The Importance of Language

I have been asked a few times lately if I think boredom has a facial expression associated with it.  Research suggests that it does not have a facial expression.  More intriguingly though is to look at the relationship between between communication and the emotions.  Research (e.g. Ekman) has suggested that the facial expressions related to emotion exist in order to communicate an individual’s internal state to coordinate group actions.  Many of the emotions that we communicate have existed for many generations.  The term boredom, in the english language, was only recorded in literature by the late 19th century.  As far as emotions or affective sates, this is relatively recent.  This means that we have not evolved linked physiological responses such as facial expressions to represent this state.  However, we have other abilities that allow us to determine whether an individual is bored or not, namely theory of mind.  This higher level process allows for us to use both context and an individual’s misfit between our expectations of an appropriate response in that context to determine if an individual is bored.  In other words, when we expect to be interesting in a conversation, and we do not have the necessary signs of interest, then we can infer that an individual is experiencing boredom.

Dissertating and Random Thoughts

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.

Social Media Analysis is Really Just About Effect Size

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 works 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.

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.

Repeated Measures: Problems and Directions

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.

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.

The Facets of Emotions

Lately I have been musing on what is an emotion. My realization is that emotions serve the purpose of modifying interactions with the environment. Emotions, as such, are comprised of a few facets.

The primary facet is arousal. It is these changes in arousal that led to the evolution of emotions. High states of arousal such fear or anxiety prime the individual to respond to the environment in a beneficial manner.

Additionally, there is the valence of an emotion. When an emotion is positive it is encouraging the type of interaction with the environment that the individual is already engaged in. For example, to experience happiness is to know that the environment is beneficial to the individual and encourages a similar type of engagement. For negative emotions, the opposite is true. When experience fear, an individual must change his or her interaction with the environment in order to respond to the object that is creating fear.

Finally, there is the cognitive component of the emotion. This is comprised of the situational features that allow for the individual to recognize the emotional state that he or she is experiencing. This includes conscious realizations such as triggers of an emotion (e.g. a gun) and automatic responses (e.g. facial feedback).

Does boredom represent a state of low or high arousal? We already know that it is a negative emotion. We also already know that one of the key situational features of boredom is the inability to maintain cognitive focus, so a lapse in attention.

Tenets to Guide My Research

-Evolutionary theory teaches us that organisms have evolved through natural selection.

-This selection occurs at multiple levels, as ascribed by multilevel selection theory.

-The forces that drive evolution occur outside of the individual, which is the environment.

-This is true for traits and behaviors that can be described as genic or memic in origin.

-The environment is comprised of both the natural world and the social world of ideas. The later is synonymous with culture.

-Behaviors and environments interact. As such, behaviors should be analyzed as both originating from and acting on the environment.

-Behaviors serve the purpose of allowing an individual to engage with the environment.

-Before engaging with the environment, all possible behaviors are activated and then a single behavior is selected.

-Factors effect which behavior is selected. This is basically the argument made by Mischel, a cognitivist’s approach to the situationist perspective.

-Learning, the internalization of knowledge and the creation of representations, is central to this approach.

-Learning comes in two forms. The first is experience. The second is through social means such as communication and education.

-Evolved psychological mechanisms can also influence the selection of behaviors.

-One such example of this emotions, which serve to influence how an individual engages in an environment.

-Emotions are activated by situational features, and as such are products of the environment in which the individual is acting. Again, importance is placed on the interaction with the environment.

-Perception is merely a type of behavior.

Egosyntonic and Egodystonic: An Evolutionary Perspective

This analysis suggested that the extremity of a trait is insufficient to characterize a trait as adaptive or maladaptive. Additionally, this analysis has also suggested that multiple adaptive forms of a trait exist as adaptation is a product of the interaction between a trait and an environmental context that increases fitness. However, this analysis has yet to characterize psychopathology as maladaptive. In a traditional FFM approach, the severity of psychopathology is not a product of just the extremity of a single traits, but a product of the interaction of multiple, extreme traits (Costa & Widiger, 1994; Trull & Sher, 1994; Costa & Widiger, 2002; Warner, et al., 2004). However, from an evolutionary perspective, these personality styles represent unique adaptive strategies. As such, to determine if an individual’s unique personality style is a valid adaptive strategy is to determine whether the interaction between this style and the environment increases fitness. To this end, there are two necessary levels of analysis. The first is the individual level in which the individual’s ability to engage the environment to increase fecundity and longevity is assessed, while the second is the group level where the individual’s ability to contribute to the fitness of the group is assessed.

The first level of analysis, the individual level, focuses on adaptation as an individual’s inability to engage with the world in a way that increases fitness. In other words, this is a poorness of fit of a personality style in an environment. The first source of this inability is the personality style. For example, the high level of neuroticism associated with depression might inhibit action in the individual (Trull & Sher, 1994). Without action, the individual is unable to engage with the world and is therefore unable to benefit from the environment, leading to maladaptation. The second source of the individual’s inability to engage the world is constraints the environment places upon the individual Much of these constraints are part of the natural environment. However, constraints can also come from the individual’s culture. An individual’s culture is part of the environmental context that interacts with a trait. As cultural evolution occurs at a rate that far exceeds biological evolution, it is possible that a culture would impose constraints distinct from those imposed by the natural environment. In the right environment, ADHD might be adaptive as some individuals diagnosed as a child turn into highly successful adults who benefit from the near endless energy in the pursuit of an interest (MacDonald, 2005). However, in a modern context ADHD is seen as maladaptive as it leads the individual to be disruptive in an educational setting  (MacDonald, 1995). Given this analysis, at the individual level some psychopathologies are the product of an individual being unable to engage with the world in an adaptive manner, with maladaptation stemming from constraints imposed by the natural environment or the individual’s culture (Nesse & Williams, 1996; MacDonald, 2005).

An individual’s inability to engage with the environment characterizes disorders that are egodystonic. Egodystonic disorders are differentiated from egosyntonic disorders, as only egodystonic disorders cause the individual distress. Commonly, Axis I disorders such as depression and bipolar disorder considered to be egodystonic. The high level of neuroticism in depression  is hypothesized to inhibit an individual’s action (Trull & Sher, 1994), which may limit an individual’s ability to engage the world. This loss of fitness leads to the distress that characterizes egodystonic disorders, as the individual is aware that their personality style is not a valid adaptive strategy. Importantly, this view of egodystonic as the inability to engage the world in an adaptive manner can explain why the manic phases of bipolar disorder are egosyntonic while the depressive phases are egodystonic as the increased activity of the manic phases increases the individual’s activity in the world, therefore leading to increased fitness.

The second level of analysis, the group level, focuses on adaptation as an individual’s ability to contribute to the fitness of the group. Researchers have suggested that a more general purpose for personality traits is to evaluate others through social interaction (see Buss, 1991; Hogan, 1983, cited in Buss, 1995; Borkenau, 1990). Humans are invariably social creates and as such utilize interactions to increase both the individual’s and the group’s fitness. As previously stated, the social environment allows for the existence of multiple, adaptive niches within a population. As multiple adaptive strategies based on personality styles exists, one of the major adaptive pressures placed on the species is the ability to determine benefit from an interaction with another. To cite previous, the individuals low on neuroticism performed a fundamentally different role for the group than did the individuals high on neuroticism with the individuals engaging in high-risk, high-reward strategies and individuals engaging in low-risk, safe strategies respectively. While the fitness of the group increases through the interaction of multiple personality styles, some individuals will have personality styles that do not contribute to the group’s fitness. For example, the low agreeableness of the psychopath leads them to be callously manipulative (Hare, 1999) and the high levels of conscientiousness in obsessive compulsive personality disorder contributes to poor interpersonal relationships (Marks, 1987). As a result, an individual can be labeled by the group as maladapted when they provide no benefit to the fitness of the group and classified as possessing a disorder.

The inability of the individual to provide fitness to the group is central to egosyntonic disorders. Egosyntonic disorders are psychopathological disorders in which the individual does not experience distress as a result of the disorder. As the individual experiences no distress, these disorders are difficult to treat. Commonly, the personality disorders and psychopathy are described as egosyntonic. As with many other, personality styles, these disorders are patterns of adaptation that serve to increase an individual’s fitness. These personality styles allow the individuals to engage the environment in an adaptive manner, with the psychopath increasing his or her fitness through manipulating others, while for the individual with OCPD increasing fitness obsessive drive for perfection. However, as previously suggested, these personality styles do not benefit the group, with the psychopath’s fitness increasing at the cost of another individual’s fitness and the individual with obsessive compulsive personality disorder being unable to maintain interpersonal relationships. As such, individuals with egosyntonic disorders do not experience distress because they are able to still engage in the world in an adaptive manner, however, their personality style is maladaptive to the group as it does not increase the group’s fitness.


Borkenau, P. (1990). Traits as ideal-based and goal-derived social categories. Journal of Personality and Social Psychology, 58, 381-396

Bouchard, T.J. & McGue, M. (2003). Genetic and environmental influences on human psychological differences. Journal of Neurobiology, 54, 4-45.

Buss, D.M. (1991). Evolutionary personality psychology. Annual Review of Psychology. Palo Alto, CA: Annual Reviews, Inc.

Buss, D. M. (1995). Evolutionary psychology: A new paradigm for psychological science. Psychological Inquiry, 6, 1-30.

Buss, D.M. (2001). Human nature and culture: An evolutionary psychological perspective. Journal of Personality, 69(6), 955-978.

Buss, D.M. & Greiling, H. (1999). Adaptive individual differences, Journal of Personality, 67(2), 209-243.

Costa, P.T. & McCrae, R.R. (1989). The NEO-PI/NEO-FFI manual supplement. Odessa, FL: Psychological Assessment Resources.

Costa, P.T. & Widiger, T.A. (1994). Summary and unresolved issues. In P.T. Costa & T.A.Widiger (Eds.), Personality disorders and the five factor model of personality. Washington, DC: American Psychological Association.

Costa, P.T. & Widiger, T.A. (2002). Personality disorders and the five-factor model of personality (2nd ed.). Washington, DC: American Psychological Association. Farley, F.H. The Big T in personality. Psychology Today, 20, 44-52.

Figueredo, A.J., Sefcek, J.A., Vasquez, G., Brumbach, B.H., King, J.E., & Jacobs, W.J. (in press) Evolutionary personality psychology. In D.M. Buss & P. Hawley (Eds). The evolution of personality and individual differences. New York: Oxford University Press.

Funder, D.C. (2007). The personality puzzle, (4th ed), (pp. 171-219). New York: Norton & Company

Goldberg, L. R. (1993). The structure of phenotypic personality traits. American Psychologist, 48, 26-34.

Hare, R.D. (1999). Without conscience: The disturbing world of psychopaths among us. New York: Guilford Press.

Jang, K., Livesley, W. J., & Vemon, P. A. (1996). Heritability of the Big Five Personality Dimensions and Their Facets: A Twin Study. Journal of Personality, 64, 577-591.

MacDonald, K.B. (1992). A time and a place for everything: Children’s rough and tumble play in education settings. Early Education and Development, 3, 334-355.

MacDonald, K.B. (1995). Evolution, the five-factor model, and levels of personality. Journal of Personality 63(3), 525-567.

MacDonald, K.B. (2005). Personality, evolution, and development. In R. Burgess and K. MacDonald (Eds.), Evolutionary perspectives on human development, 2nd Ed, (pp. 207-242). Thousand Oaks, CA: Sage.

McCrae, R.R. (2004). Human nature and culture: A trait perspective. Journal of Research in Personality, 38, 3-14.

McCrae, R.R., Costa, P.T., & Yik, M.S.M. (1996). Universal aspects of Chinese personality structure. In M.H. Bond (Ed.), The handbook of Chinese psychology (pp. 825-247). Hong Kong: Oxford University Press.

Montag, I. & Levine, J. (1994). The five-factor personality model in applied settings. European Journal of Personality, 8, 1-11.

Nesse, R.M. & Williams, G.C. (1996). Why we get sick: The new science of Darwinian medicine. New York: Vintage Books.

Okasha, S. (2004) Multi-Level Selection and the Major Transitions in Evolution. In Proceedings Philosophy of Science Assoc. 19th Biennial Meeting – PSA2004: PSA 2004 Contributed Papers, Austin, Texas.

Piedmont, R.L. & Chae, J.H. (1997). Cross-cultural generalizability of the five-factor model of personality. Journal of Cross-Cultural Psychology, 28, 131-155.

Richards, R., Kinney, D.K., Lunde, I., Henet, M., & Merzel, A.P.C. (1988). Creativity in manic-depressives, cyclothemes, their normal relatives, and control subjects. Journal of Abnormal Psychology, 97, 261-276.

Segal, N.L. & MacDonald, K.B. (1998). Behavioral genetics and evolutionary psychology: Unified perspective on personality research. Human Biology, 70(2), 159-184.

Trull, T.J. & McCrae, R.R. (1994). A five-factor perspective on personality disorder research. In P.T.Costa & T.A. Widiger (Eds.), Personality disorders and the five-factor model of personality (pp. 59-71). Washington, DC: American Psychological Association.

Trull, T.J. & Sher, K.J. (1994). Relationship between the Five-Factor Model of personality and axis I disorders in a nonclinical sample. Journal of Abnormal Psychology, 10(2), 350-360.

Warner, M.B., Morey, L.C., Finch, J.F., Gunderson, J.G., Skodol, A.E., Sanislow, C.A., Shea, M.T., McGlashan, T.H., & Grilo, C.M. (2004). The longitudinal relationship of personality traits and disorders. Journal of Abnormal Psychology, 113(2), 217-227.

Watter, E. (2006). DNA is not destiny. Discover Magazine. Retrieved from:

Zuckerman, M. & Kuhlman, D.M. (2000). Personality and risk taking: Common biosocial factors. Journal of Personality, 68(6), 999-1029.

Psychology and Science

I recently decided that I needed to clean off a shelf that contained the last five years of psychology today, monitor on psychology, and american psychologist. I quickly read through the psychology today finding it perfect reading for the train. Today, I finally completed my collection of monitor on psychology. In reading it, I was struck by the number of articles that related psychology to science.

A few of these articles, such as Breckler’s recent article entitled “Setting the Bar Higher”, focused on the importance of psychologists being educated in a variety of traditional sciences such as biology and chemistry. I cannot disagree with importance of a science heavy education, since as psychology matures and inquiry advances, theories will needfully become more integrated, drawing on evolution, biological processes and chemical reactions, cognitive science etc. To be able to understand results, comprehend theories, and contribute meaningful to the field it will become necessary for psychologists to be educated in all of the traditional sciences.

However, most of the articles were relating psychology to other sciences in research methodologies, societal importance, public opinion, etc. Such focus on psychology as, and in relation to, science prompts the question, is psychology a science? This is is a question that I have spent much time on in the past years. To address this question is to first define science. Science is both a structure and a process.

As a process, the term science describes the scientific method, which involves the inductive and deductive reasoning to characterize phenomena, to produce hypotheses about phenomena, to make predictions about phenomena, to conduct experiments to test these. From this description, psychology inarguably engages in the process of science as it attempts to understand the human condition.

However, science as a structure is a historical model that describes the structure of a field. Famously, Kuhn laid out one such model in his book “The Structure of Scientific Revolutions” in which he focused on science as a paradigm. A paradigm is is a set of practices that define a science and includes what is observed, the kinds of questions that are asked, how questions are to be asked, and how results are interpreted.

In his description, the progression of a science as comprised of three distinct stages. The first stage is pre-science. Within this stage, there is a lack of a single central paradigm. Instead, inquiry utilizes many different methodologies, which it applies to a number of different focuses within the description of a field. The focus of this inquiry is to develop a single paradigm to guide the field of study. Eventually, these different approaches begin to come into agreement, creating a single paradigm. The creation of a single paradigm allows for the second stage, known  and allowing for normal science to commence. Normal science involves problem solving where scientists engage in problem solving in order to expand the central paradigm. However, sometimes these activities reveal problems in the original paradigm. When these issues are sufficiently great to lead scientists to question the central paradigm, the discipline enters into the third stage, crisis, where scientists resolve the issues by constructing a new paradigm that resolves the issues that forced the old paradigm into crisis. This progression is known as a paradigm shift.

Kuhn’s theory of paradigm shifts is important because it characterizes scientific progression as not being cumulative, but being characterized by major theoretical shifts. In these shifts, the new paradigm is theoretically independent from the previous paradigm. This is to say that each paradigm has a different set of questions to ask, methods of investigation, etc. For example, gravity is a theoretical construct in multiple physics paradigms. From a Newtonian perspective, gravity occurs because an object falls towards the center of a larger object. However, from an Einsteinian perspective, gravity occurs because objects deform space like a bearing on a paper towel. Large objects create large deformations, and when smaller objects pass near the large object, the object slides down these deformations towards the larger object. Despite this theoretical difference, both paradigms describe the same phenomena.

Within this view, psychology would be considered to be a pre-science as there is no central paradigm within the discipline. What exists is a number of theoretical perspective that guide inquiry. Furthermore, these perspectives could be viewed to be incommensurable, which is to say that they are distinct. For example to a Freudian, the content of dreams hold meaning as they reflect the unconscious. However, to a cognitive psychologist dreams in themselves may not be meaningful, but could be considered to be useful in learning by allowing for an individual to train the connections between neurons in order to consolidate learning or to allow for the individual to practice for situations that do not commonly occur.

However, while this analysis may seem worthwhile, Kuhn developed his theory to specifically describe traditional sciences, not social sciences. As such, his description may not be an accurate representation of how a social science may function. This uncertainty may be enough though to further confirm that while psychology engages in the process of science, it is structurally not a science. Furthermore, the suggestion that psychology is structurally a pre-science suggests that . On a more personal note, one of the reasons I chose to study psychology was the diversity of its theoretical perspectives. I have no problems with psychology potentially being a pre-science. I enjoy the scientific process. It is for this reasons that I initially began my undergraduate education studying biology and chemistry. However, the prospect of being merely a puzzle solver led me to change the focus of my education to psychology. Being directly involved in the construction of a theoretical paradigm excites me.


Kuhn. (1962). The Structure of Scientific Revolutions. University of Chicago Press: Chicago.

Society of Selves

Well, since I guess I need to start somewhere, I shall start with a favorite article of mine. Written by an evolutionary psychologist named Humphrey, he deviates from typical evolutionary psychology, in which processes such as empathy and mind reading are important and evolved mechanisms would be investigated, by instead tackling the question why such processes are necessary for the individual in the first place. To him, these processes are necessary because of consciousness is private.

Humphrey theorizes that bodily actions occur because of the individual acting. This is to say that the cause of movement is the individual self, not another individual. However, the internal component, the drive to act, is private and unknown to the observer. Secondly, he theorizes that sensation is a bodily action. When an individual senses, sensations are not the outside world acting upon the individual, but is the individual observing physiological changes. Seeing the color red is realizing the the eye is in a state of what is known to the individual as observing red.  Consciousness is the realization that one is engaged in such an act. However, this realization is both internal and private like the drive for action. As such, it is only known to the individual.

And since consciousness is private, it helps to create a barrier between individuals. From this comes the phrase society of selves. We are each a single self, and I, and society is comprised of a collection of selves, a group of Is. However, individuals  within a society do attempt to commune by utilizing empathy, theory of mind, or other sociocognitive processes. These function by allowing the individual to utilize one’s own consciousness to determine the consciousness of another. As a result, the other is never truly known, but the individual can only understand his or her interpretation of the other individual. In this sense, we will always be alone.

To me, this makes intuitive sense. Our conceptions of anything that occurs outside of the self is understand only through interpretation by the self. Language serves an important means through which this process occurs. Language is symbolic, existing outside of the individual in the shared practices of the the society. Language serves the function of acting as a shared reference to common internal representations. The word red symbolizes our conscious experience of red. Well the experience of red is common to all individuals, each individual has a unique conceptualization of red, so that when I make reference to the concept of red through its symbolic word, another can generally understand what is being referenced, but only I can understand exactly the form of what I am referencing.

Given this view, complex speech acts such as metaphors seem to function because of common ground. the word that references this concept is generally excepted by all members of a society to make reference to that internal concept. Additionally, the internal concept referenced in a metaphor is complex and nuanced in form. However, its form is expected to have enough common ground based on shared experience that the other individual would be able to interpret the concept in the way in which the speaker intended given their own conscious experiences.

Typically, this process functions smoothly. However, in some groups of individuals with psychopathology, this process may not necessarily occur in this expected manner. For example, take schizophrenics with delusions. These individuals have been suggested to have deficits in theory of mind while delusional, however during non-delusional periods these deficits disappear (Frith & Corcoran, 1996). Have these individuals lost the ability to produce socially meaningful speech due to theory of mind impairments so that delusions are merely failed metaphors? A second example is that of the individual with severe autism. Unlike schizophrenics, these individuals have chronic deficits in theory of mind that impair their ability to develop complex speech (Diesendruck 2007). Are these individuals unable top understand metaphor because to them the word that acts as a reference is not symbolic but a concrete learned association?


Frith, C.D., & Corcoran, R. (1996). Exploring Theory of Mind in People With Schizophrenia.Psychological Medicine 26: 521-30.

Diesendruck, G. (2007). Word learning without Theory of Mind: Possible but Useless. Coevolution of Language and ToM. Retrieved October 10, 2007, from

Humphrey, N. (2007.) The society of selvesPhilosophical transactions of the royal society b: biological sciences, 362 (1480). pp. 745-754.