# Model-Based Approaches to Active Perception and Control

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## Abstract

**:**

## 1. Introduction

## 2. Two Criticisms of “4-Es” Theories to Traditional Cognitive Theory

#### 2.1. A Critique of Passive Perception

#### 2.2. A Critique of Serial Information Processing

## 3. Model-Based Solutions to Active Perception and Control Problems

#### 3.1. Active Perception from a Model-Based Perspective

#### 3.2. Beyond Active Perception: Active Inference and the Embodied Nature of Inference

## 4. Comparing Alternative Conceptualizations of Active Perception and Control

and refers to two specific examples of models that have these characteristics [106,107]. Ahissar and Kleinfeld [34] (p. 53) provide another interesting illustration of duality between homeostatic (or control-theoretic) and computational perspectives:“This strikes me as a false dilemma. As an illustration of how representation and dynamics can peacefully coexist, one may consider recent computational accounts of perceptual decision-making. Here, we find models that can be understood as implementing statistical procedures, computing the likelihood ratio of opposing hypotheses (read: representations), or with equal immediacy as systems of differential equations”.

“The operation of neuronal closed loops at various levels can be considered from either homeostatic or computational points of view. All closed loops have set-points at which the values of their state variables are stable. Thus, feedback loops provide a mechanism for maintaining neuronal variables within a particular range of values. This can be termed a homeostatic function. On the other hand, since the feedback loops compute changes in the state variables to counteract changes in the external world, the change in state variables constitutes a representation of change in the outside world. As an example, we consider Wiener’s description of the sensorimotor control of a stick with one finger. The state variables are the angle of the stick and the position (angle and pivot location) of the finger. When the stick leaves a set-point as a result of a change in local air pressure, the sensorimotor system will converge to a new set-point in which the position of the finger is different. The end result, from the homeostatic point of view, is that equilibrium is re-established. From the computational point of view, the new set-point is an internal representation of the new conditions, e.g., the new local air pressure, in the external world. (We note that the representation of perturbation by state variables may be dimensionally under- or over-determined and possibly not unique.) This internal representation is ‘computed’ by the closed-loop mechanism”.

#### 4.1. Model-Based Approaches to Active Perception and Control: Conceptual Implications

#### 4.2. Who Fears Internal Models?

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- James, W. The Principles of Psychology; Dover Publications: New York, NY, USA, 1890. [Google Scholar]
- Hurley, S. The shared circuits model (SCM): How control, mirroring, and simulation can enable imitation, deliberation, and mindreading. Behav. Brain Sci.
**2008**, 31, 1–22. [Google Scholar] [CrossRef] [PubMed] - Clark, A. Being There. Putting Brain, Body, and World Together; The MIT Press: Cambridge, MA, USA, 1998. [Google Scholar]
- Gallagher, S. How the Body Shapes the Mind; Clarendon Press: Oxford, UK, 2005. [Google Scholar]
- Thompson, E.; Varela, F.J. Radical embodiment: Neural dynamics and consciousness. Trends Cogn. Sci.
**2001**, 5, 418–425. [Google Scholar] [CrossRef] - Wilson, M. Six views of embodied cognition. Psychon. Bull. Rev.
**2002**, 9, 625–636. [Google Scholar] [CrossRef] [PubMed] - Rupert, R.D. Challenges to the hypothesis of extended cognition. J. Philos.
**2004**, 101, 389–428. [Google Scholar] [CrossRef] - Haugeland, J. Mind Embodied and Embedded. In Mind and Cognition: 1993 International Symposium; Academica Sinica: Taipei, Taiwan, 1993. [Google Scholar]
- Dewey, J. The Reflex Arc Concept in Psychology. Psychol. Rev.
**1896**, 3, 357–370. [Google Scholar] [CrossRef] - Peirce, C.S. Philosophical Writings of Peirce; Dover Publications: New York, NY, USA, 1897. [Google Scholar]
- Piaget, J. The Construction of Reality in the Child; Routledge: Abingdon, UK, 1954. [Google Scholar]
- Gibson, J.J. The Ecological Approach to Visual Perception; Houghton Mifflin Harcourt: Boston, MA, USA, 1979. [Google Scholar]
- O’Regan, J.K.; Noe, A. A sensorimotor account of vision and visual consciousness. Behav. Brain Sci.
**2001**, 24, 883–917. [Google Scholar] [CrossRef] - Newell, A.; Simon, H.A. Human Problem Solving; Prentice-Hall: Upper Saddle River, NJ, USA, 1972. [Google Scholar]
- Engel, A.K.; Maye, A.; Kurthen, M.; König, P. Where’s the action? The pragmatic turn in cognitive science. Trends Cogn. Sci.
**2013**, 17, 202–209. [Google Scholar] [CrossRef] [PubMed] - Pezzulo, G.; Cisek, P. Navigating the Affordance Landscape: Feedback Control as a Process Model of Behavior and Cognition. Trends Cogn. Sci.
**2016**, 20, 414–424. [Google Scholar] [CrossRef] [PubMed] - Clark, A.; Grush, R. Towards a Cognitive Robotics. Adapt. Behav.
**1999**, 7, 5–16. [Google Scholar] [CrossRef] - Friston, K. The free-energy principle: A unified brain theory? Nat. Rev. Neurosci.
**2010**, 11, 127–138. [Google Scholar] [CrossRef] [PubMed] - Grush, R. The emulation theory of representation: Motor control, imagery, and perception. Behav. Brain Sci.
**2004**, 27, 377–396. [Google Scholar] [CrossRef] [PubMed] - Pezzulo, G. Grounding Procedural and Declarative Knowledge in Sensorimotor Anticipation. Mind Lang.
**2011**, 26, 78–114. [Google Scholar] [CrossRef] - Pezzulo, G.; Castelfranchi, C. The Symbol Detachment Problem. Cogn. Process.
**2007**, 8, 115–131. [Google Scholar] [CrossRef] [PubMed] - Toussaint, M. Probabilistic inference as a model of planned behavior. Kuenstliche Intell.
**2009**, 23, 23–29. [Google Scholar] - Churchland, P.S.; Ramachandran, V.S.; Sejnowski, T.J. A critique of pure vision. In Large-Scale Neuronal Theor. Brain; The MIT Press: Cambridge, MA, USA, 1994; pp. 23–60. [Google Scholar]
- Doya, K.; Ishii, S.; Pouget, A.; Rao, R.P.N. (Eds.) Bayesian Brain: Probabilistic Approaches to Neural Coding, 1st ed.; The MIT Press: Cambridge, MA, USA, 2007. [Google Scholar]
- Friston, K. A theory of cortical responses. Philos. Trans. R. Soc. Lond. B Biol. Sci.
**2005**, 360, 815–836. [Google Scholar] [CrossRef] [PubMed] - Rao, R.P.; Ballard, D.H. Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci.
**1999**, 2, 79–87. [Google Scholar] [CrossRef] [PubMed] - Von Helmholtz, H. Concerning the perceptions in general. In Treatise on Physiological Optics; Southall, J.P.C., Ed.; Dover: New York, NY, USA, 1866; Volume 3. [Google Scholar]
- Hinton, G.E. To recognize shapes, first learn to generate images. Prog. Brain Res.
**2007**, 165, 535–547. [Google Scholar] [PubMed] - Hinton, G.E. Learning multiple layers of representation. Trends Cogn. Sci.
**2007**, 11, 428–434. [Google Scholar] [CrossRef] [PubMed] - Barsalou, L.W. Perceptual symbol systems. Behav. Brain Sci.
**1999**, 22, 577–600. [Google Scholar] [CrossRef] [PubMed] - Ahissar, E.; Assa, E. Perception as a closed-loop convergence process. eLife
**2016**, 5, e12830. [Google Scholar] [CrossRef] [PubMed] - Gibson, J.J. The Senses Considered as Perceptual Systems; Houghton Mifflin: Boston, MA, USA, 1966. [Google Scholar]
- Bajcsy, R.; Aloimonos, Y.; Tsotsos, J.K. Revisiting Active Perception. arXiv
**2016**. [Google Scholar] - Ahissar, E.; Kleinfeld, D. Closed-loop Neuronal Computations: Focus on Vibrissa Somatosensation in Rat. Cereb. Cortex
**2003**, 13, 53–62. [Google Scholar] [CrossRef] [PubMed] - Donnarumma, F.; Costantini, M.; Ambrosini, E.; Friston, K.; Pezzulo, G. Action perception as hypothesis testing. Cortex
**2017**, 89, 45–60. [Google Scholar] [CrossRef] [PubMed] - Friston, K.; Adams, R.A.; Perrinet, L.; Breakspear, M. Perceptions as hypotheses: Saccades as experiments. Front. Psychol.
**2012**, 3, 151. [Google Scholar] [CrossRef] [PubMed] - Lepora, N.F. Biomimetic Active Touch with Fingertips and Whiskers. IEEE Trans. Haptics
**2016**, 9, 170–183. [Google Scholar] [CrossRef] [PubMed] - Norman, D.A.; Shallice, T. Attention to action: Willed and automatic control of behaviour. In Consciousness and Self-Regulation: Advances in Research and Theory; Davidson, R.J., Schwartz, G.E., Shapiro, D., Eds.; Springer: Berlin/Heidelberg, Germany, 1986; pp. 1–18. [Google Scholar]
- Barkley, R.A. The executive functions and self-regulation: An evolutionary neuropsychological perspective. Neuropsychol. Rev.
**2001**, 11, 1–29. [Google Scholar] [CrossRef] [PubMed] - Fuster, J.M. The Prefrontal Cortex: Anatomy, Physiology, and Neuropsychology of the Frontal Lobe; Lippincott-Raven: Philadelphia, PA, USA, 1997. [Google Scholar]
- Pezzulo, G.; Castelfranchi, C. Thinking as the Control of Imagination: A Conceptual Framework for Goal-Directed Systems. Psychol. Res.
**2009**, 73, 559–577. [Google Scholar] [CrossRef] [PubMed] - Cisek, P. Cortical mechanisms of action selection: The affordance competition hypothesis. Philos. Trans. R. Soc. B
**2007**, 362, 1585–1599. [Google Scholar] [CrossRef] [PubMed] - Cisek, P.; Kalaska, J.F. Neural mechanisms for interacting with a world full of action choices. Annu. Rev. Neurosci.
**2010**, 33, 269–298. [Google Scholar] [CrossRef] [PubMed] - Shadlen, M.N.; Kiani, R.; Hanks, T.D.; Churchland, A.K. Neurobiology of Decision Making: An Intentional Framework. In Better than Conscious?: Decision Making, the Human Mind, and Implications for Institutions; Engel, C., Singer, W., Eds.; The MIT Press: Cambridge, MA, USA, 2008. [Google Scholar]
- Lepora, N.F.; Pezzulo, G. Embodied Choice: How action influences perceptual decision making. PLoS Comput. Biol.
**2015**, 11, e1004110. [Google Scholar] [CrossRef] [PubMed] - Cisek, P. Beyond the computer metaphor: Behavior as interaction. J. Conscious. Stud.
**1999**, 6, 125–142. [Google Scholar] - Ashby, W.R. Design for a Brain; Wiley: Oxford, UK, 1952; Volume ix. [Google Scholar]
- Powers, W.T. Behavior: The Control of Perception; Aldine: Chicago, IL, USA, 1973. [Google Scholar]
- Wiener, N. Cybernetics: Or Control and Communication in the Animal and the Machine; The MIT Press: Cambridge, MA, USA, 1948. [Google Scholar]
- Cisek, P.; Pastor-Bernier, A. On the challenges and mechanisms of embodied decisions. Philos. Trans. R. Soc. Lond. B Biol. Sci.
**2014**, 369, 20130479. [Google Scholar] [CrossRef] [PubMed] - Pezzulo, G.; Barsalou, L.W.; Cangelosi, A.; Fischer, M.H.; McRae, K.; Spivey, M. The Mechanics of Embodiment: A Dialogue on Embodiment and Computational Modeling. Front. Cogn.
**2011**, 2, 1–21. [Google Scholar] [CrossRef] [PubMed] - Verschure, P.; Pennartz, C.M.A.; Pezzulo, G. The why, what, where, when and how of goal-directed choice: Neuronal and computational principles. Philos. Trans. R. Soc. Lond. B Biol. Sci.
**2014**, 369, 20130483. [Google Scholar] [CrossRef] [PubMed] - Engel, A.K.; Friston, K.J.; Kragic, D. The Pragmatic Turn: Toward Action-Oriented Views in Cognitive Science; The MIT Press: Cambridge, MA, USA, 2016. [Google Scholar]
- Shadmehr, R.; Smith, M.A.; Krakauer, J.W. Error correction, sensory prediction, and adaptation in motor control. Annu. Rev. Neurosci.
**2010**, 33, 89–108. [Google Scholar] [CrossRef] [PubMed] - Port, R.; van Gelder, T. Mind as Motion: Explorations in the Dynamics of Cognition; The MIT Press: Cambridge, MA, USA, 1995. [Google Scholar]
- Beer, R.D. The dynamics of adaptive behavior: A research program. Robot. Auton. Syst.
**1997**, 20, 257–289. [Google Scholar] [CrossRef] - Hope, T.; Stoianov, I.; Zorzi, M. Through neural stimulation to behavior manipulation: A novel method for analyzing dynamical cognitive models. Cogn. Sci.
**2010**, 34, 406–433. [Google Scholar] [CrossRef] [PubMed] - Nolfi, S. Behavior and cognition as a complex adaptive system: Insights from robotic experiments. In Handbook of the Philosophy of Science: Philosophy of Complex Systems; Hooker, C., Gabbay, D.M., Thagard, P., Woods, J., Eds.; Elsevier: Amsterdam, The Netherlands, 2009; Volume 10. [Google Scholar]
- Nolfi, S.; Floreano, D. Evolutionary Robotics. The Biology, Intelligence, and Technology of Self-Organizing Machines; The MIT Press: Cambridge, MA, USA, 2001. [Google Scholar]
- Todorov, E.; Jordan, M.I. Optimal feedback control as a theory of motor coordination. Nat. Neurosci.
**2002**, 5, 1226–1235. [Google Scholar] [CrossRef] [PubMed] - Todorov, E. Optimality principles in sensorimotor control. Nat. Neurosci.
**2004**, 7, 907–915. [Google Scholar] [CrossRef] [PubMed] - Clark, A. Surfing Uncertainty: Prediction, Action, and the Embodied Mind; Oxford University Press: Oxford, UK, 2016; ISBN 978-0-19-021701-3. [Google Scholar]
- Friston, K. What is optimal about motor control? Neuron
**2011**, 72, 488–498. [Google Scholar] [CrossRef] [PubMed] - Friston, K.; Samothrakis, S.; Montague, R. Active inference and agency: Optimal control without cost functions. Biol. Cybern.
**2012**, 106, 523–541. [Google Scholar] [CrossRef] [PubMed] - Pezzulo, G.; Rigoli, F.; Friston, K.J. Active Inference, homeostatic regulation and adaptive behavioural control. Prog. Neurobiol.
**2015**, 134, 17–35. [Google Scholar] [CrossRef] [PubMed] - Seth, A.K. The Cybernetic Bayesian Brain: From Interoceptive Inference to Sensorimotor Contingencies. In Open MIND; Metzinger, T., Windt, J.M., Eds.; MIND Group: Frankfurt, Germany, 2014. [Google Scholar]
- Conant, R.C.; Ashby, W.R. Every good regulator of a system must be a model of that system. Int. J. Syst. Sci.
**1970**, 1, 89–97. [Google Scholar] [CrossRef] - Kappen, H.J.; Gómez, V.; Opper, M. Optimal control as a graphical model inference problem. Mach. Learn.
**2012**, 87, 159–182. [Google Scholar] [CrossRef] - Penny, W.D.; Zeidman, P.; Burgess, N. Forward and Backward Inference in Spatial Cognition. PLoS Comput. Biol.
**2013**, 9, e1003383. [Google Scholar] [CrossRef] [PubMed] - Pezzulo, G.; Rigoli, F.; Chersi, F. The Mixed Instrumental Controller: Using Value of Information to combine habitual choice and mental simulation. Front. Cogn.
**2013**, 4, 92. [Google Scholar] [CrossRef] [PubMed] - Pezzulo, G.; Rigoli, F. The value of foresight: How prospection affects decision-making. Front. Neurosci.
**2011**, 5, 79. [Google Scholar] [CrossRef] [PubMed] - Solway, A.; Botvinick, M.M. Goal-directed decision making as probabilistic inference: A computational framework and potential neural correlates. Psychol. Rev.
**2012**, 119, 120–154. [Google Scholar] [CrossRef] [PubMed] - Butz, M.V. Toward a Unified Sub-symbolic Computational Theory of Cognition. Front. Psychol.
**2016**, 7, 925. [Google Scholar] [CrossRef] [PubMed] - Hemion, N.J. Discovering Latent States for Model Learning: Applying Sensorimotor Contingencies Theory and Predictive Processing to Model Context. arXiv
**2016**. [Google Scholar] - Maye, A.; Engel, A.K. A computational model of sensorimotor contingencies for object perception and control of behavior. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2011), Shanghai, China, 9–13 May 2011. [Google Scholar]
- Seth, A.K. A predictive processing theory of sensorimotor contingencies: Explaining the puzzle of perceptual presence and its absence in synesthesia. Cogn. Neurosci.
**2014**, 5, 97–118. [Google Scholar] [CrossRef] [PubMed] - Wolpert, D.M. Computational approaches to motor control. Trends Cogn. Sci.
**1997**, 1, 209–216. [Google Scholar] [CrossRef] - Bickhard, M.H. Representational content in humans and machines. J. Exp. Theor. Artif. Intell.
**1993**, 5, 285–333. [Google Scholar] [CrossRef] - Meyniel, F.; Schlunegger, D.; Dehaene, S. The Sense of Confidence during Probabilistic Learning: A Normative Account. PLoS Comput. Biol.
**2015**, 11, e1004305. [Google Scholar] [CrossRef] [PubMed] - Friston, K.; FitzGerald, T.; Rigoli, F.; Schwartenbeck, P.; Pezzulo, G. Active Inference: A Process Theory. Neural Comput.
**2016**, 29, 1–49. [Google Scholar] [CrossRef] [PubMed] - Friston, K.; FitzGerald, T.; Rigoli, F.; Schwartenbeck, P.; O’Doherty, J.; Pezzulo, G. Active inference and learning. Neurosci. Biobehav. Rev.
**2016**, 68, 862–879. [Google Scholar] [CrossRef] [PubMed] - Pezzulo, G.; Cartoni, E.; Rigoli, F.; Pio-Lopez, L.; Friston, K. Active Inference, epistemic value, and vicarious trial and error. Learn. Mem.
**2016**, 23, 322–338. [Google Scholar] [CrossRef] [PubMed] - Pezzulo, G.; Ognibene, D. Proactive Action Preparation: Seeing Action Preparation as a Continuous and Proactive Process. Motor Control
**2011**, 16, 386–424. [Google Scholar] [CrossRef] - Pio-Lopez, L.; Nizard, A.; Friston, K.; Pezzulo, G. Active inference and robot control: A case study. J. R. Soc. Interface
**2016**, 13. [Google Scholar] [CrossRef] [PubMed] - Maisto, D.; Donnarumma, F.; Pezzulo, G. Nonparametric Problem-Space Clustering: Learning Efficient Codes for Cognitive Control Tasks. Entropy
**2016**, 18, 61. [Google Scholar] [CrossRef] - Donnarumma, F.; Maisto, D.; Pezzulo, G. Problem Solving as Probabilistic Inference with Subgoaling: Explaining Human Successes and Pitfalls in the Tower of Hanoi. PLoS Comput. Biol.
**2016**, 12, e1004864. [Google Scholar] [CrossRef] [PubMed] - Barrett, L.F.; Quigley, K.S.; Hamilton, P. An active inference theory of allostasis and interoception in depression. Phil. Trans. R. Soc. B
**2016**, 371, 20160011. [Google Scholar] [CrossRef] [PubMed] - Pezzulo, G. Why do you fear the Bogeyman? An embodied predictive coding model of perceptual inference. Cogn. Affect. Behav. Neurosci.
**2013**, 14, 902–911. [Google Scholar] [CrossRef] [PubMed] - Seth, A.K.; Friston, K.J. Active interoceptive inference and the emotional brain. Philos. Trans. R. Soc. B
**2016**, 371, 20160007. [Google Scholar] [CrossRef] [PubMed] - Adams, R.A.; Shipp, S.; Friston, K.J. Predictions not commands: Active inference in the motor system. Brain Struct. Funct.
**2013**, 218, 611–643. [Google Scholar] [CrossRef] [PubMed] - Kilner, J.M.; Friston, K.J.; Frith, C.D. Predictive coding: An account of the Mirror Neuron system. Cogn. Process.
**2007**, 8, 159–166. [Google Scholar] [CrossRef] [PubMed] - Friston, K.; Frith, C. A Duet for one. Conscious. Cogn.
**2015**, 36, 390–405. [Google Scholar] [CrossRef] [PubMed] - Dindo, H.; Donnarumma, F.; Chersi, F.; Pezzulo, G. The intentional stance as structure learning: A computational perspective on mindreading. Biol. Cybern.
**2015**, 109, 453–467. [Google Scholar] [CrossRef] [PubMed] - Donnarumma, F.; Dindo, H.; Pezzulo, G. Sensorimotor coarticulation in the execution and recognition of intentional actions. Front. Psychol.
**2017**, 8, 237. [Google Scholar] [CrossRef] [PubMed] - Donnarumma, F.; Dindo, H.; Iodice, P.; Pezzulo, G. You cannot speak and listen at the same time: A probabilistic model of turn-taking. Biol. Cybern.
**2017**, 111, 165–183. [Google Scholar] [CrossRef] [PubMed] - Friston, K.; Mattout, J.; Kilner, J. Action understanding and active inference. Biol. Cybern.
**2011**, 104, 137–160. [Google Scholar] [CrossRef] [PubMed] - Pezzulo, G.; Iodice, P.; Donnarumma, F.; Dindo, H.; Knoblich, G. Avoiding accidents at the champagne reception: A study of joint lifting and balancing. Psychol. Sci.
**2017**. [Google Scholar] [CrossRef] [PubMed] - Allen, M.; Friston, K.J. From cognitivism to autopoiesis: Towards a computational framework for the embodied mind. Synthese
**2016**, 1–24. [Google Scholar] [CrossRef] - Hohwy, J. The Predictive Mind; Oxford University Press: Oxford, UK, 2013. [Google Scholar]
- Metzinger, T.; Wiese, W. (Eds.) The Philosophy of Predictive Processing; Open Mind: Frankfurt, Germany, 2017. [Google Scholar]
- Friston, K. Life as we know it. J. R. Soc. Interface
**2013**, 10, 20130475. [Google Scholar] [CrossRef] [PubMed] - Friston, K.; Levin, M.; Sengupta, B.; Pezzulo, G. Knowing one’s place: A free-energy approach to pattern regulation. J. R. Soc. Interface
**2015**, 12, 20141383. [Google Scholar] [CrossRef] [PubMed] - Bruineberg, J.; Kiverstein, J.; Rietveld, E. The anticipating brain is not a scientist: The free-energy principle from an ecological-enactive perspective. Synthese
**2016**, 2016, 1–28. [Google Scholar] [CrossRef] - Gallagher, S.; Allen, M. Active inference, enactivism and the hermeneutics of social cognition. Synthese
**2016**, 2016, 1–22. [Google Scholar] [CrossRef] - Botvinick, M. Commentary: Why I Am Not a Dynamicist. Top. Cogn. Sci.
**2012**, 4, 78–83. [Google Scholar] [CrossRef] [PubMed] - Beck, J.M.; Pouget, A. Exact inferences in a neural implementation of a hidden Markov model. Neural Comput.
**2007**, 19, 1344–1361. [Google Scholar] [CrossRef] [PubMed] - Bogacz, R.; Brown, E.; Moehlis, J.; Holmes, P.; Cohen, J.D. The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks. Psychol. Rev.
**2006**, 113, 700–765. [Google Scholar] [CrossRef] [PubMed] - Kiefer, A.; Hohwy, J. Content and misrepresentation in hierarchical generative models. Synthese
**2017**, 2017, 1–29. [Google Scholar] [CrossRef] - Orlandi, N. Bayesian Perception Is Ecological Perception. Available online: http://mindsonline.philosophyofbrains.com/wp-content/uploads/2015/09/Orlandi-Minds-2015.pdf (accessed on 8 June 2017).
- Głladziejewski, P. Predictive coding and representationalism. Synthese
**2016**, 193, 559–582. [Google Scholar] [CrossRef] - Cummins, R.C. Meaning and Mental Representation; The MIT Press: Cambridge, MA, USA, 1989. [Google Scholar]
- Friston, K.; Rigoli, F.; Ognibene, D.; Mathys, C.; Fitzgerald, T.; Pezzulo, G. Active inference and epistemic value. Cogn. Neurosci.
**2015**, 6, 187–214. [Google Scholar] [CrossRef] [PubMed] - Montague, P.R.; King-Casas, B. Efficient statistics, common currencies and the problem of reward-harvesting. Trends Cogn. Sci.
**2007**, 11, 514–519. [Google Scholar] [CrossRef] [PubMed] - Rubin, J.; Ulanovsky, N.; Nelken, I.; Tishby, N. The Representation of Prediction Error in Auditory Cortex. PLoS Comput. Biol.
**2016**, 12, e1005058. [Google Scholar] [CrossRef] [PubMed] - FitzGerald, T.H.; Dolan, R.J.; Friston, K.J. Model Averaging, Optimal Inference, and Habit Formation; Frontiers Media SA: Lausanne, Switzerland, 2014. [Google Scholar]
- Friston, K.; Schwartenbeck, P.; FitzGerald, T.; Moutoussis, M.; Behrens, T.; Dolan, R.J. The anatomy of choice: Active inference and agency. Front. Hum. Neurosci.
**2013**, 7, 598. [Google Scholar] [CrossRef] [PubMed] - Friston, K.; Shiner, T.; FitzGerald, T.; Galea, J.M.; Adams, R.; Brown, H.; Dolan, R.J.; Moran, R.; Stephan, K.E.; Bestmann, S. Dopamine, Affordance and Active Inference. PLoS Comput. Biol.
**2012**, 8, e1002327. [Google Scholar] [CrossRef] [PubMed] - Kanai, R.; Komura, Y.; Shipp, S.; Friston, K. Cerebral hierarchies: Predictive processing, precision and the pulvinar. Philos. Trans. R. Soc. B
**2015**, 370, 20140169. [Google Scholar] [CrossRef] [PubMed] - Saraf-Sinik, I.; Assa, E.; Ahissar, E. Motion Makes Sense: An Adaptive Motor-Sensory Strategy Underlies the Perception of Object Location in Rats. J. Neurosci.
**2015**, 35, 8777–8789. [Google Scholar] [CrossRef] [PubMed] - Voigts, J.; Herman, D.H.; Celikel, T. Tactile object localization by anticipatory whisker motion. J. Neurophysiol.
**2015**, 113, 620–632. [Google Scholar] [CrossRef] [PubMed] - Pfeifer, R.; Bongard, J.C. How the Body Shapes the Way We Think; MIT Press: London, UK, 2006. [Google Scholar]
- Friston, K.; Schwartenbeck, P.; FitzGerald, T.; Moutoussis, M.; Behrens, T.; Dolan, R.J. The anatomy of choice: Dopamine and decision-making. Philos. Trans. R. Soc. Lond. B Biol. Sci.
**2014**, 369, 20130481. [Google Scholar] [CrossRef] [PubMed] - Pezzulo, G. An Active Inference view of cognitive control. Front. Theor. Philos. Psychol.
**2012**, 3, 487. [Google Scholar] [CrossRef] [PubMed] - Roy, D. Semiotic schemas: A framework for grounding language in action and perception. Artif. Intell.
**2005**, 167, 170–205. [Google Scholar] [CrossRef] - Roy, D.; Hsiao, K.; Mavridis, N.; Gorniak, P. Ripley, Hand Me the Cup: Sensorimotor Representations for Grounding Word Meaning. Available online: https://www.media.mit.edu/cogmac/publications/asru03.pdf. (accessed on 9 June 2017).
- Jeannerod, M. Neural simulation of action: A unifying mechanism for motor cognition. NeuroImage
**2001**, 14, S103–S109. [Google Scholar] [CrossRef] [PubMed] - Pezzulo, G. Coordinating with the Future: The Anticipatory Nature of Representation. Minds Mach.
**2008**, 18, 179–225. [Google Scholar] [CrossRef] - Pezzulo, G. Tracing the Roots of Cognition in Predictive Processing; Open MIND: Frankfurt, Germany, 2017. [Google Scholar]
- Jeannerod, M. Motor Cognition; Oxford University Press: Oxford, UK, 2006. [Google Scholar]
- Behrens, T.E.J.; Woolrich, M.W.; Walton, M.E.; Rushworth, M.F.S. Learning the value of information in an uncertain world. Nat. Neurosci.
**2007**, 10, 1214–1221. [Google Scholar] [CrossRef] [PubMed] - Mathys, C.; Daunizeau, J.; Friston, K.J.; Stephan, K.E. A bayesian foundation for individual learning under uncertainty. Front. Hum. Neurosci.
**2011**, 5, 39. [Google Scholar] [CrossRef] [PubMed] - Pezzulo, G.; van der Meer, M.A.A.; Lansink, C.S.; Pennartz, C.M.A. Internally generated sequences in learning and executing goal-directed behavior. Trends Cogn. Sci.
**2014**, 18, 647–657. [Google Scholar] [CrossRef] [PubMed] - Buzsáki, G.; Peyrache, A.; Kubie, J. Emergence of Cognition from Action. Cold Spring Harb. Symp. Quant. Biol.
**2014**, 79, 41–50. [Google Scholar] [CrossRef] [PubMed] - Buzsáki, G.; Moser, E.I. Memory, navigation and theta rhythm in the hippocampal-entorhinal system. Nat. Neurosci.
**2013**, 16, 130–138. [Google Scholar] [CrossRef] [PubMed] - Pezzulo, G.; Kemere, C.; van der Meer, M. Internally generated hippocampal sequences as a vantage point to probe future-oriented cognition. Ann. N. Y. Acad. Sci.
**2017**, 1396, 144–165. [Google Scholar] [CrossRef] [PubMed] - Pfeiffer, B.E.; Foster, D.J. Hippocampal place-cell sequences depict future paths to remembered goals. Nature
**2013**, 497, 74–79. [Google Scholar] [CrossRef] [PubMed] - Redish, A.D. Vicarious trial and error. Nat. Rev. Neurosci.
**2016**, 17, 147–159. [Google Scholar] [CrossRef] [PubMed] - Buzsáki, G. Rhythms of the Brain; Oxford University Press: Oxford, UK, 2006; ISBN 978-0-19-530106-9. [Google Scholar]
- Friston, K. Hierarchical Models in the Brain. PLoS Comput. Biol.
**2008**, 4, e1000211. [Google Scholar] [CrossRef] [PubMed] - Barsalou, L.W. Ad hoc categories. Mem. Cogn.
**1983**, 11, 211–227. [Google Scholar] [CrossRef] - Rigoli, F.; Pezzulo, G.; Dolan, R.; Friston, K. A Goal-Directed Bayesian Framework for Categorization. Front. Psychol.
**2017**, 8, 408. [Google Scholar] [CrossRef] [PubMed] - Stoianov, I.; Genovesio, A.; Pezzulo, G. Prefrontal Goal Codes Emerge as Latent States in Probabilistic Value Learning. J. Cogn. Neurosci.
**2015**, 28, 140–157. [Google Scholar] [CrossRef] [PubMed] - Anderson, M.L. Embodied Cognition: A Field Guide. Artif. Intell.
**2003**, 149, 91–130. [Google Scholar] [CrossRef] - Pezzulo, G.; Barsalou, L.W.; Cangelosi, A.; Fischer, M.H.; McRae, K.; Spivey, M.J. Computational Grounded Cognition: A new alliance between grounded cognition and computational modeling. Front. Psychol.
**2013**, 3, 612. [Google Scholar] [CrossRef] [PubMed] - Thaker, P.; Tenenbaum, J.B.; Gershman, S.J. Online learning of symbolic concepts. J. Math. Psychol.
**2017**, 77, 10–20. [Google Scholar] [CrossRef] - Tenenbaum, J.B.; Kemp, C.; Griffiths, T.L.; Goodman, N.D. How to grow a mind: Statistics, structure, and abstraction. Science
**2011**, 331, 1279–1285. [Google Scholar] [CrossRef] [PubMed] - Clark, A. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci.
**2013**, 36, 181–204. [Google Scholar] [PubMed] - Verschure, P.F.M.J.; Voegtlin, T.; Douglas, R.J. Environmentally mediated synergy between perception and behaviour in mobile robots. Nature
**2003**, 425, 620–624. [Google Scholar] [CrossRef] [PubMed] - Wu, Z.; Yamaguchi, Y. Input-dependent learning rule for the memory of spatiotemporal sequences in hippocampal network with theta phase precession. Biol. Cybern.
**2004**, 90, 113–124. [Google Scholar] [CrossRef] [PubMed] - Carvalho, J.T.; Nolfi, S. Cognitive Offloading Does Not Prevent but Rather Promotes Cognitive Development. PLoS ONE
**2016**, 11, e0160679. [Google Scholar] [CrossRef] [PubMed] - Stepp, N.; Turvey, M.T. The Muddle of Anticipation. Ecol. Psychol.
**2015**, 27, 103–126. [Google Scholar] [CrossRef]

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**MDPI and ACS Style**

Pezzulo, G.; Donnarumma, F.; Iodice, P.; Maisto, D.; Stoianov, I.
Model-Based Approaches to Active Perception and Control. *Entropy* **2017**, *19*, 266.
https://doi.org/10.3390/e19060266

**AMA Style**

Pezzulo G, Donnarumma F, Iodice P, Maisto D, Stoianov I.
Model-Based Approaches to Active Perception and Control. *Entropy*. 2017; 19(6):266.
https://doi.org/10.3390/e19060266

**Chicago/Turabian Style**

Pezzulo, Giovanni, Francesco Donnarumma, Pierpaolo Iodice, Domenico Maisto, and Ivilin Stoianov.
2017. "Model-Based Approaches to Active Perception and Control" *Entropy* 19, no. 6: 266.
https://doi.org/10.3390/e19060266