ICCM: Problem Solving Skills

Current studies suggest that individuals estimate the value of their choices based on observed feedback. Here, we ask whether individuals also update the value of their unchosen actions, even when the associated feedback remains unknown. One hundred seventy-eight individuals completed a multi-armed bandit task, making choices to gain rewards. We found robust evidence suggesting latent value updating of unchosen actions based on the chosen action’s outcome. Computational modeling results suggested that this effect is mainly explained by a value updating mechanism whereby individuals integrate the outcome history for choosing an option with that of rejecting the alternative. Properties of the deliberation (i.e., duration/difficulty) did not moderate the latent value updating of unchosen actions, suggesting that memory traces generated during deliberation might take a smaller role in this specific phenomenon than previously thought. We discuss the mechanisms facilitating credit assignment to unchosen actions and their implications for human decision-making.

This is an in-person presentation on July 21, 2024 (11:00 ~ 11:20 CEST).

Computational modeling is a powerful approach for discerning individual differences in memory function. The model-based assessments discussed in this paper rely on estimating an individual's rate of memory decay– a stable and idiographic parameter that the model can capture. However, this paper aims to demonstrate prior knowledge as a confounding factor in these model-based assessments and seeks to parse out the error using Maximum Likelihood Estimations. The metric of individualized memory performance, termed Speed of Forgetting, was significantly lower for facts known beforehand. Still, these facts were identified with 81% accuracy by recovered base-level activation estimations blind to the ground-truth data. A proposal for future model-based assessments to account for prior knowledge is discussed.

This is an in-person presentation on July 21, 2024 (10:00 ~ 10:20 CEST).

Analogical reasoning is a core cognitive process that involves mapping knowledge structures, and may depend on how mental representations are encoded and retrieved. Successful analogical reasoning can enable analogical transfer between a previous and new concept or problem. Theories and models were developed to explain analogical reasoning and transfer. However, challenges with interacting cognitive processes, generalization, and cognitive plausibility remain. Here, we attempt to address challenges by leveraging previous work with a cognitive analogical reasoning framework and a subsequent extension. The model starts with procedural knowledge about how do a problem solving task and learns its solution. It then "reads" and represents problem isomorphs, and initiates analogical transfer to solve them. We present results and limitations with our approach.

This is an in-person presentation on July 21, 2024 (10:40 ~ 11:00 CEST).

Complex problem solving (CPS) is a fundamental capability of humans. It is often studied through microworlds, with the Tailorshop-scenario as a well-investigated prominent example. This paper addresses several research questions for CPS in the Tailorshop scenario: Firstly, it examines the impact of background knowledge vs. understanding underlying dynamics. Secondly, it investigates the predictability of a participants' performance, particularly when considering their assumptions about the scenario's mechanisms. Finally, it discusses the suitability of the Tailorshop as a scenario for cognitive modeling of CPS. Thereby, we discuss some of the measures that have been proposed to assess CPS performance, considering CPS from an perspective of predictive modeling.Based on our results, we conclude that effective prediction of outcomes in complex tasks necessitates uniform impact of actions throughout, facilitating comprehension of both overarching strategies and smaller adjustments crucial in real-world problem-solving domains.

This is an in-person presentation on July 21, 2024 (10:20 ~ 10:40 CEST).