INTRINSIC MOTIVATIONS AND OPEN-ENDED DEVELOPMENT IN ANIMALS, HUMANS, AND ROBOTS: AN OVERVIEW

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INTRINSIC MOTIVATIONS AND OPEN-ENDED DEVELOPMENT IN ANIMALS, HUMANS, AND ROBOTS: AN OVERVIEW

This editorial article introduces the Frontiers Research Topic and Electronic Book (eBook) on Intrinsic Motivations (IMs), which involved the publication of 24 articles with the journals Frontiers in Psychology – Cognitive Science and Frontiers in Neurorobotics. The main objective of this Frontiers Research Topic is to present state-of-the-art research on IMs and open-ended development from an interdisciplinary perspective involving human and animal psychology, neuroscience, and computational perspectives. We first introduce in this section the main themes and concepts on IMs from different interdisciplinary perspectives. These themes and concepts have been reviewed more extensively in other works (e.g., see Barto et al., 2004; Oudeyer and Kaplan, 2007; Mirolli and Baldassarre, 2013; Barto, 2013), but they are briefly reported here both to meet the needs of the reader new to the field and to introduce the concepts and terms we use in the succeeding sections. In the next four sections, we give an overview of the Topic contributions grouped by four themes. A final section draws the conclusions.

Autonomous development and lifelong open-ended learning are hallmarks of intelligence. Higher mammals, and especially humans, engage in activities that do not appear to directly serve the goals of survival, reproduction, or material advantage. Rather, many activities seem to be carried out “for their own sake” (Berlyne, 1966), play being a prime example, but including other activities driven by curiosity and interest in novel stimuli or surprising events. Autonomously setting goals and working to acquire new forms of competence are also examples of activities that often do not confer obvious evolutionary benefit. Activities like these are thus said to be driven by intrinsic motivations (Baldassarre and Mirolli, 2013a). IMs facilitate the cumulative and virtually open-ended acquisition of knowledge and skills that can later be used to accomplish fitness-enhancing goals (Singh et al., 2010; Baldassarre, 2011). IMs continue during adulthood, and they underlie several important human phenomena such as artistic creativity, scientific discovery, and subjective well-being (Ryan and Deci, 2000b; Schmidhuber, 2010). IMs were proposed within the animal literature to explain aspects of behavior that could not be explained by the dominant theory of motivation postulating that animals work to reduce physiological imbalances (Hull, 1943). The term “intrinsic motivation” was first used to describe a “manipulation drive” hypothesized to explain why rhesus monkeys would engage with mechanical puzzles for long periods of time without receiving extrinsic rewards (Harlow et al., 1950).

Other studies showed how animal instrumental actions can be conditioned with the delivery of apparently neutral stimuli: for example, monkeys were trained to perform actions to gain access to a window from which they could observe conspecifics (Butler, 1953), and mice were trained to perform actions that resulted in clicks or in moving the cage platform (Kish, 1955). The psychological literature on IMs initially linked them to the perceptual properties of stimuli, such as their complexity, novel appearance, or surprising features (Berlyne, 1950, 1966). Later, IMs were also related to action, in particular to the competence (“effectance”) that an agent can acquire to willfully make changes in its environment (White, 1959). This relation of IMs with action and their effects was later linked to the possibility of autonomously setting one’s own goals (Ryan and Deci, 2000a). Computational approaches, in particular machine learning and autonomous robotics, are concerned with IMs and open-ended development as these are thought to have the potential to lead to the construction of truly intelligent artificial systems, in particular systems that are capable of improving their own skills and knowledge autonomously and indefinitely. The relation of these studies with those on IMs in psychology were first highlighted by Barto et al. (2004) and Singh et al. (2005). The investigation of IMs from a computational perspective can lead to theoretical clarifications, in particular with respect to the computational mechanisms and functions that might underlie IMs (Mirolli and Baldassarre, 2013). IM mechanisms have been classified as being either knowledge-based or competence-based (Oudeyer and Kaplan, 2007): the former based on measures related to the acquisition of information, and the latter on measures related to the learning of skills. More recently, knowledge-based IMs have been further divided into novelty-based IMs and prediction-based IMs (Baldassarre and Mirolli, 2013b; Barto et al., 2013). Novelty-based IMs are elicited by the experience of stimuli that are not in the agent’s memory (e.g., novel objects, or novel object-object or object-context combinations); prediction-based IMs are related to events that surprise the agent by violating its explicit predictions.

These distinctions have been formalized in the computational models proposed in the literature. Seminal works in machine learning (Schmidhuber, 1991), later developed to function in robots (Oudeyer et al., 2007), have proposed algorithms rewarding actions that allow the agent to improve the quality of a “predictor” component with which it anticipates the effects that such actions produce on the environment. Other researchers have proposed robots capable of detecting and focussing on novel stimuli (e.g., Marsland et al., 2005), or systems capable of detecting anomalies in datasets (Nehmzow et al., 2013). Additional research threads have focussed on action and control, in particular on IMs guiding the autonomous acquisition of motor skills (Barto et al., 2004), on the decision about which of several skills to practice at any time (Schembri et al., 2007; Santucci et al., 2013), and on the the autonomous formation of goals guiding skill acquisition (Baranes and Oudeyer, 2013). Other computational mechanisms related to the idea of IMs are being proposed in the growing field of active learning, in particular in relation to supervised learning systems (Settles, 2010). Recent neuroscientific investigations are revealing brain mechanisms that possibly underlie the IM systems investigated in the behavioral and computational literature. However, unfortunately such investigations are carried out under agendas different from the one on IMs, e.g., in relation to dopamine, memory, motor learning, goal-directed behavior, and conflict monitoring, so comprehensive views are still missing. A large body of research shows how the hippocampus, a brain compound system playing pivotal functions for memory, has the capacity to detect the novelty of various aspects of experience, from the novelty of single items to the novelty of item-item and item-context associations (Ranganath and Rainer, 2003; Kumaran and Maguire, 2007).

This detection is then capable of triggering the release of neuromodulators, such as dopamine, that modulate the functioning and learning processes of the hippocampus itself and other brain areas, e.g., of the frontal cortex involved in higher cognition, action planning, and action execution (Lisman and Grace, 2005). Other studies have shown that unexpected stimuli can activate the superior colliculus, a midbrain structure that plays a key role in oculomotor control, which in turn causes phasic bursts of dopamine affecting trial-and-error learning processes happening in basal ganglia, a brain region known to be involved in learning to select actions and other cortex contents (Redgrave and Gurney, 2006). Dopamine signals have also been shown to have an interesting direct relationship with information seeking (Bromberg-Martin and Hikosaka, 2009). Noradrenaline, another neuromodulator targeting a large part of brain, has been shown to be involved in signaling violations of the agent’s expectations (Sara, 2009). The failure (Carter et al., 1998) or success (Ribas-Fernandes et al., 2011) in accomplishing goals and sub-goals, possibly themselves set by IMs, has been shown to have neural correlates that might affect succeeding motivation, engagement, and learning. Bio-inspired/bio-constrained computational modeling is linking some of these neuroscientific results to specific computational mechanisms, e.g., in relation to dopamine (e.g., see the pioneering work of Kakade and Dayan, 2002, and Mirolli et al., 2013) and goal-directed behavior (Baldassare et al., 2013). The 24 interdisciplinary contributions to the present Research Topic can be clustered into four groups. The first group of six contributions (IMs and brain and behavior) focuses on different types of IM mechanisms implemented in the brain. The second group of five contributions (IMs and attention) focuses on the role of IMs in attention. The third group of eight contributions (IMs and motor skills) focuses on IMs as drives for the acquisition of manipulation and navigation skills, often with an emphasis on their function in enabling cumulative, open-ended development. Finally, the fourth group of five contributions (IMs and social interaction) focuses on the relationship between IMs and social phenomena, a novel area of investigation of IMs that is increasingly attracting the attention of researchers.

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