The relationship between reward context and inhibitory control, does it depend on BMI, maladaptive eating, and negative affect?

Background Recent studies suggest that higher Body Mass Index (BMI) is associated with reduced inhibitory control in contexts of palatable food. However, due to limitations of previous studies, it remained the question whether this reduction is specific to food contexts, and whether it generalizes to other contexts of reward, such as money. This main question was addressed in the current study. In addition, we explored the effect of maladaptive eating and stress regarding inhibitory control across the contexts that differed in terms of reward. Methods In total, 46 participants between 19 and 50 years old (39% males and 61% females) with an average BMI of 23.5 (SD = 3.9) participated. Participants filled out questionnaires and performed a go/no-go task (indexing inhibitory control) with three conditions (neutral, food, and money condition). Results Relatively high (above median) BMI was associated with challenged inhibitory control in the food relative to the neutral context, but not in the money relative to neutral context. Explorative analyses suggested that maladaptive eating and stress were associated with reduced inhibitory control in the food context. Only rumination was associated with reduced inhibitory control in the money context. Conclusions The effects of BMI, maladaptive eating behavior, and stress on inhibitory control were specific to the food context, and did not generalize to a non-intrinsic reward condition, operationalized with money pictures. Our results imply that (research on) interventions directed at improving inhibitory control in relation to overweight and obesity, should consider food-reward context.

Predication-Error-Based Intrinsically Motivated Saccade Learning

The quick, simultaneous movements of both eyes in the same direction is called a saccade, and the process of developing an internal model for the eyes’ movement-control based on visual stimuli is called saccade learning. All humans use this type of eye motion to bring salient objects to the foveal locations of the retina, even if the objects are located randomly in the surrounding environment. To begin with, infants are not able to perform this type of eye motion, but sensory information motivates them to start learning saccadic behavior. In this paper, a sensory prediction-error-based intrinsically motivated model is proposed for learning saccadic eye movements, and this approach is more consistent with biological systems for saccade learning. Predicted Coding/Biased Competition using Divisive Input Modulation (PC/BC-DIM) network is used for saccade learning using sensory prediction errors. The quantification of sensory prediction errors provides an intrinsic reward. A simulated humanoid agent, iCub, is used to assess and quantify the performance of the proposed model. The performance metrics used for this purpose are percentage mean post-saccadic distance and standard deviation. The mean post-saccadic distance for the proposed model was less than 1°, which is biologically plausible.

PENGARUH IMBALAN INTRINSIK TERHADAP SIKAP PEGAWAI YANG DIMEDIASI OLEH PERSEPSI DUKUNGAN ORGANISASI

This study aims to examine the effect of Intrinsic Rewards on Employee Attitudes mediated by Perceptions of Organizational Support at the Social Service of Gayo Lues Regency. The sampling method in this research uses sensus sampling with a sample of 115 respondents. The results of this study indicate that the variables of intrinsic reward and perceived organizational support have a significant effect on employee attitudes. Furthermore, the perceived organizational support variable partially mediates the intrinsic rewards of employee attitudes.

Animals in flow – Towards the scientific study of intrinsic reward in animals

The concept of flow, a state of complete absorption in an intrinsically rewarding activity, has played a pivotal role in advancing notions of human well-being beyond minimising suffering towards promoting flourishing and thriving. While flow has played a fundamental role in human positive psychology, it has not yet been explored in non-human animals, leaving an enormous void in our understanding of intrinsic motivation in animals. As ethology and related fields keep progressing in uncovering complex cognitive and affective capacities of non-human animals, we propose the time is ripe to translate the concept of flow to animals. We start by embedding flow in the topic of intrinsic motivation and describe its impact on positive human psychology and potentially positive animal welfare. We then disambiguate flow from related concepts discussed in the animal literature. Next, we derive experimental approaches in animals from the canonical characteristics of flow in humans and provide guidelines for both inducing and assessing flow by focusing on two characteristics that do not necessarily depend on self-report, namely resistance to distraction and time distortion. Not all aspects of the human flow experience are (yet) translatable, but those that are may improve quality of life in non-human animals.

Introduction

This Introduction sets the scene for the rest of the book by raising the problem of evil; a problem that for theist and atheist alike, makes us doubt the value of the world. To value the world requires that we find something of final positive value. When we experience final positive value we feel pleasure, however the intrinsic reward of pleasure cannot always satisfy us. We must find a value that works even when our lives are going badly. It is proposed that, while the quest for moral value cannot satisfy this requirement, aesthetic value can.

Generating individual intrinsic reward for cooperative multiagent reinforcement learning

Multiagent reinforcement learning holds considerable promise to deal with cooperative multiagent tasks. Unfortunately, the only global reward shared by all agents in the cooperative tasks may lead to the lazy agent problem. To cope with such a problem, we propose a generating individual intrinsic reward algorithm, which introduces an intrinsic reward encoder to generate an individual intrinsic reward for each agent and utilizes the hypernetworks as the decoder to help to estimate the individual action values of the decomposition methods based on the generated individual intrinsic reward. Experimental results in the StarCraft II micromanagement benchmark prove that the proposed algorithm can increase learning efficiency and improve policy performance.