A short proof of a theorem of Falmagne

Falmagne (J. Math. Psychol. 18 (1978) 52) proved the sufficiency of the Block-Marschak inequalities (in: I. Olkin, S. Ghurye, W. Hoefding, W. Madow, H. Mann (Eds.), Contributions to Probability and Statistics, Stanford University Press, Stanford, CA, 1960, pp. 97-132) and normalization equalities for a complete system of choice probabilities to be induced by rankings. Here, we give a considerably shorter proof of this result. Our approach combines Möbius inversion and network flows.     

Finding ERP-signatures of target awareness: Puzzle persists because of experimental co-variation of the objective and subjective variables

Using masking techniques combined with electrophysiological recordings is a promising way to study neural correlates of visual awareness, as shown in recent studies. Here I comment on the following puzzling aspects typical for this endeavour that have made obstacles for a potentially even more impressive progress. First, the continuing practice of confounds between objective stimulus variables and subjective dependent measures. Second, complexity of timing the emergence of subjective conscious percept which is partly due to complex interactivity between target and mask. Third, the ambiguity of the concept of neural correlates of awareness.     

Using habituation in machine learning

Habituation, a decrement in response to a stimulus that is presented repeatedly without ill effect, can be identified in almost all animals. It can also be used in machine learning to provide a variety of different applications, such as novelty detection, recency encoding, and temporal signal pre-processing. This paper examines how habituation can be mathematically modelled, and discusses how well these models fit the revised characteristics of habituation. It then demonstrates how the models can be combined with neural networks in order to realise the various applications. Finally, some simple experimental results are presented that demonstrate the effectiveness of the methods.     

Mathematical foundations of consciousness

We employ the Zermelo–Fränkel Axioms that characterize sets as mathematical primitives. The Anti-foundation Axiom plays a significant role in our development, since among other of its features, its replacement for the Axiom of Foundation in the Zermelo–Fränkel Axioms motivates Platonic interpretations. These interpretations also depend on such allied notions for sets as pictures, graphs, decorations, labelings and various mappings that we use. A syntax and semantics of operators acting on sets is developed. Such features enable construction of a theory of non-well-founded sets that we use to frame mathematical foundations of consciousness. To do this we introduce a supplementary axiomatic system that characterizes experience and consciousness as primitives. The new axioms proceed through characterization of so-called consciousness operators. The Russell operator plays a central role and is shown to be one example of a consciousness operator. Neural networks supply striking examples of non-well-founded graphs the decorations of which generate associated sets, each with a Platonic aspect. Employing our foundations, we show how the supervening of consciousness on its neural correlates in the brain enables the framing of a theory of consciousness by applying appropriate consciousness operators to the generated sets in question.     

Brain and mind operational architectonics and man-made “machine” consciousness

To build a true conscious robot requires that a robot’s “brain” be capable of supporting the phenomenal consciousness as human’s brain enjoys. Operational Architectonics framework through exploration of the temporal structure of information flow and inter-area interactions within the network of functional neuronal populations [by examining topographic sharp transition processes in the scalp electroencephalogram (EEG) on the millisecond scale] reveals and describes the EEG architecture which is analogous to the architecture of the phenomenal world. This suggests that the task of creating the “machine” consciousness would require a machine implementation that can support the kind of hierarchical architecture found in EEG.

Neural computation as a tool to differentiate perceptual from emotional processes: The case of anger superiority effect

Research findings in social and cognitive psychology imply that it is easier to detect angry faces than happy faces in a crowd of neutral faces [Hansen, C. H., & Hansen, R. D. (1988). Finding the face in the crowd – An anger superiority effect. Journal of Personality and Social Psychology, 54(6), 917–924]. This phenomenon has been held to have evolved over phylogenetic development because it was adaptive to quickly and accurately detect a potential threat in the environment. However, across recent studies, a controversy has emerged about the underlying perceptual versus emotional factors responsible for this so-called anger superiority effect [Juth, P., Lundqvist, D., Karlsson, A., & Ohman, A. (2005). Looking for foes and friends: Perceptual and emotional factors when finding a face in the crowd. Emotion, 5(4), 379–395; Purcell, D. G., Stewart, A. L., & Skov, R. B. (1996). It takes a confounded face to pop out of a crowd. Perception, 25(9), 1091–1108]. To tease apart emotional and perceptual processes, we used neural network analyzes of human faces in two different simulations. Results show that a perceptual bias is probably acting against faster and more accurate identification of anger faces compared to happy faces at a purely perceptual level. We suggest that a parsimonious hypothesis related to the simple perceptual properties of the stimuli might explain these behavioral results without reference to evolutionary processes. We discuss the importance of statistical or connectionist analysis for empirical studies that seek to isolate perceptual from emotional factors, but also learned vs. innate factors in the processing of facial expression of emotion.     

Using an AI creativity system to explore how aesthetic experiences are processed along the brain’s perceptual neural pathways

With the increased sophistication of AI techniques, the application of these systems has been expanding to ever newer fields. Increasingly, these systems are being used in modeling of human aesthetics and creativity, e.g. how humans create artworks and design products. Our lab has developed one such AI creativity deep learning system that can be used to create artworks in the form of images and videos. In this paper, we describe this system and its use in studying the human visual system and the formation of aesthetic experiences. Specifically, we show how time-based AI created media can be used to explore the nature of the dual-pathway neuro-architecture of the human visual system and how this relates to higher cognitive judgments such as aesthetic experiences that rely on these divergent information streams. We propose a theoretical framework for how the movement within percepts such as video clips, causes the engagement of reflexive attention and a subsequent focus on visual information that are primarily processed via the dorsal stream, thereby modulating aesthetic experiences that rely on information relayed via the ventral stream. We outline our recent study in support of our proposed framework, which serves as the first study that investigates the relationship between the two visual streams and aesthetic experiences.     

Network Connectivity Dynamics,Cognitive Biases,and the Evolution of Cultural Diversity in Round-Robin Interactive Micro-Societies

The distribution of cultural variants in a population is shaped by both neutral evolutionary dynamics and by selection pressures. The temporal dynamics of social network connectivity, that is, the order in which individuals in a population interact with each other, has been largely unexplored. In this paper, we investigate how, in a fully connected social network, connectivity dynamics, alone and in interaction with different cognitive biases, affect the evolution of cultural variants. Using agent-based computer simulations, we manipulate population connectivity dynamics (early, mid, and late full-population connectivity); content bias, or a preference for high-quality variants; coordination bias, or whether agents tend to use self-produced variants (egocentric bias), or to switch to variants observed in others (allocentric bias); and memory size, or the number of items that agents can store in their memory. We show that connectivity dynamics affect the time-course of variant spread, with lower connectivity slowing down convergence of the population onto a single cultural variant. We also show that, compared to a neutral evolutionary model, content bias accelerates convergence and amplifies the effects of connectivity dynamics, while larger memory size and coordination bias, especially egocentric bias, slow down convergence. Furthermore, connectivity dynamics affect the frequency of high-quality variants (adaptiveness), with late connectivity populations showing bursts of rapid change in adaptiveness followed by periods of relatively slower change, and early connectivity populations following a single-peak evolutionary dynamic. We evaluate our simulations against existing data collected from previous experiments and show how our model reproduces the empirical patterns of convergence.     

Enhanced proliferation of progenitor cells following long-term potentiation induction in the rat dentate gyrus

The dentate gyrus (DG) is among the few areas in the mammalian brain where production of new neurons continues in the adulthood. Although its functional significance is not completely understood, several lines of evidence suggest the role of DG neurogenesis in learning and memory. Considering that long-term potentiation (LTP) is a prime candidate for the process underlying hippocampal learning and memory, these results raise the possibility that LTP and neurogenesis are closely related. Here, we investigated whether or not LTP induction in the afferent pathway triggers enhanced proliferation of progenitor cells in the DG. LTP was induced by tetanic stimulation in perforant path-DG synapses in one hemisphere, and the number of newly generated progenitor (BrdU-labeled) cells in the DG was quantified. Compared with the control hemisphere (stimulated with low-frequency pulses), the LTP-induced hemisphere contained a significantly higher number of newly generated progenitor cells in the dorsal as well as ventral DG. When CPP, an NMDA receptor antagonist, was administered, tetanic stimulation neither induced LTP nor enhanced progenitor cell proliferation, indicating that NMDA receptor activation, rather than tetanic stimulation per se, is responsible for enhanced progenitor proliferation in the control animal. Our results show that tetanic stimulation of perforant path sufficient to induce LTP increases progenitor proliferation in adult DG in an NMDA receptor-dependent manner.