Copycat Decision Making

In chapter five o Douglas Hofstadter’s book Fluid Concept he details the workings of his copycat program. The copycat architecture he designs is focused not only on discovering mappings between situations but on how it perceives and makes sense of situations it is presented with. Again Hofstadter mentions the vital concept of perception integration into artificial intelligence programming. He also states that the actual process of integrating perception into an artificial intelligence program is extremely difficult. Considering the aspects of copycat that deal with the fluid change between analogies and concepts applied to particular input, it is almost equally difficult to integrate those. Hofstadter describes the ability of the copycat program to do this switching between analogies is an emergence of its component flexible parts, which reminds me of a certain description of human consciousness I have read before. The fluid ability to apply different rules and heuristics to a single situation, within the copycat program, emerges through the flexibility and almost simultaneously productivity of its component parts like the slipnet, coderack, and workspace. I find it very interesting that Hofstadter likens two of these elements to a particular aspect of the human brain such as the slipnet emulating long term memory, the workspace designed to function like working or short term memory, and the coderack which is not directly correlated to a human brain aspect but can be thought of as a storage area where elements are selected by the current processes predictable actions and an element of randomness.


This function of copycat to stochastically (selecting an element through prediction of the current goal with an integration of randomness) select elements from the coderack is intriguing to me. What aspect in our human brain completes this similar function? It doesn’t sound like a subconscious task to me, more like a conscious decision making aspect that involves directly selecting pertinent details to a current situation. Furthermore, what is the main factor that determines the selection of a particular element over another within our mental processes? The answer I believe Hofstadter already described, perception. Perception is vital to the decision process especially when applying multiple heuristics to a single situation and finding the appropriate one. Due to human individuality the different applications and understanding of individual perception can produce multiple distinct workings and solutions to a single problem; this is where the randomness element of the decision making must play a part. Hofstadter has clearly integrated some thought provoking aspects into this copycat program.

High Level Perception

In chapter four of Douglas Hofstadter’s book on Fluid Concepts, he details the necessary integration of high level perception within our cognitive processes. Furthermore, if one is to research or build an artificial intelligence model then one must consider the aspect of high level perception. Hofstadter explains high level perception as the flexible, semantic, and multi faceted process by which we humans take sensory input and begin to relate it to familiar concepts. High level perception can be processed in different ways by different individuals when determining personal experiences, beliefs, goals, and external context. A major aspect of high level perception is the semantic aspect or the drawing of meanings out of situations, this involves concepts playing a major role in processing of a semantic thought, the more meaning or meanings derived from an individual perception the more concepts it takes into consideration.


This mention of high level perception which Hofstadter details sparked some interesting thoughts in my mind. One does not think daily about certain almost unconscious processes like that of high level perception, yet all mentally healthy humans exhibit it when we think of conceptual aspects related to any input we receive. It seems to me that thought looping and deriving a number of distinct analogies related to a particular input could go hand in hand with high level perception. Hofstadter also mentions that certain artificial intelligence modeling has focused strictly on conceptual processes without incorporating perceptual processing, this approach he argues is not a satisfactory way to model the human mind, and I would have to strongly agree. Human minds are made and molded by the stream of environmental input they receive. If we were to cut out this essential perceptual process we would all be likened to rocks or vegetables. It is our adaptability, distinct reactions, and cognitive conceptual analogous thoughts that make those perceptual inputs so vital to our lives and how we think. I also wondered what part of our brain deals with this aspect of high level perception, I am guessing somewhere in the frontal lobe, but am not sure. One thing is certain, as Hofstadter mentions, high level perception is a vital part of our cognitive processes, and without it humans could not form concepts, analogies, and sense conceptual spheres as quickly and as easily as we do today.

Eliza Effect

In preface four of Douglas Hofstadter’s book of Fluid Concepts he begins with making an interesting point about certain A.I. computer programs and their lack of grasping real world concepts. Hofstadter details that if a computer program does not have a knowledge base to extract data from and construct representational models it cannot grasp the actual concept of a thing. A program can have many variables to deal with and one without a conceptual knowledge base to refer to is not affected by the change of those variable names, however a computer program with a pre determined knowledge base can exhibit a conceptual analogous attitude if it extracts existing information about a particular variable and constructs a model or conceptual sphere around that particular variable. Therefore, if a programmer changed the name of a particular variable with a reference in the knowledge base it would greatly affect the programs functions, analogies, and models dealing with that variable. Another interesting aspect Hofstadter describes is the Eliza Effect, or the susceptibility of people to infer far more understanding into a given string of words (phrase or sentence) than is warranted. Most computer programs, ones without any analogous or conceptual ability, would never make the mistake of producing the Eliza Effect, because they cannot infer deeper into an aspect. It is human nature to seek knowledge and define concepts according to relations, analogies, and personal experience. The sheer fact that we are conceptual and analogous thinkers’ pre exposes all humans to the Eliza Effect. We have a natural born desire to relate and categorize things into groups and associations, this is something computer programs do not usually do or are even designed to do. However, when building A.I. programs, which emulate human thought processes, the Eliza Effect must be considered as Hofstadter wrote. Considering this, if one was ever to make a computer program that could emulate and even reproduce the Eliza Effect for any given input or concept, then we could be one step closer to constructing a true artificial intelligence.

Human vs. Numbo

In the last part of chapter three in Douglas Hofstadter’s book Fluid Concepts, Daniel Defays details the similarities of the Numbo program, creating and computing number problems, to the human method of the same task. The Numbo programs essential task in this manner was to shed light on the understanding of how humans exhibit mental fluidity and smooth processing in determining the solutions to such numeric problems. The author explains that a strict comparison of the Numbo program and human performance is not possible at this time due to many differences between the two. The most profound reason for denying a strict comparison is the difference in the knowledge base between humans and the program, followed by a lack of human characteristics within the program, such as taking into consideration the linear order of available elements. Lastly, the design of Numbo’s architecture did not exhibit the importance of numeric relationships and the measureable degree of similarity between numbers that most humans demonstrate almost subconsciously. Thinking about the major differences between Numbo and humans taking in to account these numeric problems makes me think that human perception and subconscious thought might have a role in all of this.

The Numbo program, in my mind, does not exhibit the complex perceptual qualities and pattern recognition as we humans do in this case, this aspect is perhaps arguable by some people, but nonetheless Numbo does have certain qualities of pattern recognition. Through our experience and basic mathematical learning it seems we humans have a decent amount of pattern recognition and simple computational qualities subconsciously built in to our brain processes. This would be very difficult for a computer program to duplicate, but is likely possible in the near future due to exponential growth of technology. The first logical step would be for a machine to experience and learn, somehow bettering itself, to acquire such abilities that humans’ exhibit pertaining to the computation and immediate pattern recognition of these particular mathematical problems. To concretely define these human processes of learning and computation, in this case, seems to be the bigger and more immediate milestone than determining how a machine or program processes such data. The author makes a similar point in saying that human protocols are highly questionable and not well defined, for it is extremely difficult to keep track of every single process a human completes when solving a similar Numbo problem.

Unintelligent Number Biases

In this first section of chapter three in Douglas Hofstadter’s Fluid Concepts Daniel Defays discusses the workings of the program Numble. An adaption of the concept of a word jumble that chooses a random number goal (between 1 and 150) and attempts to computationally reach the target by picking random numbers (between 1 and 12) and using addition subtraction and multiplication to reach the number goal. While explaining the workings of the Numble program the author touches on a very interesting aspect of the mathematical solution process of people, which details that humans can see certain patterns and solutions immediately and other solutions elude us almost completely. He uses a good example in the text of a number goal of eighty seven and the set of numbers eight three nine ten and seven.

Target: 87
Bricks: 8 3 9 10 7

It seems almost immediate that most humans can recognize the solution of ((9 x 10) – 3) or ((8 x 10) + 7) mainly because we are accustomed to dealing with ten based numbers and for most people these are the easiest numbers to deal with in mathematical problems. However the solution (9 x 7) + (8 x 3) is rarely the first resolution to the problem detected by humans. The author makes another inference into why we prefer the ten based solution to this problem, because of our priori knowledge (familiar concepts and patterns pertaining to input) that eighty or ninety is close to the target number eighty seven. While testing a random crypto problem generator and solver program I noticed similar instances where I personally could immediately see a solution to the problem, usually one, zero, or ten based, yet the computer program picked a drastically different, usually shorter more quickly solvable solution. Having no bias or preference towards certain number patterns and priori, except what it was given, the computer program took a completely different path to solving the problem than I did. It is as if the lack of biases towards pattern recognition gave the program a different aspect of creativity and effectiveness. The section preceding chapter three, Hofstadter mentions that the randomness aspect of his Jumble construction program gives it a greater sense of intelligence and speed compared to a deductive reasoning or strictly pattern prediction method, as I put these theories to the test I am starting to clearly see the evidence and method to his madness.