Cognitive Adequacy in Brain-Like Intelligence

A variety of disciplines have dealt with the design of intelligent algorithms --- among them Artificial Intelligence and Robotics. While some approaches were very successful and have yielded promising results, others have failed to do so which was -- at least partly -- due to inadequate architectures and algorithms that were not suited to mimic the behavior of biological intelligence. Therefore, in recent years, a quest for "brain-like" intelligence has arosen. Soft- and hardware are supposed to behave like biological brains -- ideally like the human brain. This raises the questions of what exactly defines the attribute "brain-like", how can the attribute be implemented and how tested. This chapter suggests the concept of cognitive adequacy in order to get a rough estimate of how "brain-like" an algorithm behaves.

[1]  Daniel Strüber,et al.  MEG alpha activity decrease reflects destabilization of multistable percepts. , 2002, Brain research. Cognitive brain research.

[2]  C S Herrmann,et al.  Magnetoencephalographic responses to illusory figures: early evoked gamma is affected by processing of stimulus features. , 2000, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[3]  M. Minsky.  The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind , 2006 .

[4]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[5]  Maurice Merleau-Ponty.  The structure of behavior , 1963 .

[6]  E. Kandel,et al.  Memory: From Mind to Molecules , 1999 .

[7]  R. Parasuraman.  The attentive brain , 1998 .

[8]  Professor Dr. Dr. h.c. Hermann Haken,et al.  Synergetic Computers and Cognition , 1991, Springer Series in Synergetics.

[9]  Daniel Crevier,et al.  Ai: The Tumultuous History Of The Search For Artificial Intelligence , 1993 .

[10]  Wolfgang Bibel.  Perspectives on Automated Deduction , 1991, Automated Reasoning: Essays in Honor of Woody Bledsoe.

[11]  Axel Mecklinger,et al.  Gamma activity in human EEG is related to highspeed memory comparisons during object selective attention , 2001 .

[12]  M. Posner.  Chronometric explorations of mind : the third Paul M. Fitts lectures, delivered at the University of Michigan, September 1976 , 1978 .

[13]  L. Shastri,et al.  From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony , 1993, Behavioral and Brain Sciences.

[14]  J. Fuster,et al.  Prefrontal Cortex and the Bridging of Temporal Gaps in the Perception‐Action Cycle , 1990, Annals of the New York Academy of Sciences.

[15]  Peter Mombaerts,et al.  From mind to molecule , 2001, Nature Neuroscience.

[16]  N. Furstenau,et al.  A chaotic attractor model of cognitive multistability , 2004, IEEE International Conference on Systems, Man and Cybernetics.

[17]  D. Brainard,et al.  Mechanisms of color constancy under nearly natural viewing. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[18]  W. Freeman,et al.  Change in pattern of ongoing cortical activity with auditory category learning , 2001, Nature.

[19]  Steffen Hölldobler,et al.  On the Adequateness of the Connection Method , 1993, AAAI.

[20]  H. Dreyfus.  What Computers Can't Do: The Limits of Artificial Intelligence , 1978 .

[21]  M. Gazzaniga,et al.  Cognitive Neuroscience: The Biology of the Mind , 1998 .

[22]  Hermann Haken.  Synergetic Computers & Cognition , 2004 .

[23]  Christoph Herrmann,et al.  Considering adequacy in neural network learning , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[24]  Michael Thielscher,et al.  On the adequateness of AI-systems (extended abstract) , 1995 .

[25]  John R. Anderson,et al.  Rules of the Mind , 1993 .

[26]  Henning Scheich,et al.  Learning-induced plasticity in animal and human auditory cortex , 2005, Current Opinion in Neurobiology.

[27]  J. Searle,et al.  Minds, Brains and Science , 1988 .

[28]  Harold E. Yuker,et al.  Can you believe your eyes? : over 250 illusions and visual oddities , 1992 .

[29]  Frank W. Ohl,et al.  Chips in Your Head , 2007 .

[30]  J. Stroop.  Studies of interference in serial verbal reactions. , 1992 .

[31]  Rolf Eckmiller,et al.  Neurobionics: An Interdisciplinary Approach to Substitute Impaired Functions of the Human Nervous System , 1993 .

[32]  D. Senkowski,et al.  Effects of task difficulty on evoked gamma activity and ERPs in a visual discrimination task , 2002, Clinical Neurophysiology.

[33]  桐山 伸也.  "The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind," Marvin Minsky, Simon & Schuster, 2006(私のすすめるこの一冊,コーヒーブレイク) , 2007 .

[34]  Ron Chrisley,et al.  Embodied artificial intelligence , 2003, Artif. Intell..

[35]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[36]  A. Abbott.  Neuroprosthetics: In search of the sixth sense , 2006, Nature.