Understanding the Mind

Representations and Computations

In order to understand how the mind works there have been lots of different approaches, how people do such tasks and their different ways of thinking. First, we should describe what mind is, it is defined as whom responsible of thought, feeling with respect to one’s reasoning, conscious and unconscious experiences, perception, memory, emotion and imagination (Pandya, 2011). How mind works and how mind evolved still not explained fully yet and protects its mysterious state, mind is also referred to as a system of ‘organs of computation’ that allows individuals understand us, each other and the environment around us (Pinker, 2009). Brain states are different than thoughts and feelings but both of them are still real, important part is that these high order psychological states such as emotion and cognition can be understood and can be explained in psychology but only with the help of science (Barrett, 2009). Figuring out how the relationship between mind and the brain works with respect to human cognition is a struggle that has been faced by the researchers and was a step stone for development of disciplines like neuro science and cognitive science (Shi, 2017). The question of how mind works can be answered in two ways while some believes that mind is not a machine some believes that mind is machine-like. Results that are done so far shows that mind not being a machine still not supported with evidences yet, which increases the tendency to accept that mind is just like a machine (Stern, 2018).

According to Thagard (1996) on his book Mind: Introduction to cognitive science, he explains this as:

“Thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures.”

Computational and representational understanding of mind (CRUM) is an approach, which refers that thinking is done with computations operating on representations which is a computer-science based hypothesis. While mental representations are similar to analogous to data structures, computational procedures are similar to algorithms.

Mostly it has been thought as the neural computations over neural representations in cognition (Piccinini, 2018). In order to understand human cognition, functions of cognition should be broken down using computational models, such that this computational model should be able to explain cognition and perform similar cognitive tasks, computational models will also give a wider perspective in understanding of other related disciplines such as computational neuroscience, cognitive science and artificial intelligence having a common point which is the computational models (Kriegeskorte & Douglas, 2018). There are also suggestions that with Alan Turing’s mathematical model and his computing device which is called the “Turing machine” is very similar in ways actions of the mental processes of the computational theory of mind such that how Turing machine has a central processor and a memory store (Rescorla, 2014). According to Sprevak, (2017) in The Turing Guide: Life, Work, Legacy he stated that:

“A Turing machine is an abstract mathematical model of a human clerk. Imagine that a human being works by himself, mechanically, without undue intelligence or insight, to solve a mathematical problem.” (p.7)

Even though some researchers undervalue the role of representations and even called approaches that are ‘anti-representational’, study suggested that in order to understand this complex mechanism of intelligence and it’s neural activity ignoring the representation and only taking computation into account will not be enough and will be just like taking a one step back in (O’Brien & Opie, 2009) overall suggesting that both computational and representational approach have their own powers that are complementing each other. Moreover, to understand the high-order human cognitive abilities in a wider perspective, studies suggested that symbolic representations are necessary, but to be able to understand wide range high-order processes of the human cognition there should be a combination with respect to analogy, symbolic and representations (Forbus, Liang & Rabkina, 2017). Individual’s cognitive processes can also explain by representational models by showing the neuron-based patterns which shows the explanatory power of representational models (Diedrichsen & Kriegeskorte, 2017), in addition a study suggested that the mechanism of beliefs and the mental processes behind those beliefs might be representations that are meaningful probabilities of individuals (Seitz, Paloutzian & Angel, 2018).

Representational models have a huge role in explanation of this cognitive processes but understanding the connection between the representation and the neural activity has its challenges, a study suggested that these cognitive processes can be explained with representational geometry where they try to explain the brain information processing and different activities of neurons by representations, researchers explained how representational geometry can explain beyond the vision such as auditory perception, memory and action and motor control (Kriegeskorte & Kievit, 2013).

Logic

Thinking about logic and logical reasoning starts with Greek philosopher Aristotle where he explains logic as an instrument that individuals use in order to know or make sense of anything (Johnson-Laird, 2006; Jung, Wranke, Hamburger, & Knauff, 2014), and claims that logic occurs when we gather information with production of notions, judgements and conclusions, and it is special subject matter of cognition processes (Chernikova, 2014). Logic is another contribution to cognitive science, such that it helps to do recognize what is valid or what is not valid and distinguish between those two with investigating such rules, most of the information that exists in human mind mostly is the end product of logical systems while providing plenty of representations about information and transforming this output as beliefs (Malinowski & Palczewski, 2011).

Moreover, it is possible to distinguish logic under the umbrella of computational and representational logic. Computational logic focuses on the use computers to analyze logical formalism, it works with how formal logic is done on computers and how the computation related problems are solved in computers, computational logic mostly used as a tool on building software and hardware and in addition to improvement of online services (Paulson, 2018). On the other hand representational logic or logical representations are concerned with probabilistic logic and their represented input problems, it is mostly used linguistic semantics (Beltagy, Roller, Cheng, Erk & Mooney, 2016). Symbolic reasoning can be taking into account of a sub-dimension of logic as well, studies suggested that this symbolic reasoning gives individuals the ability to represent numbers, logical relationships and mathematical rules, but in addition researchers think this symbolic reasoning as a form of embodied reasoning where it gives individuals the ability to formulate things logically and arithmetically as well as represent notations externally and implement this principles with perceptual and sensorimotor systems (Landy, Allen & Zednik, 2014).

Even though some tried to reject the role of logic in human reasoning and human cognition, studies did not showed clear evidence due to lack of design and lack of relevance which made the role of logic in human thinking inevitable (Sefranek, 2014). The idea that logic fully cannot explain human cognitive processes can be accepted but it still does not change the fact that logic plays a huge role in explaining cognitive processes, such that results of a study showed that modeling logical symbolic systems can represent and describe cognition with respect to nervous activity of propositional logic (Balcerak, 2018). From the perspective of probability and deduction, studies show that mental models can explain how individuals reason potential possibilities and underlines the fact that the model postulates the reasoning itself is probabilistic (Johnson-Laird, Khemlani & Goodwin, 2015). It should not be forgotten that thinking logic as a concept of only including deduction, the reasoning is extremely focused and fixated into deductive reasoning and every extend of it taking into account as a part of deduction, due to diversity and complexity of human cognition these concept should be a step stone but the creation of such different models are needed in order to fully understand such concepts (Bonnefon, 2013). It has been proven that infants look longer towards ambiguous objects due to their ability to have logical inference and understand the inconsistency while identifying a such object, it has been stated that at this potential deduction they gave physiological responses as well, in conclusion logical structures are involved and a product of human mind (Cesana-Arlotti, Martin, Teglas, Vorobyova, Cetnarski & Bonatti, 2018). In our daily life unconsciously or not we use logic, but more than that according to a study logic is also the center for building beliefs as well, propositions that are accepted by everyone shows the logic’s role in building common beliefs (Miller, Pfau, Sonenberg, & Kashima, 2017). Logic provided a perspective for mental representations. There is also hypothesis that logic and it’s relationship to explain artificial intelligence has become a new field itself which is called logic-based artificial intelligence (Bringsjord, 2008), which shows to the importance of logic while explaining such factors and how logic is the step and center to understand these cognitive capacities. Artificial intelligence systems are nourished by logic because of their common understanding, researches suggested artificial intelligence systems should focus on logic but not only logic, since logic cannot tolerate any inconsistency and due to complexity of human cognition (Sowa, 2014).

To understand the action of thinking, operations on representation are essential to make conclusions in logic, where rules of inference combine with a set of premises. Inference refers to the thinking process and basic information, premises refers to the conclusion that is drawn with alleged information. Logic can be divided into predicate logic and propositional logic, while predicate logic can be explained as first-order logic that is used as a synonym and it refers to the other logic that has a similar syntax, the propositional logic can be explained as logic that includes letters of a sentence and makes logical connectives. Predicate logic can be referred to as extended logics as well due to its power to expressiveness with the comparison to propositional logic (Groote & Tverentina, 2003).

For language and the cognition, logical connectives (“and”, “or”, “if”, “then”, “if and only if”) plays a huge role in propositional reasoning which refers to the model where deduction depends on if, or, and not to explain semantic processes. For an example, there is a golf club which always has been represented a place for people who are rich and famous does hang out, this may leave an individual in wonder whether or not they would fit into it even though they are wealthy but obscure (Sloutsky & Goldvarg, 2004). Likewise, logical connectives can be used in simple propositions, studies showed that these different logical connectives are represented in the brain and their activations of processing differ such that depending on whether it is logical, logical meaning of compounds or conjunctive (Baggio, Cherubini, Pischedda, Blumenthal, Haynes & Reverberi, 2016).

Moreover, with the influence of the earlier studies that done in Japanese, Mandarin and Turkish, current study suggested that language is the expression of logic, but in means of logical connectives when there is more than two logical operators in a sentence it becomes vulnerable to ambiguity (Pagliarini, Crain & Guasti, 2018). A research showed that logic is a way of problem-solving in our everyday life (Pezzuti, Artistico, Chirumbolo, Picone & Dowd, 2014). It is the fundamental cognitive activity, individuals use logic in their decision-making processes, in correctness and in reasoning logic plays a key role (Wolenski, 2016). Overall, in conclusion logic can be the foundation of computer science and plays a key role in human cognition, it can describe the human cognition at bases but if it will become a model for a computer program the high-level cognition the understanding of formal logic will not be enough.

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