Writing on CTM

What is the computational theory of mind? How is human reasoning implemented in the brain, according to classical cognitive science?

The computational theory of mind (CTM) states that the brain processes information algorithmically. Within cognitive science, computational processes such as problem solving, and language comprehension are crucial to understanding the mechanisms and strategies employed by the mind. Classical cognitive science involves empirical methods to comprehensively understand human reasoning. This essay will formulate information on what CTM is, how human reasoning is implemented within the brain with links to classical cognitive science and notably, argue that the mind is wholly physical, and it is due to the structure of the brain that we can process information about the world.

CTM suggests that the mind utilises similar computer like processes via mental representations and algorithms for information processing. The theory views cognitive processes as computations and suggests that the human brain uses algorithms for cognitive processes. These algorithms rely on mental representations, which act as internal symbols that guide our thinking. For example, when trying to solve a puzzle, we use mental representations such as, visual, spatial, semantic, conceptual, procedural, and metacognitive processes. All of which work together when cognitive tasks are being performed. The key and crucial claim for the CTM theory of mind, is that like computers, we process our input data according to rules that produce output. This is due to the physical structure of our brain that gives us the ability to quickly process information. To highlight the claim, the pocket calculator can be used as an example, highlighted in philosopher Andy Clarks reading, Mindware. According to Clark, cognitive processes can be understood as computations carried out by the brain by comparing the calculator to the human mind. Calculators manipulate symbols, process information according to rules, and transform it via input and output in the same way CTM views the mind as a computational system. As a pocket calculator performs complex calculations, Clark validates the claim that the mind processes information according to rules.

According to CTM, we process our input data according to rules that produce output. The Turing machine can highlight the idea that the brain utilises an algorithmic process. The Turing machine, a theoretical device for computational processes operates on symbols influenced by syntax and semantics. The syntax of a Turing machine involves the rules for symbol manipulation and state transitions, while the semantics relates to the meaning and purpose of the symbols and guides the machine’s behaviour. Syntax governs how symbols are read, written, and moved, while semantics determines the machine’s output or computation based on encountered symbols. Together, syntax and semantics define the rules and functionality of a Turing machine. The syntax of a Turing machine enables it to process information as it establishes the structure, and behaviour of the machine surrounding the rules it permits. Similarly, the brain processes information symbolically, through its own syntax and semantics. Just as a Turing machine operates on symbols using syntax and semantics, the brain processes information symbolically, follows rule-based procedures, and derives meaning from the manipulation of those symbols. An example of this can be formulated through language comprehension in a sentence, the syntax acts as the rules within the sentence (subject, verbs, and phrases). Whilst the semantics within the sentence are the meanings associated with each individual word.

CTM relies on the claim that the physical structure of our brain is what allows us to process information. Cognitive science highlights key claims surrounding how human reasoning is implemented within the brain. Such claims include, symbolic reasoning, information processing, computational processes, serial processing, and modularity. The claims formulate a framework to validate the intricate process which human reasoning is implemented in the brain. The field suggests that the brain engages in symbol manipulation guided by predetermined rules or algorithms to facilitate information processing. In addition, it suggests that discrete areas specialize in different cognitive domains like language, perception, and reasoning. This viewpoint maintains that human reasoning follows a sequential progression, where subsequent stages rely on prior ones. The physical framework is plausible as many cases formulate the truth behind such claims. An example that demonstrates the plausibility of the framework in cognitive science is the result of brain damage in physical altercations and the effect it has on people’s mental health capacity. Such examples, validate evidence for the relationship between brain function and cognitive ability.

The implementation of human reasoning in the brain, involves the notion of cognitive architecture. A materialistic approach to the mind reveals that our ability to process information quickly and follow rules. According to a materialist perspective, mental states and processes are nothing more than electrochemical processes within the brain. During cognitive tasks, such as reasoning, the brain employs these neural representations and carries out computations. According to materialism, these cognitive processes arise from the physical processes in the brain. The brain’s neural architecture enables rapid information processing and rule-following, processes like a computer. This foundation supports our reasoning abilities, problem-solving skills, and decision-making. Cognitive architecture is an ongoing area of research. Scientists explore the neural mechanisms and brain regions involved in various cognitive processes to deepen our understanding of how human reasoning is implemented in the brain.

CTM has been subject to limitations and doubts, many of which have forced mixed perspectives on human reasoning. Such doubts have increased popularity surrounding a varied understanding of the mechanisms underpinning human cognition. Critiques of CTM challenge the reductionist assumption that mental processes can be completely captured via algorithms and symbolic manipulations. They argue greatly that such approaches simplify the complexity of human cognition, failing to account for the contextual and nuanced aspects of reasoning. An example of algorithmic processing and symbolic manipulation is addition within mathematics. Adding numbers such as 2 + 3 involves following rules to combine symbols for an answer. Critics of CTM argue that this oversimplifies human cognition, ignoring the contextual and nuanced aspects of reasoning. They believe that human reasoning involves complexities beyond algorithms and symbols, which cannot be fully captured by this approach.

The theory is also critiqued for its limitations regarding the significance of the body as well as the mind and the environment surrounding our minds and bodies that they believe helps shape human reasoning. More importantly, many believe that CTM lacks the ability to accurately model the brains computational process. In response to the criticisms and limitations, different perspectives have emerged that provide a broader understanding of human reasoning. Connectivism highlights the importance of distributed neural networks and parallel processing in cognition. This perspective posits that cognitive processes arise from interactions between interconnected nodes, rather than relying solely on symbolic manipulation. Furthermore, as suggested many are hesitant to believe in CTM due to its formulaic nature, and its lack of ability to capture intricate cognitive processes our brains are capable of is likely due to several reasons such as, uncertainty and ambiguity, contextual dependencies, and flexible thinking. Similarly, critics again argue that a more intricate approach is needed to fully understand and explain the complexity of human cognition.

Assumptions regarding information processing, algorithms, and mental representations underpin these theories. Due to its emphasis on computation and information processing, CTM may be able to comprehend and simulate human reasoning. By using algorithms and symbolic manipulation, mental processes, including reasoning, can be understood. A closely related example of this is that of Advanced AI systems. Such systems have been developed to understand and produce texts resembling human language through extensive language training, such as deep language models. As a result of the algorithms and symbolic manipulation employed by these models, language is processed and generated. Such models can replicate aspects of human reasoning by leveraging large volumes of data and language patterns. As a result, they can comprehend context, make logical deductions, utilise the correct tones and generate responses that mimic the reasoning of humans. Embracing the principles of computation and information processing deepens our comprehension of the mechanisms driving human cognition.

In conclusion, CTM and classical cognitive science have together shed light on human reasoning by emphasizing information processing, algorithms, and mental representations. While recognizing the existence of limitations and alternative viewpoints, the theories mentioned have played an essential role in progressing our understanding of the human mind. They establish a solid foundation for greater investigation to unravel the complexity of human cognition and reasoning. It is evident that through our brains intricate structure we can process information surrounding us in a complex manner.

References:

1. Block, Ned (1990). The computer model of mind. In Daniel N. Osherson & Edward E. Smith (eds.), An Invitation to Cognitive Science. MIT Press.
2. Clark, A. (2001). Mindware: An introduction to the philosophy of cognitive science. Oxford University Press. Pg 1-27.
3. Crane, Tim (1995). The Mechanical Mind: A Philosophical Introduction to Minds, Machines and Mental Representation. New York: Routledge.
4. Fodor, J. A. (1975). The language of thought. Harvard University Press.
5. Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information. MIT Press.