Capacities for abstract thinking and problem‐solving are central to human cognition. Processes of abstraction allow the transfer of experiences and knowledge between contexts helping us make informed decisions in new or changing contexts. While we are often inclined to relate such reasoning capacities to individual minds and brains, they may in fact be contingent on human‐specific modes of collaboration, dialogue, and shared attention. In an experimental study, we test the hypothesis that social interaction enhances cognitive processes of rule‐induction, which in (...) turn improves problem‐solving performance. Through three sessions of increasing complexity, individuals and groups were presented with a problem‐solving task requiring them to categorize a set of visual stimuli. To assess the character of participants’ problem representations, after each training session, they were presented with a transfer task involving stimuli that differed in appearance, but shared relations among features with the training set. Besides, we compared participants’ categorization behaviors to simulated agents relying on exemplar learning. We found that groups performed superior to individuals and agents in the training sessions and were more likely to correctly generalize their observations in the transfer phase, especially in the high complexity session, suggesting that groups more effectively induced underlying categorization rules from the stimuli than individuals and agents. Crucially, variation in performance among groups was predicted by semantic diversity in members’ dialogical contributions, suggesting a link between social interaction, cognitive diversity, and abstraction. (shrink)

Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model that incorporates varying degrees of prior belief in class-conditional independence, learns whether or not independence holds, (...) and adapts its behavior accordingly. Theoretical results from two simulation studies demonstrate that classification behavior can appear to start simple, yet adapt effectively to unexpected task structures. Two experiments—designed using optimal experimental design principles—were conducted with human learners. Classification decisions of the majority of participants were best accounted for by a version of the model with very high initial prior belief in class-conditional independence, before adapting to the true environmental structure. Class-conditional independence may be a strong and useful default assumption in category learning tasks. (shrink)

The nosological diagnosis is a particular type of nontheoretical diagnosis consisting of identifying the disease that afflicts the patient without explaining the underlying etiopathological mechanisms. Its origins are within the essentialist point of view on the nature of diseases, which dates back at least to 18th-century taxonomy studies. In this article, we propose a model of nosological diagnosis as a two-phase process composed of the categorization of inductive inferences and argumentations by analogy. In the inductive phase, disease entities are identified (...) by means of typicality-based categorization processes, and meaningful clinical samples are learned ; in the subsequent phase, those samples are used as the bases of argumentations by analogy to obtain a diagnosis for a given patient. This model extends the prototype resemblance theory of disease including also the exemplar theory proposed in cognitive science and, moreover, it frames the clinical activity of nosological diagnosis and how it can be explained within the theory of argumentation. According to it, diagnosis based on the recognition of a typical syndrome is explained in terms of the prototype theory of categorization and the antisymmetrical argumentation by analogy, while diagnosis based on a comparison with a previous clinical case is explained by the exemplar theory of categorization and by the symmetrical argumentation by analogy. (shrink)

Riassunto: Lo scopo di questo lavoro è fornire una spiegazione filosofico-cognitiva della diagnosi nosologica legando i classici temi filosofici delle forme del ragionamento scientifico con gli aspetti cognitivi del ragionamento di senso comune. In filosofia della medicina sono state proposte varie teorie della diagnosi per analizzare e spiegare il ragionamento diagnostico; una di queste è la diagnosi nosologica: un particolare tipo di diagnosi a-teorica e basata sulla similarità. In questo lavoro mostriamo come la diagnosi nosologica si possa considerare come un (...) procedimento scientifico composto sia da processi induttivi di categorizzazione e concettualizzazione che di argomentazioni per analogia; i processi di categorizzazione coinvolti sono spiegabili usando due delle teorie dei concetti proposte nella scienza cognitiva: la teoria dei prototipi e la teoria degli esemplari; mentre le argomentazioni usate sono analogie sia simmetriche che anti-simmetriche. Infine, presentiamo un modello della diagnosi nosologica che lega in un quadro coerente queste precedenti analisi; in questo modello la diagnosi nosologica basata sul riconoscimento del quadro morboso tipico, la sindrome, è spiegata in termini di teoria dei prototipi e di argomentazioni analogiche anti-simmetriche; mentre la diagnosi nosologica basata su di un precedente caso clinico è spiegata con la teoria degli esemplari e con le usuali argomentazioni analogiche simmetriche. Parole chiave: Diagnosi; Categorizzazione; Analogia; Induzione; Nosologia Analogical Arguments and Categorization Processes in Nosological Diagnosis -: The purpose of this work is to provide a philosophical-cognitive explanation of nosological diagnosis by linking the classical philosophical themes of scientific reasoning to cognitive aspects of common sense reasoning. In philosophy of medicine, various theories of diagnosis have been proposed to analyze and explain diagnostic reasoning; one of these is nosological diagnosis: a particular type of atheoretical diagnosis based on similarity. In this paper, we show how nosological diagnosis can be considered as a scientific process composed of both inductive processes of categorization and argumentation by analogy; the categorization processes involved can be explained using two of the theories of concepts proposed in cognitive science: the theory of prototypes and the theory of exemplars; while argumentation uses both symmetrical and anti-symmetrical analogies. Finally, we present a model of nosological diagnosis that binds these previous analyses into a coherent framework; in this model, nosological diagnosis based on the recognition of a typical syndrome is explained in terms of prototype theory and anti-symmetrical analogical argumentation; while nosological diagnosis based on previous clinical case studies is explained by the theory of exemplars and the usual symmetrical analogical argumentation. Keywords: Diagnosis; Categorization; Analogy; Induction; Nosology. (shrink)

The idea that perceptual and cognitive systems must incorporate knowledge about the structure of the environment has become a central dogma of cognitive theory. In a Bayesian context, this idea is often realized in terms of “tuning the prior”—widely assumed to mean adjusting prior probabilities so that they match the frequencies of events in the world. This kind of “ecological” tuning has often been held up as an ideal of inference, in fact defining an “ideal observer.” But widespread as this (...) viewpoint is, it directly contradicts Bayesian philosophy of probability, which views probabilities as degrees of belief rather than relative frequencies, and explicitly denies that they are objective characteristics of the world. Moreover, tuning the prior to observed environmental frequencies is subject to overfitting, meaning in this context overtuning to the environment, which leads (ironically) to poor performance in future encounters with the same environment. Whenever there is uncertainty about the environment—which there almost always is—an agent's prior should be biased away from ecological relative frequencies and toward simpler and more entropic priors. (shrink)

The bias–variance tradeoff is a theoretical concept that suggests machine learning algorithms are susceptible to two kinds of error, with some algorithms tending to suffer from one more than the other. In this letter, we claim that the bias–variance tradeoff is a general concept that can be applied to human cognition as well, and we discuss implications for research in cognitive science. In particular, we show how various strands of research in cognitive science can be interpreted in light of the (...) bias–variance tradeoff, giving insight into individual differences in learning, the nature of cognitive processes, and debates in cognitive science research. (shrink)