Can reduced cognitive load lead to some kind of mental or physical “atrophy”?

Can reduced cognitive load lead to some kind of mental or physical “atrophy”?

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Muscles, if you don't use them, degenerate. Everyone who has had a broken leg in a cast for a few weeks and then seen the thin stick that reemerged once the cast was removed, can attest to that fact.

But does the same happen to the brain? Does mental ability or brain matter atrophy, if life is to easy for us because all the products we use are so highly usable that we don't have to think anymore?

Assuming you're getting at a related idea to your other recent question (Does a more ergonomic and user friendly interface/device make the human brain work less?), I wouldn't worry about user friendliness causing mental atrophy by precluding the need for thought. Thought continues well beyond matters of control to matters of application and optimization. (E.g., can I use my washing machine on AstroTurf? How much high-efficiency detergent do I really need? Can I still use the old stuff?) When systems work particularly well together, entirely new areas of thought may emerge (e.g., the Industrial Revolution and the Italian Renaissance depended largely on the stability and prosperity of their host societies).

However, the "use-it-or-lose-it" principle may apply to specific skills and even general abilities and physical senses to some extent. Secondary language proficiency is one such popularly recognized example. I'd also recommend checking out the Scientific American Frontiers miniseries, Changing Your Mind (some episodes are available on YouTube). One particular episode with which I'm familiar features a research participant wearing a blindfold for a long period of time, during or after which a neuroimaging method (probably fMRI, but I can't quite remember) shows activity in her visual cortex when she's processing tactile stimuli. These sort of repurposing processes probably occur naturally throughout neurological development, especially in youth when neuroplasticity is greatest. AFAIK, neurological models of learning and development in general describe a lot of processes involving pruning of unused connections and reinforcement of frequently used connections. Others here probably know a lot more about these topics than I do, but it seems clear enough even to me that to some extent, atrophy is a natural and even adaptive part of the learning process. Even without referring to neurological substrates, one can argue that learning often involves the process of elimination, so the atrophy of ideas representing eliminated possibilities (e.g., the world is flat?) is desirable in some ways.

Of course, not all atrophy is desirable, and not all results in adaptive repurposing of the relevant brain structures. My former doctoral advisor once told me a particularly disturbing story about the punishment of one of the assassins of Archduke Franz Ferdinand, whose death played a role in sparking the first World War. I've looked into it a bit since, and suspect the story is apocryphal, but it's interesting nonetheless. Supposedly the assassin was locked away in a completely dark cell for so long that he went blind. If, as I suspect, this is mere apocrypha, I don't know whether this can happen in general, but the loss of vision in subterranean species of fish, lizards, and insects is well-documented in evolutionary theory.

Week 11: Performance Load Q3

In my opinion, I think psychology is important in everything we do. As long as you are working with somebody or the job requires communication- psychology is important to understand as you are understanding how people work and how you can adjust yourself to suit their needs and develop versatility for yourself.

But looking at this question specifically, I strongly agree that psychology is neccessary in design. Many of my design tutors and lecturers in Edith Cowan University often reinforce (Kueh, Ormsby, Hadad, Price, 2015 personal communication) that as designers we will be working closely with clients and working with the audience as well. Designers will need to understand their target audience as they are the users of the designer’s product. It is not just the target audience but designers will also need to learn cooperative skills with their clients and printers (print people) as they are heavily invovled in a graphic designer’s job.

Kueh (2014, personal communication) often explained to me that design is not for aesthetics, it is always for a purpose and with the user/audience in mind. When you design for a product or a graphic- you design with a strong purpose that is applicable for the target audience. In my previous projects, I would often research behind the target audience – who they are and what they are about. This helped me in finding out more about them and how they work so that I can design better products for them. I did this for my Vector Illustration KABWA Project which I discussed in Q2 of the Performance Load portfolio.

Shane Henderson in his CCA1108 Week 11, (2015) examines web design and argues that it is not about the web’s aesthetics that draws users in, but the usability of the web design. It is always important to consider the user as they are the ones who are using it.

By understanding psychology as designers, we can understand the target audience better and know what they want. It is also easier in the sense that we can create products specifically for that audience and create a product that is user-friendly for that audience. This can also lead to an improvement in percieved credibility as Fogg (2003) explains that credibility is awarded when the designer fulfils the user’s expectations such as responsiveness, design and purpose. This can lead to an increase in trust and expertise (credibility) between the designer and the target audience.

Fogg, B. J. (2003) Credibility and the World Wide Web. In Persuasive Technology: Using Computers to Change What We Think and Do. (pp.122-125 & pp.147.181). Amsterdam: Morgan Kaufmann Publishers.

Henderson, S (2015) Week 11 Lecture Notes [Lecture Notes via PDF] retrieved from Edith Cowan University Blackboard.


To address growing academic interest in the psychology of resource scarcity, this review synthesizes prior work and existing theoretical perspectives into a parsimonious integrative theoretical framework. We advance the notion that resource scarcity operates through two psychological pathways: a scarcity-reduction route and a control-restoration route. The route that the consumer follows critically depends on the extent to which they appraise the resource discrepancy as mutable. Given the interdisciplinary nature of this phenomenon, this review offers the ability to bridge connections across different academic domains and suggests several viable paths forward. We hope this review will serve as a catalyst for researchers interested in studying the psychology of resource scarcity from a self-regulatory perspective.


The following Literature Review will study the effect of Intrinsic Cognitive Load on Problem Solving and study elements like Cognitive Overload, Intrinsic Cognitive Overload and problem solving in detail with respect to themes like age, gender and education and explore their effect on the above. The author has mentioned in detail along with suitable examples and by drawing references from past researches, theories and experiments the effect of Intrinsic Cognitive load on Problem Solving, with intention to find out suitable conditions to prevent or reduce buildup of cognitive load maximum.


Based on John Sweller’s cognitive load theory, the basis of cognitive overload can be described as the overload arising out of the relentless need to be efficient. David Kirsh in his article ‘A FEW THOUGHTS ON COGNETIVE OVERLOAD’ [1] has described COGNETIVE OVERLOAD as the unaddressed overload that has arisen out of multi-tasking, interruption and information overload. In today’s world, everybody has so many work tasks assigned to them and so many obligations to fulfill that multitasking has become our way of life to cope up with our ever growing need of perfection or at least for completion of our assigned work and tasks. Kirsh believes that cognitive overload is an ambiguous term and no one really knows what or how many factors are responsible for causing cognitive overload. Kirsh believes that overload of information and information demand make people feel information anxiety and therefore they suffer from cognitive overload.

According to a report, “Dying for Information? – an investigation into the effect of information overload in the U.K and World-wide” [2] written by writer Paul Waddington [3] for Reuters, United Kingdom, a 350 pages report based on a survey of 1313 managers in the U.K, U.S, Australia, Hong Kong and Singapore – the key findings were:

Two thirds of manager report tension with work colleagues, and loss of work satisfaction because of stress associated with information overload.

One third of managers suffer from ill health, as a direct consequence of stress associated with information overload. This figure increases to 43% among senior managers.

Almost one thirds (62%) of managers testify their personal relationships suffer as a direct result of information overload.

43% of managers think important decisions are delayed and the ability to make decisions is affected as a result of having too much information.

44% of managers believe the cost of collating information exceeds its value to business.

Even though the above given survey points out towards Information overload as the main cause of Cognitive overload, it cannot be proved to be the only reason for causing Cognitive Overload in people. Kirsh believes that information is relentlessly pushed at us and no matter how much we get we feel we need more of it and that of a better quality and focus that is there is not only too much of information supply towards us causing the Information Overload but also a great demand of too much information that also acts as a factor causing Information Overload in people. Inadequacy of proper work place infrastructure can also be a cause of Cognitive Overload as lack of adequate work infrastructure can lead to slowing down of work which in turn hampers the Information supply which leads to Information Overload.

Looking at the studies, articles and investigative surveys we can consider four components as the main causes or reasons of COGNETIVE OVERLOAD in people. They are:

Too much information supply,

Too much information demand,

The need to deal with multi-tasking and interruption, and

The inadequate work place infrastructure to reduce metacognition.


Cognitive Load Theory (CLT) was developed and formulated by an Australian educational psychologist DR. John Sweller in late 1980s. Taking cognitive psychology as the basis, Dr. Sweller developed the Cognitive load theory out of study of the theory of problem solving. Dr. Sweller refers to the Cognitive load as the COGNITIVE amount of mental effort being used in Working Memory (WM). DR. Sweller latter differentiated the Cognitive load theory (CLT) into three types:

Intrinsic Cognitive theory

Extraneous Cognitive theory

Cognitive load theory suggests that learning will be best under conditions that are in line with human cognitive architecture. The structure of human cognitive architecture, while not known exactly in detail, is discernible through the results of experimental research. Recognizing George Miller’s information processing research showing that short term memory’s capacity is limited in the number of elements it can contain simultaneously, Sweller constructs a theory that treats schemas, or combinations of elements, as the cognitive structures that make up an individual’s knowledge base.


Applications of cognitive load theory by DR. Sweller are best applied in the area of instructional design of cognitively complex or technically challenging material. His concentration is on the reasons that people have difficulty learning material of this nature. Cognitive load theory has many implications in the design of learning materials which must, if they are to be effective, keep cognitive load of learners at a minimum during the learning process. While in the past the theory has been applied primarily to technical areas, it is now being applied to more language-based discursive areas. [4]


Intrinsic cognitive load is the inherent level of difficulty associated with a specific instructional topic. The term was first used in the early 1990s by Chandler and Sweller. According to them, all instruction has an inherent difficulty associated with it. This inherent difficulty may not be altered by an instructor. However, many schemas may be broken into individual “subschema” and taught in isolation, to be later brought back together and described as a combined whole. [5]

If the load is imposed by the nature of what is to be learned, including the number of information elements and their interactivity, it is known as intrinsic cognitive load. [6]

Thus Intrinsic cognitive load unlike extrinsic cognitive load is not based on the external matters like instructions given to participants, instead it builds up due to stress of solving many difficult problems at a time or due to boredom and practice of solving many similar problems.

First described by Chandler and Sweller, intrinsic cognitive load is the idea that all instruction taken by human mind frame has an inherent difficulty associated with it. Intrinsic load theory is the “thinking” part of the cognitive load theory. The amount of intrinsic load affects the learning capacity. Another key component is the way in which the material is presented. [7]

It puts a strain on our working memory thus interrupting our thinking capability and hindering problem solving. The similarity of the material adds up after we have solved a few problems and constantly barriers from solving new but similar problems, which can be helped by changing the basic type of problem or providing fresh problem for the subject’s load to break and enabling them to invest fresh energy into fresh perspective.

For instance, if one were learning the mechanics of 2+2 for the first time then one would have to think about the combination of two items with another two items. Essentially one would have two items, than one item and then another item.

Intrinsic cognitive theory refers to the inherent complexity of the learning material. According to Sweller, 2010, it can “only be altered by changing the nature of what is learned or by the act of learning itself”.

For instance, a simple mathematical problem of adding two numbers places less cognitive load on the brain than a complex algebraic task. Intrinsic load is also determined by the prior learning or expertise level of the learner. An algebraic problem may be extremely challenging, but it may not place a large cognitive burden on an audience who are experts in the subject. You cannot do much with the learning material to reduce this load. [8]


Problem solving consists of using generic or ad hoc methods, in an orderly manner, for finding solutions to problems. Some of the problem-solving techniques developed and used in artificial intelligence, computer science, engineering, mathematics, or medicine are related to mental problem-solving techniques studied in psychology. It is a mental process. Problem solving refers to a state of desire for reaching a definite ‘goal’ from a present condition that either is not directly moving toward the goal, is far from it, or needs more complex logic for finding a missing description of conditions or steps toward the goal. In psychology, problem solving is the concluding part of a larger process that also includes problem finding and problem shaping.

Considered the most complex of all intellectual functions, problem solving has been defined as a higher-order cognitive process that requires the modulation and control of more routine or fundamental skills. Problem solving has two major domains: mathematical problem solving and personal problem solving where, in the second, some difficulty or barrier is encountered. Further problem solving occurs when moving from a given state to a desired goal state is needed for either living organisms or an artificial intelligence system.

While problem solving accompanies the very beginning of human evolution and especially the history of mathematics, the nature of human problem solving processes and methods has been studied by psychologists over the past hundred years. Methods of studying problem solving include introspection, behaviorism, simulation, computer modeling, and experiment.


The early experimental work of the Gestaltists in Germany placed the beginning of problem solving study. Later this experimental work continued through the 1960s and early 1970s with research conducted on relatively simple laboratory tasks of problem solving.

Choosing simple novel tasks was based on the clearly defined optimal solutions and their short time for solving, which made it possible for the researchers to trace participants’ steps in problem-solving process. Researchers’ underlying assumption was that simple tasks such as the tower of Hanoi (9) correspond to the main properties of “real world” problems and thus the characteristic cognitive processes within participants’ attempts to solve simple problems are the same for “real world” problems too simple problems were used for reasons of convenience and with the expectation that thought generalizations to more complex problems would become possible. Perhaps the best-known and most impressive example of this line of research is the work by Allen Newell and Herbert Simon (10). Other experts have shown that the principle of decomposition improves the ability of the problem solver to make good judgment.

A very long series of experiments generated by cognitive load theory over the last decade has indicated some instructional techniques that can be used as alternatives to conventional procedures. The use of reduced goal-specificity or goal-free problems was the first technique investigated.

A goal-free equivalent of the above geometry problem asks problem solvers to “find the value of as many angles as possible” rather than to specifically “find a value for angle x.” It was reasoned that goal-free problems would eliminate the use of a means-ends strategy and its attendant misdirection of attention and imposition of a heavy cognitive load.

Furthermore, a goal-free strategy should direct attention only to those aspects of a problem essential to schema acquisition: problem states and their associated moves. Many experiments demonstrated repeatedly that goal-free problems facilitated learning.

Sweller provided additional evidence for a reduced cognitive load associated with goal-free problems using production system models. Ayres and Sweller used cognitive load theory to predict major sources and locations of errors during geometry problem solving.

Cooper and Sweller suggested that worked examples could have the same effect as goal-free problems. They used algebra worked examples of the following type:

(a + b)/c = d solve for a a+b=dc a=dc-b

in order to follow this example, it is only necessary to attend to each line and the algebraic rule needed for the transformation to the next line.

As was the case for goal-free problems, this activity corresponds closely to that required for schema acquisition. It might be expected that studying such worked examples should result in more rapid schema acquisition than solving the equivalent problems by means-ends analysis. They indicate that extraneous cognitive load should be eliminated. Total cognitive load is an amalgam of at least two quite separate factors: extraneous cognitive load which is artificial because it is imposed by instructional methods and intrinsic cognitive load over which instructors have no control.

The primary determinant of intrinsic cognitive load is element interactivity. If the number of interacting elements in a content area is low it will have a low cognitive load with a high cognitive load generated by materials with a high level of element interactivity. On this analysis, intrinsic cognitive load is determined largely by element interactivity.

Halford, Maybery and Bain (11) provided evidence for the importance of element interactivity as a source of cognitive load. Using transitive inference problems they hypothesized that integrating the two premises should generate the heaviest cognitive load because element interactivity is at its highest at this point. Extraneous cognitive load can severely reduce instructional effectiveness, it may do so only when coupled with a high intrinsic cognitive load.

In contrast, if intrinsic cognitive load is high because of high element inveracity, adding a high extraneous cognitive load may result in a total load that substantially exceeds cognitive resources, leading to learning failure.


Age plays a big factor in determining the cognitive load a person goes through. Different age groups generally have different task works to go through in their daily life and therefore the cognitive load they take is different as well. In other words, the cognitive load they go through or take up is directly related to the age group they belong to. Those in the higher age group feel more cognitive load than those in lower or younger age group.


One hundred and twenty-one participants, aged 11–25 years, were given a language-based memory task in the form of a wordlist consisting of 15 concrete and 15 abstract words, presented either visually, acoustically, or a combination of both audio and visual presentation. The study found that the presence of cognitive overload was greater in the older academic age participants than in the younger groups and that as academic experience increased, the visual presentation of the target stimuli produced greater levels of recall than was the case with acoustic and audio-visual presentation. Overall the findings indicate that cognitive overload increases with age, as the younger-age groups were found to have significantly higher levels of word recall in the audio-visual condition than the older groups. [12]

Age factor also is responsible for changes in the behavior and body movement in adults. A task test conducted by Psychologists Korotkevich Y, Trewartha KM, Penhune VB and Li KZ of Department of Psychology, Centre for Research in Human Development, Concordia University of Montreal, Canada determines through its findings that age factor plays a huge role in motor movements in adults. The findings suggest that age-related decline in motor movements are caused due to reduced cognitive load capacity.

A dual-task paradigm was used to examine the effect of cognitive load on motor reprogramming. We propose that in the face of conflict, both executive control and motor control mechanisms become more interconnected in the process of reprogramming motor behaviors. If so, one would expect a concurrent cognitive load to compromise younger adults’ (YAs) motor reprogramming ability and further exacerbate the response reprogramming ability of older adults (OAs). Nineteen YAs and 14 OAs over learned a sequence of key presses. Deviations of the practiced sequence were introduced to assess motor reprogramming ability.

A Serial Sevens Test was used as the cognitive load. A 3D motion capture system was used to parse finger movements into planning and motor execution times. Global response time analysis revealed that under single-task conditions, during proponent transitions, OAs responded as quickly as YAs, but they were disproportionately worse than YAs during conflict transitions. Under dual-task conditions, YAs performance became more similar to that of OAs. Movement data were decomposed into planning and movement time, revealing that under single-task conditions, when responding to conflicting stimuli YAs reduced their movement time in order to compensate for delayed planning time however, additional cognitive load prevented them from exhibiting this compensatory hastening on conflict transitions. It was found that age-related declines in response reprogramming were linked to reduced cognitive capacity. Findings suggest that cognitive capacity, reduced in the case of OAs or YAs under divided attention conditions, influences the ability to flexibly adapt to conflicting conditions. [13]

We can conclude that age factor plays a very huge role when it comes down to cause a difference in cognitive load capacity. Those in younger or lower age group have a better cognitive capacity than those in older age group.We also noticed that increase in age causes decline in motor movements due to buildup of cognitive load.


Gender generally plays a huge role in determining the cognitive capacity of individuals in different work task roles. Different tasks have required different cognitive load capacity and individuals from both genders take up the task roles differently according to their own cognitive load intake capacity.

Tests set up by Hwang, Ming-Yueh Hong, Jon-Chao Cheng, Hao-Yueh Peng, Yu-Chi and Wu, Nien-Chen of National Taiwan Normal University, Taipei to determine which set of gender performs better under cognitive load capacity. They collected and analyzed data of 220 students of 6th grade which indicated that girls did have a higher cognitive load and more competition anxiety from synchronous types of competitive games, but they showed beliefs in technology acceptance constructs that were similar to that of boys. Even with high cognitive load and competition anxiety, the boys and girls didn’t show a decrease in their perceived ease of playing and sense of usefulness in using this game to learning Chinese characters for two types of competitive games, and they both showed a positive attitude and intentions to play the game. [14]

The test paper on “Gender effects when learning manipulative tasks from instructional animations and static presentations” submitted by Mona Wong, Juan C. Castro-Alonso, Paul Ayres, and Fred Paas of School of Education, University of New south Wales, Australia explored the effect of gender on the effectiveness of static and animated presentations when learning a manipulative task using two different learning platforms – real Lego bricks (physical environment) in Experiment 1 and computerized images of the bricks (virtual environment) in Experiment 2.

Gender vs. Presentation format interaction patterns were found in both experiments. Females performed better than males when learning manipulative tasks with the instructional animations, however no gender differences were found for the statics presentations. [15]

Tests carried out by Cheng-Chieh Chang and Fang-Ying Yang of Department of Earth Sciences, National Taiwan Normal University, Taiwan has shown that gender differences were apparent with respect to scientific articles, chat rooms and information searches. While male subjects seemed to feel overloaded with articles, female subjects showed higher engagement in chat rooms and information searches. Instructors can make use of these gender differences in curriculum design. For example, some concept-related questions can be formulated in a more informal way and posted in the chat room to enhance females’ interest in the topic. For male learners, the reading of scientific articles may be broken down into sections and accompanied with activities that take less mental effort, such as information searches, to compensate for the high cognitive cost. [16]

Thus with gender studies and cognitive load buildup we can say that both males and females show equal interest towards problem solving(games) and do not show any decline in continuing interest for the game. Also from the second study we noticed that there is no gender difference for static presentations however, girls learn better than boys when it comes to learning manipulative tasks.


Education can have a huge effect on cognitive intake capacity of an individual. Education plays a huge role in determining the cognitive load intake capacity in different individuals. People who are more educated can somehow bear more cognitive load then those who are slightly less educated. By education we also mean those who have more knowledge or have learned more about a topic than other people involved in that topic. We can see that through a test conducted by Paul Ayres where he examined the effectiveness of instructional strategies that lower cognitive load by reducing task complexity (intrinsic cognitive load).

Three groups of 13-year-old students were required to learn a mathematical task under different conditions. One group (Isolated) followed a strategy that used part-tasks where the constituent elements were isolated from each other (element isolation). A second group (Integrated) received whole tasks where all elements were fully integrated, and a third group (Mixed) followed a mixed strategy progressing from part-tasks to whole-tasks. Results indicated that the part-task strategy was effective in lowering cognitive load for all students, but only benefitted learning for students with low prior knowledge. In contrast, students with a higher prior knowledge learned significantly more having studied whole tasks during instruction compared with part-tasks. The mixed-mode method proved to be ineffective for both levels of prior knowledge. [17]

The above test concluded that those students who had higher prior knowledge about the topic were more effective in learning the mathematical task due to a much lower cognitive load that those students who had low prior knowledge.


Intrinsic cognitive load can play a huge role in determining the learning capacity of an individual. It is seen in case studies that those students who study in increasing intrinsic cognitive load find it difficult to learn very complex information that those students that study in reduced intrinsic cognitive load. This is found because to learn very complex information in increasing cognitive load requires a very high working memory capacity. Students may find it difficult because the complex nature of study due to high intrinsic load may exceed their working memory capacity,

This was seen by a study conducted by Pollock, Chandler and Sweller in the year 2002, where they presented students with very high complex information in isolated elements form thus reducing the intrinsic cognitive load followed by a presentation of same information with links between elements indicated. Another group was presented with the fully interacting material twice. It was seen that those students who were presented information in isolated form performed better on subsequent tests. [18]

Another such instance where lower or reduced intrinsic cognitive load has resulted in better or increased learning capacity in students can be seen in a paragraph from a book ‘Knowledge Visualization and Visual Literacy in Science Education’ written by Anna Ursyn. In this book the author writes how higher intrinsic cognitive load was developmentally challenging to learners and how through training the learners about basic concepts in the content area helped in reducing the intrinsic cognitive load, which further resulted in learners finding it easier to learn and be motivated to learn. [19]

Thus we see an interdependent effect of education and intrinsic cognitive overload on each other where training helps us reduce cognitive load and which further helps in learning or solving problem and being motivated to continue to do so.

Cognitive load is often associated to memory and learning and thus direct researches with problem solving are rather a few. Through this paper the author has tried to shed light on various aspects that affect intrinsic cognitive load and how that affects problem solving. The author studied in detail about core of what is cognitive overload, the Cognitive Overload Theory, Intrinsic Cognitive overload and how they impact problem solving skills of an individual by going through various past researches and experiments and studying their results and the experimenter’s interpretation of those results. The pioneer in this field is the work of Sweller who has shown cognitive load’s effect and how it is affected by several things. This paper has an elaborate discussion of some of the most important themes in respect to intrinsic cognitive overload and problem solving like: age, wherein increase in age leads to increase in cognitive load, thus younger population will have less intrinsic load and will be better in problem solving than elders. The author has studied gender and how it impacts intrinsic cognitive load thus affecting problem solving and discovered that the results remain mostly same irrespective of gender. Another important theme discussed is education with respect to buildup of cognitive overload thus impacting problem solving skills where author found that both are dependent on each other and form a cyclic process where education helps in less buildup of cognitive load which further helps in solving problem easily and rather effectively.

  1. 1. ‘A FEW THOUGHTS ON COGNETIVE OVERLOAD’ by David Kirsh 1(30), 2000 –
  2. 2. “Dying for Information? – an investigation into the effect of information overload in the U.K and World-wide” by Paul Waddington(1996) –

Sweller, J., Instructional Design in Technical Areas, (Camberwell, Victoria, Australia: Australian Council for Educational Research (1999).

  1. 5. Chandler, P Sweller, J. (1991). “Cognitive Load Theory and the Format of Instruction”. Cognition and Instruction. 8(4): 293-332.
  2. 6. What is cognitive? By Connie Malamed –

9.Tower of Hanoi- Hofstadter, Douglas R. (1985). Metamagical Themas : Questing for the Essence of Mind and Pattern. New York: Basic Books

10.Allen Newell and Herbert Simon- NEWELL, A., SHAW, J. C., & SIMON, H. A. Elements of a theory of human problem solving. Psychological Review, 1958, 65, 151-166

11.Halford Maybery and Bain- Halford, Maybery, and Bain (1986) and Maybery, Bain, and Halford (1986)

Problems with reasoning, problem-solving and judgment

  • Individuals with TBI may have difficulty recognizing when there is a problem, which is the first step in problem-solving.
  • They may have trouble analyzing information or changing the way they are thinking (being flexible).
  • When solving problems, they may have difficulty deciding the best solution, or get stuck on one solution and not consider other, better options.
  • They may make quick decisions without thinking about the consequences, or not use the best judgment.

What can be done to improve reasoning and problem-solving?

  • A speech therapist or psychologist experienced in cognitive rehabilitation can teach an organized approach for daily problem-solving.
  • Work through a step-by-step problem-solving strategy in writing: define the problem brain¬storm possible solutions list the pros and cons of each solution pick a solution to try evalu¬ate the success of the solution and try another solution if the first one doesn’t work.

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Make your donor fill in the blanks

What’s brown and sticky?

This is my wife’s favorite joke in the world. And it puts me in mind of the power of either asking the person who gets your communication to fill in the blanks or evoking their curiosity, compelling them to read on.

Of course the answer is at the end. How would I do a blog post about building interest through questions and not put the answer at the end?

While it may be fatal to our feline companions, curiosity is a basic human motivator. Jerome Kagan, one of the forefathers of development psychology found in a 1972 paper that what he termed “uncertainty resolution” is a primary motivator of human behavior. We are hardwired to want to know.

This can work to our advantage in direct marketing. More and more research indicators that the donating decision isn’t a yes/no dyad it’s a series of microconversions that lead up to the act of pulling out the credit card/checkbook/wallet/etc. and giving the gift of a life saved or changed.

Our goal, then, is to shepherd the potential donor through the little steps that lead to that big step. One of these is often the decision to open, whether it’s a physical or virtual envelope.

A good question in a subject line or intriguing statement in teaser copy can help draw in a prospect. One of my favorite subject lines of all time was one that was intended to show our gratitude, but could be read one of two ways:

Look what you’ve done

When you opened the email, it was telling the story of what the person’s support had meant: the small dent they put in the world that year.

But I’d be surprised if at least some of the people who opened it (and it had a 25%+ open rate) didn’t think we were saying:

Incidentally, you might think of this as a bait and switch, but not shockingly, no one complained about being tricked into being thanked profusely.

You can also create an information gap. Think of the teaser of your local news: “Coming up after the break: what common household object could kill you today?” Sometimes, asking the question that your email aims to answer can get people to read and read all the way through. Just like brown and sticky things.

There’s a specific manifestation of completionism that is particularly interesting. It started with Austrian waiters.

See? “It started with Austrian waiters.” You can’t help but read on to resolve the uncertainty that comes with a statement like that.

Psychologist Bluma Zeigarnik was watching waiters in Vienna. She noticed that their memory was astoundingly good for orders that were in progress, but very bad for those that were already served. She went back to the lab and found that our memories work well for unfinished or incomplete things (know as the Zeigarnik effect). Subsequent testing has shown it works best for tasks that are very important to a person.

There are a good number of ways to deploy this:

  • Don’t put a period (or God help you, an exclamation point) on the end of your subject line. Punctuation there (other than a question mark) signals that the thought is complete and you need not read on.
  • Test out two-step opt-ins. This is the not only the perfect compromise by the people on your Web site that want to reduce form friction and the ones who think you need your participants middle initial and blood type (you don’t the first people are right). Simply have the person fill out the most basic information on the first screen (usually just email address). Then ask for some of the important information on screen two, with a big friendly opt-in button (note: it should not say opt-in) right there for the taking.
  • Finally, you should end your content with an idea that the content will continue on in another letter, post, email, etc.. There’s a reason every Bond movie ends with “James Bond will return” — there’s always more to the story. So tune in tomorrow for the end of story week when we talk about story arcs and hero’s journeys.

* What’s brown and sticky? A stick.

10 Ways to Boost Your Cognitive Fitness and Longevity

We normally associate the term cognitive development with babies and children. While many adults do not think of developing themselves cognitively, they should do so, particularly since studies show that reduced cognitive function can age us prematurely and reduce life expectancy. It is well known in the medical community that people who have advanced stages of Alzheimer's or dementia do not live as long as those free from these conditions.

You can be many years younger than your chronological age by making certain lifestyle choices, including those that tax or challenge the brain. Research over the past 20 years has shown that certain regions of the adult brain can generate new neurons and new synapses. (Here's one recent study, for example.) In essence, whenever we learn something new, engage in new activities, or even ponder a new concept, the brain will rewire itself in response to these activities. Just like babies, adults can keep growing their brain and protect cognitive functioning as they age.

There are many positive ways to build better cognition and to lessen the chances of developing diminished cognitive ability, dementia, or Alzheimer's later on in life, all of which make us act old and feel old. Here are ten of them.

Exercise to improve cognitive function.
Exercise increases blood flow to the hippocampus, which is the part of the brain responsible for memory. One recent study found that the loss of tissue density in the brain was less in those who were aerobically fit, which is another way of saying fit people have better cognitive functioning. Many other studies show that exercise increases one's ability to learn, handle stressful situations, make clear decisions and recall facts and memories.

Watch TV and read "actively."
The difference between watching "The Bachelorette" and watching an educational science show is how active your brain has to be. Watching TV is cognitively enriching when it takes effort to understand what you're watching, or sparks questions, ideas or "aha" moments. The same is true for reading. A celebrity tabloid magazine takes less brain power to flip through than, say, a magazine such as Smithsonian. Develop new connections in your brain by reading something that's instructive instead of merely entertaining. After reading or watching TV, make yourself recall what you just learned. This exercise boosts retention.

Take up a new hobby.
Increase cognitive enrichment by taking on a new active pursuit that requires learning, as opposed to merely attending a baseball game or concert. Some examples include: gardening, antiquing, taking up an instrument, raising chickens, learning a foreign language or selling items on the Internet. Read books, talk to experts, take classes, attend conferences or join organizations related to your hobby. All of this learning activity develops new connections between neurons, which helps offset cell loss due to aging or disease.

Solve all types of puzzles.
Puzzles are an outstanding way to build new connections in the brain. There are many types of puzzles other than crosswords. These include acrostics, cryptograms, syllacrostics and many other word-oriented brain teasers. Some brain teasers don't involve words at all, such as Sudoku. It's particularly good for your brain to seek out a variety. Or start with one type, and as you get better, switch to another type of puzzle. Your brain will be challenged anew with each particular type of puzzle. Switching from a puzzle that's easy to a more difficult or unfamiliar type stimulates new brain activity, or learning, as your brain now has to generate new memories in order to master the new challenge.

Play board games and card games.
Games that involve strategy are excellent for the brain, especially those that involve puzzle solving or new learning of some sort, such as Scrabble, Wheel of Fortune, Jeopardy, Trivial Pursuit, Monopoly and Who Wants to Be a Millionaire -- all available in digital form as well. Chess and checkers are excellent games because almost every game is unique, requiring a different set of strategies each time. Card games can similarly help preserve cognitive functioning because the player continues to perfect the most effective strategies according to the opponent's playing style. You can also play card games with a computer!

Visit museums, zoos, and historical sites.
There are many specialty museums as well as zoos and historical sites that will help you build better cognition. To get the most out of the visit from a cognitive standpoint, don't be a passive visitor. Read the signage next to the exhibits, try to repeat the key information to yourself and then do it again once or twice during or after your visit. Not only will you retain what the exhibits were about, but with some occasional recall attempts, you increase the odds of being able to recall the information months or even years later.

Become a student again.
Many continuing education courses are available that do not require being in a degree program -- you merely sign up for one or two courses whenever you feel like it. Relatively inexpensive courses are available through community colleges. As a student, you will get many chances to learn new things, and most instructors will give you tests that will force you to recall the information learned. Nondegree classes are offered in many areas, from technical subjects to local community history, public speaking, relationships, poetry and other friendly topics.

Attend workshops.
Workshops, conferences, and other gatherings where professionals in their field share their knowledge offer another way to build cognitive function through active learning. While these are commonly offered in a person's profession, you may find many others connected with hobbies and personal interests. One that came across my desk recently, for example, was a workshop on how to trace your family's ancestry. Another was amateur backyard astronomy.

Reduce stress.
People with high amounts of stress are more likely to suffer from cognitive problems than those who are free of stress. While medications can reduce the symptoms of stress, they do not cure the problem or help you understand the root cause of the stress, which is key. Since many meds require ever-increasing dosages to be effective, and many have side-effects, it is important to consider reducing stress in more natural ways, including exercise, naps, individual counseling, meditation, relaxing hobbies, spiritual growth and other means.

Address depression.
Depressed individuals are more likely to suffer from cognitive problems later in life than those who are free of depression. As with stress, many people who are depressed merely run to their family doctor and say, "Can you give me something for being depressed?" and walk away with a prescription. No attempt is made to find out what is causing the depression in the first place, let alone cure it. As with stress, there are ways to bring about a long-lasting solution to depression besides medication, including individual counseling, exercise, spiritual growth, career rejuvenation, goal setting, and other techniques.


Although children begin their formal education with a very positive view of mathematics [1, 2], as they progress through education, many of them develop negative feelings and attitudes (cf., [3]). The feeling of tension, apprehension and fear experienced when faced with maths content has been termed mathematical anxiety [4]. Maths anxiety interferes with mathematics performance and it leads to negative maths-related attitudes and self-perceptions, an avoidance of elective maths courses, and it also affects people’s career opportunities and career choices [5]. Whereas the negative consequences of maths anxiety on educational outcomes are well-known, it is less clear whether maths anxiety also affects people’s behaviour in other contexts.

It is known that, at the population level, low numeracy is associated with reduced economic productivity [6], higher rates of unemployment, mental and physical illness, as well as higher rates of arrest and incarceration [7]. Low numeracy also impairs risk comprehension, and leads to biases in judgment and decision making (see [8, 9] for reviews). Nevertheless, the link between low numeracy and maths anxiety is not so clear-cut. Several studies investigated the effects of maths anxiety in educational contexts. Although correlations of around -.30 have been reported between mathematics anxiety and mathematics achievement (see [5, 10] for meta-analyses), these correlations might be inflated, because mathematics achievement in these studies is typically based on measures collected in high-stakes test situations, which are especially likely to induce anxiety (cf., [11]). Other studies (e.g., [12]) found a link between maths anxiety and arithmetic performance in experimental settings. Although these findings suggest that maths anxiety can lead to suboptimal maths performance, it is not the case that maths anxiety is necessarily associated with low levels of numeracy or poor maths performance (cf., [13, 14]).

Lab-based investigations have revealed the cognitive mechanisms underpinning the negative effects of maths anxiety. When solving arithmetical problems, participants with high maths anxiety have higher error rates, they have problems with rejecting incorrect solutions (even when these are highly implausible), and these effects are especially prominent in the case of more complex problems which require a carry operation (e.g., [15, 16]). Participants with high maths anxiety also often show a characteristic pattern of responding quickly to problems, displaying a “local avoidance” of the uncomfortable test situation (cf., [13, 16]), and probably decreasing their chances of generating correct responses. Studies have also indicated that maths anxiety has an online effect on maths performance through hampering working memory resources [12, 17].

It is easy to imagine how maths anxiety might emerge in, and impact on some everyday situations, such as estimating the price of purchases, splitting the bill in a restaurant, choosing between electricity suppliers, or deciding on investment options. For example, Silk and Parrott [18] reported that participants’ maths anxiety increased after they were presented with statistical information about the health risks associated with consuming genetically modified food. These authors also reported a negative association between maths anxiety and numeracy, as well as between maths anxiety and the ability to interpret health statistics. Nevertheless, the effect of maths anxiety on the everyday functioning of individuals, and, in particular, their ability to make good decisions is unknown. Thus, the aim of the following studies was to explore the link between mathematical anxiety and a short test which was found to be highly predictive of individuals’ decision-making skills: the Cognitive Reflection Test (CRT [19]). Additionally, we were also interested in whether the link between maths anxiety and cognitive reflection is also present when participants’ maths knowledge and maths achievement are taken into account. In order to investigate these questions, in Experiment 1, we used a test of basic mathematical knowledge, which was specifically developed for university students [20]. This test can also be considered as a measure of numeracy, and it was administered in a low-stakes situation, as part of the research study. In Experiment 2, participants’ final secondary school maths grades were used. This was intended as an ecologically valid indicator of maths achievement in an academic setting.

The CRT [19] is a short test measuring a person’s tendency to override an intuitively compelling response, and to engage in further reflection which can lead to a correct solution. Although the CRT was not intended as a decision-making test per se, but as a “simple measure of one type of cognitive ability” ([19], p. 26), both Frederick [19] and others (e.g., [21–23] demonstrated that it was highly predictive of performance on tests of decision-making ability. As an example, consider the following item:

A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost? ______ cents

Although the correct response is 5 cents, many participants give the response “10 cents”, which seems to pop into mind effortlessly. Cognitive reflection involves the ability to effectively monitor and correct impulsive response tendencies.

Cognitive reflection was found to be negatively related to temporal discounting (i.e., the tendency to prefer smaller, immediately available rewards over larger rewards which will be available later), and positively related to choosing gambles with higher expected values [19]. Further studies showed that the CRT was also related to some typical heuristics and biases [21–23]. One could argue that these relationships are unsurprising, given that the CRT is based on mathematical word problems, and many of these judgment and decision-making tasks also contain numerical information. However, the CRT was also related to tasks containing no numerical information (for example, syllogistic reasoning, which measured the effect of beliefs on logical reasoning see [23]). Furthermore, although the CRT correlates with measures of intelligence and numeracy (e.g., [19, 24]) it was found to explain additional variance in reasoning and decision-making tasks when it was administered together with measures of intelligence and numeracy [21, 23, 25]. Overall, these results demonstrate that the CRT is a very powerful predictor of a person’s ability to make unbiased judgments and rational decisions on a variety of tasks.

In sum, in the studies that we report below we tested the hypothesis that maths anxiety might be related to performance on the CRT. We based this prediction on the observation that maths anxiety leads to a tendency to generate and accept responses quickly, without taking the opportunity to check solutions and correct mistakes, and to accept incorrect responses even when these are highly implausible (cf., [16]). In other words, it seems that maths anxiety reduces cognitive reflection.

It could be argued that a potential link between maths anxiety and cognitive reflection would be unsurprising, given that the CRT problems contain numerical content. Thus, we also aimed at showing that even when the effect of mathematical abilities or mathematical achievement is controlled for, the link between maths anxiety and cognitive reflection would still be present. We based this prediction on earlier claims that the CRT is “not just another numeracy scale” (cf., [21] p.361), but also a potent indicator of a person’s ability to avoid tempting, easy-to-generate heuristic responses [19]. This prediction is also in line with Campitelli and Gerrans [25] who found that the ability to inhibit the effect of beliefs on logical reasoning explained additional variance in the CRT, when the effects of numeracy were already taken into account. The prediction that the link between the CRT and maths anxiety is not fully mediated by maths knowledge or maths achievement is key to our account, as we aim to show that mathematical anxiety is negatively related to individuals’ ability to avoid the typical pitfalls of reasoning and decision-making.

Additionally, in line with a recent study [26] we wanted to control for the effects of test anxiety. This is important, as maths anxiety and test anxiety show moderate correlations, and it has been argued that maths anxiety could be just a form of test anxiety ([27], although see [28]).

In summary, in the studies that we report below, we tested the prediction that maths anxiety is linked to relatively poor performance on the CRT, even when the effects of general mathematical knowledge or school maths achievement, and test anxiety are taken into account. We tested this prediction in two populations: secondary school and university students. In our final study we also investigate the cognitive mechanisms behind the link between maths anxiety and cognitive reflection.

Why the universe is empty

Alien life is likely, but there is none that we can see. Therefore, it could be the case that somewhere along the trajectory of life's development, there is a massive and common challenge that ends alien life before it becomes intelligent enough and widespread enough for us to see—a great filter.

This filter could take many forms. It could be that having a planet in the Goldilocks' zone—the narrow band around a star where it is neither too hot nor too cold for life to exist—and having that planet contain organic molecules capable of accumulating into life is extremely unlikely. We've observed plenty of planets in the Goldilock's zone of different stars (there's estimated to be 40 billion in the Milky Way), but maybe the conditions still aren't right there for life to exist.

The Great Filter could occur at the very earliest stages of life. When you were in high school bio, you might have the refrain drilled into your head “mitochondria are the powerhouse of the cell." I certainly did. However, mitochondria were at one point a separate bacteria living its own existence. At some point on Earth, a single-celled organism tried to eat one of these bacteria, except instead of being digested, the bacterium teamed up with the cell, producing extra energy that enabled the cell to develop in ways leading to higher forms of life. An event like this might be so unlikely that it's only happened once in the Milky Way.

Or, the filter could be the development of large brains, as we have. After all, we live on a planet full of many creatures, and the kind of intelligence humans have has only occurred once. It may be overwhelmingly likely that living creatures on other planets simply don't need to evolve the energy-demanding neural structures necessary for intelligence.

Cognitive psychology

In this paper the author has tried to understand inductive reasoning in detail with focus in study of Rips and Carey. The types of inductive reasoning mainly causal, categorical and analogy has been studied in detail and also why is deductive reasoning more full proof and substantial for a proper conclusion has been discussed.

To reason out either we deduce from several facts and come to a conclusion, called deductive reasoning or we predict, combine all bits of information, i.e., we induce all the matter together to be able to hold an opinion. In inductive reasoning we cannot directly “logically” conclude, rather we can predict or forecast or a probable conclusion. Thus Inductive reasoning is the process of reasoning from specific facts or observations to reach a likely conclusion that may explain the facts. The inductive reasoner then may use that probable conclusion to attempt to predict future specific instances (Johnson-Laird, 2000). Therefore, it can be called as the process of making “generalized” decisions after either observing, or witnessing, repeated and similar instances of the thing one is reasoning about.

For example- Statement 1: This crow is black.

Statement 2: That crow is black. A third crow is black.

Therefore, all crows are black.

Example- Statement1: This marble from the bag is green.

Statement 2: That marble from the bag is green. A third marble from the bag is green.

Therefore, all the marbles in the bag green.

Thus we begin with the few data that we have, and then determine what general conclusions can logically be derived from those data, or, any particular past theories that could be related or associated with the data. For example, the probability of the child becoming asthmatic is greatly increased if even at least one parent is asthmatic or has symptoms of so, and from that, since the premises can be linked we can conclude that asthma may be inherited. That is certainly a reasonable hypothesis given the data. However, induction does not prove that the theory is correct. There are often alternative theories that are also supported by the data. For example, the behavior of the depressed parent may cause the child to be depressed, not the genes. What is important in induction is that, the theory does indeed offer a logical explanation of the data. To conclude that the parents have no effect on the depression of the children is not supportable given the data, and would not be a logical conclusion.

Furthermore, a single opposing finding, statement or theory would disprove our conclusion. For example, suppose that we notice that all the people enrolled in your mathematics course are on the honor’s list. From these observations, we can reason inductively that all students who enroll in mathematics course are excellent students and have high marks to prove so. However, unless we can observe the marks averages of all people who ever have taken or ever will take mathematics, we will be unable to prove the conclusion. Furthermore, a single poor student who happened to enroll in the course would disprove our conclusion. Still, after large numbers of observations, we might conclude that we had made enough observations to reason inductively. Because of such reasons deductive reasoning is preferred.

Since we predict in induction, that means we predict future, and future can be of multiple type with no certainty of only a particular thing occurring.

According to the new Riddle of Induction by Goodman,1983 to understand the problem Goodman posed, it is helpful to imagine some arbitrary future time t, say January 1, 2026. For all green things we observe up to time t, such as parrots and well-watered plants, both the predicates green and grue apply. Likewise for all blue things we observe up to time t, such as peacocks or blue butterflies, both the predicates blue and bleen apply. On January 2, 2026, however, parrots and well-watered plants are now bleen and peacocks or blue butterflies are now grue. Clearly, the predicates grue and bleen are not the kinds of predicates we use in everyday life or in science, but the problem is that they apply in just the same way as the predicates green and blue up until some future time t. From our current perspective (i.e., before time t), we cannot say which predicates are more projectable into the future: green and blue or grue and bleen.

In the context of justifying rules of induction, this becomes the problem of confirmation of generalizations for Goodman. However, the confirmation is not a problem of justification but instead it is a problem of precisely defining how evidence confirms generalizations. It is with this turn that grue and bleen have their philosophical role in Goodman’s view of induction.

Given possible alternative futures, we need to choose between which one to consider for the prediction. For example, in the alphabet series problem A, B, C, ?, most people would replace the question mark with an D. But we cannot know for sure that the correct alphabet id D. A mathematical formula could be proposed that would yield any number at all as the next number. Partly we choose D because it seems simple to us. It is a less complex formula than others we might choose. And partly we choose it because we are familiar with it. We are used to ascending series of alphabets. But we are not used to other complex series where alpahabets are not placed individually in ascending order. Inductive reasoning forms the basis of the empirical method (Holyoak & Nisbett, 1998). In it, we cannot logically leap from saying, “All observed instances to date of X are Y” to saying, “Therefore, all X are Y.” It is always possible that the next observed X will not be a Y. For example, we may say that all crows that we have ever seen are black. However, we cannot form the conclusion then that all crows are black because the next crow might be grey. In research, when we reject the null hypothesis (the hypothesis of no difference), we use inductive reasoning. We never know for sure whether we are correct in rejecting a null hypothesis.

When we are drawing inferences we choose some to induce predicates from than others. For example, hearing about our country model winning Miss international would make us more proud compared to neighboring country model winning. This is so because of proximity, in fact, the notion of proximity is central to understanding induction, because similarity between cases has been found to be one of the main determinants of inductive strength.

The seminal study of inductive reasoning was that of Rips (1975). This work looked at how adults project properties of one category of animals to another. Subjects were told to assume that on a small island, it had been discovered that all members of a particular species (of birds or mammals) had a new type of contagious disease. Then the subjects judged for various other species what proportion would also have the disease. For example, if all rabbits had this disease, what proportion of dogs would have it? Rips used a variety of animal categories in the premise and conclusion roles, with the categories having a known similarity structure derived using multidimensional scaling techniques.

It was found that two factors consistently promoted inferences from a premise category to a conclusion category. First, similarity between premises and conclusions promoted strong inferences. For example, subjects made stronger inferences from rabbits to dogs than from rabbits to bears. Second, the typicality of the premise, with respect to its superordinate category, was critical in promoting inferences. (Typicality of rabbit, for example, would be measured in terms of its distance from the representation of its superordinate, mammal, in a multidimensional scaling solution.) The result was that more typical premise categories led to stronger inferences than did atypical premise categories. For example, with the bird stimuli, having blue jay as a premise category led to stronger inferences overall than did having goose as a premise category. Using multiple regression analyses, Rips (1975) found distinct contributions of premise conclusion similarity and premise typicality. Interestingly, there was no evidence for a role of conclusion typicality. For example, all other things being equal, people would be as willing to draw a conclusion about a blue jay or about a goose, despite the difference in typicality of these two categories. Premise-conclusion similarity and premise typicality promote induction, but that typicality of the conclusion category does not seem to affect inductive strength.

In another study done by Nisbett, Krantz, Jepson, and Kunda (1983), who also asked subjects to draw inferences about items (animals, people, and objects) found on a remote island. For example, subjects were told to imagine that one member of the Barratos tribe was observed to be obese, and they estimated the proportion of all members of this group that would be obese. Likewise, the subjects were told that one sample of the substance “floridium” was observed to conduct electricity, and they estimated the proportion of all members of this set that had floridium conducted electricity.

There were several interesting findings from the Nisbett et al. study, most relevant is that the subjects were very sensitive to perceived variability of the conclusion category. For a variable category such as Barratos people (and their potential obesity), the subjects were rather unwilling to make strong inferences about other Barratos, after just one case. But for a homogenous category such as floridium samples, the subjects were willing to generalize the observation of electrical conductance to most or all of the population. This result, that subjects were more willing to draw inferences about homogenous conclusion categories, makes a striking comparison to the results of Rips (1975). Whereas Rips found that typicality of the conclusion did not affect inductive strength, Nisbett etal.’s(1983) results show that conclusion categories do matter, at least in terms of their variability. The criteria for what makes a good premise category are different than the criteria for what makes a good conclusion category.

The Rips (1975) task has been adapted for testing with children, first by Carey (1985). There are a number of important reasons to study inductive reasoning in children. Such studies could show how inductive abilities develop, perhaps guiding or constraining accounts of fully developed, adult inductive reasoning. In comparing two models that equally account for adult data, if one model can also give an explanation of the course of development, then that model ought to be favored. For example, a pattern of age-related changes in reasoning about animals could reflect the growth of children’s knowledge or theories about living things.

Of course, with these different reasons for studying the development of induction, there is always the challenge of whether to attribute a change in performance to development of reasoning processes or development of knowledge.

There are several major kinds of inductive reasoning, including causal inference, categorical inference, and analogical inference.

In a causal inference, one reasons to the conclusion that something is, or is likely to be, the cause of something else. For example, from the fact that one hears the sound of piano music, one may infer that someone is (or was) playing a piano. But although this conclusion may be likely, it is not certain, since the sounds could have been produced by an electronic synthesizer.

The philosopher David Hume observed that we are most likely to infer causality when we observe covariation over time: First one thing happens, then another. If we see the two events paired enough, we may come to believe that the first causes the second. Perhaps our greatest failing -We demonstrate confirmation bias, which may lead us to errors such as illusory correlations (Chapman & Chapman, 1967, 1969, 1975). Furthermore, we frequently make mistakes when attempting to determine causality based on correlational evidence alone. Correlational evidence cannot indicate the direction of causation. Suppose we observe a correlation between Factor A and Factor B. We may find one of three things:

  1. it may be that Factor A causes Factor B
  2. it may be that Factor B causes Factor A or
  3. some higher order, Factor C, may be causing both Factors A and B to occur together.

Based on the correlational data we cannot determine which of the three options indeed causes the observed phenomenon. A related error occurs when we fail to recognize that many phenomena have multiple causes. For example, A fire accident can be caused due to several causes, a short circuit or gas leak or a lighter. Once we have identified one of the suspected causes of a phenomenon, we may commit what is known as a discounting error. We stop searching for additional alternative or contributing causes. Confirmation bias can have a major effect on our everyday lives. For example, we may meet someone, expecting not to like her. As a result, we may treat her in ways that are different from how we would treat her if we expected to like her. She then may respond to us in less favorable ways. She thereby “confirms” our original belief that she is not likable. If our original beliefs are thereby confirmed (Sternberg, 1997), this effect is referred to as a self-fulfilling prophecy (Harber & Jussim, 2005).

In a categorical inference, one makes a judgment about whether something is, or is likely to be, a member of a certain category. For example, upon seeing an animal one has never seen before, a person with a limited knowledge of dogs may be confident that what he is seeing is a dog but less certain about the specific species.

People generally use both bottom-up strategies and top-down strategies that is, they use both information from their sensory experiences and information based on what they already know or have inferred previous (Holyoak & Nisbett, 1988). Bottom-up strategies are based on observing various instances and considering the degree of variability across instances. From these observations, we abstract a prototype. Once a prototype or a category has been induced, the individual may use focused sampling to add new instances to the category. He or she focuses chiefly on properties that have provided useful distinctions in the past. Top-down strategies include selectively searching for constancies within many variations and selectively combining existing concepts and categories.

The third type of inductive reasoning is analogical inference. In reasoning by analogy, one applies what one has learned to another domain. Aristotle stated the formulae for two possible analogical inferences: “As A is to B, so C is to D” and “As A is in B, so C is in D.” Analogical inference involves applying the outcomes of a known situation to a new or unknown situation. An analogical argument is an explicit representation of a form of analogical reasoning that cites accepted similarities between two systems to support the conclusion that some further similarity exists. In general (but not always), such arguments belong in the category of inductive reasoning, since their conclusions do not follow with certainty but are only supported with varying degrees of strength.

Some classic case study where reasoning by analogy was used,

In a much-cited case (Donoghue v. Stevenson 1932 AC 562), the United Kingdom House of Lords found the manufacturer of a bottle of ginger beer liable for damages to a consumer who became ill as a result of a dead snail in the bottle. The court argued that the manufacturer had a duty to take “reasonable care” in creating a product that could foreseeably result in harm to the consumer in the absence of such care, and where the consumer had no possibility of intermediate examination. The principle articulated in this famous case was extended, by analogy, to allow recovery for harm against an engineering firm whose negligent repair work caused the collapse of a lift (Haseldine v. CA Daw & Son Ltd. 1941 2 KB 343). By contrast, the principle was not applicable to a case where a workman was injured by a defective crane, since the workman had opportunity to examine the crane and was even aware of the defects (Farr v. Butters Brothers & Co. 1932 2 KB 606).

In 1934, the pharmacologist Schaumann was testing synthetic compounds for their anti-spasmodic effect. These drugs had a chemical structure similar to morphine. He observed that one of the compounds—meperidine, also known as Demerol—had a physical effect on mice that was previously observed only with morphine: it induced an S-shaped tail curvature. By analogy, he conjectured that the drug might also share morphine’s narcotic effects. Testing on rats, rabbits, dogs and eventually humans showed that meperidine, like morphine, was an effective pain-killer (Lembeck 1989: 11 Reynolds and Randall 1975: 273).

An analogical argument has the following form:

  1. S is similar to T in certain (known) respects.
  2. S has some further feature Q.
  3. Therefore, T also has the feature Q, or some feature Q* similar to Q.

(1) and (2) are premises. (3) is the conclusion of the argument. The argument form is inductive the conclusion is not guaranteed to follow from the premises.

S and T are referred to as the source domain and target domain, respectively

An application of analogies in reasoning can be seen in politics. Analogies can help governing bodies come to conclusions (Breuning, 2003). The analogies also can be effectively used to conveying the justification of the decision to the public (Breuning, 2003). However, the use of analogies is not always successful. This highlights both the utility and possible pitfalls of using analogies in political deliberation. In 2010, opponents of the war in Afghanistan drew an analogy to Vietnam to argue for withdrawing from Afghanistan. They asserted that the failure of U.S.policies to lead to a conclusive victory were analogous between Vietnam and Afghanistan. Somemembers of government then turned the tables, using an analogy to Vietnam to argue that withdrawal from Afghanistan could lead to mass slaughter, as they asserted happened in Vietnam after the Americans left. Thus, analogies can end up being largely in the eye of the beholder rather than in the actual elements being compared. Analogies are also used in everyday life as we make predictions about our environment. We connect our perceptions with our memories by means of analogies. The analogies then activate concepts and items stored in our mind that are similar to the current input. Through this activation, we can then make a prediction of what is likely in a given situation (Bar, 2007). For example, predictions about global warming are being guided in part by people drawing analogies to times in the past when the people believed either that the atmosphere warmed up or did not.

Whether a given individual believes in global warming depends in part upon what analogy or analogies the individual decides to draw.

The above paper studied inductive reasoning in detail and we saw the various positive points of how we can induce several inferences from various information together and predict to get our inference. However, one can only predict and not draw confident conclusion which sets back reasoning by induction from deduction comparatively. We saw the three types of induction mainly causal induction, categorical induction and reasoning by analogy of which analogy is mostly used.

Watch the video: Minimize the Noise: Methods to Reduce Cognitive Load (August 2022).