Second Wave Positive Psychology: Exploring the Positive–Negative Dialectics of Wellbeing

Positive psychology has tended to be defined in terms of a concern with ‘positive’ psychological qualities and states. However, critics of the field have highlighted various problems inherent in classifying phenomena as either ‘positive’ or ‘negative.’ For instance, ostensibly positive qualities (e.g., optimism) can sometimes be detrimental to wellbeing, whereas apparently negative processes (like anxiety) may at times be conducive to it. As such, over recent years, a more nuanced ‘second wave’ of positive psychology has been germinating, which explores the philosophical and conceptual complexities of the very idea of the ‘positive.’ The current paper introduces this emergent second wave by examining the ways in which the field is developing a more subtle understanding of the ‘dialectical’ nature of flourishing (i.e., involving a complex and dynamic interplay of positive and negative experiences). The paper does so by problematizing the notions of positive and negative through seven case studies, including five salient dichotomies (such as optimism versus pessimism) and two complex processes (posttraumatic growth and love). These case studies serve to highlight the type of critical, dialectical thinking that characterises this second wave, thereby outlining the contours of the evolving field.

Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding

The most biologically-inspired artificial neurons are those of the third generation, and are termed spiking neurons, as individual pulses or spikes are the means by which stimuli are communicated. In essence, a spike is a short-term change in electrical potential and is the basis of communication between biological neurons. Unlike previous generations of artificial neurons, spiking neurons operate in the temporal domain, and exploit time as a resource in their computation. In 1952, Alan Lloyd Hodgkin and Andrew Huxley produced the first model of a spiking neuron; their model describes the complex electro-chemical process that enables spikes to propagate through, and hence be communicated by, spiking neurons. Since this time, improvements in experimental procedures in neurobiology, particularly with in vivo experiments, have provided an increasingly more complex understanding of biological neurons. For example, it is now well understood that the propagation of spikes between neurons requires neurotransmitter, which is typically of limited supply. When the supply is exhausted neurons become unresponsive. The morphology of neurons, number of receptor sites, amongst many other factors, means that neurons consume the supply of neurotransmitter at different rates. This in turn produces variations over time in the responsiveness of neurons, yielding various computational capabilities. Such improvements in the understanding of the biological neuron have culminated in a wide range of different neuron models, ranging from the computationally efficient to the biologically realistic. These models enable the modelling of neural circuits found in the brain. In recent years, much of the focus in neuron modelling has moved to the study of the connectivity of spiking neural networks. Spiking neural networks provide a vehicle to understand from a computational perspective, aspects of the brain’s neural circuitry. This understanding can then be used to tackle some of the historically intractable issues with artificial neurons, such as scalability and lack of variable binding. Current knowledge of feed-forward, lateral, and recurrent connectivity of spiking neurons, and the interplay between excitatory and inhibitory neurons is beginning to shed light on these issues, by improved understanding of the temporal processing capabilities and synchronous behaviour of biological neurons. This research topic aims to amalgamate current research aimed at tackling these phenomena.