This chapter presents a systematic theory of generalized (or universal) Fechnerian scaling, based on the intuition underlying Fechner’s original theory. The intuition is that subjective distances among stimuli are computed by cumulating small discriminability values between “neighboring” stimuli. A stimulus space is supposed to be endowed by a dissimilarity function, computed from a discrimination probability function for any pair of stimuli chosen in two distinct observation areas. On the most abstract level, one considers all possible chains of stimuli leading from stimulus a to stimulus b and back to a, and takes the infimum of the sums of the dissimilarities along these chains as the subjective distance between a and b. In arc-connected spaces, the cumulation of dissimilarity values along all possible chains reduces to their cumulation along continuous paths, leading to a fully fledged metric geometry. In topologically Euclidean spaces, the cumulation along paths further reduces to integration along smooth paths, and the geometry in question acquires the form of a generalized Finsler geometry. The chapter also discusses Fechner’s original derivation of his logarithmic law, observation sorites paradox, a generalized Floyd--Warshall algorithm for computing metric distances from dissimilarities, and an ultra-metric version and data-analytic application of Fechnerian scaling.

Response inhibition refers to an organism’s ability to suppress unwanted impulses, or actions and responses that are no longer required or have become inappropriate.In the stop-signal task, participants perform a response time task (go task), and occasionally, the go stimulus is followed by a stop signal after a variable delay, indicating subjects to withhold their response (stop task). The main interest of modeling is in estimating the unobservable latency of the stopping process as a characterization of the response inhibition mechanism. Here we analyze and compare the underlying assumptions of different models, including parametric and non-parametric versions of the race model. New model classes based on the concept of copulas are introduced and a number of unsolved problems facing all existing models are pointed out.

The investigation of processes involved in merging information from different sensory modalities has become the subject of research in many areas, including anatomy, physiology, and behavioral sciences. This field of research termed "multisensory integration’’ is flourishing, crossing borders between psychology and neuroscience. The focus of this chapter is on measures of multisensory integration based on numerical data collected from single neurons and in behavioral paradigms:spike numbers, reaction time, frequency of correct or incorrect responses in detection, recognition, and discrimination tasks. Defining that somewhat fuzzy term, it has been observed that at least some kind of numerical measurement assessing the strength of crossmodal effects is required. On the empirical side, these measures typically serve to quantify effects of various covariates on multisensory integration like age, certain disorders, developmental conditions, training and rehabilitation, in addition to attention and learning. On the theoretical side, these measures often help to probe hypotheses about underlying integration mechanisms like optimality in combining information or inverse effectiveness, without necessarily subscribing to a specific model.