The importance of motivation and emotion for explaining human cognition.

Lake et al. discuss building blocks of human intelligence that are quite different from those of artificial intelligence. We argue that a theory of human intelligence has to incorporate human motivations and emotions. The interaction of motivation, emotion, and cognition is the real strength of human intelligence and distinguishes it from artificial intelligence.

From da Vinci's Flying Machines to a Theory of the Creative Process.

Steps involved in the creative process have been described in previous research, yet the exact nature of the process still remains unclear. In the current study, we take this investigation further, referring to two flying machines developed by Leonardo da Vinci and his other notes. Nine iterative steps are described with a focus on motivation and cognition: (a) vision and curiosity; (b) social recognition; (c) asking questions; (d) analogical thinking; (e) trial and error; (f) abductive reasoning; (g) incubation and forgetting; (h) overinclusive thinking, latent inhibition, and illumination; and (i) schema elaboration. The influence of da Vinci's socio-historic context is also briefly discussed. The analyses show how general psychological mechanisms can explain extraordinary acts of creativity. The steps discussed can be further formalized in future research to advance the modeling of creativity.

Complex Problem Solving: What It Is and What It Is Not.

Computer-simulated scenarios have been part of psychological research on problem solving for more than 40 years. The shift in emphasis from simple toy problems to complex, more real-life oriented problems has been accompanied by discussions about the best ways to assess the process of solving complex problems. Psychometric issues such as reliable assessments and addressing correlations with other instruments have been in the foreground of these discussions and have left the content validity of complex problem solving in the background. In this paper, we return the focus to content issues and address the important features that define complex problems.