1.1.5 Thought

Thought

Thoughts are often divided into two categories:

1. Propositional thought – which deals with facts and ideas
2. Imaginal thought – which is related to visual imagery

Neurons associated with thought are located in the cortex, but how they are organised to produce thought is unknown. A hierarchical model suggests that specialised neurons function for the production of thought, but this is very circumstantial still today.

Thought disorder is mostly assessed by understanding the use of language by the patient. A number of examples include: not following grammatical rules, incomprehensible speech, or the use of idiosyncratic words.

The Possible Relationship with Language

Whether language forms our thoughts and actions, or whether our thoughts and beliefs shape our language, has been a topic long investigated by researchers and psychologists (Boroditsky, 2001). When one expresses a thought in a language, do they begin with a completely shaped idea and then “translate” it into a sequence of words? Or is it the other way around, where the thought is not entirely formed until the sequence of words is assembled? If we consider the former view, a thought could be formed without the presence of language. In the latter view, however, every thought is totally dependent upon, and is not dissimilar from, language (Fedorenko and Varley, 2016).

Some philosophers like Humboldt support the concept of language being wired with thought. He quotes: “…the idea is born, becomes an object and returns, perceived anew as such, into the subjective mind. For this, language is inevitable,”. This implies that without language we would not have the ability to think or even have the capability to express ourselves at all (Zinken, 2008).

As a counter-argument, there are people with global aphasia, who have been able to provide evidence of fantastic addition and subtraction capabilities, solve logic problems, love music, think about the other person’s thoughts, and have strived successfully in life. These individuals have almost no capacity to comprehend or produce language.

Findings from neuroimaging studies demonstrate that when healthy adults understand a sentence, they then can strongly engage the Broca’s area. However, this was not possible when they were given the task to perform other nonlinguistic tasks like arithmetic, listening to music or even storing information in working memory. Many aspects of thought engage distinct brain regions and do not depend on the language (Fedorenko, Behr, and Kanwisher, 2011).

Thus, to conclude, there are many aspects of thought that are found to engage distinct brain regions from, and thus, do not depend on, language (Fedorenko and Varley, 2016).

Concepts, Prototypes and Cores: Deductive and Inductive Reasoning

Concepts are mental categories that group together objects, activities, relations, abstraction, or qualities that have common properties. This helps to reduce complexity. Psychological experiments with concepts have revealed that our cognitive schema is more in line with the use of ‘fuzzy concepts’ than ‘well-defined concepts’.

‘Fuzzy concepts’: share some resemblance, but have no core properties. The members vary in how similar they are to the concept.

‘Well-defined concepts’: will have fewer properties and are easier to learn and use.

Normally, reasoning processes can be divided into two types: inductive/forward and deductive/backwards (Fischer, 1998). In the former inductive reasoning process, a person initially observes numerous individual facts and then proceeds to form a conclusion about an idea or principle grounded on those facts. There still exists a likelihood that a conclusion could be wrong, even though the idea or principle supporting that conclusion is true. This is due to the fact that conclusions cannot be generalized, which they are, by the learner, who does not observe all related examples (Johnson-Laird, 1994).

In contrast, a deductive reasoning process involves a person establishing one or more sets of models. This they do whilst solving a given problem, purely based on general knowledge and solid formed principles. Following this, a person draws a conclusion or proposes a solution based on the mental model or set of models.

Inductive and deductive reasoning processes have diverse features and can be deployed in various ways while solving complex problems (Scavarda, 2006). However, it is vital to check the soundness of the conclusions or solutions before authenticating a mental model. This is done by probing for counterexamples. In a case where counterexamples are not considered, the solutions can be taken as valid and the conclusions can be believed as true (Shin, 2019).

We are susceptible to a variety of logical fallacies involving the use of inductive reasoning, such as confirmation bias, conjunctions fallacies like the ‘Monty Hall problem,’ and our inclination to both over-estimate and under-estimate the frequency of uncommon events.

A clinical trial involved carrying out neuroimaging to test the neurophysiological predictions made by these two models of reasoning. Ten normal volunteers performed deductive and inductive reasoning tasks. There was also a recording of their regional cerebral blood flow pattern using PET imaging. The volunteers were provided sentences to process. The deduction condition resulted in activation of the left inferior frontal gyrus (Brodmann areas 45, 47). The induction condition resulted in the activation of a large area including the left medial frontal gyrus, the left cingulate gyrus, and the left superior frontal gyrus (Brodmann areas 8, 9, 24, 32) (Goel, 1997).

Problem-Solving Strategies, Algorithms and Heuristics

According to the problem-solving literature, problems are categorized based on clarity (well-defined vs. ill-defined) or on the underlying cognitive processes (analytical, memory retrieval, and insight) (Sprugnoli, et al., 2017). Problem-solving strategies are actions people take deliberately to accomplish anticipated objectives (Liao and Jiao, 2022). The foremost step in problem-solving includes defining the problem, followed by creating a representation in the working memory. When a problem is identified, it is vital to clarify exactly what it is and establish the cause prior to seeking a solution. This solution-seeking process should comprise of contributions from those directly involved in the problematic situation. This aids individuals by bringing forward their viewpoint, appreciating why any modification in practice is essential and what changes to expect as a result of the steps taken. Some approaches to identifying and analyzing problems in practice include the Five Whys analysis, fishbone diagram, problem tree analysis, and Seven-S Model (Hewitt-Taylor, 2012).

Psychologists use an algorithm in circumstances when accuracy is crucial or when each decision needs to follow the same process (Bobadilla-Suarez and Love, 2018). An algorithm is a set of step-by-step procedures that delivers an accurate answer to a particular problem. On the other hand, if time is of the essence, an algorithm is likely not the best choice for psychologists. They then make use of Heuristics to solve problems. It is this when the problem-solver notices an event in the environment. Heuristics are generally used in the everyday state of affairs, like figuring out the best route to get from point A to point B.

Knowing which approach to use for problem-solving is important, however, one needs to make choices based on their requirements for timeliness as well as accuracy.

References:

(1) Bobadilla-Suarez, S., Love, B. C. (2018). ‘Fast or frugal, but not both: decision heuristics under time pressure’, Journal of Experimental Psychology: Learning, Memory, and Cognition, 44(1), pp. 24-33.

(2) Boroditsky, L. (2001) ‘Does language shape thought? Mandarin and English speakers’ conceptions of time’, Cognitive Psychology, 43(1), pp. 1-22.

(3) Fedorenko, E. and Varley, R. (2016) ‘Language and thought are not the same thing: evidence from neuroimaging and neurological patients’, Annals of the New York Academy of Sciences, 1369(1), pp. 132-153.

(4) Fedorenko, E., Behr, M. K., and Kanwisher, N (2011) ‘Functional specificity for high-level linguistic processing in the human brain’, Proceedings of the National Academy of Sciences, 108(39), pp. 16428-16433.

(5) Fischer, R (1998) ‘Public relations problem solving: heuristics and expertise’, Journal of Public Relations Research, 10(2), pp. 137–153.

(6) Goel, V., Gold, B., Kapur, S., Houle, S. (1997) ‘The seats of reason? An imaging study of deductive and inductive reasoning’, NeuroReport, 24;8(5), pp. 1305-1310.

(7) Hewitt-Taylor, J. (2012). ‘Identifying, analysing and solving problems in practice’, Nursing Standard (through 2013), 26(40), pp. 35-41.

(8) Johnson-Laird, P. N. (1994) ‘Mental models and probabilistic thinking’, Cognition, 50(1-3), pp. 189–209.

(9) Liao, M. and Jiao, H. (2022) ‘Modelling multiple problem-solving strategies and strategy shift in cognitive diagnosis for growth’, British Journal of Mathematical and Statistical Psychology, 10.

(10) Luo J., Li W., Qiu J., Wei D., Liu Y., Zhang Q. (2013), ‘Neural basis of scientific innovation induced by heuristic prototype’, PLoS ONE 8.

(11) Scavarda, A. J., Bouzdine‐Chameeva, T., Goldstein, S. M., Hays, J. M., Hill, A. V. (2006) ‘A methodology for constructing collective causal maps’, Decision Sciences, 37(2), pp. 263–283.

(12) Shin, H. S. (2019) ‘Reasoning processes in clinical reasoning: from the perspective of cognitive psychology’, Korean Journal of Medical Education, 31(4), pp. 299-308.

(13) Sprugnoli, G., Rossi S., Emmendorfer A., Rossi A., Liew S.-L., Tatti E., et al. (2017) ‘Neural correlates of Eureka moment’, Intelligence, 62, pp. 99–118.

(14) Zinken, J. (2008) ‘The metaphor of ‘linguistic relativity’, History & Philosophy of Psychology, 10(2), pp. 1–10.