Lake et al. applaud the advances made in artificial intelligence (AI), but argue that future research should focus on the most impressive form of intelligence, namely, natural/human intelligence. In brief, the authors argue that AI does not resemble human intelligence. The authors then discuss the building blocks of human intelligence, for example, developmental start-up software including intuitive physics and intuitive psychology, and learning as a process of model building based on compositionality and causality, and they stress that “people never start completely from scratch” (sect. 3.2, last para.)

We argue that a view of human intelligence that focuses solely on cognitive factors misses crucial aspects of human intelligence. In addition to cognition, a more complete view of human intelligence must incorporate motivation and emotion, a viewpoint already stated by Simon: “Since in actual human behavior motive and emotion are major influences on the course of cognitive behavior, a general theory of thinking and problem solving must incorporate such influences” (Simon Reference Simon1967, p. 29; see also Dörner & Güss Reference Dörner and Güss2013).

Incorporating motivation (e.g., Maslow Reference Maslow1954; Sun Reference Sun2016) in computational models of human intelligence can explain where goals come from. Namely, goals come from specific needs, for example, from existential needs such as hunger or pain avoidance; sexual needs; the social need for affiliation, to be together with other people; the need for certainty related to unpredictability of the environment; and the need for competence related to ineffective coping with problems (Dörner Reference Dörner2001; Dörner & Güss Reference Dörner and Güss2013). Motivation can explain why a certain plan has priority and why it is executed, or why a certain action is stopped. Lake et al. acknowledge the role of motivation in one short paragraph when they state: “There may also be an intrinsic drive to reduce uncertainty and construct models of the environment” (sect. 4.3.2, para. 4). This is right. However, what is almost more important is the need for competence, which drives people to explore new environments. This is also called diversive exploration (e.g., Berlyne Reference Berlyne1966). Without diversive exploration, mental models could not grow, because people would not seek new experiences (i.e., seek uncertainty to reduce uncertainty afterward).

Human emotion is probably the biggest difference between people and AI machines. Incorporating emotion into computational models of human intelligence can explain some aspects that the authors discuss as “deep learning” and “intuitive psychology.” Emotions are shortcuts. Emotions are the framework in which cognition happens (e.g., Bach Reference Bach2009; Dörner Reference Dörner2001). For example, not reaching an important goal can make a person angry. Anger then characterizes a specific form of perception, planning, decision making, and behavior. Anger means high activation, quick and rough perception, little planning and deliberation, and making a quick choice. Emotions modulate human behavior; the how of the behavior is determined by the emotions.

In other situations, emotions can trigger certain cognitive processes. In some problem situations, for example, a person would get an “uneasy” feeling when all solution attempts do not result in a solution. This uneasiness can be the start of metacognition. The person will start reflecting on his or her own thinking: “What did I do wrong? What new solution could I try?” In this sense, human intelligence controls itself, reprogramming its own programs.

And what is the function of emotions? The function of emotions is to adjust behavior to the demands of the current situation. Perhaps emotions can partly explain why humans learn “rich models from sparse data” (sect. 4.3, para. 1), as the authors state. A child observing his or her father smiling and happy when watching soccer does not need many trials to come to the conclusion that soccer must be something important that brings joy.

In brief, a theory or a computational model of human intelligence that focuses solely on cognition is not a real theory of human intelligence. As the authors state, “Our machines need to confront the kinds of tasks that human learners do.” This means going beyond the “simple” Atari game Frostbite. In Frostbite, the goal was well defined (build an igloo). The operations and obstacles were known (go over ice floes without falling in the water and without being hit by objects/animals). The more complex, dynamic, and “real” such tasks become – as has been studied in the field of Complex Problem Solving or Dynamic Decision Making (e.g., Funke Reference Funke2010; Güss Tuason & Gerhard Reference Güss, Tuason and Gerhard2010), the more human behavior will show motivational, cognitive, and emotional processes in their interaction. This interaction of motivation, cognition, and emotion, is the real strength of human intelligence compared with artificial intelligence.