Abductive reasoning in neural-symbolic systems

Abduction is or subsumes a process of inference. It entertains possible hypotheses and it chooses hypotheses for further scrutiny. There is a large literature on various aspects of non-symbolic, subconscious abduction. There is also a very active research community working on the symbolic (logical) characterisation of abduction, which typically treats it as a form of hypothetico-deductive reasoning. In this paper we start to bridge the gap between the symbolic and sub-symbolic approaches to abduction. We are interested in benefiting from developments made by each community. In particular, we are interested in the ability of non-symbolic systems (neural networks) to learn from experience using efficient algorithms and to perform massively parallel computations of alternative abductive explanations. At the same time, we would like to benefit from the rigour and semantic clarity of symbolic logic. We present two approaches to dealing with abduction in neural networks. One of them uses Connectionist Modal Logic and a translation of Horn clauses into modal clauses to come up with a neural network ensemble that computes abductive explanations in a top-down fashion. The other combines neural-symbolic systems and abductive logic programming and proposes a neural architecture which performs a more systematic, bottom-up computation of alternative abductive explanations. Both approaches employ standard neural network architectures which are already known to be highly effective in practical learning applications. Differently from previous work in the area, our aim is to promote the integration of reasoning and learning in a way that the neural network provides the machinery for cognitive computation, inductive learning and hypothetical reasoning, while logic provides the rigour and explanation capability to the systems, facilitating the interaction with the outside world. Although it is left as future work to determine whether the structure of one of the proposed approaches is more amenable to learning than the other, we hope to have contributed to the development of the area by approaching it from the perspective of symbolic and sub-symbolic integration.

The Nencki Affective Picture System (NAPS): Introduction to a novel,standardized, wide-range,high-quality,realistic picture database

Selecting appropriate stimuli to induce emotional states is essential in affective research. Only a few standardized affective stimulus databases have been created for auditory, language, and visual materials. Numerous studies have extensively employed these databases using both behavioral and neuroimaging methods. However, some limitations of the existing databases have recently been reported, including limited numbers of stimuli in specific categories or poor picture quality of the visual stimuli. In the present article, we introduce the Nencki Affective Picture System (NAPS), which consists of 1,356 realistic, high-quality photographs that are divided into five categories (people, faces, animals, objects, and landscapes). Affective ratings were collected from 204 mostly European participants. The pictures were rated according to the valence, arousal, and approach–avoidance dimensions using computerized bipolar semantic slider scales. Normative ratings for the categories are presented for each dimension. Validation of the ratings was obtained by comparing them to ratings generated using the Self-Assessment Manikin and the International Affective Picture System. In addition, the physical properties of the photographs are reported, including luminance, contrast, and entropy. The new database, with accompanying ratings and image parameters, allows researchers to select a variety of visual stimulus materials specific to their experimental questions of interest. The NAPS system is freely accessible to the scientific community for noncommercial use by request at http://naps.nencki.gov.pl.     

The 5C’s Model of Successful Partnerships in Elite Beach Volleyball Dyads

An athlete–athlete partnership is a form of athletic dyad in which both members equally share power and responsibility. Although research on the coach–athlete relationship is prevalent, research on the unique interpersonal dynamics of athlete–athlete partnerships in elite sport is sparse, particularly in beach volleyball. The purpose of the present study was to explore the interpersonal components of successful, elite athlete–athlete beach volleyball partnerships through the lens of interdependence theory and Jowett and colleagues’ 3?+?1C’s theory of coach–athlete relationships. Interpretative Phenomenological Analysis was applied to semistructured interviews with four Olympic-level beach volleyball players (3 male, 1 female). Results revealed 5 higher order themes: (a) compatibility, (b) commitment, (c) complementarity, (d) coorientation, and (e) closeness, which became the key constructs in the proposed 5C’s model of the successful athlete–athlete partnership. Interpersonal awareness, interpersonal maturation, and context were identified as 3 overarching meta-themes, whereas interdependence connected all interpersonal components. For example, in consultation, increasing athlete individual and interpersonal awareness (e.g., in the areas of personal and dyadic philosophy, personal and shared values, and individual and dyadic coping) requires careful contextualization and thoughtful implementation. Future studies need to examine diverse samples of athlete–athlete dyads to advance interpersonal theory in sport and add to emerging theories of performance behavior and expertise in sport.

Lay Summary: Successful beach volleyball partnerships share a philosophy and commitment to their sport. Desired partners are supportive and adaptive, are compatible, depend on one another, and continually appraise and reflect on their relationship to grow as individuals and as teammates.