The causal view of categories assumes that categories are represented by features and their causal relations. To study the effect of causal knowledge on categorization, researchers have used Bayesian causal models. Within that framework, categorization may be viewed as dependent on a likelihood computation (i.e., the likelihood of an exemplar with a certain combination of features, given the category's causal model) or as a posterior computation (i.e., the probability that the exemplar belongs to the category, given its features). Across three experiments, in combination with computational modeling, we offer evidence that categorization is better accounted for by assuming that people compute posteriors and not likelihoods, though both probabilities are closely related. This result contrasts with existing analyses of causal-based categorization, which assume that likelihood computations give a good approximation of human judgments. We also find that people are able to compute likelihoods in a closely related task that elicits judgments of consistency rather than category membership judgments. Our analyses show that people do use causal probabilistic information as prescribed by a Bayesian model but that they flexibly compute likelihoods or posteriors depending on the task. We discuss our results in relation to the relevant literature on the topic.
Associative accounts of category learning have been, for the most part, abandoned in favor of cognitive explanations (e.g., similarity, explicit rules). In the current work, we implement an Adaptive Linear Filter (ALF) closely related to the Rescorla and Wagner learning rule, and use it to tackle three learning tasks that pose challenges to an associative view of category learning. Across three computational simulations, we show that the ALF is in fact able to make the predictions that seemed problematic. Notably, in our simulations we use exactly the same model and specifications, attesting to the generality of our account. We discuss the consequences of our findings for the category learning literature.
La metodología Aula Invertida está siendo cada vez más utilizada en la educación superior para enfrentar los desafíos de la formación remota en contextos de pandemia, así como para mejorar los resultados de aprendizaje de los estudiantes. Sin embargo, la evidencia empírica sobre la relación entre esta metodología y la participación estudiantil es aún limitada e inconsistente. Este estudio indagó los efectos de la implementación de la metodología Aula Invertida en línea, desde la perspectiva de estudiantes universitarios de cuarto año de la carrera de psicología. El diseño de investigación fue comparativo, razón por la cual se aplicó una encuesta antes y después del uso de Aula Invertida en un curso troncal de la formación en psicología social-comunitaria. La encuesta fue elaborada para indagar específicamente sobre el uso de Tecnologías de la Información y la Comunicación, la gestión del tiempo, la colaboración entre pares y la participación en las actividades. Los resultados muestran que los estudiantes redujeron el uso de plataformas basadas en la nube durante el semestre, que valoran el uso de las Tecnologías de la Información y Comunicación en el curso evaluado, y que valoran la colaboración entre pares durante las actividades implementadas. Las conclusiones del estudio resaltan la relevancia de privilegiar el uso de ciertos recursos digitales en la formación de los estudiantes, al mismo tiempo que la necesidad de prestar mayor atención en este tipo de investigaciones a la influencia del equipo docente en la participación estudiantil.
In the category learning literature, similarity models have monopolized a good deal of research. The prototype and exemplar models are both based on the idea that people represent the structure of categories and category instances in the physical world in a mental similarity space. However, evidence for these models comes mainly from paradigms that provide subjects with deterministic feedback (i.e., exemplars belong to their corresponding categories with probability = 1). There is evidence that results obtained with deterministic feedback paradigms may not generalize well under probabilistic feedback conditions (i.e., where exemplars belong to their corresponding categories with probability less than 1). In this current work, we also suggest that probabilistic feedback may better reflect natural conditions, which is another important reason to pursue probabilistic feedback research. Thus, in the current work we set up a category learning experiment with probabilistic feedback and use it to evaluate different models. In addition to the two similarity models discussed above, we also use an associationist model that does not rely on the similarity construct. To compare our three models, we rely on computational modeling, which is a standard way of model comparison in cognitive psychology. Our results show that our associationist model outperforms similarity models on all our model evaluation measures. After presenting our results, we discuss why the similarity-based models fail, and also suggest some future lines of research that are possible using probabilistic feedback procedures.
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