Psychological AI – Via language to motives

Guest contribution by | 16.01.2020 | Processes & methods | 0 comments

​Language expresses the motives of each person. This insight is nothing new, on the contrary, it is already widely used in psychology. Unfortunately, capturing motives from language is a costly and time-consuming process, as this has so far been done by manual assessment. Now, for the first time, it is possible to analyse human language automatically and validly in relation to underlying motives. Based on the latest social psychological research in combination with the powerful possibilities of artificial intelligence, a solution has been developed that can also read “between the lines”: Psychological AI.

For which business areas can such a solution be used and how does it work?

No successful implementation without domain knowledge

Choosing the right psychological construct is crucial to the success of an AI solution. It is important to capture only those features that enable long-term predictive power of behavior, especially the motives and processes of information processing.

People are guided by motives. Motives are internal action leaders and cause us humans to think in certain directions or to behave in certain ways. For example, if we know what drives us, this is particularly relevant in a professional context, because motives tell us which professional role suits us best.

In general, three basic motives are distinguished:

  • the motive for power, status and leadership,
  • the motive according to performance and development and
  • the motive for harmonious relationships with other people.

These motives were formulated by the social psychologist David McClelland and have been extensively researched in psychology.¹

Motives are also distinguished between explicit and implicit motives. People are aware of explicit motives and can give precise information about the intention behind their behaviour. Implicit motives are largely unconscious to humans, which means that they cannot express the intention behind their behaviour precisely. So if an AI wants to understand the motives of people, it has to grasp the implicit motives.

In addition to the three basic motives, the legs, the head and the heart are important features that enable long-term prediction of behaviour:

  • It’s the legs that move us forward. This also applies to the motives that answer the question of “why”. These are the basic motives described above for leadership, performance and relationship, which are measured in language.
  • The head processes information, either via the path of risk avoidance or the path of the profit-seeker.
  • And we often associate feelings with the heart. Moods and emotionality have a great influence on the legs and the head. Current emotional states such as happiness, sadness and fear differ.

After all, the head, the heart and the legs have to move in the same direction to influence behaviour. If the right motif is addressed with the right emotion in the right focus, work becomes easier. If not, we have to do more work for the same result.

The importance of understanding the context

People intuitively code the right term in the right context, classical algorithms have problems with this. It is therefore important that an AI also has an understanding of context that is based on classical psychological coding manuals in order to assign a motif to certain terms and word combinations. In practice, however, such a language analysis is not yet supported by many solutions. However, it is – as you can easily see in the following example – a very important function to correctly analyze language and its motives:

  • Gold conducts electricity better than wood.
  • He conducts the project with passion and will lead it to success.

Since you quickly grasp the context, you will naturally understand the different meanings. But for a conventional machine, the different meaning of “conducting” is not so easy to understand. Only through a deep understanding of the meaning of individual words and sentence combinations in context can a motif be correctly assigned. A machine does not have the intuitive, human understanding of context and must therefore “learn” to understand different meanings. In order to teach it this understanding, different methods from word processing and text comprehension can be used. These methods are summarised under the term Natural Language Processing (NLP).

Opportunities for different business areas

By analysing the motives with Psychological AI, it is possible for the first time to use it reliably on a large scale in important business areas. In the end, a motive can always be understood as a purpose. It can be used to build brands, ensure customer experiences and create a corporate culture.

Currently, Psychological AI includes solutions for the optimisation of recruiting, marketing and customer contacts in the form of three products: Talent Intelligence, Brand Intelligence and Customer Intelligence.

Talent Intelligence supports users in addressing the right candidates in a gender-appropriate manner and in understanding the motives of their candidates. By formulating their job advertisements in a targeted manner, they can reach more suitable applicants for open positions in the future. In addition, a standardised motive test, which evaluates the explicit and implicit motive, is carried out to analyse the fit with the job. This knowledge is used for decision-making purposes, but also for long-term personnel development.

Brand Intelligence supports customers in communicating the purpose of their brand more clearly and, above all, in a way that is appropriate for the individual. In doing so, the software compares the content in its creation with the effect on the persona and helps to strike the right note.

Ultimately, Customer Intelligence supports organisations in ensuring a communication experience through targeted and individualised conversation management. This is done by analysing dialogues with customers, taking into account their motives.

Conclusion

The capture of motifs from speech is costly and time-consuming. For many companies, it is therefore obvious to follow paths to understand people and their motives faster and more cost-effectively. At the same time, reliability is also important. The described Pyschological AI has the claim to reliably understand people and their motives. It learns continuously on the basis of words that we use thousands of times a day in our language use. And thus it helps companies to consciously perceive the unconscious and subsequently even to convince people communicatively. You are welcome to try it out.

 

Note:

If you want to experience Pyschological AI live, you can simply arrange a web session: https://www.100worte.de/

Psychological AI - check it out live

[1] Information about David McClelland

Cankat Demirkol
Cankat Demirkol

Cankat Demirkol is studying business informatics at Heilbronn University. During his practical semester he is working in the field of online marketing at 100 Worte Sprachanalyse GmbH. There he is responsible for the administration of social media channels, runs the 100 Worte blog and is in charge of search engine optimisation.

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