Evaluative processes have their roots in early evolutionary history, as survival is dependent on an organism’s ability to identify and respond appropriately to positive, rewarding or otherwise salubrious stimuli as well as to negative, noxious, or injurious stimuli. Consequently, evaluative processes are ubiquitous in the animal kingdom and are represented at multiple levels of the nervous system, including the lowest levels of the neuraxis. While evolution has sculpted higher level evaluative systems into complex and sophisticated information-processing networks, they do not (...) come to replace, but rather to interact with more primitive lower level representations. Indeed, there are basic features of the underlying neuroarchitectural plan for evaluative processes that are common across levels of organization—including that of evaluative bivalence. (shrink)

Rutherford and Lindell (2011) review the theoretical and empirical research conceptualizing emotion and emotional processing within an approach-avoidance framework. This is accompanied by an extensive discussion of the cerebral lateralization of approach-avoidance. Berntson, Norman, and Cacioppo (2011) extend this discussion by presenting a bivariate evaluative model of emotion which adopts a valence-based (positive, negative) dictum. Here we discuss this latter model in the context of an approach-avoidance perspective.

Common methods for analysing response time (RT) tasks, frequently used across different disciplines of psychology, suffer from a number of limitations such as the failure to directly measure the underlying latent processes of interest and the inability to take into account the uncertainty associated with each individual's point estimate of performance. Here, we discuss a Bayesian hierarchical diffusion model and apply it to RT data. This model allows researchers to decompose performance into meaningful psychological processes and to account optimally for (...) individual differences and commonalities, even with relatively sparse data. We highlight the advantages of the Bayesian hierarchical diffusion model decomposition by applying it to performance on Approach–Avoidance Tasks, widely used in the emotion and psychopathology literature. Model fits for two experimental data-sets demonstrate that the model performs well. The Bayesian hierarchical diffusion model overcomes important limitations of current analysis procedures and provides deeper insight in latent psychological processes of interest. (shrink)

Numerous species use different forms of communication in order to successfully interact in their respective environment. This article seeks to elucidate limitations of the classical conduit metaphor by investigating communication from the perspectives of biology and artificial neural networks. First, communication is a biological natural phenomenon, found to be fruitfully grounded in an organism’s embodied structures and memory system, where specific abilities are tied to procedural, semantic, and episodic long-term memory as well as to working memory. Second, the account explicates (...) differences between non-verbal and verbal communication and shows how artificial neural networks can communicate by means of ontologically non-committal modelling. This approach enables new perspectives of communication to emerge regarding both sender and receiver. It is further shown that communication features gradient properties that are plausibly divided into a reflexive and a reflective form, parallel to knowledge and reflection. (shrink)