Gender of Seuxuality Theories

· Essentialists: desire is biological and evolutionary; cause of desire is genetically programmed reproductive functions specific to males and females; consequences are male independence in reproduction and female-centered child-rearing practice and passivity are the cause, rather than the result, of gendered social institutions

· Social Constructionists: desire is sociological and contextual; causes of desire are social institutions and social interaction signal and sanction “male” and “female”- gendered norms of behavior; consequences are support for or opposition to sex/gender-segregated reproductive and social practices depends on social definitions of men, women, and sexuality

· Integrative: desire is contextual and physical; causes of desire are bodies, environments, relationships, families, governments shaping sexuality; consequences are policies address some biological differences such as pregnancy and work, and emphasize the impact of social forces, interaction, and societal programs

Gender of Sexuality Vocabulary

· Whatever…I’m too tired to care.

Theories of Choice, Network, and Scripts and Norms

· Scripting theories assume that patterns of sexual conduct in a culture are sexually derived, that human beings possess no biological instincts about how to act sexually, that through a process of acculturation lasting from birth to death, individuals acquire patterns of sexual conduct that are appropriate to their culture, and people may not enact the scripts provided by their culture exactly but instead my make minor adaptations to suit their own needs

· Choice Theory: concerned with how people utilize the resources available to them in the pursuit of one or more specific goals: choosing partners on the long-term based on overall value, skill, scarcity, likeness in aspirations à private choices

· Network Theories: based on dyads; assumes that partnerships may conform to certain regularities that have been observed regarding social relationships more generally, that the features of the relationship itself will be important in determining what activities will occur, and that sexual dyads do no exist in a vacuum but are instead embedded within larger networks of social relationships

Study the sexual response cycle!

Evil Preacher’s fraud exposed?

Section 5 #8, 9, 10

8. In a study with five endocrinologists, five residents, and five medical students, a number of variables were measured centering around a clinical interview conducted with a patient. In the interview, accuracy differed: 4/5 experts were correct, 1/5 residents were correct, and 0/5 medical students were correct in there diagnoses. This is due to the greater knowledge base of experts and more information stored in LTM with which to confirm or disconfirm the patient’s symptoms as being part of the diagnosed disease. The length of the interview also differed; it was longest for the med students, shorter for the residents, and shortest for the experts. The experts are definitely using optimal stopping – they are taking in just enough information to make the correct decision and not wasting time to take in irrelevant or erroneous information. The number of findings (or the inferences made by the doctors) was highest for the residents, next highest for the medical students, and lowest for the experts. This is because…??? The observations, or number of accounts of verbatim info that was provided by the patient, fared the same way. The ratio of observations to findings was only 2:1 for the expert though, while it was nearly 3:1 for the novice. This could be due to the greater organization of knowledge on part of the expert as well as the ability to recognize patterns better and apply them to the current situation.

9. One way that experts differ from novices is in how they represent problems. Experts tend to represent them in terms of derived features. They will take a lot of time to set up the problem, while novices will just try to begin solving right away. This can be seen in weather forecasters; expert forecasters will see different types of fronts in cloud patters, while novices will simply start working with those given cloud patterns. Another way they differ is the extent to which they rely on LTM: experts rely more on it, and novices rely less. In ATC, the experts are better able to space and sequence; they’ve seen patterns of airplanes before and are able to recognize the pattern of planes as one they’ve seen before. Novices aren’t able to do this as well, and their working memory is compromised. Finally, experts rely on less information to make more accurate decisions. This is because they can field out information that is either erroneous or redundant. Novices tend to take in all of the data; they don’t know what is relevant. In commanding officers, instructors, and students involved with submarines, evidence shows that the commanding officers knew more than the instructors who knew more than the students.

10.

1.
–sensory memory: large capacity, short duration, literal code

-short term memory/working memory: small capacity (7 +/- 2), duration small(?), code is auditory

-long term memory: capacity is huge, duration is very large (possibly permanent), code is episodic for events and semantic for meaning

-STM is the bottleneck in the system because of its very limited capacity

2.
-see “answer to number 2” in notes

3.

-tower controller: gives the directions for taxiways and runways

-TRACON controller: aka radar controller (?), gives directions for upside-down wedding cake airspace above the airport

-enroute controller: aka center controller, monitors and controls aircraft at high altitudes traveling at high speeds between departure and arrival along the airways between TRACONS

- when control is formally passed between controllers, this is a hand-off Joel is hurting

Interestingly, our third hypothesis was unsupported. We proposed that the framing effect would be more pronounced in high-payoff scenarios as opposed to low-payoff scenarios. This would have been shown with an interaction between frame-type (gain versus loss) and payoff type (high versus low) with a larger discrepancy of sure-option responding between gain-framed and loss-framed scenarios in high-payoff scenarios. This was not the case; while there was an interaction, the discrepancy was actually much larger in the low-payoff scenarios and not at all significant in the high-payoff scenarios. This evidence is contradictory to previous findings. Kuhberger, et al. (2002) http://seattletimes.nwsource.com/html/sports/ that the framing effect was larger in scenarios offering a high payoff and proposed that the results were simply due to the magnitude of the consequences involved. (For example, both choices for a low-payoff situation seem equally small while in a high-payoff situation, the choices seem to offer distinctly different outcomes). Our data, however, did not follow this trend. Perhaps the subjects focused more intently on the high-payoff scenarios and were thus less susceptible to their frames than the general content of the scenarios themselves. Low-payoff scenarios, on the other hand, inherently offered consequences of less importance. Subjects may therefore have been less concerned with the actual content of the scenario and more susceptible to its frame.

Results

Before any statistics could be calculated, the data had to be converted into an analyzable format. For each subject’s answer sheet, each sure-option response was coded as “1” and each risky-option response was coded as “0.” These numbers were then entered into a spreadsheet using Microsoft Excel. Each subject responded legibly to all questions, so no correction for error was needed and all data was used. Because “1” represented a sure-option response, averages of the scores represented the percentage of total sure-option responding.

For each hypothesis, a statistical analysis was calculated using a two-tailed, paired t-test. To test the existence of the framing effect, the percentages of total sure-option responding were compared between gain-framed questions and loss-framed questions. Fig. 1 shows that the percentage of sure-option responses for the gain-framed questions (M = .5795) was significantly higher than sure-option responses for the loss-framed questions (M = .4545; t = 2.62, p < .0121). To see if subjects were generally less risky in scenarios involving a larger payoff, differences in sure-option responses were examined between high-payoff and low-payoff scenarios. Fig. 2 shows significant differences between high-payoff (M = .6193) and low-payoff responses (M=.4148; t = 4.02, p < .0002). To examine any differences in the prominence of the framing effect in high-payoff and low-payoff questions (and particularly if this prominence is greater in the high-payoff questions), an interaction was calculated. The difference of sure-option responding (between gain-framed questions and loss-framed questions) was, surprisingly, significantly larger in low payoff scenarios (mean difference = .2386, t = 4.33, p < .0001). and not significant in the high payoff scenarios (mean difference = .0114, t = .15 , p < .881). This did, however, lead to an interaction as shown in Fig. 3 (t = 2.49, p < .017).

The Framing Effect: Conclusion

We devised our experiment to observe the framing effect under specific conditions. The design of the experiment included scenarios that were either framed as a gain or a loss and also with either a large or a small payoff, and subjects were asked to respond to the scenarios with either a sure-option choice or a risky-option choice. The results show a significantly higher percentage of sure-option responses to gain-framed questions than to loss-framed questions. Subjects also chose significantly more sure-option responses when the scenario had a high payoff as opposed to a low payoff, and an interaction was shown with the difference of sure-option responding between gain-framed and loss-framed scenarios differing across the payoff type.

The construct of humor in humans is a topic of great intrigue. It is generally accepted that humor has a stress-moderating effect in humans (Abel and Maxwell, 2002), but to what degree and in what instances does this effect apply? For example, Yovetich, Alexander, and Hudak (1990) studied the effect of humor on threat-induced anxiety and stress. Results show that humor led to a general decrease self-reported anxiety as well as increased smiling activity, but that heart rate was not lowered. Does humor only affect cognition, and if so, what other types of stress (besides threat-induced anxiety) will cause the same effect? Also, it has been shown that one’s own perception of humor can be easily influenced and manipulated (Olson 1992). Is humor, again, only a function of malleable human cognitive processes?

The Mary Starks Whitehouse article was an interesting read. She seems to be intent on the fact that as we grow older, we really lose both awareness of our bodies and the capability to, in everyday living, move freely within them. I was particularly struck by the comparison of adult movement to children’s; I wholeheartedly agree with the fact that we will often say one thing while our body language emits something entirely different. (I actually found myself conversing with a friend last night who said something offensive; he asked what was wrong, I quietly said “nothing,” and my body language led him to not necessarily believe me.) I never really explicitly thought about how rare this is in children, but it makes perfect sense. I don’t think the change that occurs as children grow up is a bad thing, but rather one of the first marks of maturity. Imagine if our body language always coincided with how we are feeling or what we are thinking; this may prove troublesome.