Adaptive Control of Thought Model of Memory

 

Adaptive Control of Thought
 

Hello Everyone, so today I will be giving you some brief information about network theory of memory has been developed by John Robert Anderson. Called the Adaptive Control of Thought (ACT) model of memory, it has evolved the almost 40 years of its existence, and various versions (ACT-*, ACT-R) exist.

Based on analogies to computers, ACT has given rise to several computer simulations of cognitive processing of different task. ACT model distinguish among three kinds of memory systems: Working Memory, Declarative Memory and Procedural Memory.

 

Declarative Memory

Declarative Memory (also known as descriptive knowledge) is knowledge that we are conscious of and can verbalize. It is information we directly encode from the environment and doesn’t require much synthesization. It emphasizes what one needs to do to solve an issue rather than how to solve it. In essence, it is content that can be recited or memorized.

 Declarative memory stores information in networks that contains nodes. There are different types of nodes, including those corresponding to spatial images or to abstract propositions. As with others network models, ACT models allow both for activation of any node and for spreading activation to connected nodes. Example of declarative memory include facts, world history, or rules for solving mathematical equations. 

Procedural Memory

Procedural Memory (also known as imperative knowledge) is knowledge you use while performing a task, but may not be able to verbalize. It is information encoded from synthesizing and observing transformations of the environment (behaviors).  Procedural memory is about how we do something.

This memory store represents information in production rules. Production rules specify a goal to achieve, one or more conditions that must be true for the rule to apply, and one or more actions that result from applying the rule. Examples of procedural memory include behaviors we do habitually, such as riding a bike or driving a car.

Another example, a typical college student could use this production rule: “If the goal is to study actively and attentively (goal) and the noise level in the dormitory is high (condition) and the campus library is open (condition), then gather your study materials (action) and take them to the library (action) and work there (action).” Okay, that example was a bit contrived. But psychologists, computer scientists, and others have used production rules to build computer programs that simulate human problem solving.

J. R. Anderson’s (1983) proposal not meant merely to address the question of knowledge representation. Instead, his aim was to create a theory of cognitive architecture, a theory of how human cognition actually operates in practice. He proposed a system that includes both memory storage and particular processing structures. Interestingly, the broad goal led him to develop proposals about knowledge representation that fit well with those of researchers whose aims were more focused.

In the ACT models, Working Memory is actually that part of declarative memory that a very activated at any particular moment. The production rules also become activated when the nodes in the declarative memory that correspond to the conditions of the relevant production rules are activated. When production rules are executed, they can create new nodes within declarative memory. Thus, ACT models have been described as very “activation-based” models of human cognition (Luger,1994).

 

History

Canadian psychologist John Anderson has an extensive background researching and developing his Adaptive Control of Thought model. His underlying assumption is that knowledge can be reduced into a theory or model, and therefore that a system can be created to perform human cognitive tasks. This assumption rests on the belief, articulated by Anderson in his 1990 book, that “we can understand a lot about human cognition without considering in detail what is inside the human head. Rather, we can look in detail at what is outside the human head and try to determine what would be optimal behavior given the structure of the environment and the goals of the human”.

With the goal in mind of creating a model to depict human knowledge, Anderson developed the Human Associative Memory (HAM) model alongside cognitive psychologist Gordon Bower in 1973. This model computed the mathematical theories of human cognition prevalent in the 1950s and 1960s. However, the model only accounted for human memory and did not accomplish Anderson’s mission of showing that all higher cognitive processes (memory, language, problem solving, imagery, deduction and induction) have the same underlying system. Replacing the HAM model, Anderson developed the ACT model in 1976, which was able to account for these higher cognitive processes.

In 1990, Anderson developed another version of ACT which he named ACT*. His hypothesis was that ACT*, alike ACT, showed that the mind is unitary (all of thoughts and mental faculties can be explained by the same underlying system) and that our experiences are stored in different facilities (linguistic, geometric, etc.) depending on their subject matter. Information comes through a ‘buffer’, known as working memory, which determines whether the information should be stored as declarative and later retrieved, or as procedural knowledge and executed in the moment to match current activity. The model looks as follows:

The original ACT model only accounted for one kind of ‘cognitive unit’ that the mind would process, also known as ‘chunks’. These units were words and statements like ‘hate’ or ‘my mom is nice’. And Anderson speculated that working memory is able to process around 5 chunks at once before becoming overwhelmed.

However, in the ACT* model, information could come in the form of spatial images and temporal strings as well as abstract propositions. What counted as a cognitive unit was expanded. Temporal strings encode the order of a set of items; spatial images encode the spatial configuration of a cognitive unit; abstract propositions encode meaning. Another key element of ACT* was its suggestion that all information begins as declarative, and combines with method learning to produce procedural knowledge.

Moreover, ACT suggests that information was serially processed, whereas ACT* suggests information could be processed simultaneously (parallel processing). The last notable difference is that the ACT* theory added a ‘sub-symbolic’ component: a feature that could determine what meaning is activated when we run into a cognitive unit with many possibilities. For example, if we encounter the sentence “The robber took money from the bank,” the word ‘bank’ has two meanings: a financial institution, or the land sloping down next to a body of water. The sub-symbolic component of ACT* activates the financial institution meaning because it knows that meaning is processed in relation to money or robbery.

A few years later, Anderson, alongside his colleagues at Carnegie Mellon University, developed the most recent model of ACT by combining the original with rational analysis. This model is known as ACT-R and predicts behavior based on the idea that humans act in ‘optimal’ (see: rational) ways. The ACT-R is essentially a production system, like a machine, that operates according to ACT*. The theory was moved to a computer program, on which researchers can download the ACT-R code, input information about a specific task, and analyze people’s predicted performances.

I hope you found this article helpful & learnt something new. I would be glad to know your thoughts on it in the comment section below. If you like this article, share it with your friends and colleagues!

THANK YOU 

REFRENCES 

https://thedecisionlab.com/reference-guide/neuroscience/adaptive-control-of-thought/

Cognitive psychology, 5th edition Kathleen M. Galotti