What are intrinsic motivations? A biological perspective

By Gianluca Baldassarre. 2011

109 citations recorded by the source corpus.

The concept of “intrinsic motivation”, initially proposed and developed within psychology, is gaining an increasing attention within cognitive sciences for its potential to produce open-ended learning machines and robots. However, a clear definition of the phenomenon is not yet available. This theoretical paper aims to clarify what intrinsic motivations are from a biological perspective. To this purpose, it first shows how intrinsic motivations can be defined contrasting them to extrinsic motivations from an evolutionary perspective: whereas extrinsic motivations guide learning of behaviours that directly increase fitness, intrinsic motivations drive the acquisition of knowledge and skills that contribute to produce behaviours that increase fitness only in a later stage. Given this difference, extrinsic motivations generate learning signals on the basis of events involving body homeostatic regulations, whereas intrinsic motivations generate learning signals based on events taking place within the brain itself. These ideas are supported by presenting some examples of biological mechanisms underlying the two types of motivations. The paper closes by linking the theory to the current major computational views on intrinsic motivations and by listing the main open issues of the field.

Subfields: Cognitive Neuroscience, Developmental and Educational Psychology, Reinforcement Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Developmental and Educational Psychology

In Reinforcement Learning

Citations in the corpus

What can the brain teach us about building artificial intelligence? (2016, background): “& Baldassarre, G. (2015) Selection of cortical dynamics for motor”
Active learning of inverse models with intrinsically motivated goal exploration in robots (2013, positive): “logical concept of intrinsic motivations [90, 35, 14] which relate to mechanisms that drive a learning agent to perform dierent activities for their own sake, without requiring any 5 external reward [96, 12, 109, 110, 65, 68, 94, 97, 48, 7, 76, 64, 40]. Dierent criteria were elaborated, such as the ”
Self-organization of early vocal development in infants and machines: the role of intrinsic motivation (2014, background): “Such models concretely formalize concepts of intrinsic motivation described in the psychology literature into algorithmic architectures that can be experimented in computers and robots (Schmidhuber, 1991; Barto et al., 2004; Oudeyer and Kaplan, 2007; Baldassarre, 2011).”
GRAIL: A Goal-Discovering Robotic Architecture for Intrinsically-Motivated Learning (2016, background): “) but also in the absence of direct biological pressure [5].”
The strategic student approach for life-long exploration and learning (2012, methodology): “As a generalization of exploration methods in re-inforcement learning, such as [21], ideas have been suggested such as planning to be surprised [59] or the combination of empirical learning progress with visit counts [60].”
Intrinsic motivations and open-ended development in animals, humans, and robots: an overview (2014, background): “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).”
Intrinsically Motivated Learning in Natural and Artificial Systems (2013, background): “…activities more directly related to survival and reproduction, intrinsic motivations lead the agent to develop behaviours less directly related to basic needs, such as play [see Baldassarre (2011) for a proposal of a stronger distinction between the two types of motivations done on this basis].”