ICCM: Decision Making

In this study, we propose a roadmap for the analysis of various factors on cognitive models' parameters and utilizing different cognitive models to better understand the human decision making process in a binary choice task. Our experiment of a binary choice task is a biased coin flip game, where users predict the outcome of 150 trials of biased coin flips without knowing the coin's bias. In a previous study, we conducted a factorial ANOVA on the biased coin flip game to identify factors that significantly influence users' decision making strategies, such as Gender, Win rate visibility, and the coin's bias value. In this paper, we employed genetic algorithms to identify cognitive models that fit users' behaviors the best in specific scenarios for each combination of the effective factors. Subsequently, we fitted linear models to examine the relationship between the identified parameters and the influential factors. By analyzing and interpreting the coefficients of these linear models, we aim to gain insights into how these factors affect users' decision making processes and understand human decision making better. Our proposed roadmap serves as a valuable resource for researchers aiming to interpret cognitive model parameters for diverse user behaviors. By providing a systematic approach to investigating the relationships between influential factors and cognitive model parameters, this work provides a deeper understanding of human decision making processes and baselines for future modeling approaches in this domain.

This is an in-person presentation on July 21, 2023 (11:00 ~ 11:20 UTC).

Cognitive and sensorimotor functions are usually assessed separately and therefore also modeled individually although they are strongly intertwined. One way to link these two levels conceptually is sensorimotor abstraction. It is the simplification of complex sensorimotor experiences, and it might enable goal-directed planning in situations with high uncertainties. We propose a computational model for dynamic decision-making that employs two distinct layers, a (lower) sensorimotor control layer holding sub-symbolic information, and a (higher) cognitive control layer holding abstracted information as symbols. In this two-layer architecture information about action control is passed upwards in the hierarchy, abstracted, and used to generate explicit action intentions which are passed downwards again. The hierarchization of model components is intended to represent the different levels of regulatory control (automated vs. fully conscious). We also use different forms of modeling for the individual layers. We employ predictive coding for sensorimotor and ACT-R for cognitive control. An agent equipped with the two-layer architecture is situated in a grid world and tasked to reach a finish line. However, the environment poses challenges on motor control by causing perturbations in the action execution of traversal reflecting varying uncertainty encountered in the real world. Here we describe a straightforward approach to the multi-layer architecture and relate it to the embodied cognition perspective. We also discuss possible extensions that we plan to introduce which depict fundamental cognitive functions such as representing the visual environment in varying granularity.

This is an in-person presentation on July 21, 2023 (11:20 ~ 11:40 UTC).

Prior research has found interference effects (IEs) in decision making, which violate classical probability theory (CPT). We developed a model of IEs called the probability theory + noise (PTN) model and compare its predictions to an existing quantum model called the Belief-Action Entanglement (BAE) model. The PTN assumes that memory operates consistently with CPT, but noise in the retrieval process produces violations of CPT. Using parameter space partitioning, we identified that both models can produce all qualitative patterns of IEs. We found that the BAE tends to produce IE distributions with a larger variance compared to the PTN. We also show that PTN predicts a relationship we term the conditional attack probability equality (CAEP) which is violated in previously reported data. The CAEP holds for the PTN regardless of chosen parameter values. However, the BAE is not constrained by the CAEP.

This is an in-person presentation on July 21, 2023 (11:40 ~ 12:00 UTC).

We investigate the viability of the drift-diffusion framework to account for behaviour on magnitude comparison tasks. Data from both published studies on magnitude comparison and a simulation are analysed to estimate the key drift-diffusion model parameters, using the EZ-diffusion method and HDDM package. All methods resulted in linear mappings between drift rate and difficulty (indexed using 1 - smaller/larger), with an intercept that was consistently close to zero for non-symbolic tasks. The EZ method was rapid and simple to apply, but subject to bias when using aggregate data or when accuracy was very high. In contrast, the HDDM tool produced results that were less biased, but individual differences were under-estimated. We conclude that application of parameter estimation methods, particularly in research on individual differences, requires careful consideration of their limitations.

This is an in-person presentation on July 21, 2023 (12:00 ~ 12:20 UTC).