Summary Architectures of Intelligence
How can the human mind occur in the physical universe – John Anderson (2007)
Chapter 1: Cognitive Architecture
Newell introduced the term cognitive architecture into cognitive science through an analogy
to computer architectures, which Fred Brooks (1962) in turn introduced into computer science
through an analogy to the architecture of buildings. Architecture is the art of specifying the
structure of the building at a level of abstraction sufficient to assure that the builder will
achieve the functions desired by the user. Computer architecture however, was more focused
on the product of the design rather than the designing itself. Cognitive architecture is therefore
referred to as “the fixed (or slowely varying) structure that forms the framework for the
immediate processes of cognitive performance and learning.”
Architecture involves relating its structure to a function. The structure in here referred
to as the agent itself and its function is to enable cognition. Before, when scientists wanted to
understand cognition, had to either focus of the structure, or human behaviour. To understand
the mind however, we need an abstraction that gets its essence. There is though still a lot of
debate of what is the best abstraction. Taken all together: A cognitive architecture is a
specification of the structure of the brain at level of abstraction that explains how it achieves
the function of the mind.
The architectural program that will be discussed mostly pays attention to three things:
the brain, mind (functional cognition), and the architectural abstractions that link them. In the
rest of this chapter, some shortcut theories (or scientific approaches that ignore a part) will be
discussed. The first one is the Classical Information-Processing theory of Psychology that
ignores the brain. The problem with this behavioural approach is that the structure is
completely left out. One of the more prominent researches was the Sternberg experiment in
which some numbers were presented, and then the subject had to search a probe-number. This
approach was criticised because the explanations were biologically implausible. Long was
this criticism ignored until connectionism arose. The rise of neural imaging further showed
the importance of also taking the brain into account.
The second shortcut is the eliminative connectionism which ignores the mind. They
did realise that the brain structure generates human behaviour, but maybe that is just enough
to understand the mind, and you get functionality for free. The goal is to come up with an
abstract description of the computational properties of the brain and then use this description
to explain various behavioural phenomena. One of the biggest eliminative connectionist
, success is the past-tense model of Rummelhart and McClelland (1986). When children start to
learn the past tense, both regular and irregular inferences are know per word. After some time
however, they start to over generalise the rules for regular words to irregular words. After
some more time they finally learn the proper past-tenses resulting in a U-shape in
performance.
Until Rummerhart and McClelland there was no clear explanation for this, but they
could and even built a working neuronal network model that performed like the empirical
data. In such a model the use of the features strengthens the relationship between them. This
means, when errors are made, also false connections can be made. Thus, they claim, they have
achieved the function of a rule without ever having to consider the rules in their explanation.
This view however rests on a sleight of hand, in which they map activation pattern over
activation pattern, not considering anything of human speech productions. How input patterns
are produced or what happens to the output patterns to yield parts of coherent speech. That,
the model does explain the functional aspects of the entire system. The above mentioned
criticism is not for connectionism per se, but rather for any model that disregards the overall
architecture and its function.
The last shortcut, which ignores the architecture, is called rational analysis, also called
ecological psychology (Gibson, 1966) for instance. In this, rather than focussing on the
architecture as the key abstraction, focus on the adaptation to the environment. The Bayesian
statistical methodology is maybe the most prominent application for understanding human
cognition. The Bayesian approach claims the following, but first note that this approach does
not claim that people make these calculations explicitly. Rather, we just do not have to worry
about how people do it.
1. We have a set of prior constraints about the nature of the world we occupy.
2. Given various experiences, one can calculate the conditional probability that
various states of the world provided them.
3. Given the input, one can calculate the posterior probabilities from the prior and
conditional probabilities.
4. After making this calculation, one engages in Bayesian decision making and takes
the action that optimises our expected utilities.
One of the most prominent examples of rational analysis experiments is about e-mail
sending and replying. It appears that the chance that one sends you an e-mail decreases
logarithmically when time since the last occurrence increases. This is related to the memory
retention function (i.e. how well memories are remembered after last usage). Both these
How can the human mind occur in the physical universe – John Anderson (2007)
Chapter 1: Cognitive Architecture
Newell introduced the term cognitive architecture into cognitive science through an analogy
to computer architectures, which Fred Brooks (1962) in turn introduced into computer science
through an analogy to the architecture of buildings. Architecture is the art of specifying the
structure of the building at a level of abstraction sufficient to assure that the builder will
achieve the functions desired by the user. Computer architecture however, was more focused
on the product of the design rather than the designing itself. Cognitive architecture is therefore
referred to as “the fixed (or slowely varying) structure that forms the framework for the
immediate processes of cognitive performance and learning.”
Architecture involves relating its structure to a function. The structure in here referred
to as the agent itself and its function is to enable cognition. Before, when scientists wanted to
understand cognition, had to either focus of the structure, or human behaviour. To understand
the mind however, we need an abstraction that gets its essence. There is though still a lot of
debate of what is the best abstraction. Taken all together: A cognitive architecture is a
specification of the structure of the brain at level of abstraction that explains how it achieves
the function of the mind.
The architectural program that will be discussed mostly pays attention to three things:
the brain, mind (functional cognition), and the architectural abstractions that link them. In the
rest of this chapter, some shortcut theories (or scientific approaches that ignore a part) will be
discussed. The first one is the Classical Information-Processing theory of Psychology that
ignores the brain. The problem with this behavioural approach is that the structure is
completely left out. One of the more prominent researches was the Sternberg experiment in
which some numbers were presented, and then the subject had to search a probe-number. This
approach was criticised because the explanations were biologically implausible. Long was
this criticism ignored until connectionism arose. The rise of neural imaging further showed
the importance of also taking the brain into account.
The second shortcut is the eliminative connectionism which ignores the mind. They
did realise that the brain structure generates human behaviour, but maybe that is just enough
to understand the mind, and you get functionality for free. The goal is to come up with an
abstract description of the computational properties of the brain and then use this description
to explain various behavioural phenomena. One of the biggest eliminative connectionist
, success is the past-tense model of Rummelhart and McClelland (1986). When children start to
learn the past tense, both regular and irregular inferences are know per word. After some time
however, they start to over generalise the rules for regular words to irregular words. After
some more time they finally learn the proper past-tenses resulting in a U-shape in
performance.
Until Rummerhart and McClelland there was no clear explanation for this, but they
could and even built a working neuronal network model that performed like the empirical
data. In such a model the use of the features strengthens the relationship between them. This
means, when errors are made, also false connections can be made. Thus, they claim, they have
achieved the function of a rule without ever having to consider the rules in their explanation.
This view however rests on a sleight of hand, in which they map activation pattern over
activation pattern, not considering anything of human speech productions. How input patterns
are produced or what happens to the output patterns to yield parts of coherent speech. That,
the model does explain the functional aspects of the entire system. The above mentioned
criticism is not for connectionism per se, but rather for any model that disregards the overall
architecture and its function.
The last shortcut, which ignores the architecture, is called rational analysis, also called
ecological psychology (Gibson, 1966) for instance. In this, rather than focussing on the
architecture as the key abstraction, focus on the adaptation to the environment. The Bayesian
statistical methodology is maybe the most prominent application for understanding human
cognition. The Bayesian approach claims the following, but first note that this approach does
not claim that people make these calculations explicitly. Rather, we just do not have to worry
about how people do it.
1. We have a set of prior constraints about the nature of the world we occupy.
2. Given various experiences, one can calculate the conditional probability that
various states of the world provided them.
3. Given the input, one can calculate the posterior probabilities from the prior and
conditional probabilities.
4. After making this calculation, one engages in Bayesian decision making and takes
the action that optimises our expected utilities.
One of the most prominent examples of rational analysis experiments is about e-mail
sending and replying. It appears that the chance that one sends you an e-mail decreases
logarithmically when time since the last occurrence increases. This is related to the memory
retention function (i.e. how well memories are remembered after last usage). Both these
