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One Size Doesn’t Fit All: Idiographic Computational Models Reveal Individual Differences in Learning and Meta-Learning Strategies

Theodros Haile
University of Washington ~ Psychology
Chantel Prat
University of Washington, United States of America
Prof. Andrea Stocco
University of Washington ~ University of Washington

Learning occurs through the interaction of working memory (WM), declarative memory (LTM) and reinforcement learning (RL). There are vast individual differences in learning mechanism deployment and it is often difficult to assess the relative contributions of these systems during learning through behavioral measures. Collins (2018), forwarded a working memory - reinforcement learning combined model that addresses this issue but seems to lack a robust declarative memory component. In this project we built four (two single-mechanism RL and LTM, and two integrated RL-LTM) idiographic learning models based on the ACT-R cognitive architecture. We aimed to examine individual differences and fit parameters that could explain preferential use of learning mechanisms using the Collins (2018) stimulus-response association task. We found that multiple models provided best-fits for individual learners with more variability in learning and memory parameters observed even within the best fitting models.

Are individual differences persistent? Last updated 3 years ago

Do you have any sense as to whether particular participants would exhibit the same behavior (i.e., be best described by the same model that best described the participant in your experiment) if you were to test the participant again at a different time or on an analogous task? Or said another way, do you feel that individual differences in your ex...

Jim Treyens 0 comments
Questions Last updated 3 years ago

Hi, thanks for this really interesting paper! I am completely on board with this push towards individual-level models. :-) I have a few questions. 1. Did you also look at RT as a measure to distinguish strategies? For example, I can imagine that a model of an LTM-based strategy would predict a effect of memory strength on RT (so slower responses...

Dr. Maarten van der Velde 1 comment

cool stuff! I was wondering whether the identified differences in dominant model between participants was associated with differences in behaviour, e.g., response to task conditions, RT, learning rate... and how does your declarative model differ from IBL? In the absence of blending? Maybe it would be interesting to add a comparison to IBL to the ...

Dr. Marieke Van Vugt 1 comment
Other strategies? Last updated 3 years ago

Hello, Great paper. While reading it, I wondered if there could be other strategies that could explain the data. Are there other strategies you looked at that could not explain the data? Are there other strategies that you considered but for whatever reason did not implement? Two strategies that come to mind that could integrate the LTM and R...

Dr. Tim Halverson 1 comment

We regret to inform you that, due to sudden and unforseen circumstances, Theodros Haile will not be able to present this paper. Please find it in the proceedings. The other authors will attempt to answer questions posted here.

Terry Stewart 0 comments
Cite this as:

Haile, T., Prat, C., & Stocco, A. (2020, July). One Size Doesn’t Fit All: Idiographic Computational Models Reveal Individual Differences in Learning and Meta-Learning Strategies. Abstract published at Virtual MathPsych/ICCM 2020. Via