Man and Machine

Full course description

Psychological hypotheses are often specified in the form of computational models. Precision, transparency and the heuristic value of these models on the one hand, and the availability of sufficient computing capability on the other explain their popularity. Cognitive psychological theories have increasingly come to depend on symbolic architectures for problem-solving, reasoning and knowledge acquisition and/or on connectionist models of aspects of human learning, categorisation, perception, memory and attention. In biological psychology, theories are developed and assessed using models of the behaviour of networks of neurons. In this course, students will discuss several influential architectures and algorithms, in conjunction with various biopsychological phenomena that shaped them.

The course will start with a reflection on the nature of cognitive science and artificial intelligence and our ability to forecast future technological developments. Students will also pay attention to changes in the division of labour between man and machine and how these will impact psychological practice. Next, students will study creativity and search models. The question “Can computers be creative?” of course also invites students to reflect on human creativity. Learning will take centre stage in problems relating to connectionist models and to ACT-R, one of the most influential cognitive architectures in which classical, symbolic and connectionist principles have been integrated. Research into higher cognitive skills based on ACT-R models has for example, led to educational innovations.

During the last part of the course, several subjects that have posed problems for classical cognitive science will be discussed. The role of emotions is discussed in an assignment relating to social robotics and cyberpsychology. Students will discuss time, a factor that is often neglected after studying examples of how dynamic systems theory is applied in psychological research (e.g. motor development and attitude polarisation). Thirdly, classical cognitive science often disregarded the physical and social environment of the subject. Hence, problem descriptions are offered that focus on distributed cognition, man-machine interaction, team cognition, autonomous agents, and ethical questions raised in the context of the development of new technologies, and the way in which people would need to cope with them.

The final assessment for this course is pass or fail - and not a numerical grade between 0,0 and 10,0.