Computational Affective Modelling I

1.9.2026 - 30.9.2027

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Syventävät opinnot

This course (in two Parts I and II) presents a introduction to modelling of affective phenomena. It has a particular focus on theories of human suffering, or mental pain. The basic premise is that we can use the modern theory of artificial intelligence (AI) to model and understand human suffering or mental pain. Both humans and sophisticated AI agents process information about the world in order to achieve goals and obtain rewards, which is why AI can be used as a model of the human brain and mind, and this includes affective phenomena. This course intends to make the theory accessible to a relatively general audience, requiring only some relevant scientific background (see prerequisites); it is based on the e-book available at https://www.cs.helsinki.fi/u/ahyvarin/painintl/(Avautuu uuteen välilehteen) .

Part I begins with the fundamental assumption that human suffering is mainly caused by frustration. Frustration means the failure of an agent (whether AI or human) to achieve a goal or a reward it wanted or expected. Frustration is inevitable because of the overwhelming complexity of the world, limited computational resources, and scarcity of good data. The concept of threat, related to risk and fear, provides a complementary approach to modelling suffering. Fundamental in such modelling is the idea of learning, or adaptation to the environment. While AI uses machine learning, humans and animals adapt by a combination of evolutionary mechanisms and ordinary learning. Another important concept here is the dual-process nature of human cognition: the division into logical-symbolic and neural-network information processing.

Sisältää

Tarkista sinulta vaadittavat suoritukset oppilaitoksen sivuilta.

Ei aktiivisia toteutuksia tällä hetkellä.

Lisätiedot ja ilmoittautuminen

Hinta ja ilmoittautumisaika: Tarkista tieto korkeakoulun sivuilta

Siirry korkeakoulun sivuille(Avautuu uuteen välilehteen)

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Koulutusalat

Tietojenkäsittely ja tietoliikenne

Laajuus

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DATA20060

After the course (Part I), the student

- has become acquainted the basic concept of modelling affective phenomena, such as suffering, using the theory of autonomous agents and machine learning

- understands the importance of frustration in modelling human suffering, and how it can be modelled in an AI agent

- understands how threat and risk can be modelled in a probabilistic AI framework

- understands the idea of dual processing in a cognitive system and its implications for action selection and affective phenomena

Study materials: All material is provided in the MOOC, which includes text pages, multimedia content (e.g. figures, short recorded lectures), and multiple-choice questions (the last serving both for learning and assessment).

Literature: While the MOOC contains everything, it is quite precisely based on an e-book written by Aapo Hyvärinen, see https://www.cs.helsinki.fi/u/ahyvarin/painintl/(Avautuu uuteen välilehteen) , so that this Part I of the course corresponds to Part I of the book. The factual contents of the MOOC and the e-book are basically equivalent, except that some details (basically the footnotes) are left out of the MOOC, while multimedia content will be created for the MOOC merely as pedagogical devices without increasing the factual content.

Completion methods: Independent study (MOOC)

Assessment practices and criteria: Assessment based on multiple-choice questions in the MOOC system.

Activities and methods in support of learning: This is a MOOC and based on independent study of material on the MOOC web pages.

Target groups: Primarily, advanced students (starting from 3rd year Bachelor’s) of data science and computer science. Secondarily, students in any related disciplines since the prerequisites are kept quite low, especially International Bachelor’s of Science and Liberal Arts and Sciences (typically in 3rd year). The course is not obligatory in any program. Being a MOOC, it is open to “everybody” under the usual conditions of UH MOOCs.

Teaching period: This MOOC will be available any time.

Recommended time or stage of studies: 3rd year Bachelor's of CS, or any year of DS/CS master. For non-CS students, typically an optional course in a Master’s degree, but in International Bachelor’s in Science and Liberal Arts and Sciences, advanced (e.g. 3rd year) Bachelor’s students.

Study module: This is part of the specialization studies module "Cognition in Brain and Machines" in the data science master’s program.

Expiry of studies: to be decided

Language of Instruction: English

Järjestäjä

Helsingin yliopisto

Yhteystiedot

uni-help@helsinki.fi

Tiedot ja toiminnallisuudet

Computational Affective Modelling I

Sisältää

Lisätiedot ja ilmoittautuminen

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Oppimateriaali

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