Sponsors Committees

Topics in cognitive science, 2018

Cognitive modeling is the effort to understand the mind by implementing theories of the mind in computer code, producing measures comparable to human behavior and mental activity. The community of cognitive modelers has traditionally met twice every 3 years at the International Conference on Cognitive Modeling (ICCM). In this special issue of topiCS, we present the best papers from the ICCM meeting. (The full proceedings are available on the ICCM website.) These best papers represent advances in the state of the art in cognitive modeling. Since ICCM was for the first time also held jointly with the Society for Mathematical Psychology, we use this preface to also reflect on the similarities and differences between mathematical psychology and cognitive modeling.

PsycEXTRA Dataset

In the past twenty or so years the scientific community has made impressive advancements in the modeling and simulation of general human cognition. This progress has led to the beginnings of wide-spread applications and use. In fact, we are now at a point where the community can begin to make fairly accurate predictions as to how this technology will be used in the next twenty-plus years. Accordingly, the purpose of this panel is to engage the community at large regarding the future needs and requirements associated with building cognitive models for various scientific and engineering endeavors. Specifically, this panel will discuss and make recommendations with regard to the future functionality of cognitive modeling that could be encompassed in nextgeneration capabilities. To do this, we will concentrate on four different domain areas. These are: academic use of cognitive modeling, cognitive model development, neuroscience-related issues, and practical applications of cognitive modeling.

Unified Dynamic Model of the Mind: A Comprehensive Framework for Cognitive Science, 2025

Abstract The Unified Dynamic Model of the Mind presents a comprehensive framework for understanding cognitive processes as dynamic, interrelated, and continuously evolving. This model integrates principles from cognitive science, neuroscience, psychology, and artificial intelligence to explore the intricate interactions between perception, memory, reasoning, and emotional dynamics. By bridging conscious and unconscious processes, the framework provides insights into how mental representations adapt in response to internal and external stimuli. Developed collaboratively through the Storm Platform at Stanford University, this work highlights practical applications in fields such as education, healthcare, and AI development. It emphasizes the role of cultural and environmental factors in shaping cognition while offering innovative solutions for addressing individual differences and decision-making processes. The integration of computational models and dynamic systems theory underscores the model's potential for advancing both theoretical understanding and practical applications in cognitive science and beyond. This research represents a significant step toward unifying disparate cognitive theories into a cohesive framework, paving the way for future exploration and interdisciplinary collaboration.

2022

In the past three decades, there has been an increasing growth in the adoption of computational models of cognition as a tool to further understand the cognition of the human mind (Norris & Cutler, 2021). Computational models of cognition utilise mathematical models through computation to study a complex system or a specific mechanism by providing a quantitative explanation of a set of behavioural data (Wilson & Collins, 2019). Considering the need for computational methods of knowledge and studying the way computational models are constructed can allow for a better understanding of the benefits, as well as the pitfalls of, computational models of cognition.

2004

The cognitive neuroscience revolution has profoundly altered the nature of theories in cognitive psychology. For many years, the only charge of these theories was to account for purely behavioral data from cognitive experiments that typically were performed on healthy young adults. Now, however, the validity of a cognitive theory may also be challenged by data from a wide variety of other sources-including functional magnetic resonance imaging (fMRI), neuropsychological patient studies, recordings of event-related potentials (ERPs), transcranial magnetic stimulation studies, and singleunit recordings. Clearly the converging evidence provided by these many methods adds tremendous new constraints to the underlying theories, and thereby almost guarantees faster progress in our understanding of the behaviors of interest. But the huge variety of data sources that a successful theory must resolve also greatly increases the difficulty of theory construction. In fact, a new type of theory is required, and along with it, new methods for constructing those theories.

2015

Cognitive modeling can provide important insights into the underlying causes of behavior, but the validity of those insights rests on careful model development and checking. We provide guidelines on five important aspects of the practice of cognitive modeling: parameter recovery, testing selective influence of experimental manipulations on model parameters, quantifying uncertainty in parameter estimates, testing and displaying model fit, and selecting among different model parameterizations and types of models. Each aspect is illustrated with examples.

Computational modeling has long been one of the traditional pillars of cognitive science. Unfortunately, the computer models of cognition being developed today have not kept up with the enormous changes that have taken place in computer technology and, especially, in human-computer interfaces. For all intents and purposes, modeling is still done today as it was 25, or even 35, years ago. Everyone still programs in his or her own favorite programming language, source code is rarely made available, accessibility of models to non-programming researchers is essentially non-existent, and even for other modelers, the profusion of source code in a multitude of programming languages, written without programming guidelines, makes it almost impossible to access, check, explore, re-use, or continue to develop. It is high time to change this situation, especially since the tools are now readily available to do so. We propose that the modeling community adopt three simple guidelines that would ensure that computational models would be accessible to the broad range of researchers in cognitive science. We further emphasize the pivotal role that journal editors must play in making computational models accessible to readers of their journals.

Human Brain Mapping, 1999

This article describes a computational modeling architecture, 4CAPS, which is consistent with key properties of cortical function and makes good contact with functional neuroimaging results. Like earlier cognitive models such as SOAR, ACT-R, 3CAPS, and EPIC, the proposed cognitive model is implemented in a computer simulation that predicts observable variables such as human response times and error patterns. In addition, the proposed 4CAPS model accounts for the functional decomposition of the cognitive system and predicts fMRI activation levels and their localization within specific cortical regions, by incorporating key properties of cortical function into the design of the modeling system. Hum. ᭜ Human Brain Mapping 8:128-136(1999) ᭜ ᭜ Computational Modeling: 4 CAPS ᭜ ᭜ 129 ᭜ ᭜ Computational Modeling: 4 CAPS ᭜ ᭜ 131 ᭜

2010

The approach presented in this paper addresses the question of the proper scientific basis for Human Factors modeling and proposes an architectural framework for integrating federated models and simulations. It is intended to be applicable to a broad range of scenarios across domains. Indeed, broad applicability and integration of modeling and simulation techniques are essential to their effectiveness and validation. Our approach is grounded in the concept of unified theories of cognition, implemented computationally ...

Psychological Methods, 2017

The question of cognitive architecture -how cognitive processes are temporally organized -has arisen in many areas of psychology. This question has proved difficult to answer, with many proposed solutions turning out to be spurious. Systems Factorial Technology provided the first rigorous empirical and analytical method of identifying cognitive architecture, using the Survivor Interaction Contrast (SIC) to determine when people are using multiple sources of information in parallel or in series. Although the SIC is based on rigorous nonparametric mathematical modeling of response time distributions, for many years inference about cognitive architecture has relied solely on visual assessment. Houpt and Townsend (2012) recently introduced null hypothesis significance tests, and here we develop both parametric and nonparametric (encompassing prior) Bayesian inference. We show that the Bayesian approaches can have considerable advantages.