Special issue on problem-solving,creativity and spatial reasoning

Problem-solving, creativity and spatial reasoning are high level abilities of cognitive systems with high potential for synergies. However, they have been treated separately by different fields. This special issue presents research work on these topics, aiming to observe their interrelations in order to create theoretical approaches, methodologies and computational tools to advance work on creativity and spatial problem-solving in cognitive systems.     

New perspectives on theories linking cognition,emotion, and context: A proposal from the Theory of Analysis of Demand

Both scholars and practitioners acknowledge that the major factors explaining behavior are cognition, emotion, and context. However, existing theories tend to only focus on a combination of two. Furthermore, not all models are rooted in a specific theory of mind. Finally, there is no consistent definition of ‘mind.’ To address these issues, we review the major models explaining behavior. We then describe the Theory of Analysis of Demand (TAD), an interactionist (individual-context) model of functioning of mind that thoroughly addresses the conjoint interplay of cognition, emotion, and context. A key concept of the TAD is emotional symbolization, the process of relating one’s experiences of the external context with an inevitable emotional reaction. By considering an intersection among cognition, emotion, and context, TAD fills the gap in the existing literature and expands our understanding of behavior. Moreover, we describe the TAD intervention methodology, Individual-Setting of intervention-Organization technique, which explores an individual’s demand for intervention and the underlying emotion-, cognition-, and context-related categories (i.e., emotional symbolization) that prompt the request. Last, we discuss the potential benefits and boundary conditions of the TAD to integrate existing approaches.     

A real‐world rational agent: unifying old and new AI

Explanations of cognitive processes provided by traditional artificial intelligence were based on the notion of the knowledge level. This perspective has been challenged by new AI that proposes an approach based on embodied systems that interact with the real‐world. We demonstrate that these two views can be unified. Our argument is based on the assumption that knowledge level explanations can be defined in the context of Bayesian theory while the goals of new AI are captured by using a well established robot based model of learning and problem solving, called Distributed Adaptive Control (DAC). In our analysis we consider random foraging and we prove that minor modifications of the DAC architecture renders a model that is equivalent to a Bayesian analysis of this task. Subsequently, we compare this enhanced, “rational,” model to its “non‐rational” predecessor and a further control condition using both simulated and real robots, in a variety of environments. Our results show that the changes made to the DAC architecture, in order to unify the perspectives of old and new AI, also lead to a significant improvement in random foraging.     

From commonsense to science,and back: The use of cognitive concepts in neuroscience

Commonsense cognitive concepts (CCCs) are the concepts used in daily life to explain, predict and interpret behaviour. CCCs are also used to convey neuroscientific results, not only to wider audiences but also to the scientific inner circle. We show that translations from CCCs to brain activity, and from brain data to CCCs are made in implicit, loose and unsystematic ways. This results in hard to connect data as well as possibly unwarranted extrapolations. We argue that the cause of these problems is a covert adherence to a position known in philosophy of mind as ‘mental realism’. The most fruitful way forward to a clearer and more systematic employment of CCCs in cognitive neuroscience, we argue, is to explicitly adopt interpretivism as an alternative for mental realism. An interpretative stance will help to avoid conceptual confusion in cognitive science and implies caution when it comes to big conclusions about CCCs.     

Revisiting the past and back to the future: Horizons of cognition and emotion research

ABSTRACT

To commemorate that Cognition & Emotion was established three decades ago, we asked some distinguished scholars to reflect on past research on the interface of cognition and emotion and prospects for the future. The resulting papers form the Special Issue on Horizons in Cognition and Emotion Research. The contributions to Horizons cover both the field in general and a diversity of specific topics, including affective neuroscience, appraisal theory, automatic evaluation, embodied emotion, emotional disorders, emotion-linked attentional bias, emotion recognition, emotion regulation, lifespan development, motivation, and social emotions. We hope that Horizons will spark constructive debates, while offering guidance for the future growth and development of research on the interface between cognition and emotion. Finally, we provide an update on how Cognition & Emotion has fared over the past year, and announce some changes in editorial policies and the editorial board.     

From cybernetics to brain theory,and more: A memoir

While structured as an autobiography, this memoir exemplifies ways in which classic contributions to cybernetics (e.g., by Wiener, McCulloch & Pitts, and von Neumann) have fed into a diversity of current research areas, including the mathematical theory of systems and computation, artificial intelligence and robotics, computational neuroscience, linguistics, and cognitive science. The challenges of brain theory receive special emphasis. Action-oriented perception and schema theory complement neural network modeling in analyzing cerebral cortex, cerebellum, hippocampus, and basal ganglia. Comparative studies of frog, rat, monkey, ape and human not only deepen insights into the human brain but also ground an EvoDevoSocio view of “how the brain got language.” The rapprochement between neuroscience and architecture provides a recent challenge. The essay also assesses some of the social and theological implications of this broad perspective.     

Re-framing the characteristics of concepts and their relation to learning and cognition in artificial agents

In this work, the problems of knowledge acquisition and information processing are explored in relation to the definitions of concepts and conceptual processing, and their implications for artificial agents.The discussion focuses on views of cognition as a dynamic property in which the world is actively represented in grounded mental states which only have meaning in the action context. Reasoning is understood as an emerging property consequence of actions-environment couplings achieved through experience, and concepts as situated and dynamic phenomena enabling behaviours.Re-framing the characteristics of concepts is considered crucial to overcoming settled beliefs and reinterpreting new understandings in artificial systems.The first part presents a review of concepts from cognitive sciences. Support is found for views on grounded and embodied cognition, describing concepts as dynamic, flexible, context-dependent, and distributedly coded.That is argued to contrast with many technical implementations assuming concepts as categories, whilst explains limitations when grounding amodal symbols, or in unifying learning, perception and reasoning.The characteristics of concepts are linked to methods of active inference, self-organization, and deep learning to address challenges posed and to reinterpret emerging techniques.In a second part, an architecture based on deep generative models is presented to illustrate arguments elaborated. It is evaluated in a navigation task, showing that sufficient representations are created regarding situated behaviours with no semantics imposed on data. Moreover, adequate behaviours are achieved through a dynamic integration of perception and action in a single representational domain and process.