expectable environment and depend on that environment for their functioning, yet infants of many species form bonds to objects not typical in any species' environment, or even to sources of maltreatment. Learning theory is faulted for making predictions contradicted by the maltreatment data and for a lack of formal mechanisms to account for the secure base and separation effects. The contiguity analysis is criticized for its inability to account for the emergence of certain response patterns during separation, and the opponent-process model is called into question because of its failure to fit important affective dynamics of social separation (a central focus of this theory). Recommendations for future theories of attachment are offered.

In 1986, Osherson, Stob and Weinstein asked whether two variants of anomalous vacillatory learning, TxtFex** and TxtFext**, could be distinguished [3]. In both, a machine is permitted to vacillate between a finite number of hypotheses and to make a finite number of errors. TxtFext**-learning requires that hypotheses output infinitely often must describe the same finite variant of the correct set, while TxtFex**-learning permits the learner to vacillate between finitely many different finite variants of the correct set. In this paper we show that TxtFex** ≠ TxtFext**, thereby answering the question posed by Osherson, et al. We prove this in a strong way by exhibiting a family in TxtFex*2 TxtFext**.

In Benaïm and Ben Arous (2003) is solved a multi-armed bandit problem arising in the theory of learning in games. We proposea short and elementary proof of this result based on a variant of the Kronecker lemma.

The conceptual and investigative tools for the analysis of social behavior can be expanded by integrating biological theory, control systems theory, and Pavlovian conditioning. Biological theory has focused on the costs and benefits of social behavior from ecological and evolutionary perspectives. In contrast, control systems theory is concerned with how machines achieve a particular goal or purpose. The accurate operation of a system often requires feed-forward mechanisms that adjust system performance in anticipation of future inputs. Pavlovian conditioning is ideally suited to subserve this function in behavioral systems. Pavlovian mechanisms have been demonstrated in various aspects of sexual behavior, maternal lactation, and infant suckling. Pavlovian conditioning of agonistic behavior has been also reported, and Pavlovian processes may likewise be involved in social play and social grooming. Several further lines of evidence indicate that Pavlovian conditioning can increase the efficiency and effectiveness of social interactions, thereby improving their cost/benefit ratio. We extend Pavlovian concepts beyond the traditional domain of discrete secretory and other physiological reflexes to complex real-world behavioral interactions and apply abstract laboratory analyses of the mechanisms of associative learning to the daily challenges animals face as they interact with one another in their natural environments.