Caffeine is found naturally in so many of our foods and beverages, we take it for granted. On top of that, it’s often referenced for its positive effects on attention and mental alertness.

Not only is caffeine found abundantly available in natural and supplemented foods and beverages, you’ll also find it in products sold over the counter for fatigue, migraines and colds.

But what are caffeine’s effects on our thinking? Is it helping or hindering our thought processes? Let’s find out…

Blood levels of caffeine peak in as few as 15 minutes and on average 45 minutes after ingestion.  Some studies suggest that over 80 percent of U.S. adults and children ingest caffeine on a daily basis (Brunye et al., 2010).

Many studies indicate that the primary role caffeine has affecting our behavior is its effect on blocking the inhibitory properties of endogenous adenosine. So what? you say. Well, that inhibition results in increased dopamine, norepinephrine and glutamate. Caffeine ingestion leads to increased stimulation of your heart (cardio) and even anti-asthmatic actions.

Many studies have demonstrated that caffeine leads to enhanced cognitive performance involving various tasks (Brunye et al., 2010). It is often cited for its positive effects on vigilance, mental alertness, feeling of well-being and arousal.  Caffeine also has a positive effect on various domains of attention (Trayambak et al., 2009). 

Many studies show caffeine reduces response times and error rates in simple reaction time tasks, choice reaction time, and visual vigilance. Your brain appears to love caffeine too. Brain processes that have also been shown to benefit from caffeine include visual selective attention, task switching, conflict monitoring and response inhibition.

Different types of tasks are used when measuring caffeine’s effect on different types of attention.  Sustained attention — e.g., attention over a prolonged time period — has been most studied. A large body of data shows caffeine positively influences sustained attention.  Sustained attention often is measured by using a continuous performance task. For example, participants view a stream of stimuli (often letters) and are required to respond whenever a predetermined target is presented. Task length varies considerably.

Research also shows caffeine has positive effects on selective attention — the process of attending to meaningful sources while ignoring irrelevant ones.  The research findings are indecisive; some research has failed to find a positive relationship between caffeine ingestion and selective attention. 

Selective attention most often is measured by four main tasks. The visual search task is least often used to measure caffeine’s effects on selective attention.

A visual search task consists of participants identifying a predetermined target stimulus while ignoring a number of distractors.  For example, a conjunction search requires participants to identify a target by at least two different attributes (e.g., find a blue capital A).  These types of tasks are useful because in daily life, often it is necessary to identify objects by several attributes.

Moderate doses of caffeine — 200-300 mg — often are used in research, although doses over 500 mgs sometimes are used. The general finding is that more than moderate use does not offer additional benefits, and higher doses sometimes lead to negative effects.

So go ahead and have that cup of coffee or can of Coke. It’s likely to help your thinking… as long as you don’t overdo it.

References

Brunye et al. (2010).  Caffeine Modulates Attention Network Function.  Brain and Cognition,  Vol. 72, 181-182.

Trayambak et al. (2009).  Effect of Caffeine on Sensory Vigilance Task Performance-l: Under Low Demanding Condition.  Indian Journal of Social Science Researchers, 6, 8-16.

The relationship between flavor evaluative conditioning and contingency awareness was investigated in two experiments (Wardle et al., 2007).  In both experiments evaluative conditioning was seen only in those participants who were aware of the contingencies.  According to the researchers, the results of these experiments contradicted earlier findings, where evaluative conditioning occurred in participants who showed no awareness of the contingencies. 

How did they research these issues and what did they find?

Experiment 1 was designed to replicate the evaluative conditioning effect reported by Baeyens et al. (1990) and Dickinson & Brown (2007), and to further explore whether or not it is possible to detect any level of contingency awareness at all in flavor evaluative conditioning. 

To reiterate, findings by Baeyens et al. (1990) and Dickinson & Brown (2007) showed that evaluative conditioning occurred without awareness. In Experiment 1, a within-subjects design using two flavors as CSs were used.  During conditioning, one flavor was always paired with sugar (positive US) and the other with a bitter tasting substance (negative US). 

In the test phase, participants tasted the CS flavor and then completed the evaluative and contingency rating scales for the flavor before moving on to taste the next flavor. 

The results showed evaluative conditioning occurred, and the participants liked the flavor that had been coupled with sugar more than the flavor coupled with the bitter substance. The awareness test revealed that only aware participants displayed evaluative conditioning.   

The objective of Experiment 2 was to replicate the results of the first experiment.  The main weakness of Experiment 1 was that the vanilla flavor used had a slight yellowish coloring that might have influenced participants’ answers given on the contingency test. 

In Experiment 2, colorless flavors were used.  The finding in Experiment 2 showed, as in Experiment 1, an evaluative conditioning effect, and only participants that showed awareness displayed evaluative conditioning.  The findings of the two studies by Wardle et al. were inconsistent with those of Baeyens et al. (1990) and Dickinson & Brown (2007), studies that showed an evaluative conditioning effect in effect in the absence of awareness. 

Wardle et al. (2007) pointed out that the previous studies showing evaluative conditioning in the absence of awareness were methodologically flawed.  The main weakness in the Baeyens et al. (1990) study was that the measure of awareness was different in the testing and conditioning phases. 

Another weakness was the measure of awareness was always administered after the evaluative test, which could have weakened participants’ ability to recall contingencies (lacked counterbalancing).  Wardle et al. (2007) pointed out problems with the Dickinson & Brown (2007) study, including increased complexity due to presentation of four contingencies and an analysis of contingency awareness based on aggregate scores. 

They suggested that basing awareness on aggregate scores might miss some contingency awareness for a subset of individual CSs, or a subset of participants.  Further analysis of the data from the Dickinson & Brown (2007) study showed that the aggregate scores overlooked the fact that over a third of the participants in the experiment were aware of at least three of the four contingencies.

 
References  

Baeyens, F., Eelen, P., & Van Den Bergh, O. (1990a).  Contingency awareness in evaluative conditioning: A case for unaware affective-evaluative learning.  Cognition & Emotion, 4, 3-18. 

Baeyens, F., Eelen, P., Van den Bergh, O., & Crombez, G. (1990).  Flavor-flavor and color-flavor conditioning in humans.  Learning and Motivation, Vol. 21, Issue 4, Pages 434-455.

Dickinson, A., & Brown, KJ. (2007).  Flavor evaluative conditioning is unaffected by contingency knowledge during training with color-flavor compounds.  Learning & Behavior, 35, 36-42.  

Wardle, SG., Mitchell, CJ., & Lovibond, PF. (2007).  Flavor evaluative conditioning and contingency awareness.  Learning & Behavior, 35 (4), 233-241.

Carrot juice photo available at Shutterstock

Simply put, this means that our preferences for brands, products, people and other things can be influenced and even modified by the presence of something we like or dislike strongly.

Evaluative conditioning has also been associated with the development of food likes and dislikes.  Humans develop a dislike for foods that are followed by negative consequences such as nausea, rashes, diarrhea, and breathing problems (Pelchat & Rozin, 1982).  Taste aversions are derived from various situations, such as food poisoning, allergic reactions, over consumption and some medical treatments (Batsell & Brown, 1998). 

Although the majority of developed food aversions are attributed to the taste or flavor of food, a proportion of aversions are related to smell (de Silva & Rachman, 1987). 

Change in food likes has been shown with flavor-flavor pairings: pairing of a neutral flavor (conditioned stimulus, or CS) with a liked or disliked flavor (unconditioned stimulus, or US) that can result in a change in liking of the CS flavor. Flavor-flavor conditioning appears to be a potent tool for increasing liking for isolated tastes and specific foods (Eertmans et al., 2001). Liking for unsweetened vegetables and unfamiliar teas increases after they have been consumed sweetened on a number of occasions (Eertmans et al., 2001; Capaldi, 1996).  Willingness to try new foods increases after providing people with verbal information that the foods taste good. This may imply that flavor-flavor or food-flavor conditioning can also occur by using written messages (Pelchat & Pliner, 1995).

It has also been demonstrated that flavor-flavor conditioning can occur through observation (Baeyens et al., 1996). With observational evaluative conditioning, participants observe a social model being exposed to a CS-US association.  The model tastes a food and shows his or her reaction by facial expression or other gestures.  When observers rate the target stimulus after observing the model’s reaction an evaluative conditioning effect can be observed.

Baeyens and colleagues (1990) hypothesized that the pairing of a neutral flavor (CS) with an already liked (or disliked) flavor (US) should result in an increase (or decrease) in liking for the originally neutral flavor.   Sugar was used as a positive US, and a bitter tasting substance as a negative US, the flavor of the drink served as the CS. An evaluative conditioning effect was observed in the flavor-flavor, negative condition. However, the evidence for positive flavor-flavor conditioning was weak at best.  When children are presented neutral foods as rewards or the foods are paired with attention from adults, the food appears to produce increases in preference (Eertmans et al., 2001).

Evaluative conditioning has been proposed to occur in the presence and absence of awareness (Wardle et al., 2007).  Evaluative conditioning and its relationship with awareness will be addressed in the next article: Change in Food Likes/Dislikes 2.  Stay tuned for part two.

References

Baeyens, F., Eelen, P., Van den Bergh, O., & Crombez, G. (1990).  Flavor-flavor and color-flavor conditioning in humans.  Learning and Motivation, Vol. 21, Issue 4, Pages 434-455.

Batsell, WR., & Brown, AS. (1998).  Human-flavor aversion conditioning: a comparison of traditional and cognitive aversions.  Learning and Motivation, 29, 383-396.

Capaldi, ED. (1996).  Conditioned food preferences.  In Capaldi, E.D. (ed.) Why We Eat What We Eat: The Psychology of Eating.  American Psychological Associaiton, Washington DC, pp. 53-80.  

De Houwer, J., Thomas, S., & Baeyens, F. (2001).  Associative Learning of Likes and Dislikes: A Review of 25 years of Research on Human Evaluative Conditioning.  Psychological Bulletin, Vol. 127, No.6, 853-869. 

De Silva, P., & Rachman, S. (1987).  Human food aversions: nature and acquisition.  Behavior, Research and Therapy, 25, 457-468.  

Eertmans, A., Baeyens, F., & Van den Bergh, O. (2001).  Food likes and their relative importance in human eating behavior: review and preliminary suggestions for health promotion. Health Education Research: Theory and Practice, Vol.16, No.4, pp. 443-456.

Pelchat, ML., & Pliner, P. (1995).  “Try it You’ll like it’: effects of information on willingness to try novel foods.  Appetite, 24, 153-166. 

Pelchat, ML., & Rozin, P. (1982).  The special role of nausea in the acquisition of food dislikes by humans. Appetite, 3, 341-351. 

Wardle, SG., Mitchell, CJ., & Lovibond, PF. (2007).  Flavor evaluative conditioning and contingency awareness.  Learning & Behavior, 35 (4), 233-241.

Lettuce in a cone photo available from Shutterstock

Q. Why doesn’t the current model of treating obesity — only telling people what and how much to eat — work for most people?

A. Great question, because the current model definitely doesn’t work. I can’t remember who said it, but there’s a saying that if you want to gain weight, go on a diet. Diets have a failure rate of about 95 percent. People may lose weight initially but then they usually gain it back and then some.

So this model doesn’t work for many reasons. For one thing, genetics plays a prominent role in our weight. This is why you can have two people who eat the same foods in the same quantities look very different. One may be thin; the other may be considered “overweight.” Our bodies are more complicated than the “calories in, calories out” equation assumes.

Instead of putting people on diets, which don’t work anyway, why not promote engaging in healthy behaviors directly? (By the way, many people have to do some pretty unhealthy things to lose weight and maintain that weight loss.) Instead of focusing on weight loss, focus on moving your body by doing activities that you enjoy — research has shown that fitness is critical for health — eating nutritious foods, getting enough sleep and listening to your body, along with other healthy self-care habits. Practicing these habits does help a person become healthier, whether they lose weight or not.

On a side note, there are plenty of skinny people who aren’t healthy, who smoke, lead sedentary lives and don’t nourish their bodies. But because they’re genetically predisposed to a slimmer frame, we don’t single them out and just assume that they’re healthy. The key is to encourage everyone to engage in healthy habits.

Health At Every Size is an approach that shifts the focus from weight to health. You can learn more here.   Also, check out these interviews on Weightless with Linda Bacon, author of Health At Every Size: The Surprising Truth About Your Weight — Part 1 and Part 2 and Evelyn Tribole, co-author of Intuitive Eating: A Revolutionary Program That Works — Part 1  and Part 2.

I also just wrote a post about healthy eating  on Weightless, which readers might be interested in.

Q. Are there reliable predictors for individuals who may become anorexic or bulimic?

A. Eating disorders are complicated and serious illnesses. A variety of complex factors contribute, including genetics, neurobiology and the environment. There’s a saying that genetics load the gun, and environment pulls the trigger.

Eating disorders tend to run in families. Also, traits such as perfectionism and cognitive rigidity can increase risk. Our appearance- and diet-obsessed culture serves as a trigger along with dieting. For people who are genetically and biologically vulnerable to eating disorders, dieting can serve as a gateway to an ED. But, again, there must be a genetic or biological vulnerability in order for the environment to play a role.

Q. Is there a relationship between ethnicity and anorexia? Or ethnicity and bulimia?

A. In the past, eating disorders were thought of as illnesses that solely strike white women. While the rates of eating disorders seem to be lower in ethnic groups like Asians and African-Americans, eating disorders can affect anyone, regardless of race, ethnicity, age, income, size, shape or sex. As one of my fave bloggers, Carrie Arnold of ED Bites, wrote in a post, “If you’re human, you can get an eating disorder.”

Q. What is the most commonly diagnosed eating disorder? Is there a substantial difference between the number of men and women diagnosed?

A. The most common eating disorder is binge eating disorder. According to the Binge Eating Disorder Association, it affects more than eight million men and women.

In general, more women do struggle with eating disorders. But plenty of men do, too. We used to think that about 10 percent of people with eating disorders were men. But it may be as high as 25 percent. Again, anyone can struggle with an eating disorder.

Q. What is your resolution for the new year?

A. My resolution is to listen to myself and my needs more when it comes to the decisions in my life, big or small. It could be everything from moving my body to eating to pursuing certain creative projects.

Q. Where do you see yourself in five years?

A. I see myself running after a few kids and writing my heart out.

One of the key findings in this area is that the difference between human brains and brains of other species are often quantitative rather than qualitative.  Comparative studies do reveal differences, but they also reveal many similarities that can lead to a better understanding of evolution and brain development.  The primary benefits of using the comparative approach are that simpler brains found in other species make it more likely that brain-behavior relationships will be revealed, and there are fewer ethical restrictions applied to the study of other species.

Researchers who work with our closest relatives, chimps, assume that the things learned about chimps’ brains and behavior could be applied to understanding human brains and behavior.  Researchers can also make meaningful comparisons with more distantly related species: slugs, fruit flies, rats and cats.  Brain-behavior comparisons across species provide information that is difficult to obtain from studying a single species. 

It is important to keep in mind the differences between species are as important as the similarities.  Identifying similarities and differences give us a better understanding of the structure and function of the brain.

Here are some examples of how the comparative approach can be applied:

• Rat behavior is complex, and most structures of the rate brain are similar to those of the human brain.  The cortical function of lab rats is very similar to that of humans.
• Slugs provide important information on how neurons interconnect to produce behavior.  The slug’s nervous system is relatively simple making it easier to the study than humans.
• Fruit flies provide useful information concerning the genetic basis of behavior because many generations of fruit flies with genetic alterations can be bred rapidly in the lab.

The comparative approach is used for three primary lines of research: understanding basic brain mechanisms, designing animal models of human neurological disorders and describing the evolutionary development of the brain.

Researchers produce neurological disorders in animals, manipulate variables in an effort to understand the cause of the disorders and then design treatments to combat the disorder.  Parkinson models have been developed in rats, mice and monkeys.  Studying rats that had a disease similar to Parkinson’s developed the drug L-dopa.

In conclusion, using the comparative approach has led to important discoveries in area of basic brain function, disease treatments, and knowledge concerning the evolutionary development of the brain.

Nerve cell photo available from Shutterstock

Memory re-consolidation occurs when a memory is retrieved and it becomes unstable, thus allowing new information to be incorporated into the memory. When the memory is unstable its re-stabilization process can be blocked or weakened. This process of memory re-consolidation has been shown to help weaken the memory of the negative emotions associated with the fearful memories (Schiller et al., 2010). This does not imply that conscious memory of the event is weakened, rather, it implies that the negative tone of the memory is weakened.

Research in this area has led to using extinction processes that have been shown to weaken fear-associated memories.

Past research has shown extinction training to be successful in the treatment of fear memories, but it has been shown that fear may return under stressful situations. A newer non-invasive method that involves precise timing of extinction training with the conditioned stimuli has been shown to permanently weaken fearful memories (Schiller et al., 2010).

Further research into non-invasive modes of memory re-consolidation — which may serve as a treatment for blocking fearful memories in post-traumatic stress disorder and other anxiety disorders — would be very useful.  Unwanted memories are often fearful memories.  Fearful memories may be the root cause of some anxiety disorders.  Blocking or weakening these memories can lead to decreases in anxiety and other similar disorders.  The therapeutic possibilities of this method are far reaching.

Blocking Fear Memories

The re-consolidation hypothesis suggests that memories are reshaped each time they are retrieved (Alberini et al., 2005).  This view is in disagreement with the view that memory formation is a one time process, often referred to as consolidation (Mcgaugh, 2000).    Research investigating declarative and motor memory suggests information that is presented during the re-consolidation window may lead to the impairment or modifying of older memories (Schiller et al., 2010).

Pharmacological interventions during re-consolidation result in either inhibition or erasing of old memories (Nader et al., 2000).  The problem with this line of treatment is the potential toxicity.  According to Schiller and colleagues (2010), many of the substances that have been used, in various species, for blocking old memories are toxic to humans.

As mentioned earlier, a non-invasive method proposed by Schiller and colleagues (2010) has been shown to weaken the re-occurrence of fear memories after extinction training.  To test their hypothesis in humans, two experiments were designed to investigate whether extinction training during the re-consolidation window (less than 6 hours) could block the return of an extinguished fear.   The results showed that the recovery of a fear memory could be blocked if extinction training is conducted during a time window when the fear memory is undergoing re-consolidation.

Further Research

The study conducted by Schiller and colleagues provided evidence that a non-invasive technique can be used to change fear memories by replacing them with non-fearful memories.  Unlike past research that showed the re-occurrence of fearful memories after extinction training, this method seemed to imply that permanent changes in memory can occur.

This line of research is still in its infancy and needs to be replicated to strengthen its value.  These results and the results of future research could have important implications for anxiety disorders.  This non-invasive treatment represents a safe alternative for memory re-consolidation when compared to pharmacological interventions. 

Further investigation into this method will also allow us to learn more about memory, its formation, how it relates to anxiety disorders and how its changes are ubiquitous.

References

Alberini, CM. (2005). Mechanisms of memory stabilization: are consolidation and reconsolidation similar or distinct processes?  Trends Neurosci, 28, 51-56.

Mcgaugh, JL. (2000).  Memory- a century of consolidation.  Science, 287, 248-251.

Nader, K., Schafe, GE., & Ledoux, JE. (2000).  Fear memories require protein synthesis in the amygdala for reconsolidation after retrieval.  Nature, 406, 722-726.

Schiller, D., Monfils, MH., Raio, CM., Johnson, DC., Ledoux, JE., & Phelps, EA. (2010).  Preventing the fear in humans using reconsolidation update mechanisms.  Nature, Vol. 463(7), January.

Girl on a train photo available from Shutterstock

The model says attention is either automatic or controlled.  In automatic processing cognition occurs with little effort, is automatic given a specific stimulus, and doesn’t interfere with other mental processes. Controlled processing is cognitively expensive, relies mainly on serial processing and is responsible for self-regulation. 

Focusing attention is dependent on top-down processing while automatic attention is more focused on bottom-up processing.  Bottom-up processing is mainly triggered by the presence of environmental stimuli, while top-down processing is dependent on information in memory, including expectation of what might occur while engaging in the task. 

It is generally assumed these different types of processes may involve different cortical circuitry.  Ability to focus attention may be affected by the presence of various sensory cues.  The ability to focus attention is limited, and the more complex the sensory environment the harder to focus on a particular task.  The amount of effort required to complete a specific task is also important when considering the implications of the attentive process.  If the task is routine little effort is required, but if the task is novel or not as familiar more effort is required. 

Understanding attention helps us identify problems with multi-tasking and allows us the opportunity to set up an optimal learning environment.  Knowledge about human attention has led to restrictions on cell phone use while driving.  Attention capacities are limited, and using a cell phone while driving limits other attention processes.  Assuming everything remains routine while driving, we may not experience any problems, as we’re engaging in automatic processing.

But once something unexpected occurs, such as a car pulling out in front of us, and we shift to controlled processing which is not as rapid as automatic, problems may occur. 

In considering the limitations of attention imagine the following scenario: you find a parking spot that is tight and requires parallel parking.  One of the first things you will probably do is turn the radio down.  You turn the radio down so you can focus on getting the car in the parking space. 

We can only focus attention on one task at a time.  Trying to multi-task, such as studying and watching TV simultaneously, leads to a decrease in performance on each task.     

Understanding attention helps us to understand the different processes required for functioning in our everyday environments, and helps us in identifying neurological problems that need to be identified and treated.

Rock climber photo available from Shutterstock

Interviews with customers in supermarkets and restaurants have shown that people consider the sensory properties of food as an important value influencing their choice of food purchased (Furst, et al., 1996).  If food is not perceived as appealing in terms of appearance, smell, taste, and texture it probably will not be eaten (Hetherington & Rolls, 1996). 

Although food preferences are by no means the only influences on eating behavior, likes and dislikes are very important factors. This article will briefly discuss the influence food preferences have on eating behavior.  

Food Likes and Dislikes

The influence likes and dislikes have on eating behavior has been demonstrated in several aspects of eating, including meal duration, rate of eating, amount eaten, (Spitzer & Rodin, 1981) and frequency of eating (Woodward et al., 1996).

Discrepancies have also been reported between food preferences and food consumption (Eertmans et al., 2001).  As an example, Lucas and Bellisle found (1987) that individuals who, on the basis of their sensory evaluation (measured with spit and taste tests), preferred medium to high sucrose or aspartame levels in a dairy product actually chose lower levels for intake.  It appears that these incongruencies between food likes and consumption are influenced both directly and indirectly by factors other than just food preferences. 

Tuorila and Pangborn (1988) obtained questionnaire information about women’s intended and reported ingestion of four foods and one category of food: milk, cheese, ice cream, chocolate and high fat foods.  They found that liking of food was a stronger predictor of consumption than health beliefs about the food or consumption of the food.  Woodward and colleagues (1996) found that self-reported frequency of food intake could be better predicted by liking and parents’ consumption of the foods rather than perceptions of health benefits of the foods.  Wardle (1993) also found that taste was a more reliable predictor of food intake than health considerations. 

Steptoe and colleagues developed the Food Choice Questionnaire as a multidimensional measure of motives related to food choice (1995).  They found sensory appeal, health, convenience and price as the most important factors influencing eating behavior. Five other factors were rated as less important: mood, natural content, weight control, familiarity and ethical concern. 

The best predictor of vegetable and fruit intake in children is whether or not they like the taste or flavor of these foods (Resnicow et al., 1997).  Beauchamp and Mennella (2009) suggest that in order to get children to eat nutritious foods it is important that they develop enthusiasm for these foods, implicating the importance of food likes for short-term and long-term consumption.   
         
The evidence concerning the impact of food likes on eating behavior is not completely decisive, but the preponderance of evidence suggests that food likes play a major role in eating behavior (Eertmans et al., 2001; Beauchamp & Mennella, 2009; Rozin, 1990). 

It is important to note that food “liking,” or pleasure derived from food, is relatively unstable and is just one of many factors that influence eating behaviors (Donaldson, et al, 2009).  But this doesn’t negate the importance of liking and its contribution to eating behavior. 

References noted within this article are available upon request.

Dessert image available from Shutterstock.

These external factors include things such as portion size, labeling, variety of food we eat, and how much attention we pay when we’re eating (or whether we’re distracted by socializing, for instance). Even the plate size can affect how we eat.

Now, some researchers have suggested that external factors may play a larger role in eating behavior than internal factors, such as hunger, satiety, flavor, macro-nutrient content, and so on (Wansink et al., 2007; Levitsky, 2005; Wansink, et al., 2005). 

What’s behind these external or environmental factors and their role in how we eat?

External (Environmental) Factors

At one time it was widely assumed people ate when they were hungry and stopped when they were full, as behavior responded to physiological signals.  More recently, a variety of factors have been shown to influence food intake, e.g. cultural factors, influences of the food industry, environmental factors and more (Vartanian et al., 2008; Rozin, 1996; Wansink et al., 2009).  Of those factors, external factors appear to be some of the strongest influences on eating behavior.

There is a substantial amount of evidence showing that external factors can influence people’s eating behavior (Remick et al., 2009; Herman et al., 2005). Some of these external factors include portion size, socializing, variety, labeling and plate shape (Wansink, 2004).  These factors often work together or with other influences to shape food intake.  Primarily, external factors affect our eating behaviors by interfering with consumption norms or they disrupt our ability to monitor how much we have eaten (Wansink et al., 2009). 

For many people deciding how much to eat or drink is a burden, so instead of spending to much time thinking about it, they instead rely on consumption norms to help when choosing how much to eat.   Consumption may be influenced by how much one typically buys or consumes. 

Consumption may also be influenced by other cues or norms in the environment.  Package size, variety, utensil size, or presence of others my suggest a consumption norm that affects how much one drinks or eats.  These norms suggest an optimal quantity that we should eat or drink (Wansink et al., 2009; Wansink et al., 2004).

External factors have been shown to bias one’s estimate of how much they have eaten.  Even when people were given information that larger package sizes often cause individuals to underestimate consumption by 20%, many of these people in field and lab studies incorrectly said they were not affected by package size (Wansink, 1996). 

A major determinant of how much one eats in a distracting environment is whether or not the person is trying to monitor their intake.  Eating is a multidimensional process that is sometimes difficult to monitor.  This can lead to individuals focusing more on food choice than food volume. 

For example, people eating at an Italian restaurant correctly believed that if they ate butter with each slice of their bread they would consume fewer fat calories per slice than they would if they dipped their bread in olive oil. This became problematic as they compensated for this reduction in fat calories by eating 23% more bread over the course of the meal (Wansink & Linder, 2003).     

References available upon request.

Woman eating a hot dog image available from Shutterstock.

The intent of this article is to provide some basic tips on reading research reports.  I will assume you already have at a least a basic understanding of different methods and statistical procedures used in analyzing research data. (In order to maximize the benefits of reading a research report it is important to have at least a basic understanding of research methods and statistics.)

1. Consider where the report is published.

Most of the research you are reading should come from peer-reviewed scientific journals.  Of course, there is useful information that can be found in other locations, but I would try to focus most of my reading on reports published by peer-reviewed journals.

2. Why am I reading this report?

Ask yourself, what is my purpose for reading this report?  It is important to read articles that are relevant to your research interests.  There are many articles that may be published in your general area of interest.  However, you will find that many of these articles do not pertain to your specific interest.  Again, ask yourself why am I reading this?  Can I gain knowledge that will assist me in further in understanding my research interests?

3. Read abstracts.

The abstract provides a basic overview of the paper.  The abstract is usually less than 200 words and provides basic information from the paper’s major sections — introduction, methods, results and discussion.  At this point you will probably have a good idea of whether you want to read the entire paper.

4. Read headings and briefly overview other sections.

If you have read the abstract and it has piqued your interest, then go ahead and briefly look at the various headings.  The point is to perform a moderate overview of the entire paper.

5. Focus on relevant sections.

Once you have looked over the paper, you should have a general idea of the specific areas that are relevant. Sometimes the entire paper is relevant.  As you focus on the areas that interest you, take notes, highlight and notice references that may lead to further reading.  It is important that you highlight these references as you read the paper.  You probably won’t remember the references by the time you have completed reading. If you are not able to highlight write the references on a piece of paper.

6. Look at references.

Look at the highlighted references.  When looking at the references repeat the same process as mentioned above.

If this seems to you like an arduous task you are right.   A thorough examination of the research is painstaking.  However, in order to gain a comprehensive understanding, and knowledge in your area of interest, laborious activity is essential.

Reliability is a measure of the internal consistency and stability of a measuring device.

Validity gives us an indication of whether the measuring device measures what it claims to. 

Internal consistency is the degree in which the items or questions on the measure consistently assess the same construct.  Each question should be aimed at measuring the same thing.  Internal consistency is often measured using Cronbach’s Alpha — a super-correlation of all the items on the scale. If the score is .70 or higher the measurement is acceptable.  However, .80 or higher is preferable.  It is also important to consider the context when considering the score that reflects internal consistency.   

Stability is often measured by test / retest reliability. The same person takes the same test twice and the scores from each test are compared. A high correlation between the two test scores implies the test is reliable.  In most circumstances a correlation of at least .70 is considered acceptable.  However, this is a general guideline and not a statistical test. 

Interrater reliability is another reliability coefficient that is sometimes used in assessing reliability.  With interrater reliability different judges or raters (two or more) make observations, record their findings and then compare their observations. If the raters are reliable then the percentage of agreement should be high.    

When asking if a measure is valid we are asking if it measures what is supposed to.  Validity is a judgment based on collected data, not a statistical test.  There are two primary ways to determine validity: existing measures and known group differences.

The existing measures test determines if the new measure correlates with existing relevant valid measures.  The new measure should be similar to measures that have been recorded with already-established valid measuring devices.

Known group differences determine whether the new measure distinguishes between known group differences.  An illustration of known group differences is seen when different groups are given the same measure, and are expected to score differently.  As an example, if you were to give Democrats and Republicans a test assessing the strength of certain political views, you would expect them to score differently.  Their views are substantially different on many issues.  If these two groups scored differently, as expected, we could say that the measure indicates validity — measurement of what it claims to measure.

When designing new measuring devices it is imperative to consider their reliability and validity. A measure can be reliable and not valid.  But a valid measure is always a reliable measure.

Neurons, their connections and their signaling mechanisms are responsible for learning and memory. Following the brain hypothesis, which stated that the brain is responsible for all behavior, the neuron hypothesis stated that the basic unit of brain structure and function is the nerve cell (neuron and glia cells).  If we are to understand the brain and its complex activities it is important to understand neurons as a basic unit.

Neurons allow the body to communicate.  In understanding the biological bases of behavior it is important to gain an understanding of neurons and neurotransmitters.  The neuron is composed of three main parts: axon, cell body and dendrites.

What follows is a basic description of how neurons communicate (via electrical-chemical- electrical signaling).

The dendritic spine receives information from the synapse, where a neurotransmitter binds to a receptor on the dendrites’ membrane (axodendritic). The information then is sent to the neuron’s cell body where the information is processed.  The axon,which carries impulses away from the cell body, carries information via electrical impulse to its end (terminal) where neurotransmitters (chemical substances) are released into the synapse,a tiny junction between neurons. They traverse to the other side and bind to receptors on another neuron’s dendrite.  The electrical-chemical-electrical process is repeated.

Neurotransmitters are very important regarding neuronal communication. These chemical substances transmit messages from neuron to neuron at the synapse.  They also transmit messages from neurons. Scientists have identified over 100 different types of neurotransmitters.

Proper functioning of neurotransmitters and neurotransmitter receptors are imperative for neuronal communication.  The three different types of neurons — sensory, interneuron and motor — vary in structure and function.

In essence, you are your brain, and your brain is largely composed of neurons, so understanding these neurons is imperative to understanding yourself.  Consider the plasticity of the brain and how forming and strengthening synapses shape who you are.  As you engage in different activities, interact with people, see new places, recall, daydream, mentally rehearse, and engage in various other cognitive activities you strengthen existing synapses and also form new ones.  This plasticity allows you to consistently modify who you are.

Photo credit: HENNING DALHOFF / SCIENCE PHOTO LIBRARY

The neuron hypothesis was a major influence on modern neuropsychology.

The neuron hypothesis has three key aspects:

• Neurons are discrete, autonomous cells that interact but are not physically connected.
• They send electrical signals that have a chemical basis.
• They communicate with one another by using chemical signals.

Want to learn more about the amazing neuron? Read on…

Discovering the Neuron

Descartes described neurons as hollow filled tubes.  However, when Anton van Leeuwenhoek examined nerves with a microscope, this is not what he found (Kolb, Whishaw, 2009).

As microscopes became more powerful, the neuron and its various parts became more visible.  Eventually this led Theodor Schwann to suggest that cells are the basic structural units of the nervous system (neurons and glial cells).

An important development in the visualizing of cells was the introduction of staining, which permits us to differentiate among various parts of the nervous system.  Anatomist Camillo Golgi used a silver-staining technique to become the first to visualize an entire neuron and all of its processes.

Electrical Activity and Behavior

Italian physicist Luigi Galvani found that wires used to electrically stimulate a frog’s nerve causes muscle contractions.  Galvani got the idea that electrical stimulation may cause movement after observing that frog legs hanging from a metal wire twitched during an electrical storm.

Fritsch and Hitzig demonstrated that stimulating the cortex electrically produced movement.  The technique of stimulating the cortex consisted of placing a thin uninsulated wire onto or into the cortex and sending a small electrical current through the uninsulated tip of the wire.  Today researchers use transcranial magnetic stimulation (TMS) to induce electrical activity into the brain.  This technique allows researchers to study how the brain produces behavior and which parts of the brain participate in specific actions.

Neurons as Basis of Learning

British psychologist Charles Scott Sherrington investigated how nerves connect to muscles. He suggested there is no continuous connection.  He theorized that junctions connect neurons and that additional time is required to cross the junction.  He referred to these junctions or gaps as synapses.

Otto Loewi found that chemicals carry messages across the synapse. Loewi’s discovery prompted a further discovery, that a synapse releases chemicals to influence the adjacent cell.  Neuropsychologist Donald Hebb proposed a learning theory: individual cells, activated at the same time, form connecting synapses or strengthen existing ones and become a functional unit.   He proposed that the new or strengthening units are the basis of memory.

Acceptance of the idea that the brain is plastic and is constantly changing at each of its billions of synapses revolutionizes our view of the brain from one that represents “self” by a static structure to one that represents self by dynamic, ongoing reorganization. (Kolb & Whishaw, 2009, p.23)

In Part 2 of The Amazing Neuron, we’ll see specifically how neurons communicate with one another and explore more about their functioning.

References

Kolb, B., & Whishaw, IQ. (2009).  Fundamentals of Human Neuropsychology 6th Ed .   New York, NY: Worth.

Photo credit: HENNING DALHOFF / SCIENCE PHOTO LIBRARY

There are three main types of descriptive methods: observational methods, case-study methods and survey methods. This article will briefly describe each of these methods, their advantages, and their drawbacks. This may help you better understand research findings, whether reported in the mainstream media, or when reading a research study on your own.

Observational Method

With the observational method (sometimes referred to as field observation) animal and human behavior is closely observed.  There are two main categories of the observational method — naturalistic observation and laboratory observation. 

The biggest advantage of the naturalistic method of research is that researchers view participants in their natural environments.  This leads to greater ecological validity than laboratory observation, proponents say.  

Ecological validity refers to the extent to which research can be used in real-life situations. 

Proponents of laboratory observation often suggest that due to more control in the laboratory, the results found when using laboratory observation are more meaningful than those obtained with naturalistic observation. 

Laboratory observations are usually less time-consuming and cheaper than naturalistic observations.   Of course, both naturalistic and laboratory observation are important in regard to the advancement of scientific knowledge.

Case Study Method

Case study research involves an in-depth study of an individual or group of indviduals.  Case studies often lead to testable hypotheses and allow us to study rare phenomena.  Case studies should not be used to determine cause and effect, and they have limited use for making accurate predictions.   

There are two serious problems with case studies — expectancy effects and atypical individuals. Expectancy effects include the experimenter’s underlying biases that might affect the actions taken while conducting research.  These biases can lead to misrepresenting participants’ descriptions.  Describing atypical individuals may lead to poor generalizations and detract from external validity. 

Survey Method

In survey method research, participants answer questions administered through interviews or questionnaires.  After participants answer the questions, researchers describe the responses given. In order for the survey to be both reliable and valid it is important that the questions are constructed properly.  Questions should be written so they are clear and easy to comprehend.

Another consideration when designing questions is whether to include open-ended, closed-ended, partially open-ended, or rating-scale questions (for a detailed discussion refer to Jackson, 2009).  Advantages and disadvantages can be found with each type:

Open-ended questions allow for a greater variety of responses from participants but are difficult to analyze statistically because the data must be coded or reduced in some manner.  Closed-ended questions are easy to analyze statistically, but they seriously limit the responses that participants can give.  Many researchers prefer to use a Likert-type scale because it’s very easy to analyze statistically. (Jackson, 2009, p. 89)

In addition to the methods listed above some individuals also include qualitative (as a distinct method) and archival methods when discussing descriptive research methods.   

It is important to emphasize that descriptive research methods can only describe a set of observations or the data collected. It cannot draw conclusions from that data about which way the relationship goes — Does A cause B, or does B cause A?

Unfortunately, in many studies published today, researchers forget this fundamental limitation of their research and suggest their data can actually demonstrate or “suggest” causal relationships. Nothing could be further from the truth.

References

Jackson, S.L. (2009).  Research Methods and Statistics:  A Critical Thinking Approach 3rd edition.  Belmont, CA: Wadsworth.

In a series of experiments, Stanovich and West (2008) examined the association between cognitive ability and two key critical thinking skills: avoidance of myside bias and avoidance of oneside bias (when people prefer one-sided arguments over arguments presenting multiple perspectives). 

In Experiment 1, natural myside bias was investigated in 15 different propositions. In Experiment 2, myside bias and one-sided bias was studied. In Experiment 3, associations between thinking dispositions, in addition to cognitive ability, and one-sided and myside bias were investigated.

In Experiment 1, the researchers concluded, there was “no evidence at all that myside bias effects are smaller for students of higher cognitive ability” (p. 140).

The main purpose of Experiment 2 was to investigate the association of cognitive abilities with myside and oneside bias. “The results… were quite clear-cut. SAT total scores displayed a nonsignificant 7.03 correlation with the degree of myside bias and a correlation of .09 with the degree of one-side bias (onebias1), which just missed significance on a two-tailed test but in any case was in the unexpected direction” (p. 147). It was also revealed that stronger beliefs usually imply heavier myside bias.

In Experiment 3 “the degree of myside bias was uncorrelated with SAT scores,” and “[t]he degree of one-side bias was uncorrelated with SAT scores” (p. 156). Myside bias was weakly correlated with thinking dispositions. Oneside bias showed no correlation with thinking dispositions.

The final two sentences or the research report read: “Our results thus indicate that intelligence — as defined by traditional psychometric indicators — turns out to be surprisingly independent of critical thinking tendencies. Cognitive ability measures such as the SAT thus miss entirely an important quality of good thinking” (p. 161). The good news is critical thinking abilities are malleable, and in fact, probably more malleable than intelligence.  Avoidance of oneside bias is another rational thinking skill that is not related to intelligence.   

Hybrid Reasoning Deficiency

Myside bias is derived from a focal bias and from contaminated mindware that causes us to look at the world from an egocentric perspective.  A focal bias occurs when one focuses on a mental model or frame already formed.  The mental model is taken as focal and all subsequent reasoning is based on this model rather than alternatives; the latter would be too computationally expensive (require too much thinking).  Egocentrism involves taking our thoughts and projecting them onto the minds of others. 

Summary

Several studies have shown no correlation between intelligence and myside bias.  Overconfidence effects leading to focal bias has been modestly associated with intelligence in a few studies.  In these studies, participants with higher intelligence have shown slightly lower overconfidence.  ”Again, though, these are statistically significant but modest associations — ones that leave plenty of room for the dissociation that defines dysrationalia in this domain (highly unwarranted overconfidence in an individual of high intelligence)” (Stanovich, 2009, p. 114). 

The strongest evidence for the lack of association between intelligence and myside bias comes from studies investigating what Stanovich and West term natural myside bias.  Natural myside bias is displayed when information is evaluated in a biased manner when no instructions or cues are given to avoid doing so (typical performance conditions). 

However, it is highly likely that individuals with higher intelligence would show less myside bias if they were told they should take multiple perspectives or to avoid biased thinking (optimal performance conditions). 

If you tell an intelligent person what the rational requirement is they are likely to do better than a less intelligent person.  But, concerning myside bias and many other rational thinking skills, when given no explicit cues (as in everyday judgment and decision making) of the rational requirement intelligent people do no better than less intelligent people.  It is also important to distinguish between within-group designs and between-group designs.  The former may provide cues to what the rational requirement is, whereas the latter does not contains cues.      

You can also read Part 1 of this essay about myside bias.

References

Stanovich, K., West, R. (2008).  On the failure of cognitive ability to predict myside and oneside thinking biases.  Thinking & Reasoning, 14 (2), 129 – 167

Stanovich, K. E. (2009). What intelligence tests miss: The psychology of rational thought. New Haven, CT: Yale University Press.