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Based on a synthesis of findings from psychology, neuroscience, and machine learning, we propose a unified theory of curiosity as a form of motivated cognition. Curiosity, we propose, is comprised of a family of mechanisms that range in complexity from simple heuristics based on novelty, salience, or surprise, to drives based on reward and uncertainty reduction and finally, to self-directed metacognitive processes. These mechanisms, we propose, have evolved to allow agents to discover useful regularities in the world – steering them toward niches of maximal learning progress and away from both random and highly familiar tasks. We emphasize that curiosity arises organically in conjunction with cognition and motivation, being generated by cognitive processes and in turn, motivating them. We hope that this view will spur the systematic study of curiosity as an integral aspect of cognition and decision making during development and adulthood.

Drive and motivation are central to affective neuroscience. Here, we describe the development of conceptualizations from early behaviorist theories to contemporary theories linking motivation closely to reward. Current experimental data suggest key roles of drive and motivation in the wanting, liking, and learning processes underlying the pleasure cycle supporting survival of individuals and species. In particular, the underlying functional neuroanatomy of drive and motivation is now becoming clearer in humans and other mammals, which provides hope for novel more effective interventions for the pervasive problems of drive and motivation in affective and addictive disorders.

This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment.

In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.

To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states.

In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.

This study aims to examine how brands can leverage on advergames as an interactive marketing tool to foster prosumer culture and build a sacred brand. Drawing from game theory, this research scrutinizes how advergame format (cooperative vs noncooperative) influences consumers’ perceived brand sacredness by harnessing positive brand relationship quality (BRQ) and intention to prosume. It also examines how reward types moderate the relationship between advergame format and advergamers’ BRQ.

Three different studies were conducted. Study 1 develops a measure to capture advergamers’ intention to prosume. Study 2 uses survey to collect data from brand-controlled gaming community platform. Study 3 is an experiment that uses 2 (game format: cooperative vs noncooperative) × 2 (reward type: hedonic vs utilitarian) between-subject format.

Study 1 provides a reliable and valid measure to capture “intention to prosume.” The results of Study 2 elucidate that (non) cooperative advergame format generates strong cold (hot) BRQ, leading to intention to prosume, which, in turn, drives brand sacredness. The results of Study 3 elucidate that using (utilitarian) hedonic rewards strengthens the impact of (non) cooperative advergame format on (cold) hot BRQ.

This research has examined the roles of cooperative vs noncooperative game design formats and hedonic vs utilitarian reward formats. Future research may focus on other possible advergame design formats and reward types.

This research provides insights to advergame marketers toward designing appropriate hedonic or utilitarian game rewards to strengthen the impact of cooperative vs noncooperative advergame format on brand sacredness through enhancing BRQ and intention to prosume among the target advergamers.

This research applies game theory in the advergaming context to manoeuvre game format and rewards so that a sustainable prosumption culture is built, which has strong beliefs about the sacredness of the brand.

Decision-making is one of the key technologies for self-driving cars. The high dependency of previously existing methods on human driving data or rules makes it difficult to model policies for different driving situations.

In this research, a probabilistic decision-making method based on the Markov decision process (MDP) is proposed to deduce the optimal maneuver automatically in a two-lane highway scenario without using any human data. The decision-making issues in a traffic environment are formulated as the MDP by defining basic elements including states, actions and basic models. Transition and reward models are defined by using a complete prediction model of the surrounding cars. An optimal policy was deduced using a dynamic programing method and evaluated under a two-dimensional simulation environment.

Results show that, at the given scenario, the self-driving car maintained safety and efficiency with the proposed policy.

This paper presents a framework used to derive a driving policy for self-driving cars without relying on any human driving data or rules modeled by hand.

This article seeks to establish the extent to which a major bank’s (Finbank) reward strategy for managers demonstrates the plausibility of Lawler’s model of reward strategy. This model argues that business strategy implies individual and organisational behaviours which dictate the content of reward strategy. In addition, three critical elements of the reward system need to be aligned for the system to be effective: the organisation’s core values; the implementation process; and the structure. The data collected demonstrate that Lawler’s model presents three major complications. It over‐simplifies the process of strategy formulation and content; it gives equal importance to values, structure and process although the data showed that the last was of the greatest significance; and it is represented as unitarist and deterministic.

Reward research has focussed on level (what individuals are paid) and structure (relationship between different levels of reward). Less emphasis has been given to reward mix decisions, i.e. the relative proportions of each element making up overall reward. This paper seeks to examine the determinants of reward mix.

Interview based research with reward consultants as key organisational observers and participants in reward mix decision making.

Benchmarking has led to the development of reward mix norms. Organisations are under pressure to conform to these norms, moderated by leadership beliefs, the occurrence of events and the extent to which organisations' change capability can overcome strong institutional forces.

The results question agency theory based explanations of reward mix determination and point towards resource dependence and institutional theory perspectives being more suitable theoretical frameworks.

The model developed allows reward managers to consider how the moderating variables, to the dominant mimetic pressure faced, could be manipulated for their firm to allow greater differentiation of the reward mix.

Academically the work contributes to a programme of research into reward determination from a constructionist perspective and aims to provide greater theoretical robustness to the subject. Practically, the findings may prompt practitioners to think more consciously about the drivers of their firm's reward mix. Policy makers may use the stronger theoretical base for understanding the determinants of reward mix choices and the extent to which organisational free choice and institutionally determined choice influence final choices in reward policy decision making.

The management control and performance measurement literature reflects a long history of discussion related to organizational, team, and individual rewards. Yet, much of the research and guidance in the academic and managerial literature has been inadequate. Reflecting work on three current research studies, this chapter examines the gaps in our current understanding of the relationship of performance measurement, rewards, and performance and suggests some research questions that are of significant interest.

A self‐help guide to achieving success in business. Directed more towards the self‐employed, it is relevant to other managers in organizations. Divided into clear sections on creativity and dealing with change; importance of clear goal setting; developing winning business and marketing strategies; negotiating skills; leadership; financial skills; and time management.

This study purports to examine the effects of a joint reward system (JRS) under a new product development (NPD) setting by identifying four neglected aspects of JRS that contains a procedural view (participation of reward decision and reward contingent on NPD phases) and a monetary view (risk‐free to participate and over‐reward incentive) in a conceptual model, and then to empirically test their effects on knowledge sharing and NPD performance.

Using regression analysis, the proposed model was tested on 233 valid respondents (112 in R&D, 50 in marketing, and 71 in manufacturing), including 92 from electronics firms, 87 from semiconductor firms, 29 from biotechnology firms, and 25 from pharmaceutical firms in Taiwan.

The results indicated that risk‐free to NPD project members is the most salient aspect of JRS on knowledge sharing and NPD performance. Joint determination of reward allocation was found to be a favorable JRS for only marketing and NPD performance. Rewards contingent on NPD phases have shown conflicting results between R&D and marketing. No relationship was found for over‐reward incentive on knowledge sharing and NPD performance. Despite the mixed effects of JRS, knowledge sharing is a strong predictor of NPD performance.

This study extends understanding of the complexities of rewards on knowledge sharing and NPD success by decomposing and testing four unique aspects of JRS, which sheds a new light on NPD researches.