The feeling of grip: novelty, error dynamics, and the predictive brain

According to the free energy principle biological agents resist a tendency to disorder in their interactions with a dynamically changing environment by keeping themselves in sensory and physiological states that are expected given their embodiment and the niche they inhabit (Friston in Nat Rev Neurosci 11(2):127–138, 2010. doi:10.1038/nrn2787). Why would a biological agent that aims at minimising uncertainty in its encounters with the world ever be motivated to seek out novelty? Novelty for such an agent would arrive in the form of sensory and physiological states that are unexpected. Such an agent ought therefore to avoid novel and surprising interactions with the world one might think. Yet humans and many other animals find play and other forms of novelty-seeking and exploration hugely rewarding. How can this be understood in frameworks for studying the mind that emphasise prediction error minimisation? This problem has been taken up in recent research concerned with epistemic action—actions an agent engages in to reduce uncertainty. However that work leaves two questions unanswered, which it is the aim of our paper to address. First, no account has been given yet of why it should feel good to the agent to engage the world playfully and with curiosity. Second an appeal is made to precision-estimation to explain epistemic action, yet it remains unclear how precision-weighting works in action more generally, or active inference. We argue that an answer to both questions may lie in the bodily states of an agent that track the rate at which free energy is being reduced. The recent literature on the predictive brain has connected the valence of emotional experiences to the rate of change in the reduction of prediction error (Joffily and Coricelli in PLoS Comput Biol 9(6):e1003094, 2013. doi:10.1371/journal.pcbi.1003094; Van de Cruys, in Metzinger and Wiese (eds) Philosophy and predictive processing, vol 24, MIND Group, Frankfurt am Main, 2017. doi:10.15502/9783958573253). In this literature valenced emotional experiences are hypothesised to be identical with changes in the rate at which prediction error is reduced. Experiences are negatively valenced when overall prediction error increases and are positively valenced when the sum of prediction errors decrease. We offer an ecological-enactive interpretation of the concept of valence and its connection to rate of change of prediction error. We show how rate of change should be understood in terms of embodied states of affordance-related action readiness. We then go on to apply this ecological-enactive account of error dynamics to provide an answer to the first question we have raised: It may explain why it should feel good to an agent to be curious and playful. Our ecological-enactive account also allows us to show how error dynamics may provide an answer to the second question we have raised regarding how precision-weighting works in active inference. An agent that is sensitive to rates of error reduction can tune precision on the fly. We show how this ability to tune precision on the go can allow agents to develop skills for adapting better and better to the unexpected, and search out opportunities for resolving uncertainty and progressing in its learning.

[1]  Pierre-Yves Oudeyer,et al.  Intrinsic Motivation Systems for Autonomous Mental Development , 2007, IEEE Transactions on Evolutionary Computation.

[2]  Tobias F. Rötheli Superforecasting: the art and science of prediction , 2017 .

[3]  Jacob Feldman,et al.  Tuning Your Priors to the World , 2013, Top. Cogn. Sci..

[4]  Pierre-Yves Oudeyer,et al.  Discovering communication , 2006, Connect. Sci..

[5]  Erik Rietveld,et al.  Self-organization, free energy minimization, and optimal grip on a field of affordances , 2014, Front. Hum. Neurosci..

[6]  T. Ziemke,et al.  The Feeling of Action Tendencies: On the Emotional Regulation of Goal-Directed Behavior , 2011, Front. Psychology.

[7]  Pierre-Yves Oudeyer,et al.  Active learning of inverse models with intrinsically motivated goal exploration in robots , 2013, Robotics Auton. Syst..

[8]  Erik Rietveld,et al.  Ecological-Enactive Cognition as engaging with a field of relevant affordances , 2018, The Oxford Handbook of 4E Cognition.

[9]  Richard N. Aslin,et al.  The Goldilocks Effect: Human Infants Allocate Attention to Visual Sequences That Are Neither Too Simple Nor Too Complex , 2012, PloS one.

[10]  Jürgen Schmidhuber,et al.  Learning tactile skills through curious exploration , 2012, Front. Neurorobot..

[11]  Erik Rietveld,et al.  Optimal grip on affordances in architectural design practices: an ethnography , 2017 .

[12]  S. V. D. Cruys,et al.  Affective Value in the Predictive Mind , 2017 .

[13]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[14]  W. N. Dember,et al.  Response by rats to differential stimulus complexity. , 1957, Journal of comparative and physiological psychology.

[15]  Karl J. Friston,et al.  Free-energy and the brain , 2007, Synthese.

[16]  Matthew Ratcliffe,et al.  Feelings of Being: Phenomenology, Psychiatry and the Sense of Reality , 2008 .

[17]  Karl J. Friston,et al.  Free-Energy Minimization and the Dark-Room Problem , 2012, Front. Psychology.

[18]  Karl J. Friston Life as we know it , 2013, Journal of The Royal Society Interface.

[19]  Raymond J. Dolan,et al.  The anatomy of choice: dopamine and decision-making , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[20]  Karl J. Friston,et al.  The Dopaminergic Midbrain Encodes the Expected Certainty about Desired Outcomes , 2014, Cerebral cortex.

[21]  Andy Clark,et al.  Happily entangled: prediction, emotion, and the embodied mind , 2018, Synthese.

[22]  Karl J. Friston,et al.  Active Inference: A Process Theory , 2017, Neural Computation.

[23]  Raymond J. Dolan,et al.  Dopamine, Affordance and Active Inference , 2012, PLoS Comput. Biol..

[24]  Pierre-Yves Oudeyer,et al.  Socially guided intrinsic motivation for robot learning of motor skills , 2014, Auton. Robots.

[25]  Tom Froese,et al.  Where There is Life There is Mind: In Support of a Strong Life-Mind Continuity Thesis , 2017, Entropy.

[26]  Jürgen Schmidhuber,et al.  Learning skills from play: Artificial curiosity on a Katana robot arm , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[27]  J. Wagemans,et al.  Putting reward in art: A tentative prediction error account of visual art , 2011, i-Perception.

[28]  Jürgen Schmidhuber,et al.  Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010) , 2010, IEEE Transactions on Autonomous Mental Development.

[29]  Peter Dayan,et al.  A Neural Substrate of Prediction and Reward , 1997, Science.

[30]  Pierre-Yves Oudeyer,et al.  Information-seeking, curiosity, and attention: computational and neural mechanisms , 2013, Trends in Cognitive Sciences.

[31]  Raymond J. Dolan,et al.  Exploration, novelty, surprise, and free energy minimization , 2013, Front. Psychol..

[32]  Karl J. Friston The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.

[33]  J. Waller,et al.  Mind in Life: Biology, Phenomenology, and the Sciences of Mind , 2009 .

[34]  A. Clark Busting Out: Predictive Brains, Embodied Minds, and the Puzzle of the Evidentiary Veil , 2017 .

[35]  Karl J. Friston The free-energy principle: a rough guide to the brain? , 2009, Trends in Cognitive Sciences.

[36]  Erik Rietveld,et al.  Inviting complementary perspectives on situated normativity in everyday life , 2010 .

[37]  N. Frijda THE EMOTIONS (STUDIES IN EMOTION AND SOCIAL INTERACTION) , 2011 .

[38]  Pierre-Yves Oudeyer,et al.  From hardware and software to kernels and envelopes: a concept shift for robotics, developmental psychology, and brain sciences , 2011 .

[39]  Karl J. Friston,et al.  Cerebral hierarchies: predictive processing, precision and the pulvinar , 2015, Philosophical Transactions of the Royal Society B: Biological Sciences.

[40]  Pierre-Yves Oudeyer,et al.  Active choice of teachers, learning strategies and goals for a socially guided intrinsic motivation learner , 2012, Paladyn J. Behav. Robotics.

[41]  Erik Rietveld,et al.  A Rich Landscape of Affordances , 2014 .

[42]  Erik Rietveld Special Section: The Skillful Body as a Concernful System of Possible Actions , 2008 .

[43]  Nico H. Frijda,et al.  Emotions and Action , 2004 .

[44]  Mateus Joffily,et al.  Emotional Valence and the Free-Energy Principle , 2013, PLoS Comput. Biol..

[45]  Erik Rietveld,et al.  The anticipating brain is not a scientist: the free-energy principle from an ecological-enactive perspective , 2016, Synthese.

[46]  A. Clark Whatever next? Predictive brains, situated agents, and the future of cognitive science. , 2013, The Behavioral and brain sciences.

[47]  Pierre-Yves Oudeyer,et al.  Intrinsically Motivated Learning of Real-World Sensorimotor Skills with Developmental Constraints , 2013, Intrinsically Motivated Learning in Natural and Artificial Systems.

[48]  Pierre-Yves Oudeyer,et al.  In Search of the Neural Circuits of Intrinsic Motivation , 2007, Front. Neurosci..

[49]  Jakob Hohwy,et al.  The Neural Organ Explains the Mind , 2014 .

[50]  Anil K. Seth,et al.  The cybernetic Bayesian brain: from interoceptive inference to sensorimotor contingencies , 2014 .

[51]  E. Thelen,et al.  Development as a Dynamic System , 1992 .

[52]  Pierre-Yves Oudeyer,et al.  Curiosity-driven phonetic learning , 2012, 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL).

[53]  Erik Rietveld The skillful body as a concernful system of possible actions: Phenomena and neurodynamics , 2008 .

[54]  N. Frijda The laws of emotion. , 1988, The American psychologist.

[55]  Karl J. Friston,et al.  Active inference and epistemic value , 2015, Cognitive neuroscience.

[56]  James Dodd,et al.  Body, community, language, world , 1998 .

[57]  K. R. Ridderinkhof,et al.  No pain, no gain: the affective valence of congruency conditions changes following a successful response , 2014, Cognitive, Affective, & Behavioral Neuroscience.

[58]  D. Berlyne Curiosity and exploration. , 1966, Science.

[59]  H. Bekkering,et al.  To be precise, the details don’t matter: On predictive processing, precision, and level of detail of predictions , 2017, Brain and Cognition.