Norwegian version

Public defense: Asieh Abolpour Mofrad

Asieh Abolpour Mofrad will defend her thesis: “When Behavior Analysis Meets Machine -Learning Formation of Stimulus Equivalence Classes and Adaptive Learning in Artificial Agents” for the PhD in Behaviour Analysis.

Asieh Abolpour Mofrad is a PhD student at the PhD programme in Behaviour Analysis.

Trial lecture

The trial lecture starts at 13:00 in Zoom.

Title: How the study of atypical populations can help in building general theories of behaviour.

Public defense

The candidate will defend her thesis in English January 28, 2021 at 14:30 in Zoom.

Title of the thesis

When Behavior Analysis Meets Machine -Learning Formation of Stimulus Equivalence Classes and Adaptive Learning in Artificial Agents

Ordinary opponents

Leader of the public defense

Head of Department Magne Arve Flaten, Department of Behavioural Science, Oslo Metropolitan University


Digital defense information

Due to limitations on physical participation as a consequence of the coronavirus pandemic, the public defense will be conducted as a webinar on the zoom digital platform.

The trial lecture and the public defense use the same zoom link, which will be available on the top of this page. OsloMet students and employees log into OsloMet accounts ( Others can download Zoom ( or use a browser.

How to oppose ex auditorio

Please send your question to the host during the break, before the second opponent begins. Raise your digital hand by clicking "Participants" at the bottom of the zoom window and choose "Raise Hand" if you want to voice the question yourself after both opponents have finished their questions. The technical administrator will ask to activate your microphone. Click Yes.

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Publication of the approved PhD thesis

Request a copy of the PhD thesis by email. Include the name of the PhD candidate.

  • Abstract

    In this thesis, two well studied subjects in behavior analysis are computationally modeled; formation of stimulus equivalence classes, and adaptive learning. The former is addressed in Study I and Study II, while the latter is addressed in Study III and Study IV.


    Stimulus equivalence as a behavioral analytic approach studies cognitive skills such as memory and learning. Despite its importance in experimental studies, from a computational modelling point of view, the formation of stimulus equivalence classes has largely been under-investigated. On the other hand, adaptive learning in a broad sense, is a tool to study several cognitive tasks including memory and remembering. An appropriate model can be used as a cognitive level finder, and as a recommendation tool to optimize the training and learning sequence of tasks.


    To propose computational models that replicate formation of stimulus equivalence classes and adaptive learning. The models are supposed to be simple, flexible and interpretable in order to be suitable for analysis of human complex behavior.


    Agents endowed with Reinforcement learning, more precisely Projective Simulation and Stochastic Point Location, are used to model the interaction between experimenter and the participant through the testing/learning process. Formation of derived relations in Study I is achieved by on demand computation during the test phase trials using likelihood reasoning. In Study II, subsequent to the training phase, an iterative diffusion process called Network Enhancement is used to form derived relations, which turns the test phase into a memory retrieval phase. The solution to Stochastic Point Location in Study III aims to estimate the tolerable task difficulty level in an online and interactive settings. In Study IV, the appropriate task difficulty for training and learning is sought by using a target success rate that is usually defined beforehand by the experimenter using a method called Balanced Difficulty Task Finder.


    Proposed models for replication of equivalence relations, called Equivalence Projective Simulation (Study I) and Enhanced Equivalence Projective Simulation (Study II) could replicate a variety of settings in a matching-to-sample procedure. The models are quite flexible and appropriate to replicate results from real experiments and simulate different scenarios before performing an empirical experiment involving human subjects. In Study III, we suggest a new method to estimate the unknown point location in the Stochastic Point Location problem domain using the mutual probability  flux concept and we prove that the proposed solution outperforms the legacy solution reported in the literature. The probability of receiving correct response from the participant is also estimated as a measure of reliability of participant's performance. In Study IV, we propose a model that is able to suggest a manageable difficulty level to a learner based on online feedback via an asymmetric adjustment technique of difficulty.


    We aimed for models that are flexible, interpretative without a need of extensive pre-training of the model. By resorting to the theory of Projective Simulation, we propose an interpretable simulator for equivalence relations that enjoys the advantage of being easy to configure. By virtue of the Stochastic Point Location model, it is possible to eliminate the need for prior-knowledge about the participant while also avoiding complex modelling techniques. Although not pursued in this thesis, those two lines of modelling could be used in a complementary setting. For instance, adaptive learning can be integrated in the training phase of matching-to-sample or titrated delayed matching-to-sample procedures as suggested in Study IV.


    human complex behavior, learning and memory, stimulus equivalence classes, arbitrary matching-to-sample, titrated delayed matching-to-sample, artificial intelligence, reinforcement learning, adaptive learning, stochastic point location

  • Questions?

    Who can answer questions prior to trial lecture and public defense?

    Send e-mail to PhD administration for Faculty of Health Sciences.