Cognitive Offloading Does Not Prevent but Rather Promotes Cognitive Development

We investigate the relation between the development of reactive and cognitive capabilities. In particular we investigate whether the development of reactive capabilities prevents or promotes the development of cognitive capabilities in a population of evolving robots that have to solve a time-delay navigation task in a double T-Maze environment. Analysis of the experiments reveals that the evolving robots always select reactive strategies that rely on cognitive offloading, i.e., the possibility of acting so as to encode onto the relation between the agent and the environment the states that can be used later to regulate the agent’s behavior. The discovery of these strategies does not prevent, but rather facilitates, the development of cognitive strategies that also rely on the extraction and use of internal states. Detailed analysis of the results obtained in the different experimental conditions provides evidence that helps clarify why, contrary to expectations, reactive and cognitive strategies tend to have synergetic relationships.

[1]  C. Hofsten,et al.  Preparation for grasping an object: a developmental study. , 1988, Journal of experimental psychology. Human perception and performance.

[2]  L. Munari How the body shapes the way we think — a new view of intelligence , 2009 .

[3]  M K Kaiser,et al.  How baseball outfielders determine where to run to catch fly balls. , 1995, Science.

[4]  Stefano Nolfi,et al.  Evolution of Implicit and Explicit Communication in Mobile Robots , 2010, Evolution of Communication and Language in Embodied Agents.

[5]  Stefano Nolfi,et al.  Evolving coordinated group behaviours through maximisation of mean mutual information , 2008, Swarm Intelligence.

[6]  Brad E. Pfeiffer,et al.  Hippocampal place cell sequences depict future paths to remembered goals , 2013, Nature.

[7]  Stefano Nolfi,et al.  On the Coupling Between Agent Internal and Agent/ Environmental Dynamics: Development of Spatial Representations in Evolving Autonomous Robots , 2008, Adapt. Behav..

[8]  George E. Nasr,et al.  Cross Entropy Error Function in Neural Networks: Forecasting Gasoline Demand , 2002, FLAIRS.

[9]  S. Gilbert Strategic use of reminders: Influence of both domain-general and task-specific metacognitive confidence, independent of objective memory ability , 2015, Consciousness and Cognition.

[10]  Benjamin Schrauwen,et al.  Mobile robot control in the road sign problem using Reservoir Computing networks , 2008, 2008 IEEE International Conference on Robotics and Automation.

[11]  Rolf Pfeifer,et al.  How the body shapes the way we think - a new view on intelligence , 2006 .

[12]  Jürgen Schmidhuber,et al.  A robot that reinforcement-learns to identify and memorize important previous observations , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[13]  O. Gingerich A mind in motion , 1994, Nature.

[14]  Barbara Tversky,et al.  Visualizing Thought , 2011, Top. Cogn. Sci..

[15]  R. Mizen The embodied mind. , 2009, The Journal of analytical psychology.

[16]  Margaret Wilson,et al.  Six views of embodied cognition , 2002, Psychonomic bulletin & review.

[17]  Joel Lehman,et al.  Overcoming deception in evolution of cognitive behaviors , 2014, GECCO.

[18]  Stefano Nolfi,et al.  FARSA: An Open Software Tool for Embodied Cognitive Science , 2013, ECAL.

[19]  H. Eichenbaum,et al.  Hippocampal Neurons Encode Information about Different Types of Memory Episodes Occurring in the Same Location , 2000, Neuron.

[20]  Fred Keijzer,et al.  Representation in dynamical and embodied cognition , 2002, Cognitive Systems Research.

[21]  Evan F. Risko,et al.  Storing information in-the-world: Metacognition and cognitive offloading in a short-term memory task , 2015, Consciousness and Cognition.

[22]  Chris A. Czarnecki,et al.  Embedding Connectionist Autonomous Agents in Time: The ‘Road Sign Problem’ , 2000, Neural Processing Letters.

[23]  Stéphane Doncieux,et al.  With a little help from selection pressures: evolution of memory in robot controllers , 2012, ALIFE.

[24]  Randall D. Beer,et al.  Further Experiments in the Evolution of Minimally Cognitive Behavior: From Perceiving Affordances to Selective Attention , 2000 .

[25]  Randall D. Beer,et al.  Evolving Dynamical Neural Networks for Adaptive Behavior , 1992, Adapt. Behav..

[26]  Francesco Mondada,et al.  The marXbot, a miniature mobile robot opening new perspectives for the collective-robotic research , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[27]  Tom Ziemke,et al.  Neuromodulation of Reactive Sensorimotor Mappings as a Short-Term Memory Mechanism in Delayed Response Tasks , 2002, Adapt. Behav..

[28]  Yoonsuck Choe,et al.  Emergence of Memory in Reactive Agents Equipped With Environmental Markers , 2011, IEEE Transactions on Autonomous Mental Development.

[29]  Naoki Maeda,et al.  External Working Memory and the Amount of Distributed Cognition , 2012, CogSci.

[30]  Stefano Nolfi,et al.  Evolution of a predictive internal model in an embodied and situated agent , 2011, Theory in Biosciences.

[31]  Anders Lyhne Christensen,et al.  Evolution of Hybrid Robotic Controllers for Complex Tasks , 2015, J. Intell. Robotic Syst..

[32]  Robert F. Port,et al.  MIND IN MOTION: , 2019, Dune.

[33]  Andrew M. Wikenheiser,et al.  Decoding the cognitive map: ensemble hippocampal sequences and decision making , 2015, Current Opinion in Neurobiology.

[34]  Christina Freytag Being There Putting Brain Body And World Together Again , 2016 .

[35]  Michèle Sebag,et al.  Memory-enhanced Evolutionary Robotics: The Echo State Network Approach , 2009, 2009 IEEE Congress on Evolutionary Computation.

[36]  Dario Floreano,et al.  Exploring the T-Maze: Evolving Learning-Like Robot Behaviors Using CTRNNs , 2003, EvoWorkshops.

[37]  Pattie Maes,et al.  Toward the Evolution of Dynamical Neural Networks for Minimally Cognitive Behavior , 1996 .

[38]  J. F. Soechting,et al.  Coarticulation in Fluent Fingerspelling , 2003, The Journal of Neuroscience.

[39]  Josh C. Bongard,et al.  Evolutionary robotics , 2013, CACM.

[40]  Henrik Jacobsson,et al.  Mobile Robot Learning of Delayed Response Tasks through Event Extraction: A Solution to the Road Sign Problem and Beyond , 2001, IJCAI.

[41]  Maria Adler,et al.  Enaction Toward A New Paradigm For Cognitive Science , 2016 .

[42]  D. Lewkowicz,et al.  A dynamic systems approach to the development of cognition and action. , 2007, Journal of cognitive neuroscience.

[43]  E. D. Paolo,et al.  Enaction: Toward a New Paradigm for Cognitive Science , 2010 .

[44]  S. Gilbert Strategic offloading of delayed intentions into the external environment , 2014, Quarterly journal of experimental psychology.

[45]  A. Clark An embodied cognitive science? , 1999, Trends in Cognitive Sciences.

[46]  Jun Tani,et al.  How Hierarchical Control Self-organizes in Artificial Adaptive Systems , 2005, Adapt. Behav..

[47]  Francesco Mondada,et al.  Evolution of homing navigation in a real mobile robot , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[48]  Randall D. Beer,et al.  The Dynamics of Active Categorical Perception in an Evolved Model Agent , 2003, Adapt. Behav..