No Arabic abstract
What happen in the brain when human beings play games with computers? Here a simple zero-sum game was conducted to investigate how people make decision via their brain even they know that their opponent is a computer. There are two choices (a low or high number) for people and also two strategies for the computer (red color or green color). When the number selected by the human subject meet the red color, the person loses the score which is equal to the number. On the contrary, the person gains the number of score if the computer chooses a green color for the number selected by the human being. Both the human subject and the computer give their choice at the same time, and subjects have been told that the computer make its decision randomly on the red color or green color. During the experiments, the signal of electroencephalograph (EEG) obtained from brain of subjects was recorded. From the analysis of EEG, we find that people mind the loss more than the gain, and the phenomenon becoming obvious when the gap between loss and gain grows. In addition, the signal of EEG is clearly distinguishable before making different decisions. It is observed that significant negative waves in the entire brain region when the participant has a greater expectation for the outcome, and these negative waves are mainly concentrated in the forebrain region in the brain of human beings.
Standard lossy image compression algorithms aim to preserve an images appearance, while minimizing the number of bits needed to transmit it. However, the amount of information actually needed by a user for downstream tasks -- e.g., deciding which product to click on in a shopping website -- is likely much lower. To achieve this lower bitrate, we would ideally only transmit the visual features that drive user behavior, while discarding details irrelevant to the users decisions. We approach this problem by training a compression model through human-in-the-loop learning as the user performs tasks with the compressed images. The key insight is to train the model to produce a compressed image that induces the user to take the same action that they would have taken had they seen the original image. To approximate the loss function for this model, we train a discriminator that tries to distinguish whether a users action was taken in response to the compressed image or the original. We evaluate our method through experiments with human participants on four tasks: reading handwritten digits, verifying photos of faces, browsing an online shopping catalogue, and playing a car racing video game. The results show that our method learns to match the users actions with and without compression at lower bitrates than baseline methods, and adapts the compression model to the users behavior: it preserves the digit number and randomizes handwriting style in the digit reading task, preserves hats and eyeglasses while randomizing faces in the photo verification task, preserves the perceived price of an item while randomizing its color and background in the online shopping task, and preserves upcoming bends in the road in the car racing game.
Depression has been associated with impaired neural processing of reward and punishment. However, to date, little is known regarding the relationship between depression and intertemporal choice for gain and loss. We compared impulsivity and inconsistency in intertemporal choice for monetary gain and loss (quantified with parameters in the q-exponential discount function based on Tsallis statistics) between depressive patients and healthy control subjects. This examination is potentially important for advances in neuroeconomics of intertemporal choice, because depression is associated with reduced serotonergic activities in the brain. We observed that depressive patients were more impulsive and time-inconsistent in intertemporal choice action for gain and loss, in comparison to healthy controls. The usefulness of the q-exponential discount function for assessing the impaired decision-making by depressive patients was demonstrated. Furthermore, biophysical mechanisms underlying the altered intertemporal choice by depressive patients are discussed in relation to impaired serotonergic neural systems. Keywords: Depression, Discounting, Neuroeconomics, Impulsivity, Inconsistency, Tsallis statistics
Nature is in constant flux, so animals must account for changes in their environment when making decisions. How animals learn the timescale of such changes and adapt their decision strategies accordingly is not well understood. Recent psychophysical experiments have shown humans and other animals can achieve near-optimal performance at two alternative forced choice (2AFC) tasks in dynamically changing environments. Characterization of performance requires the derivation and analysis of computational models of optimal decision-making policies on such tasks. We review recent theoretical work in this area, and discuss how models compare with subjects behavior in tasks where the correct choice or evidence quality changes in dynamic, but predictable, ways.
The spreading dynamics of an epidemic and the collective behavioral pattern of the population over which it spreads are deeply intertwined and the latter can critically shape the outcome of the former. Motivated by this, we design a parsimonious game-theoretic behavioral--epidemic model, in which an interplay of realistic factors shapes the co-evolution of individual decision-making and epidemics on a network. Although such a co-evolution is deeply intertwined in the real-world, existing models schematize population behavior as instantaneously reactive, thus being unable to capture human behavior in the long term. Our model offers a unified framework to model and predict complex emergent phenomena, including successful collective responses, periodic oscillations, and resurgent epidemic outbreaks. The framework also allows to assess the effectiveness of different policy interventions on ensuring a collective response that successfully eradicates the outbreak. Two case studies, inspired by real-world diseases, are presented to illustrate the potentialities of the proposed model.
Human Augmentation (HA) spans several technical fields and methodological approaches, including Experimental Psychology, Human-Computer Interaction, Psychophysiology, and Artificial Intelligence. Augmentation involves various strategies for optimizing and controlling cognitive states, which requires an understanding of biological plasticity, dynamic cognitive processes, and models of adaptive systems. As an instructive lesson, we will explore a few HA-related concepts and outstanding issues. Next, we focus on inducing and controlling HA using experimental methods by introducing three techniques for HA implementation: learning augmentation, augmentation using physical media, and extended phenotype modeling. To conclude, we will review integrative approaches to augmentation, which transcend specific functions.