Dynamical aspects of behavior generation under constraints

Sensory Perception Mechanism for Preparing the Combinations of Stimuli Operation in the Architectural Experience

Sensory stimuli in an architectural space play an important role in the human perception of the indoor environment, no matter whether they are static or dynamic, isolated, or combined. By enhancing some perceptions in the sensory stimuli, the overall perceptions of an architectural space can be improved, especially for an intelligent architectural space. As yet, there are few studies reported about the sensory perception mechanism for the sensory stimuli operation in the architectural experience. In this research, a wooden micro building was prepared for the study of the sensitivity level of participants to various sensory stimuli in the same and in different sensory domains. Participants’ visual, auditory, olfactory, tactile and kinaesthesia perceptions were discussed statistically in terms of the sensitivity level. Based on the study, the effect of a single dynamic sensory stimulus (a dynamically coloured light) on the participants’ perception was studied in a paper architectural model from two aspects including preference and emotion. The dynamically coloured light was discussed statistically in terms of the level of preference. The study showed that there are significant differences among participants’ levels of sensitivity to the different sensory domains and to the different sensory stimuli. In particular, the sensitivity level to the stimulus that is the colour of a space is the highest of all stimuli. As a single changing sensory stimulus, a dynamically coloured light can lead to significant mood fluctuations and changes in the preference level. In particular, yellow is the favourite colour of light. The object of this study is expected to provide a theoretical foundation that is related to sensory choice, sensory perception enhancement and the combination forms of sensory perceptions. Based on the theoretical foundation, the perception design of overlapped multi-sensory stimuli and a single dynamic stimulus can be conducted to improve the quality of the indoor environment of normal and intelligent multi-sensory architecture.

Setting the space for deliberation in decision-making

Decision-making models in the behavioral, cognitive, and neural sciences typically consist of forced-choice paradigms with two alternatives. While theoretically it is feasible to translate any decision situation to a sequence of binary choices, real-life decision-making is typically more complex and nonlinear, involving choices among multiple items, graded judgments, and deferments of decision-making. Here, we discuss how the complexity of real-life decision-making can be addressed using conventional decision-making models by focusing on the interactive dynamics between criteria settings and the collection of evidence. Decision-makers can engage in multi-stage, parallel decision-making by exploiting the space for deliberation, with non-binary readings of evidence available at any point in time. The interactive dynamics principally adhere to the speed-accuracy tradeoff, such that increasing the space for deliberation enables extended data collection. The setting of space for deliberation reflects a form of meta-decision-making that can, and should be, studied empirically as a value-based exercise that weighs the prior propensities, the economics of information seeking, and the potential outcomes. Importantly, the control of the space for deliberation raises a question of agency. Decision-makers may actively and explicitly set their own decision parameters, but these parameters may also be set by environmental pressures. Thus, decision-makers may be influenced-or nudged in a particular direction-by how decision problems are framed, with a sense of urgency or a binary definition of choice options. We argue that a proper understanding of these mechanisms has important practical implications toward the optimal usage of space for deliberation.

Extreme learning machines for regression based on V-matrix method

This paper studies the joint effect of V-matrix, a recently proposed framework for statistical inferences, and extreme learning machine (ELM) on regression problems. First of all, a novel algorithm is proposed to efficiently evaluate the V-matrix. Secondly, a novel weighted ELM algorithm called V-ELM is proposed based on the explicit kernel mapping of ELM and the V-matrix method. Though V-matrix method could capture the geometrical structure of training data, it tends to assign a higher weight to instance with smaller input value. In order to avoid this bias, a novel method called VI-ELM is proposed by minimizing both the regression error and the V-matrix weighted error simultaneously. Finally, experiment results on 12 real world benchmark datasets show the effectiveness of our proposed methods.

Solving the "human problem": the frontal feedback model

This paper argues that humans possess unique cognitive abilities due to the presence of a functional system that exists in the human brain that is absent in the non-human brain. This system, the frontal feedback system, was born in the hominin brain when the great phylogenetic expansion of the prefrontal cortex relative to posterior sensory regions surpassed a critical threshold. Surpassing that threshold effectively reversed the preferred direction of information flow in the highest association regions of the neocortex, producing the frontal feedback system. This reversal was from the caudo-rostral bias characteristic of non-human, or pre-human, brain dynamics to a rostro-caudal bias characteristic of modern human brain dynamics. The frontal feedback system works through frontal motor routines, or action schemes, manipulating the release and reconstruction of stored sensory memories in posterior sensory areas. As an obligatory feature of frontal feedback, a central character, or self, emerges within this cortical network that manifests itself as agent in these reconstructions as well as in the experience of sensory perceptions. Dynamical-systems modeling of cortical interactions is combined in the paper with recent neuroimaging studies of "resting-state" brain activity to bridge the gap between microscopic and macroscopic levels of cortical behavior. This synthesis is used to support the proposal of an information flow reversal occurring in the hominin brain and also to explain how such a reversal generates the wide variety of cognitive and experiential phenomena that many consider to be uniquely human.