A precise assessment of various aspects of a person’s mental health with the help of standardized tests has always been an issue. Regardless of the main benefits they have, such as standardization, high reliability, and the possibility of translating the results into a scale that is easy to perceive, the psychometric tests still feature several drawbacks, with the main one being the loss of individualized approach. The very answers to the predefined list of questions do not suffice in transmitting the entire gamma of a person’s emotions and delivering a precise image of her mental state. Today, we will talk more about why the standardized psychological tests are rather a remnant from the past and not an efficient assessment tool of today.
There is a need to understand that standardized psychometric tests have been with us for almost two centuries already. It all started with Charles Darwin and his On the Origin of Species, where the British naturalist has laid down his vision of individual differences, fundamental to understanding human evolution. Francis Galton and James McKeen Cattell took further to claim that those differences should be studied systematically and studied with scientific rigor and precision (Hanson, 2021). Thus, the first Anthropometric Laboratory was established in London in 1884.
The Victorians were not the only ones trying to lay the foundation for psychometric analysis. A German philosopher Johann Friedrich Herbart has come up with a mathematical model of the mind, which was quite influential in the assessment and educational practices of the 19th century.
There was not much motion or any progress in psychometry since then until the year 1936, when Louis Leon Thurstone, having elaborated on the psychophysical theory of Weber and Fecher, developed the law of comparative judgment and factor analysis (Sellbom & Tellegen, 2019). The latter has become the core of today’s psychometric assessment practices. As a matter of fact, an examination of the 2017 issues of the Psychological Assessment journal revealed that 66 out of 114 (58%) empirical articles included some form of factor analysis (Sellbom & Tellegen, 2019). Yet, there is one problem associated with the psychometric tests of the 20th century. They were all based on statistical thinking, which would line every respondent up to a single assessment standard.
Leopold Szondi, a famous Hungarian psychiatrist and psychoanalyst, has then commented in the 1950s on this approach: “In the last decades, the specific psychological thinking has been almost completely suppressed and removed, and replaced by a statistical thinking. Precisely, here we see the cancer of testology and testomania of today” (Shapiro, 2014). The problem is that nothing much has changed as of today. The psychometric “industry” did not attain much progress in the last decades. Of course, the forms, content, and types of tests have changed, but the core principle at work is still the same – factor analysis, which significantly lacks precision and personalization.
The lack of progress in quality led the psychometric tests to a point in history when the need for developing a novice psychological testing approach has evolved naturally. Today’s standardized psychometry mostly lets the respondents self-report the measure of their anxiety (Rossi & Pourtois, 2012). When passing those tests, a person does not encounter the very sources of fear or the trigger. Instead, they just provide generic verbal responses to the no less generic questions, inquiring the description of a person’s anxiety associated with a particular situation.
That is, the entire reliability of such tests’ results is founded on how good the very respondent is at differentiating between the states of his mind. A person suffering from anxiety is not always cognizant of what’s going on with them. Hence, isn’t that kind of a biased assessment that we’re dealing with here? A patient cannot diagnose him or herself. What is more, even a person who knows that she is suffering from anxiety would like the results to be “better”. Hence, the test results are being twisted from the onset, as the respondent might not be all that honest when answering.
The latest inquiry into the efficiency of psychometric tests leaves us with a dual impression. The thing is that more and more people are starting to agree that even duly-developed psychometric tests are rather good and reliable only when measuring the overall aspects of a person’s character or abilities (Datta, 2021). Meanwhile, they are not that applicable when it comes to analyzing an anxiety-related disorder. Psychometric tests are to be used when you want to define an overall pattern of a person’s behavioral framework; they have nothing in common with detecting and assessing a particular condition.
The so popular these days questionnaires do not research a particular case of anxiety, as a human mechanism for reacting to real-life situations. They tackle a condition substituting image, which consists of the respondent’s, quite often blurred and twisted, self-reporting claims.
Imagine this: you come to see a doctor, and instead of conducting an actual physical examination he asks you to imagine it happening. For example, a neuropathologist, instead of knocking on your knee with that little hammer of his, tells you to imagine him doing it and then asks how painful was that and whether you actually felt something?
Defining one’s anxiety level and providing an accurate assessment of its further development requires an in-depth evaluation of the personalized triggers and causative factors. It goes without saying that the “questionnaires” do lack that. In order to understand a person’s condition there is a need to distinguish between the trait and state anxiety (Spielberger, Gorsuch & Lushene, 1970). The assessment and approach to a person for whom anxiety is rather a personality trait (trait anxiety) differs from the one to take to a person whose anxiety has been caused by current or past life events or conditions (state anxiety).
This is a crucial aspect of anxiety assessment. The trait anxiety level evaluation is important for a long-term assessment of the probability of anxiety-related disorders further development. While using the self-report measure of anxiety, most of the anxiety assessment is relying on Spielberger’s unidimensional approach, which envisages anxiety being construed as a single trait, where the trait and state types of anxiety are closely related (Leal, 2017). However, such an approach can be nothing closer to wrong.
Modern science already dwells on a substantial body of empirical data that overthrows this approach. For example, a 2020 study by Saviola et al. shows that trait and state types of anxiety must be looked at as various neurologically functional systems.
Fig. 1. The difference in neural response to trait and state anxiety
As we can see in Figure 1, the brain’s functional connectivity changes in Default Mode Network, depending on the kind of anxiety the brain deals with. Trait anxiety is connected with the Default Mode Network in the superior frontal gyrus (left in Figure 1). Meanwhile, state anxiety is shown to be correlated to functional connectivity of the Default Mode Network in the precuneus (right in Figure 1).Saviola’s findings justify those of the other scholars. For example, Endler and Kocovski (2001) claim that both trait and state anxieties are multidimensional constructs, with four and two dimensions, respectively. The four dimensions of trait anxiety are: i) social evaluation threat; ii) physical danger threat; iii) ambiguous threat; iv) threat in innocuous situations or daily routines. Meanwhile, the two dimensions of state anxiety are: i) cognitive worrying and autonomic-emotional worrying. That is, the two types of anxiety act on the basis of completely different neurological agents, meaning they cannot be assessed and approached from a unified point of view.
It all seems a bit complicated, doesn’t it? How do we get an accurate, reliable, and individualized result in a psychological test? Should you go through a dozen of subjective self-report tests that address various types of anxiety? Or would it be easier and more efficient to try and actually understand what is happening with you by assessing objective physiological indexes, such as levels of hormones, heart rate variability, etc.?
Sure enough, opting for the second variant would be a better choice. Nonetheless, it has already been shown that the level of physiological changes may not reflect the level of subjective anxiety (Teixeira-Silva et al., 2004). Furthermore, there is a myriad of other factors that a human body can respond to by the same or at least similar ‘symptoms’ as the ones invoked by anxiety.
When working on the concept of Anima, we decided to unite the best features of every approach there is. We’ve combined neurobiology, evidence-based psychology, physiology, and technology with the best features of psychometry (the ones that actually work) to create a product that is able to differentiate the intensity of subjective emotions – one of the key elements in the pathological loop that leads to the development of disorders (Higgins-Chen, 2019).
Figure 2. The cycle of illness anxiety
The eye-tracking technology, at the core of Anima, lets us provide an objective assessment of the respondent’s reactions within the framework of completing projectionary tasks, which is easy to interpret. Anima represents the shift in development that psychometric tests have been longing for so long, and the neurobiological testing element empowered with the help of eye tracking and deep learning technologies does just that.
A change in psychometric testing has been long overdue. It seems like Szondi’s concept of specific psychological thinking has finally found a platform that can test it with objective precision. The generalization and standardization of results are long gone, as analyzing images and not conditions cannot be considered right any longer. The ultimate combination of technology and science renders Anima capable of adding up that extra bit of personalization and assessment accuracy that all the standardized tests have been missing.
