After Chapter 4.2, you will be able to:
Every day you are faced with problems. Many of these problems you solve without any real conscious thought about what is happening. However, much like the scientific method, problem solving itself has a process. First, we must frame the problem; that is, create a mental image or schematic of the issue. Then, we generate potential solutions and begin to test them. These potential solutions may be derived from a mental set, which is the tendency to approach similar problems in the same way. Once solutions have been tested, we evaluate the results, considering other potential solutions that may have been easier or more effective in some way.
The first step in problem solving (framing the problem) may seem obvious; however, when we get “stuck” on a problem, it is most often because the manner in which we have framed the problem is inefficient or not useful.
Problem solving can be impeded by an inappropriate mental set, as well as by functional fixedness, which is demonstrated by Duncker’s candle problem. Consider the following scenario: You walk into a room and see a box of matches, some tacks, and a candle. Your task is to mount the candle on the wall so that it can be used without the wax dropping on the floor. Before reading on, try to solve the problem.
Most people find the task challenging. You might have thought of tacking the candle to the wall, but that solution doesn’t work because the wax would still drop to the floor. The key is to realize that the matchbox can serve not just as a container for the matches, but as a holder for the candle. The solution, therefore, is to tack the box to the wall and put the candle in the box. Functional fixedness can thus be defined as the inability to consider how to use an object in a nontraditional manner.
In psychology, different approaches to problem solving include trial-and-error, algorithms, deductive reasoning, and inductive reasoning.
Trial-and-error is a less sophisticated type of problem solving in which various solutions are tried until one is found that seems to work. While an educated approach may be used, this type of problem solving is usually only effective when there are relatively few possible solutions.
An algorithm is a formula or procedure for solving a certain type of problem. Algorithms can be mathematical or a set of instructions, designed to automatically produce the desired solution.
Deductive reasoning is the key to success on the MCAT, especially in the Critical Analysis and Reasoning Skills (CARS) section. Chapter 6 of MCAT Critical Analysis and Reasoning Skills Review focuses on formal logic, the cornerstone of deductive reasoning.
Deductive (top-down) reasoning starts from a set of general rules and draws conclusions from the information given. An example of deductive reasoning is a logic puzzle, as shown in Figure 4.4. In these puzzles, one has to synthesize a list of logical rules to come up with the single possible solution to the problem.
Remember that a deduction is a solution that must be true based on the information given. This is why answers on the MCAT that merely might be true (but don’t have to be) are never the correct answer.
Inductive (bottom-up) reasoning seeks to create a theory via generalizations. This type of reasoning starts with specific instances, and then draws a conclusion from them.
We make decisions every day. Some are insignificant: What should I wear today? Others are very important: Where am I going to apply to medical school? decision-making is a complicated process, but we use a number of tools, such as heuristics, biases, intuition, and emotions, to speed up or simplify the process. While useful from a time and complexity standpoint, these tools can also lead us to short-sighted or problematic solutions.
Heuristics are simplified principles used to make decisions; they are colloquially called rules of thumb. The availability heuristic is used when we try to decide how likely something is. When we use this heuristic, we make our decisions based on how easily similar instances can be imagined. Often, the use of this heuristic leads us to a correct decision, but not always. As an example, answer the following question: Are there more words in the English language that start with the letter “K” or that have “K” as their third letter?
Most people respond that there are more words that begin with the letter “K” than have “K” as their third letter. In fact, there are actually at least twice as many words in English that have “K” as the third letter than begin with “K.” Most people approach this question by trying to think of words that fit into each category. Because we’re so used to classifying words by their first letter, it is easier to think of words beginning with “K.” Thus, in this case, the availability heuristic tends to lead to an incorrect answer.
Detail questions on the MCAT often have wrong answer choices that are stated in the passage, but that fail to answer the question posed. According to the availability heuristic, students who do not truly problem solve on MCAT questions will be tempted by these familiar-sounding answers merely because they can recall that statement being mentioned in the passage. Don’t forget to use your Outline effectively, as described in Chapter 4 of MCAT Critical Analysis and Reasoning Skills Review!
The representativeness heuristic involves categorizing items on the basis of whether they fit the prototypical, stereotypical, or representative image of the category. For example, consider a standard coin that is flipped ten times in a row and lands on heads every time. What is the probability of the coin landing on heads the next time? Mathematically, the probability must still be 50 percent, but most individuals will either overestimate the probability based on the pattern that has been established, or underestimate the probability with the logic that the number of heads and tails must “even out.” Hence, like the availability heuristic, the use of the representativeness heuristic can sometimes lead us astray. Using prototypical or stereotypical factors while ignoring actual numerical information is called the base rate fallacy.
While heuristics can lead us astray, they are essential to speedy and effective decision-making. Heuristics are often used by experts in a given field. For instance, to win at chess, one must be able to think several moves ahead. On any particular turn, there may be 15 or 20 possible moves, each one of which may have multiple consequences; analyzing every possibility would take far too long. There are heuristics, however, that can quickly rule out some of the possible moves: the king must be protected, it is generally good to control the center squares, and pieces should not be put in danger when possible. In this way, heuristics provide a more efficient—although sometimes inaccurate—method for problem solving.
When a potential solution to a problem fails during testing, this solution should be discarded. This is known as the disconfirmation principle: the evidence obtained from testing demonstrated that the solution does not work. However, the presence of a confirmation bias may prevent an individual from eliminating this solution. Confirmation bias is the tendency to focus on information that fits an individual’s beliefs, while rejecting information that goes against them. Confirmation bias also contributes to overconfidence, or a tendency to erroneously interpret one’s decisions, knowledge, and beliefs as infallible. The similar phenomenon of belief perseverance refers to the inability to reject a particular belief despite clear evidence to the contrary. Together, confirmation bias, overconfidence, and belief perseverance can seriously impede a person’s analysis of available evidence.
Intuition can be defined as the ability to act on perceptions that may not be supported by available evidence. Often, people may have beliefs that are not necessarily supported by evidence, but that a person “feels” to be correct. Intuition is often developed by experience. For example, an emergency room physician, over the course of seeing thousands of patients with chest pain, may develop a keen sense of which patients are actually having a heart attack without even looking at an electrocardiogram (EKG) or a patient’s vital signs. This intuition can be more accurately described by the recognition-primed decision model: the doctor’s brain is actually sorting through a wide variety of information to match a pattern. Over time, the doctor has gained an extensive level of experience that he or she is able to access without awareness.
Emotion is the subjective experience of a person in a certain situation. How a person feels often influences how a person thinks and makes decisions. For example, a person who is angry is often more likely to engage in more risky decision-making. In addition, emotions in decision-making are not limited to the emotion experienced while the decision is being made; emotions that a person expects to feel from a particular decision are also involved. For example, if a person believes a car will make them feel more powerful, he or she may be more likely to purchase that car.
Intellectual functioning is a highly studied area of psychology. How is intelligence defined? What makes someone more intelligent than someone else? These are multifaceted questions that are difficult to answer; however, theorists have proposed models for some aspects of intelligence.
There has been much debate concerning the definition of intelligence. Howard Gardner’s theory of multiple intelligences is one of the most all-encompassing definitions, with seven defined types of intelligence: linguistic, logical–mathematical, musical, visual–spatial, bodily–kinesthetic, interpersonal, and intrapersonal. Gardner argues that Western culture values the first two abilities over the others. After all, linguistic ability and logical–mathematical ability are the two abilities tested on traditional intelligence quotient (IQ) tests.
Gardner’s multiple intelligences include linguistic, logical–mathematical, musical, visual–spatial, bodily–kinesthetic, interpersonal, and intrapersonal.
There are a number of tests and studies that have historically attempted to quantify intelligence. A founding concept behind these tests is Spearman's “g factor,” or general intelligence factor. The theory behind the existence of a g factor is based on the observation that performance on different cognitive tasks is in many cases positively correlated, indicating an underlying factor or variable is playing a role. This underlying variable of intelligence is often measured with standardized tests that generate an intelligence quotient (IQ) for the test taker. IQ tests were largely pioneered by Alfred Binet in the early twentieth century. A professor at Stanford University took Binet’s work and created what is known as the Stanford–Binet IQ test. While later iterations of the test use different methodologies to arrive at a score, it is useful to know the original formula for calculating IQ:
Equation 4.1
Using this equation, a four-year-old with intelligence abilities at the level of the average six-year-old would have an IQ of 150. The distribution of IQ scores from the original study of the Stanford–Binet IQ test is shown in Figure 4.5.
Some theorists have argued heavily for intelligence as a hereditary trait, most notably Galton in his novel Hereditary Genius. In reality, variations in intellectual ability can be attributed to many determinants, including genes, environment, and educational experiences. Intellectual ability does appear to run in families, which may be due to both genetics and the environment; some environments are simply more enriching than others. Parental expectations, socioeconomic status, and nutrition have all been shown to correlate with intelligence.
The educational system plays a significant role in the development of intelligence. Children who attend school tend to have greater increases in IQ, and IQ actually decreases slightly during summer vacations. Early intervention in childhood also improves IQ, especially for children in low-enrichment environments. Finally, both intellectually gifted and cognitively disabled children benefit from specialized educational environments. For cognitively disabled students, this is often defined as the least restrictive environment, in which they are encouraged to participate as much as possible in the regular mainstream classroom, with individualized help as needed.