I am still running subjects non-stop for my dissertation. I will be done soon. At this point I have written two data analyses written. I either expect to have two experiments (planned) or given recent findings I may have just a single experiment (unplanned). I will know next month.
Still in the process of running subjects for my dissertation. My mind is sharp, but tired. I am likely to write philosophical here for a while if I ever get a chance to update as it is merely something different. I have been thinking lately on the relationship between boredom and language, boredom and modernity, boredom and dissociation, and the meaninglessness of modern existence.
“It is not we that speak language but language that speaks us.” – Heidegger
Right now my mind is focused solely on the parasympathetic nervous system and autistic spectrum disorders after a conversation with my mother. I have other posts I will try to make shortly.
I am typically absent for periods of time but I have been more absent lately because I am in the process of working on my dissertation, which has consumed my life. My dissertation is looking at the physiological correlates of boredom in two studies. I was initially just attempting to characterize the autonomic response related to the experience of boredom. This is what I defend against. However, and increasingly so, I have become absolutely fascinated with some of the tertiary projects around my dissertation. In trying to characterize the I created a nosological frame that assess the standard things associated with boredom. Of special interest at the moment are attentional networks and dissociation. More will follow.
So I have not updated in a while. I have also not written in a while so I am assuredly rusty. Aside from being busy, conferencing at SPR, and generally being pensive about the prospects of the future, I find myself musing on the relationship between altruism and negative emotions.
I have long argued against the possibility of a truly altruistic act. My arguments draw on evolutionary psychology where the frequency of altruistic acts increase with genetic relatedness. This serves almost as the group attempting to work together for the benefit of their shared genetic material. They also deal with the motivation for altruistic acts. I generally pose the question of whether you act altruistically towards another, especially unrelated, individual because you are happy to help them or you would feel guilty not to help them. In either case you gain some direct benefit from helping another individual affectively, whether it is an increase in a rewarding sensation or a decrease in an aversive sensation respectively. As such, whether you give away one’s own resources or risk one’s safety for another individual you gain ground affectively.
Likewise, I found myself arguing about the relative importance of negative and positive emotions last night. I strongly feel that the negative emotions are more evolutionarily important. They protect us from aversive stimuli that pose an immediate or proximal threat to one’s safety. To relate it back to the motivation of altruism, we act altruistically in order to prevent negative affect. They motivate a change in the response to an environmental situation to increase the fitness of the organism. However, positive emotions have never interested me. They reward behavior in order to make an individual perseverate in a behavior. However, organisms naturally maintain a course of action until they achieve a goal unless they an environmental stimulus acts upon them making them react. This, again, is negative affect.
So the question was raised last night about affect and motivation. The example was put forth of a thirsty mouse. Thirst, while not an emotion, is a drive for the animal to act to seek water, which amounts to a change in course for the organism. When the mouse then consumes water, reward centers in the brain are activated. As such, is the reward a reward from the escape from thirst or is the reward in itself intrinsically motivating. This is analogous to negative and positive affect respectively. Do the positive affective states represent rewards for the release from negative affect or are the positive emotional states in themselves intrinsically motivating? This brings me round to altruism. I don’t believe that altruistic acts are possible because the importance of the act is to prevent negative affect. So, the question becomes do affective states follow the same pattern as altruism where the priority is the release from or prevention of negative affective states with positive affect being the reward from release? I don’t have the answer, but some day I may.
So I have spent most of my time at school working on data sets where there was a small sample size (<25) or where there was an adequate sample size for most things (>200 but <500). The first is a rather specific skill set to do quantitative analysis. The second is just the general purpose skill set of most people in psychology. One of my friends, Tyler, is a sociologist that specializes in social media. We talk about quantitative methods often. I cannot remember if it was to prove a point, out of mutual curiosity, or if he merely wanted to see what I would do with it, but he generated to samples for me. One sample was a random sample of twitter users who posted over a two week period. The other sample was of individuals who posted the word bored during the same period of time. Before cleaning up the dataset, there were about 1.6 million unique tweets. A random sample of the bored sample was used to determine if the people who posted the word bored were complaining of boredom on twitter. This was the case.
At this point, I wanted to know if I could differentiate between the two groups based on their user statistics, which served as the independent variables. This was comprised of the number of followers, the number following, and the number of tweets. I could have used logistic regression or I could I have used discriminant function analysis. I opted for discriminant function analysis because I use it less and it specifically is designed for continuous independent variables. I was also less familiar with this so it was good practice. I was able to discriminate between individuals who posted tweets that contained the word bored and the random sample. The function that differentiated these two groups was statistically significant, with p<.0001. The power of this function was also perfect, with power=1. Power and significance deal with the likelihood of accepting or rejecting the null hypothesis, that there are no differences. Statistical power is defined as the likelihood of rejecting the null hypothesis when there are no differences. Statistical significance is defined as accepting the null hypothesis when there are no differences.
I did some further analyses, such as jackknife classification to determine the percentage of correctly classified users by the function. I found that roughly half of the participants were classified correctly. This helped to explain that the function that discriminated the two groups only explained 4% of the variance, meaning that despite the massive sample size there was a very low effect size. Effect size deals with the strength of the relationship between the variables in the finding and is a rough measure of the likelihood of being about to replicate the finding in a different sample. The astonishingly low effect size led me to give up on the project after I played with it a little more. Given some of the directions in the relationship between the independent variables in the function that discriminated the two groups, I got to play with structural equation modeling to test mediated moderation. Good times, but the same issue with significance, power, and effect size remained.
This brings me to my point and why I do not remember why the data was initially handed to me. I have been highly critical about how individuals analyze social media data. Psychology, rightly, started to expect power and effect sizes to be reported because these things bring so much to light. Significant results may have high power and low effect sizes. This is possible because of the large sample size. This means that the findings may be right, but must be limited in how much father is put in them. Social media, which draws on cutting edge technology is typically analyzed through means that are years behind. The only way to analyze large sample sizes are through these added points of reference. The only thing that was important in this sample was effect size. Given the relationship between significance and power, this makes sense. Analyses of social media need to take effect size into account because this is where the strength of your findings can be determined. As an added aside, when analyzing social media, you must also factor into any model how long an individual user has been a member of the site. An individual who has been a member for years, but posts once a week is very different in behavior from someone who posts 30 times a day but has only been a member for a few months. This, I am sure, will help to explain a lot of the random variance observed in any statistically significant effect.
Given that I have been doing a fair bit of physiological and repeated measure analyses lately, I have spent much time learning and experimenting with different ways to statistically model repeated measure designs. As an added note, these studies typically have a small sample size and comparisons are made between experimental blocks during a single testing session. Commonly, repeated measure studies use repeated measure ANOVAs or general linear modeling. This means that the assumptions of sphericity and compound symmetry are rarely met, requiring the usage of alternative methods. On a more practical level, there is another issue with the use of such methods in repeated measure phyiso studies, or in many repeated measure studies. This issue comes with individual differences. Individual differences can create crossovers where the change in response for some individuals is opposite of other individuals.
In a typical setup, a baseline reading is compared to 1 or more experimental blocks. Individuals may not respond the same way to each of the experimental stimuli. My typical task for boredom studies is a vowel counting task. Some individuals, such as myself, find it incredibly boring, while other individuals may find this activity calming. Additionally, personality factors can lead to differences in the baseline baseline readings and influence the way in which individuals respond to a stimulus. These necessitate different approaches to analyzing the data.
The first alternative type of modeling is time- or cross-lagged correlations. These measure two variables at two different times and then compare the correlation between all of the variables to determine what influences change between a variable over time.
Eron, Huesman, Lefkowitz & Walder (1972).
For one study that I am working on, I am administering two measures relating to boredom. One measure is a personality measure that assesses boredom prone, while the other measure the frequency of the experience of boredom. I realize it is not physio but it is much easier to explain from this example. Traditionally, there is a strong, positive correlation between the measure of the frequency of boredom and the measure of boredom proneness. However, I hypothesize that the correlation between two administrations of the measure of the frequency of the experience of boredom is not as strong as the relationship between either of these administrations of the measure of the frequency of boredom and the administrations of the boredom proneness scale. This would suggest that the measure that assesses the frequency of boredom is more state dependent than the trait measure that assesses boredom proneness. This method overcomes some of the limitations by better modeling the relationship between the change in one measure over time given the relationship that this measure has to another measure.
Along the same lines, structural equation modeling holds such promises in modeling these complex relationships. I just have not had a sample size large enough to actually use it on this type of data before.
Another method to overcome some of the limitations previously discussed is to instead use mixed linear modeling. Mixed linear modeling overcomes the problems of crossovers and differential responses by accounting for both fixed and random effects to produce predicted values for each participant. These predicted values are then used in place of observed values in statistical comparisons. Mixed linear modeling has the added advantage of being able to manipulate the covariance structure of the model to better account for the relationship between measurement blocks. The changing of the covariance structure from the compound symmetry structure used in general linear modeling and ANOVAs can be theoretically motivated or empirically driven where the structure that provides the cleanest fit for the data is selected. Mixed linear modeling minimizes the influence of individual differences on the comparisons made between multiple measurement blocks. Mixed linear modeling also functions well with small sample sizes. As a drawback, there are no well established methods for computing power and effect sizes. Additionally, because the impact of individual differences, the random factor, is modeled out we no can no longer easily try to explain how or what individual differences led to such different responses.
I have been doing quite a bit of trying to model personality traits into mixed linear models of physiological functioning across multiple experimental blocks. While I am not certain it is the best approach statistically, I have been using a two step approach. The first is to construct a preliminary set of mixed linear models. Then, I use regression analysis or correlations to model the relationship between the change in autonomic function and a personality measure. This gives me a set of related personality traits that influence physiological function. If these are all strongly correlated in the same direction with each other and the change in autonomic tone, I use regression analysis to compute partial least squared correlations to find the strongest predictor of change in autonomic tone. I then split my sample into groups around the personality variable of interest and rerun the mixed linear model with this additional grouping variable to characterize the different observed types of physiological responses.
So I have had this for a while, but have rarely generated much content. I’ve reached a point where I am collecting participants for my dissertation. I am sitting on some stuff that will probably never see the light of day. I can also write a bit about statistics, things related to research design, and probably give my opinion on some general interest works. One of my resolutions for the new year was to write more for myself.