A brief history of psychology and AI

Psychology is the science of human experience and behavior. And while the philosophers of antiquity were already concerned with the coexistence, feelings and behavior of people, psychology only emerged as an independent science in the 19th century. The first thoughts on artificial intelligence (AI), on the other hand, can already be found among 17th century thinkers. Philosophers such as Descartes, Leibniz and Hobbes took a rather mechanistic view of human actions, thought processes and principles of coexistence. This led to the assumption that human behaviour and thinking are based on fixed rules which, if recognized, would also allow simulation via artificial intelligence.

Separate paths despite many similarities

With the emergence of psychology as a science and later brain research, there were detailed findings about human experience and behavior, learning and the brain. These findings form the basis for the development of artificial intelligence. The fields came together officially for the first time at the Dartmouth Conference in 1956*. Psychologists, philosophers, budding computer scientists and mathematicians worked on describing aspects of human intelligence so precisely that a machine could simulate them. The researchers wanted to help machines learn to use language, solve problems, develop concepts and improve themselves. The conclusion may be that AI only works as a combination of psychology and computer science. Nevertheless, after the end of the joint exchange, the researchers went their separate ways again or organized themselves into subject-specific research groups.

Overlaps between the disciplines

Although AI and psychology belong so closely together, it was mostly a one-sided relationship. Although psychological findings were used for the development of AI, there was hardly any feedback of knowledge. The research fields therefore tend to run parallel to each other rather than being interwoven, although there were always individual researchers who switched from psychology to computer science or took both disciplines into account.

Even if the methods of computer science have so far found little application in psychology, there are still overlaps: Differential psychology, for example, has always used statistical methods and numbers to describe individual differences in terms of traits or states and to measure how changeable they are and to look at correlation between different traits and states, for example the state "joyful" and the personality trait "extraverted".

One area of research that also concerns both disciplines is language: in addition to research into language acquisition and a discussion between behaviorists and cognitive psychologists about the unique position of language, there are many language-related research topics in psychology. Behaviorists view language as a behavior that, like other behaviors, is shaped by the reactions of the environment. Cognitive psychology, on the other hand, sees additional influences from feelings, beliefs, states and innate tendencies.

Language analysis, personality tests and the "Big Five"

Another central branch of research is the so-called "lexical hypothesis" as a basis for personality tests. The lexical hypothesis was first formulated in 1884 and assumes that all of a person's personality traits are somehow represented in language. The basis for researching personality was therefore all the characteristic words that can be found in dictionaries. These were extracted and mathematically grouped into personality factors. This approach gave rise to the so-called "Big Five personality factors":

1. Neuroticism (tendency to anxiety, sadness, emotionally unstable)

2. Extraversion (tendency towards sociability and optimism)

3. Openness (tendency to inquisitiveness, interest in experiences)

4. Agreeableness (tendency towards cooperation, altruism, yieldingness)

5. Conscientiousness (tendency to discipline, willingness to perform, reliability)

Another field of research in psychology deals with so-called "quantitative language analysis". Here, words are counted and organized into linguistic and psychological categories on the assumption that there are correlations between a person's use of language and their personality, processing of experiences, status, etc.

The chatbot as a therapist?

The idea that machines could use language was already circulating at the founding conference of AI in 1956. In 1966, this led to the first chatbot called Eliza, which was able to respond with structured answers to words that people entered into a computer. The chatbot simulated a therapist, and its inventor Joseph Weizenbaum was so shocked by people's quick willingness to share their deepest thoughts with a computer that he became one of the biggest critics of artificial intelligence.

The fact is that almost all the processes that led to AI dealt with language: the Neocognitron (1979), for example, recognized and translated handwriting. The Multilayer Perceptron (1986) dealt with speech recognition, as did the Convolutional Neural Network (1998) and others. For a long time, so-called "Long short-term memory (LSTM) networks" (1997) were used for text analysis. They were used by Google for speech recognition and translations in 2016, for example, or by Amazon for Alexa, before they were increasingly replaced by "transformer networks" (from 2017). In 2012, AlexNet was the first so-called "deep learning method" to surpass all traditional methods, which led to the deep learning boom. AI systems were now so good that it became conceivable to apply them to such a complex field as automatically recognizing psychological characteristics from speech.

Communication analysis thanks to a combination

Innovative solutions are now bringing together the research approaches of AI and psychology for the first time: AI makes psychological information available by analyzing language. This only works if psychology uses AI methods and vice versa, because language is an extremely complex phenomenon that requires modern and multi-layered methods in order to be mathematically understandable and processable. Psychology determines what should be measured from language. The model of communication styles clarifies what the AI should represent. In addition, it is the strength of psychology to design a good survey and develop appropriate measurement instruments, to check the data quality, to pay attention to biases and fairness in tests, to ensure the interpretability of the results and to create useful result reports.

The AI in turn codes words, word combinations, sentence structures, syntax and other aspects into numbers. In addition, millions of pieces of psychological data are collected. The combination of psychology and computer science results in meaningful quality measures for predicting psychological outcomes from language. One practical application is now, for example, to give companies, managers and employees an objective, neutral insight into the impact of their communication and to change it if necessary.