Cognitive Processes Modeling II 2500-EN-CS-PM-04
This course is devoted to a more detailed consideration of concrete models of broadly defined cognitive systems and their functioning. Having received a solid overview of general classes of models (CPM I) and main topic in cognitive sciences (Advance Topics), the students will continue to explore how particular models can be used as theories for concrete cognitive phenomena and/or bases for technological/AI solutions. Lectures will be used to explain important theoretical concepts and present examples of models, while seminar/lab/workshop class will give hands-on experience in constructing selected models and applying selected analytical tools.
Taking the perspective of embodied and situated cognition we will consider a cognitive system as an entity adapting to the changing environmental demands as well as actively structuring their niches. Therefore we will change the usual ordering of cognitive phenomena as starting from perception of stimulus and arriving at a response, and rather start from concerns of action control within environmental demands. We will proceed through the issues of motor coordination and joint action, which acknowledge the presence of others from the earliest moments of cognition in a social species, and to issues in educating attention for such action and co-action. We will show the newest methods for studying and modeling such phenomena, based mainly in the dynamical systems paradigm, with illustrations of alternative models.
The course will continue into problems more traditionally considered as ‘cognitive’, such as integration of information in individual and joint decision making, and - again individual and joint - problem solving, including issues in game theory and solving combinatorial problems, where we will present logical and dynamical models. We will arrive at problems of what is being communicated in particular cognitive tasks, the nature of communication in general, and its role in cognition. We will present models of emergence of various forms of communication including language, including agent-based models and network approaches, which allow us to take into account not only different levels (individual and collective) but also multiple timescales (developmental, cultural, on-line).
Only against this background we will tackle the cores of more traditional approaches to cognitive processes: memory, perception, attention or categorization. Main modeling approaches to memory will be presented, including traditional “data-base” thinking and alternative, more dynamic modeling of history-dependence of cognitive systems and processes. Similarly perception, selected attentional processes and categorization will be presented both in traditional models and in more action-based frameworks. We will summarize the course by showing some potential integratory models.
Learning activities:
Participatory lecture with questions/discussion at the end of each class; Seminar/workshop with in-class assignments and homework.
Learning outcomes
Upon successful completion of the course students will:
be able to describe main cognitive phenomena being modeled computationally, be aware of the main approaches to modelling them and current theoretical debates (K_W01, K_W04)
be able to describe main computational models for selected cognitive phenomena, their strengths and weaknesses (K_W04, K_U03, K_U05)
know the cognitive scientific terminology pertaining to modeling and be able to communicate concepts within interdisciplinary team (K_UO5, K_UO9)
know and be able to apply principles of model construction, interpretation and evaluation (K_W07, K_UO7, K_UO8)
be able to pose their own research questions and create their own computational models of the chosen phenomena in selected programming environments (K_U10)
be able to find information pertinent to main models in cognitive science, understand the fast pace of changes in the field (K_U12)
be sensitive to ethical issues related to the use of artificial intelligent systems and their relation to human agency within relevant ecosystems (K_W06)
Assessment criteria
a) Assessment methods: exam, ongoing work in class, homeworks, and a group project.
b) Components of the final grade and their weights:
The final grade from the lecture is based on a written exam covering the
lectures and selected literature.
The final grade from the computer lab class is based on work done in
class and a small project.
Lecture and class in computer lab will be graded separately, however:
1. Obtaining a positive grade from computer lab class is a
prerequirement for taking the exam.
2. A good grade from the class may increase the final exam grade.
Lecture final grade components:
80% Written exam.
20% Computer lab bonus.
c) Grading scale:
- over 50%: 3
- over 60%: 3+
- over 70%: 4,
- over 80%: 4+
- over 90%: 5
d) Requirements for retaking the assessment: N/A
e) Exams in the exam session:
i) Requirements for taking the exam: adequate attendance, attendance, obtaining a positive grade from computer lab class.
ii) Exam can be retaken in the case of a negative exam grade. It is not possible to retake an exam if the grade is positive.
iii) No early exam session (“zerówka”) will be offered.
Attendance rules:
Two absences permitted both in the Lecture and in the Lab
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: