Why Parents Don't Do What They Say - A Neuropsychology Explanation

Feeling frustrated because your parents seem to be contradicting what they say? Are you tired of having to guess why they are the way they are - so different from you? What in the world are they thinking?!

Bill von Hippel of University of Queensland, Australia has the answer to your questions. Try and mould your scientific minds around these research-based findings and see if you can try to comprehend more about "the old people" who ask you if you've done it with your boyfriend in front of their friends and complain about the Indian cab driver who doesn't smell so good while you're still in the cab.

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Brain atrophy in elderly leads to unintended racism, depression, and problem gambling - Bill von Hippel.

As we age, our brains slowly shrink in volume and weight. This includes significant atrophy within the frontal lobes, the seat of executive functioning. Executive functions include planning, controlling, and inhibiting thought and behavior. In the aging population, an inability to inhibit unwanted thoughts and behavior causes several social behaviors and cognitions to go awry.

In a study appearing in the October issue of Current Directions in Psychological Science, University of Queensland psychologist, Bill von Hippel, reports that decreased inhibitory ability in late adulthood can lead to unintended prejudice, social inappropriateness, depression, and gambling problems.

Regarding prejudice, von Hippel and colleagues found that older white adults showed greater stereotyping toward African Americans than younger white adults did, despite being more motivated to control their prejudices. Von Hippel suggests that “because prejudice toward African Americans conflicts with prevailing egalitarian beliefs, older adults attempt to inhibit their racist feelings, but fail.”

Age-related inhibitory losses have also been implicated in social appropriateness. Von Hippel found that older adults were more likely than younger adults were to inquire about private issues (e.g. weight gain, family problems) in public settings. Furthermore, these age differences emerged even though older and younger adults both agreed that it is inappropriate to inquire about such issues in public settings. The older adults seemed to know the social rules but failed to follow them, which is consistent with diminished frontal lobe functioning.

In late-onset depressed older adults, poor inhibition predicted increased rumination, which in turn predicted increased depression. This finding suggests that people who struggle to control their rumination begin to lose that battle as they age, with the end result being the emergence of depression late in life.

Von Hippel also found that a penchant for gambling can be toxic for older adults, as those with poor executive functioning are particularly likely to have gambling problems. Interestingly, these problems are exacerbated in the afternoon, when older adults are less mentally alert. Older adults were more likely to get into an unnecessary argument and were also more likely to gamble all their money away later rather than earlier in the day. These findings suggest a possible avenue for intervention, by scheduling their important social activities or gambling excursions earlier in the day.

While social changes commonly occur with age, they are widely assumed a function of changes in preferences and values as people get older. Von Hippel argues that there may be more to the story and that some of the changes may be unintended and brought about by losses in executive control.

Implicit Association Task: How and When It Works

My research revolved around a task called the go/no-go association task (GNAT). It is one of many implicit social cognition tasks which in layman terms, measure unconscious thoughts or preferences towards almost anything (objects, people, groups, categories). The theory behind it is from an early learning experience paradigm - the typical past behaviour/events influence future thoughts, behaviours, attitudes, stereotypes. The trick is that you don't even realize that the past is so important.

It's a speed-accuracy thing where you gotta be quick, and how much you score determines your tendency to associate one thing with another (e.g. the though that fruit is good, bugs are bad, Asians are stingy or Australians are laidback). Why is this important? Well, given that you can easily change the numbers/ranking on questionnaires (i.e., cheat) because there's a fear of being viewed as unfavourable (e.g. ranking self as more 'honest' than you really are), these implicit tasks supposedly eliminates those biases/social pressures.

No time to think. Just respond!

Sounds neat right? Like a fool-proof plan. Now we can REALLY know about people's thoughts. However, recently there has been debate on the validity of these tasks. Are they really better than explicit measures (questionnaires etc)? Why then do some implicit results similar to explicit ones? Shouldn't the implicit tell us something more? Jesse Erwin (2007) addresses the validity of the Implicit Association Task (IAT, the most famous of the family of implicit tasks) in an article as posted below:
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The IAT: How and When It Works
By Jesse Erwin

“Left…right…left…right” could be heard echoing from the Hilton Washington’s Military Room during the APS 19th Annual Convention. And although the chorus may have sounded like boot camp exercises to curious passers-by, it was merely APS Fellow Anthony Greenwald, professor of psychology at the University of Washington and recipient of the Society of Experimental Social Psychology’s 2006 Distinguished Scientist Award, administering the Implicit Association Test (IAT) en masse during his Psi Chi Distinguished Speakers address. Throughout his talk, the University of Washington Professor educated his audience about the history and validity of the IAT and, of course, provided the opportunity to experience the IAT firsthand.

“So what is the IAT?” Greenwald asks his audience. “It’s a measure of associative knowledge. And I don’t describe it as a measure of prejudice or bias, although it can be used to measure implicit prejudice or bias.”

The IAT has risen to prominence since Greenwald first published his findings in 1998; the Brief IAT of race has been taken over a million times. On the surface, the IAT asks participants to categorize words or images on a computer screen with a touch of the keyboard. These categorizations begin to require some cognitive gymnastics, however, as categories become combined. The time it takes to sort out stimuli from the combined categories provides some insight into participants’ mental associations.

“How does it work? Well, fairly simply. If two concepts are associated, it is easy to give the same response to exemplars of both,” says Greenwald. That’s a deceptively uncomplicated explanation behind one of contemporary psychology’s most influential research paradigms. “There’s little more theory underlying the IAT than the idea of association between concepts.”
But the provocative implications of the IAT have sparked controversy in both research circles and the mainstream media. So in his address, “Assessing the Validity of Implicit Association Test Measures,” Greenwald came to the IAT’s defense and discussed its psychometric worthiness.

When discussing internal validity, for example, Greenwald says “empirical research demonstrated that there are several things that might get in the way that in fact did not.” Things like participants’ familiarity with the items or lack thereof, which side of the screen categories are presented on, or whether the person is right or left handed have all been mentioned as possible confounds, but haven’t been borne out in research.

Greenwald went on to illustrate the convergent validity of the IAT with self-report using an example from the 2004 presidential election. Implicit attitudes toward each candidate correlated .73 with self-report measures. “That’s quite high,” Greenwald says. “And that’s evidence of convergence.”

Conversely, research on IAT measures of age attitudes have demonstrated evidence of discriminant validity with self report. Greenwald offers this explanation for the dichotomous results: “I think you get convergent validity when both implicit and explicit attitudes are shaped by the same influences, which means they are formed relatively late in life, such as political preferences.” For those attitudes that are formed earlier in life — in particular racial/ethnic, young/old, and male/female stereotypes — IAT results are likely to diverge from the explicit self report measures.

In a time where social desirability confounds are of pervasive concern in psychological research, one of the IAT’s greatest merits appears to be resistance to faking. Studies have demonstrated that participants rarely devise a successful faking strategy. It appears that taking one’s time is the easiest way to doctor results. “It does work,” Greenwald says of the strategy, “but it also tends to be detectable statistically.”

But as with any test, the IAT has its psychometric vulnerabilities. Greenwald describes the elasticity of the IAT, where experiences with exemplars of test categories shortly before the test can alter results. So, according to Greenwald, having a friendly interaction with a black experimenter just before the test will likely dampen evidence of bias.

Greenwald is frank in his assertion that the test-retest reliability of the IAT leaves something to be desired. “The test-retest reliability is okay for research, but not very good for an individual difference measure that you want to be diagnostic of a single person.” In order to boost its reliability, Greenwald and his colleagues resort to the “standard trick” of administering the test several times. At this point the reliability is “getting good, but still not good enough.”
The Brief IAT, and its less time-consuming administration, offers some promise in this arena. “I think we have to look at the test-retest reliability of the Brief IAT in more studies than we have so far. But the average of the first set of a relatively small set of studies that was done was around .50.”

“The last topic is the most interesting one,” Greenwald asserts. “Does the IAT predict anything interesting?” Pointing to a meta-analysis being conducted by Yale University graduate students Andy Poehlman and Eric Uhlmann, Greenwald says that the IAT performs better than self report at predicting behavior. “This IAT has incremental predictive validity relative to self-report. [The results are] statistically significant, and very clearly so in the meta-analysis.”

It’s a good start for a test that has garnered so much attention and will likely be used increasingly in the near future. But there are few certainties when using psychological tools such as the IAT and more research is needed on the topic. The jarring possibilities of how unconscious attitudes, as measured by the IAT, could affect behavior will ensure that the test garners the examination it deserves.

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Erwin, J. (2007). IAT: How and when it works. Observer, 20, 12.

Choosing the Right Stats to Use

Step 1

What is the research question? Is there Independent Variables (IV) and Dependent Variables (DV)?

E.g. Statistics anxiety (DV) is a result of a weak maths self-concept (IV), little previous experience of maths (IV), gender (IV), and age (IV).

There are two relationships between variables. Dependence and Interdependence

Dependence relationships are as illustrated above. It is typical when experiments are designed to test predictions.

Interdependent relationships on the other hand contain no IVs or DVs. Variables correlate with one another, forming an inter-reliable relationship.

Examples:

1) Correlation (r). Coffee intake vs. Being an undergraduate

2) Factor analysis.

Step 2

What are the concepts and what variables are associated with them?

E.g. Statistical anxiety is measured by the statistical anxiety rating scale 1 (low) - 100 (high)

- Math self-concept scale, 1 (low) - 70 (high)

- No. of maths or statistics courses completed (O<)

- Age (in years) and Gender

Step 3

What type of variables are they?

Quantitative vs. Qualitative

- Statistical anxiety (Quant.)

- Maths self-concept (Quant.)

- No. of courses (Quant.)

- Gender (Qual.)

Step 4

What is the sample size (n)?

Sample size effects statistical power. Statistical power is the ability of a test to find an effect when it exists. However, the danger of having too much power, is finding an effect when it doesn't exist.

When there's too much..

Type I Error : Detecting a r'ship when one does not exist (risk rises with larger n)

When there's not enough..

Type II Error : Not detecting significant r'ship when it exists (risk rises with smaller n)

Power can range from 0 (no power) to 1 (full power). The recommended power value is .80 (Cohen, 1988)

Power is affected by:

1) Sample size - Power increases with n

2) Effect size - The strength of the effect ( .20 = small, .50 = medium, .80 = large)

3) Alpha - Power increases with alpha (e.g. p < .05 has more power than p < .001)

Step 5

Choose your analysis

1) Multiple regression (dependence r'ships)

2) Between groups ANOVAs (dependence r'ships)

3) Repeated measures ANOVAs (dependence r'ships)

4) Analysis of Covariance (dependence r'ships)

5) MANOVA (dependence r'ships)

6) Exploratory Factor Analysis (interdependence r'ships)

7) Structural Equation Modelling (dependence r'ships)

8) Path Analysis (interdependence r'ships)

All content shown here are as produced by Christine Critchley of Swinburne University of Tech, 2006.

Turn around if this makes no sense..

Hi all who care to read.. I've decided to start a seperate blog to document my psych journey, hopefully fill it with insightful information from time to time. Mostly I will be using it to keep track of the important things I have learnt and all that I feel is interesting and good to know. Psychology after all, is a study of humans. With that being said, who should it benefit other than humans?

You're human, aren't you?