Before starting to discuss the most common definitions, it should be clear that here I am referring to intelligence in a strict sense. In fact, what we usually consider intelligent depends on our expectations: although ordinary people know how to play chess, a baby with the same skill would be considered a genius; a dog, even if it could only understand the rules of the game, would be nothing short of a miracle [10]. Hence, my focus lies on the most fundamental traits of intelligent behavior.
In order to delineate the strict meaning of the term, psychologists and neuroscientists have been trying to find the very essence of intelligence since the beginning of the twentieth century. According to Alfred Binet and Theodore Simon, the fathers of the IQ Test, intelligence represents the faculty of adapting oneself to new circumstances [2]. Thereafter, many researchers endorsed that adaptation is a core feature of intelligence [15]. Nevertheless, it should be noted that purely adaptive behavior seems to be not enough to distinguish an intelligent agent. Otherwise, even bacteria, which have the ability to adapt themselves to the environment [13], could be characterized as intelligent organisms. An agent equipped solely with the adaptation attribute has to adapt itself to each change in its environment, even if this change represents the return to a previously experienced condition. We expect, however, that an intelligent agent should be able to recognize an already experienced state and thus act accordingly, without any readaptation. Such behavior clearly suggests the need for memory and the ability to learn.
According to Dawkins [4], the emergence of memory in living beings represented a great advance, inasmuch as their behavior could now be influenced not only by the immediate past, but also by events in the distant past.
The psychologist Walter Dearborn has soon realized that the capacity to learn and to profit by experience represents a key aspect of intelligence [5]. Donald Hebb, whose work had an enormous influence on the field of artificial neural networks, has also highlighted the importance of learning [7]. As a matter of fact, Hebbian theory is the basis of one of the oldest and most important learning rules in neural networks. Nowadays, there is no doubt that learning is essential for all intelligent beings and it usually occurs throughout their entire lifetime. This process involves the continuous assimilation of new information, followed by the unceasing accommodation of the knowledge basis due to information arrival.
More recently, neuroscientists begun to draw attention to another essential trait of intelligence: the capacity to predict. Wolpert [16] argued that humans and animals, in order to properly activate their muscles and limbs, develop an internal model of their own motor systems and of their environment as well. This internal model is constantly updated by sensory feedback and is used by the central nervous system (CNS) to predict the consequences of its control actions [3]. Edwards et al. [6] propose that the brain creates internal models of the environment to predict imminent sensory input. Llinás [9] emphasized that prediction is the prime objective of the brain and is imperative for intelligent motricity. In [10], Llinás and Roy suggested that the CNS has evolved for the purpose of predicting the outcomes of the impending motion. Moreover, since prediction in this case should be unique, the authors also claim that this feature may have led to the emergence of self-awareness in complex living beings.
In more complex systems, such as human beings, intelligence may be also associated with many other higher predicates (emotion, creativity, consciousness, just to name a few). Nevertheless, prediction, adaptation, and learning surely represent the most basic characteristics of all intelligent organisms, even the simplest ones.
This post is an adapted extract from the paper A biologically inspired framework for the intelligent control of mechatronic systems and its application to a micro diving agent, by W. M. Bessa et al. [1]
[1] W. M. Bessa, G. Brinkmann, D. A. Duecker, E. Kreuzer, and E. Solowjow. A biologically inspired framework for the intelligent control of mechatronic systems and its application to a micro diving agent. Mathematical problems in engineering, 2018. [DOI]
[2] A. Binet and T. Simon. The Development of Intelligence in Children. Williams & Wilkins, Baltimore, 1916.
[3] S. J. Blakemore, S. J. Goodbody, and D. M. Wolpert. Predicting the consequences of our own actions: The role of sensorimotor context estimation. Journal of Neuroscience, 18(18):7511–7518, 1998.
[4] R. Dawkins. The Selfish Gene. Oxford University Press, Oxford, 1976.
[5] W. Dearborn. Intelligence and its measurement: A symposium – XII. Journal of Educational Psychology, 12(4):210–212, 1921.
[6] G. Edwards, P. Vetter, F. McGruer, L. S. Petro, and L. Muckli. Predictive feedback to v1 dynamically updates with sensory input. Scientific Reports, 7(1):16538, 2017.
[7] D. O. Hebb. The Organization of Behavior. John Wiley & Sons, Inc., New York, 1949.
[8] R. R. Llinás. I of the Vortex: From Neurons to Self. MIT Press, Cambridge, MA, 2001.
[9] R. R. Llinás and S. Roy. The ‘prediction imperative’ as the basis for self-awareness. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 364(1521):1301–1307, 2009.
[10] R. Pfeifer and C. Scheier. Understanding Intelligence. MIT Press, Cambridge, MA, 2001.
[11] H. Roy, K. Dare, and M. Ibba. Adaptation of the bacterial membrane to changing environments using aminoacylated phospholipids. Molecular Microbiology, 71(3):547–550, 2009.
[12] R. Sternberg. Beyond IQ: A triarchic theory of human intelligence. Cambridge University Press, New York, 1985.
[13] D. M. Wolpert. Computational approaches to motor control. Trends in Cognitive Sciences, 1(6):209–216, 1997.
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