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Canessa, E., Chaigneau, S. E., & Marchant, N. (2023). Use of Agent-Based Modeling (ABM) in Psychological Research. In Trends and Challenges in Cognitive Modeling (Vol. Early Access, pp. 7–20).
Abstract: In this chapter, we introduce the general use of agent-based modeling (ABM) in social science studies and in particular in psychological research. Given that ABM is frequently used in many disciplines in social sciences, as the main research tool or in conjunction with other modeling approaches, it is rather surprising its infrequent use in psychology. There are many reasons for that infrequent use of ABM in psychology, some justified, but others stem from not knowing the potential benefits of applying ABM to psychological research. Thus, we begin by giving a brief overview of ABM and the stages one has to go through to develop and analyze such a model. Then, we present and discuss the general drawbacks of ABM and the ones specific to psychology. Through that discussion, the reader should be able to better assess whether those disadvantages are sufficiently strong for precluding the application of ABM to his/her research. Finally, we end up by stating the benefits of ABM and examining how those advantages may outweigh the potential drawbacks, thus making ABM a valuable tool to consider in psychological research.
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Chaigneau, S. E., Marchant, N., Canessa, E., & Aldunate, N. (2024). A mathematical model of semantic access in lexical and semantic decisions. Lang. Cogn., Early Access.
Abstract: In this work, we use a mathematical model of the property listing task dynamics and test its ability to predict processing time in semantic and lexical decision tasks. The study aims at exploring the temporal dynamics of semantic access in these tasks and showing that the mathematical model captures essential aspects of semantic access, beyond the original task for which it was developed. In two studies using the semantic and lexical decision tasks, we used the mathematical model's coefficients to predict reaction times. Results showed that the model was able to predict processing time in both tasks, accounting for an independent portion of the total variance, relative to variance predicted by traditional psycholinguistic variables (i.e., frequency, familiarity, concreteness imageability). Overall, this study provides evidence of the mathematical model's validity and generality, and offers insights regarding the characterization of concrete and abstract words.
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Marchant, N., Canessa, E., & Chaigneau, S. E. (2023). Challenges from Probabilistic Learning for Models of Brain and Behavior. In Trends and Challenges in Cognitive Modeling (Vol. Early Access, pp. 73–84).
Abstract: Probabilistic learning is a research program that aims to understand how animals and humans learn and adapt their behavior in situations where the pairing between cues and outcomes is not always completely reliable. This chapter provides an overview of the challenges of probabilistic learning for models of the brain and behavior. We discuss the historical background of probabilistic learning, its theoretical foundations, and its applications in various fields such as psychology, neuroscience, and artificial intelligence. We also review some key findings from experimental studies on probabilistic learning, including the role of feedback, attention, memory, and decision-making processes. Finally, we highlight some of the current debates and future directions in this field.
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Marchant, N., Canessa, E., & Chaigneau, S. E. (2022). An adaptive linear filter model of procedural category learning. Cogn. Process., 23(3), 393–405.
Abstract: We use a feature-based association model to fit grouped and individual level category learning and transfer data. The model assumes that people use corrective feedback to learn individual feature to categorization-criterion correlations and combine those correlations additively to produce classifications. The model is an Adaptive Linear Filter (ALF) with logistic output function and Least Mean Squares learning algorithm. Categorization probabilities are computed by a logistic function. Our data span over 31 published data sets. Both at grouped and individual level analysis levels, the model performs remarkably well, accounting for large amounts of available variances. When fitted to grouped data, it outperforms alternative models. When fitted to individual level data, it is able to capture learning and transfer performance with high explained variances. Notably, the model achieves its fits with a very minimal number of free parameters. We discuss the ALF's advantages as a model of procedural categorization, in terms of its simplicity, its ability to capture empirical trends and its ability to solve challenges to other associative models. In particular, we discuss why the model is not equivalent to a prototype model, as previously thought.
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Ramos, D., Moreno, S., Canessa, E., Chaigneau, S. E., & Marchant, N. (2023). AC-PLT: An algorithm for computer-assisted coding of semantic property listing data. Behav. Res. Methods, Early Access.
Abstract: In this paper, we present a novel algorithm that uses machine learning and natural language processing techniques to facilitate the coding of feature listing data. Feature listing is a method in which participants are asked to provide a list of features that are typically true of a given concept or word. This method is commonly used in research studies to gain insights into people's understanding of various concepts. The standard procedure for extracting meaning from feature listings is to manually code the data, which can be time-consuming and prone to errors, leading to reliability concerns. Our algorithm aims at addressing these challenges by automatically assigning human-created codes to feature listing data that achieve a quantitatively good agreement with human coders. Our preliminary results suggest that our algorithm has the potential to improve the efficiency and accuracy of content analysis of feature listing data. Additionally, this tool is an important step toward developing a fully automated coding algorithm, which we are currently preliminarily devising.
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