A slightly different view on fluid ability and its relations with working memory, comprehension knowledge and learning

 

Lohman, D. F., Lakin, J. M., Sternberg, R. J., & Kaufman, S. B. (2009).Reasoning and intelligence. Handbook of intelligence, 419-441.‏    

https://faculty.education.uiowa.edu/docs/dlohman/Reasoning-and-Intell_Lohman-Lakin-102709.pdf

This is a wonderful chapter about reasoning, which is part of the fluid ability.  Almost every sentence in this chapter is significant.  Here I'll focus on some of the ideas that were especially interesting to me.

 

Reasoning processes, comprehension knowledge and learning

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People who solve problems efficiently usually turn their attention, sometimes even unconsciously, to different aspects of the problem than people who solve problem less efficiently.  People who are efficient problem solvers know what to look for and what to ignore.  This results from more experience that they have with similar problems and from an ability to make good use of their past experience.

Good thinking about complex problems depends on knowledge.  Expertise is based on knowledge, and experts think about problems differently than novices. Good reasoning leads to better organization of knowledge in memory.  Every reasoning process we perform depends on the efficiency of the reasoning processes we performed in the past, and on the ways we stored the products of these processes in memory.  An increasingly sophisticated knowledge base supports increasingly sophisticated forms of reasoning. A more sophisticated knowledge base has richer, more abstract associative connections between concepts and more metacognitive knowledge that links strategies to goals.  In other words, the better our reasoning processes are, the more complex our comprehension knowledge, which is the product of these reasoning processes, will be.  

Reasoning abilities are apparent even in a vocabulary test.  Individual differences in a vocabulary test can result from the extent to which we use metacognitive processes when we learn words.  For instance, do we systematically examine alternative meanings of a word when we hear it in an unfamiliar context, or do we remain with a vague understanding of it? When  we infer that a word has additional meanings, and understand them with precision, we are able reorganize our comprehension knowledge in a way that will assist us in future learning.  Researchers argue, that the ability to derive word meanings out of the contexts in which they are heard may be the cause of the high correlation that usually exists between vocabulary and reasoning tests (and also between vocabulary and g).  A wide vocabulary enables the understanding and expression of a wider variety of ideas, and assists in learning new words and concepts.  Thus language functions as a tool for expression, refinement and acquisition of thought, and the modest vocabulary test requires reasoning processes and is a product of reasoning processes. 

  

Choosing what knowledge to apply to a new problem is a nontrivial source of reasoning complexity. Developmental psychologists have long known that children reveal much about the sophistication of their reasoning by how they classify or sort objects: on the basis of an arbitrary association, or by using perceptual characteristics, or, at the highest level, by using several different abstract concepts.  Therefore, deciding how best to describe the relationships among two or more concepts is a critical step in reasoning.  Poor reasoners often settle for a vague relationship or rule rather for a more exact one.  This could be because they terminate the search for a rule or relationship too quickly, or because they do not critically examine how well candidate rules or relationships describe the data, or because they simply do not see or know the rule.

It's possible to practice and train deductive thinking (applying known rules to specific cases), or at least, it's possible to improve performance in specific tasks that measure deductive thinking. This means that deductive reasoning tests can measure different abilities in examinees who have learned strategies for solving problems like those used on the test than for examinees who must invent a strategy on the spot.  For people who practiced, performance on such tests leans much more on the retrieval of the learned strategy out of the knowledge base.  For people who did not practice, these tests are much more fluid. 

Reasoning processes require ongoing activation of executive functions like monitoring, feedback, planning, adjusting existing strategies to the new situation and inventing new strategies, and the ability to learn from past attempts to solve the problem.  Reading comprehension, for example, requires updating our mental model of the text  with new information received during reading.  For instance, if we perceive one of the characters in our mental model as a bad person, and we read about a good deed this person did, we update our mental model of him.  Children with monitoring difficulties (executive function difficulties) find it hard to do that.  They may stick with the initial impression they formed of this character, and fail to update it with later incoming information. 

Naïve interpreters of reasoning tests think that reasoning ability influences achievement level (in other words, they see causal arrows running only from reasoning ability to achievement).  But both reasoning ability and achievement are products of leaning and experience. 

Understanding a story, inferring the meaning of an unknown word, recognizing patterns and rules in information, abstracting the given information in order to create rules or principles, applying mathematical concepts to solve a problem – in these and in many other ways, successful learning requires reasoning strategies.  The best way to develop reasoning is through challenging instruction that requires students to practice familiar reasoning strategies and to invent or to learn new strategies.  Thus, learning requires reasoning, but the learning process itself develops reasoning.

In the past it was believed that it's good to adjust the instruction method to the learning style (for example, to give more verbal instruction to a people who prefers verbal learning and to give instruction with more graphs and visual material for people who prefer to learn via visual images).  Contrary to the expectations of virtually all, the profile of specific learning styles generally does not account for much of the variation in outcomes. Indeed, interactions between learning styles (such verbalizer versus visualizer) and instructional methods (such as an emphasis on visual versus verbal media) are usually small. Instead, it is advisable to adjust instruction style to the learner's cognitive abilities.  For example, a child with poor reasoning ability can be taught with more examples, and with more explicit explanations.  A child with poor comprehension knowledge can be given texts that are linguistically simpler.  Vocabulary from the text can be taught in advance.  The general information required to understand the text can be also pre-taught.

Reasoning ability and working memory.

Theories that attempt to explain reasoning processes assume there are phases in the inference process that include: A.  Representing the premises in working memory.  B.  Creating mental models of possible solutions that are derived from the premises, holding these models simultaneously in working memory and comparing them.  These processes demand significant working memory resources.  Thus limitations in working memory cause individual differences in reasoning abilities.

Since working memory limits the number of mental models that can be held simultaneously in working memory, people with a limited working memory capacity may not succeed in creating enough models to evaluate the validity of a conclusion. 

Researchers find a large overlap between fluid ability and working memory.  The authors of this chapter argue that this overlap is caused by fluid ability being measured too narrowly and working memory being measured too broadly. 

The authors claim that matrices tests that require nonverbal reasoning predict learning and academic achievement in the real world less well than measures of verbal and quantitative reasoning (for example, verbal analogies tests or mathematical series tests).  Thus, when we assess reasoning with one test like the Raven, or similar tests, we don't represent all aspects of the reasoning process.

On the other hand, working memory tests are more complex than it seems.  These tests involve understanding directions that are fairly complex (for example, in the number – letter series test), forming a strategy and sometimes altering it, performing a difficult task with high demands for attention, and keeping a high degree of continuous effort.  These tasks also require executive functions – monitoring the process, inhibiting wrong responses, and switching sets flexibly.  Thus, working memory tests contain fluid aspects.

To summarize:  reasoning abilities are not static.  They develop through experience and are easier to perform after training.  Individual differences in reasoning correlate significantly with the amount of information people can hold in working memory while performing a manipulation.  The ability to do this depends on the attention resources that a person has, on his degree of familiarity with the information, and on his experience with the performance of the required manipulation.  Thus, prior knowledge and skills determine to a large extent the level of reasoning that can be reached in reasoning tests and in reasoning situations in daily life.

When assessing fluid ability, it's important to include a broad range of tests, not only matrices tests that intentionally decrease the contribution of comprehension knowledge to the reasoning process.  It's important to include tests requiring verbal and quantitative reasoning, which are closer to the way fluid ability works in real life. 

This chapter emphasizes the importance of looking at fluid ability clinically – on the ways it is expressed in tests that do not measure it directly (for example, looking at the child's level of conceptualization when he defines a word, and at his ability to understand implicit meanings in texts).  It also emphasizes the dependence of fluid ability on other abilities like working memory and comprehension knowledge, and the interaction between them.