A Novel Behavioural Task for Researching Intrinsic Motivations
We present a novel behavioural task for the investigation of how actions are added to an agent’s repertoire. In the task, free exploration of the range of possible movements with a manipulandum, such as a joystick, is recorded. A subset of these movements
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Abstract We present a novel behavioural task for the investigation of how actions are added to an agent’s repertoire. In the task, free exploration of the range of possible movements with a manipulandum, such as a joystick, is recorded. A subset of these movements trigger a reinforcing signal. Our interest is in how those elements of total behaviour which cause an unexpected outcome are identified and stored. This process is necessarily prior to the attachment of value to different actions [Redgrave, P., Gurney, K.: The short-latency dopamine signal: A role in discovering novel actions? Nat. Rev. Neurosci. 7(12), 967–975 (2006)]. The task allows for critical tests of reinforcement prediction error theories [e.g. Schultz, W., Dayan, P., Montague, P.: A neural substrate of prediction and reward. Science 275, 1593–1599 (1997)], as well as providing a window on a number of other issues in action learning. The task provides a paradigm where the exploratory motive drives learning, and as such we view it as in the tradition of Thorndike [Animal intelligence (1911)]. Our task is easily scalable in difficulty, is adaptable across species and provides rich set of behavioural measures throughout the action-learning process. Targets can be defined in spatial, temporal or kinematic/gestural terms, and the task also allows the concatenation of actions to be investigated. Action learning requires integration across spatial, kinematic and temporal dimensions. The task affords insight into these (and into the process of integration).
T. Stafford () T. Walton L. Hetherington M. Thirkettle K. Gurney P. Redgrave Adaptive Behavior Research Group, Department of Psychology, University of Sheffield, Sheffield, UK e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected] G. Baldassarre and M. Mirolli (eds.), Intrinsically Motivated Learning in Natural and Artificial Systems, DOI 10.1007/978-3-642-32375-1 15, © Springer-Verlag Berlin Heidelberg 2013
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1 Introduction We present here, for the first time, a novel behavioural task for the investigation of intrinsically motivated action learning. We are not concerned to define precisely “intrinsically motivated” (for a treatment of this issue, see Barto 2012; Mirolli and Baldassarre 2012), rather we wish to establish a novel method which allows us to inspect those components of action learning which involve exploration and making of movements which are not predefined by the task or previous learning. Core to our approach is the idea that we can assess a component of action learning which is prior to the processes of long-term value attribution. In the task, free exploration of the range of possible movements with a manipulandum, such as a joystick, is recorded. A subset of these movements trigger a reinforcing signal. Our interest is in how those elements of total behaviour which cause an unexpected outcome are identified and stored. Bec
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