This paper reports the first empirical investigation of the hypothesis that epistemic appraisals form part of the structure of concepts. To date, studies of concepts have focused on the way concepts encode properties of objects and the way those features are used in categorization and in other cognitive tasks. Philosophical considerations show the importance of also considering how a thinker assesses the epistemic value of beliefs and other cognitive resources and, in particular, concepts. We demonstrate that there are multiple, reliably (...) judged, dimensions of epistemic appraisal of concepts. Four of these dimensions are accounted for by a common underlying factor capturing how well people believe they understand a concept. Further studies show how dimensions of concept appraisal relate to other aspects of concepts. First, they relate directly to the hierarchical organization of concepts, reflecting the increase in specificity from superordinate to basic and subordinate levels. Second, they predict inductive choices in category-based induction. Our results suggest that epistemic appraisals of concepts form a psychologically important yet previously overlooked aspect of the structure of concepts. These findings will be important in understanding why individuals sometimes abandon and replace certain concepts; why social groups do so, for example, during a “scientific revolution”; and how we can facilitate such changes when we engage in deliberate “conceptual engineering” for epistemic, social, and political purposes. (shrink)

In recent years, language has been shown to play a number of important cognitive roles over and above the communication of thoughts. One hypothesis gaining support is that language facilitates thought about abstract categories, such as democracy or prediction. To test this proposal, a novel set of semantic memory task trials, designed for assessing abstract thought non-linguistically, were normed for levels of abstractness. The trials were rated as more or less abstract to the degree that answering them required the participant (...) to abstract away from both perceptual features and common setting associations corresponding to the target image. The normed materials were then used with a population of people with aphasia to assess the relationship of abstract thought to language. While the language-impaired group with aphasia showed lower overall accuracy and longer response times than controls in general, of special note is that their response times were significantly longer as a function of a trial’s degree of abstractness. Further, the aphasia group’s response times in reporting their degree of confidence (a separate, metacognitive measure) were negatively correlated with their language production abilities, with lower language scores predicting longer metacognitive response times. These results provide some support for the hypothesis that language is an important aid to abstract thought and to metacognition about abstract thought. (shrink)

Concepts are our tools for thinking. They enable us to engage in explicit reasoning about things in the world. Like physical tools, they can be more or less good, given the ways we use them – more or less dependable for categorisation, learning, induction, action-planning, and so on. Do concept users appreciate, explicitly or implicitly, that concepts vary in dependability? Do they feel that some concepts are in some way defective? If so, we metacognize our concepts. One example that has (...) been studied is a person’s judgement about how well they have learnt a new category. There are many other forms that concept-metacognition could take. This paper offers a preliminary taxonomy of different forms of metacognition directed at concepts. It suggests that concept-metacognition may affect the way one concept from a range of candidates is selected for use, and the way a concept is relied on in reasoning. Concept-metacognition may also play a pivotal role in the social process of constructing concepts, in replacing the old and constructing the new tools for thinking. (shrink)

The renewed interest in concepts and their role in psychological theorizing is partially motivated by Machery’s claim that concepts are so heterogeneous that they have no explanatory role. Against this, pluralism argues that there is multiplicity of different concepts for any given category, while hybridism argues that a concept is constituted by a rich common representation. This article aims to advance the understanding of the hybrid view of concepts. First, we examine the main arguments against hybrid concepts and conclude that, (...) even if not successful, they challenge hybridism to find a robust criterion for concept individuation and to show an explanatory advantage for hybrid concepts. Then we propose such a criterion of individuation, which we will call ‘functional stable coactivation’. Finally, we examine the prospects of hybridism to understand what is involved in recent approaches to categorization and meaning extraction. 1 The Heterogeneity of Conceptual Representations2 Two Challenges for Hybrid Concepts: Individuation and Explanation2.1 The coordination criterion2.2 Concepts as constituents of thoughts3 Individuating Hybrids: Functional Stable Coactivation4 The Explanatory Power of Hybrid Concepts4.1 Categorization4.2 Meaning extraction4.2.1 Linguistic comprehension and rich lexical entries4.2.2 Polysemy and hybrid concepts5 Conclusion. (shrink)

In this article I shall argue that the categories a subject employs to codify her perceptions are emergent elements of the social niche her community inhabits. Hence, I defend the claim that categories are primarily elements of the social ontology a certain subject experiences. I then claim that public representations shared in a social niche play a crucial regulative role for the members of that community: in fact, they offer a rule to conceive a certain type or a certain category, (...) e.g. ‘movement’, ‘time’ or ‘space’. In this sense, categories function as normative elements. (shrink)

Each of our theories of mental representation provides some insight into how the mind works. However, these insights often seem incompatible, as the debates between symbolic, dynamical, emergentist, sub-symbolic, and grounded approaches to cognition attest. Mental representations—whatever they are—must share many features with each of our theories of representation, and yet there are few hypotheses about how a synthesis could be possible. Here, I develop a theory of the underpinnings of symbolic cognition that shows how sub-symbolic dynamics may give rise (...) to higher-level cognitive representations of structures, systems of knowledge, and algorithmic processes. This theory implements a version of conceptual role semantics by positing an internal universal representation language in which learners may create mental models to capture dynamics they observe in the world. The theory formalizes one account of how truly novel conceptual content may arise, allowing us to explain how even elementary logical and computational operations may be learned from a more primitive basis. I provide an implementation that learns to represent a variety of structures, including logic, number, kinship trees, regular languages, context-free languages, domains of theories like magnetism, dominance hierarchies, list structures, quantification, and computational primitives like repetition, reversal, and recursion. This account is based on simple discrete dynamical processes that could be implemented in a variety of different physical or biological systems. In particular, I describe how the required dynamics can be directly implemented in a connectionist framework. The resulting theory provides an “assembly language” for cognition, where high-level theories of symbolic computation can be implemented in simple dynamics that themselves could be encoded in biologically plausible systems. (shrink)

Many animals can be trained to perform novel tasks. People, too, can be trained, but sometime in early childhood people transition from being trainable to something qualitatively more powerful—being programmable. We argue that such programmability constitutes a leap in the way that organisms learn, interact, and transmit knowledge, and that what facilitates or enables this programmability is the learning and use of language. We then examine how language programs the mind and argue that it does so through the manipulation of (...) embodied, sensorimotor representations. The role language plays in controlling mental representations offers important insights for understanding its origin and evolution. (shrink)

The contributions to this special issue on cognitive development collectively propose ways in which learning involves developing constraints that shape subsequent learning. A learning system must be constrained to learn efficiently, but some of these constraints are themselves learnable. To know how something will behave, a learner must know what kind of thing it is. Although this has led previous researchers to argue for domain-specific constraints that are tied to different kinds/domains, an exciting possibility is that kinds/domains themselves can be (...) learned. General cognitive constraints, when combined with rich inputs, can establish domains, rather than these domains necessarily preexisting prior to learning. Knowledge is structured and richly differentiated, but its “skeleton” must not always be preestablished. Instead, the skeleton may be adapted to fit patterns of co-occurrence, task requirements, and goals. Finally, we argue that for models of development to demonstrate genuine cognitive novelty, it will be helpful for them to move beyond highly preprocessed and symbolic encodings that limit flexibility. We consider two physical models that learn to make tone discriminations. They are mechanistic models that preserve rich spatial, perceptual, dynamic, and concrete information, allowing them to form surprising new classes of hypotheses and encodings. (shrink)

Statistical learning refers to the ability to identify structure in the input based on its statistical properties. For many linguistic structures, the relevant statistical features are distributional: They are related to the frequency and variability of exemplars in the input. These distributional regularities have been suggested to play a role in many different aspects of language learning, including phonetic categories, using phonemic distinctions in word learning, and discovering non-adjacent relations. On the surface, these different aspects share few commonalities. Despite this, (...) we demonstrate that the same computational framework can account for learning in all of these tasks. These results support two conclusions. The first is that much, and perhaps all, of distributional statistical learning can be explained by the same underlying set of processes. The second is that some aspects of language can be learned due to domain-general characteristics of memory. (shrink)

A theory of how concept formation begins is presented that accounts for conceptual activity in the first year of life, shows how increasing conceptual complexity comes about, and predicts the order in which new types of information accrue to the conceptual system. In a compromise between nativist and empiricist views, it offers a single domain-general mechanism that redescribes attended spatiotemporal information into an iconic form. The outputs of this mechanism consist of types of spatial information that we know infants attend (...) to in the first months of life. These primitives form the initial basis of concept formation, allow explicit preverbal thought, such as recall, inferences, and simple mental problem solving, and support early language learning. The theory details how spatial concepts become associated with bodily feelings of force and trying. It also explains why concepts of emotions, sensory concepts such as color, and theory of mind concepts are necessarily later acquisitions because they lack contact with spatial descriptions to interpret unstructured internal experiences. Finally, commonalities between the concepts of preverbal infants and nonhuman primates are discussed. (shrink)

Do properties of a word's features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge lengths computed using various distance measures. Feature distinctiveness was computed as a distance measure, showing how far an object in a network is from other objects based on (...) shared and non‐shared features. Feature distinctiveness predicted order of acquisition across all measures: Words that were further away from other words in the network space were learned earlier. The best distance measures were based only on non‐shared features (object dissimilarity) and did not include shared features (object similarity). This indicates that shared features may play less of a role in early word learning than non‐shared features. In addition, the strongest effects were found for visual form and surface features. Cluster analysis further revealed that this effect is a localized effect in the object feature space, where objects' distances from their cluster centroid were inversely correlated with their age of acquisition. Together, these results suggest a role for feature distinctiveness in early word learning. (shrink)

Effects of linguistic labels on learning outcomes are well-established; however, developmental research examining possible mechanisms underlying these effects have provided mixed results. We used a novel paradigm where 8-year-olds and adults were simultaneously trained on three sparse categories (categories with many irrelevant or unique features and a single rule defining feature). Category members were either associated with the same label, different labels, or no labels (silent baseline). Similar to infant paradigms, participants passively viewed individual exemplars and we examined fixations to (...) category relevant features across training. While it is well established that adults can optimize their attention in forced-choice categorization tasks without linguistic input, the present findings provide support for label induced attention optimization: simply hearing the same label associated with different exemplars was associated with increased attention to category relevant features over time, and participants continued to focus on these features on a subsequent recognition task. Participants also viewed images longer and made more fixations when images were paired with unique labels. These findings provide support for the claim that labels may facilitate categorization by directing attention to category relevant features. (shrink)

This paper introduces a special issue of Cognitive Science initiated on the 25th anniversary of the publication of Parallel Distributed Processing (PDP), a two-volume work that introduced the use of neural network models as vehicles for understanding cognition. The collection surveys the core commitments of the PDP framework, the key issues the framework has addressed, and the debates the framework has spawned, and presents viewpoints on the current status of these issues. The articles focus on both historical roots and contemporary (...) developments in learning, optimality theory, perception, memory, language, conceptual knowledge, cognitive control, and consciousness. Here we consider the approach more generally, reviewing the original motivations, the resulting framework, and the central tenets of the underlying theory. We then evaluate the impact of PDP both on the field at large and within specific subdomains of cognitive science and consider the current role of PDP models within the broader landscape of contemporary theoretical frameworks in cognitive science. Looking to the future, we consider the implications for cognitive science of the recent success of machine learning systems called “deep networks”—systems that build on key ideas presented in the PDP volumes. (shrink)

The organization of our knowledge about the world into an interconnected network of concepts linked by relations profoundly impacts many facets of cognition, including attention, memory retrieval, reasoning, and learning. It is therefore crucial to understand how organized semantic representations are acquired. The present experiment investigated the contributions of readily observable environmental statistical regularities to semantic organization in childhood. Specifically, we investigated whether co‐occurrence regularities with which entities or their labels more reliably occur together than with others (a) contribute to (...) relations between concepts independently and (b) contribute to relations between concepts belonging to the same taxonomic category. Using child‐directed speech corpora to estimate reliable co‐occurrences between labels for familiar items, we constructed triads consisting of a target, a related distractor, and an unrelated distractor in which targets and related distractors consistently co‐occurred (e.g., sock‐foot), belonged to the same taxonomic category (e.g., sock‐coat), or both (e.g., sock‐shoe). We used an implicit, eye‐gaze measure of relations between concepts based on the degree to which children (N = 72, age 4–7 years) looked at related versus unrelated distractors when asked to look for a target. The results indicated that co‐occurrence both independently contributes to relations between concepts and contributes to relations between concepts belonging to the same taxonomic category. These findings suggest that sensitivity to the regularity with which different entities co‐occur in children's environments shapes the organization of semantic knowledge during development. Implications for theoretical accounts and empirical investigations of semantic organization are discussed. (shrink)

The study of cognitive development has been, and still is, faced with two fundamental problems. They are connected, and no satisfactory answer has been found to either. The first problem concerns a terminological distinction and the corresponding semantic contents - it is important to be clear about which characteristics make it possible to distinguish percepts from concepts proper. The second problem, which depends on such distinction, consists in determining when and how, in cognitive development, the turn from perceptual categories into (...) concepts occurs. This paper provides a clear and precise answer to both questions. (shrink)

This paper summarizes a theory of cognitive development and elaborates on its educational implications. The theory postulates that development occurs in cycles along multiple fronts. Cognitive competence in each cycle comprises a different profile of executive, inferential, and awareness processes, reflecting changes in developmental priorities in each cycle. Changes reflect varying needs in representing, understanding, and interacting with the world. Interaction control dominates episodic representation in infancy; attention control and perceptual awareness dominate in realistic representations in preschool; inferential control and (...) awareness dominate rule-based representation in primary school; truth and validity control and precise self-evaluation dominate in principle-based thought in adolescence. We demonstrate that the best predictors of school learning in each cycle are the cycle’s cognitive priorities. Also learning in different domains, e.g., language and mathematics, depends on an interaction between the general cognitive processes dominating in each cycle and the state of the representational systems associated with each domain. When a representational system is deficient, specific learning difficulties may emerge, e.g., dyslexia and dyscalculia. We also discuss the educational implications for evaluation and learning at school. (shrink)

Living things can be classified by taxonomic similarity (lions and lynx), or shared ecological habitat (ducks and turtles). The present studies used card-sorting and triad tasks to explore developmental and experiential changes in conceptual flexibility–the ability to switch between taxonomic and ecological construals of living things–as well as two processes underlying conceptual flexibility: salience (i.e., the ease with which relations come to mind outside of contextual influences) and availability (i.e., the presence of relations in one’s mental space) of taxonomic and (...) ecological relations. We were also interested in the extent to which salience and availability of taxonomic and ecological relations predicted inductive inferences. Participants were 452 six to ten-year-olds from urban, suburban, and rural communities in New England. Across two studies, taxonomic relations were overwhelmingly more salient than ecological relations, although salience of ecological relations was higher among children from rural environments (Study 1) and those who engaged in unstructured exploration of nature (Study 2). Availability of ecological relations, as well as conceptual flexibility, increased with age, and was higher among children living in more rural environments. Notably, salience, but not availability, of ecological relations predicted ecological inferences. These findings suggest that taxonomic relations are a default way to organize intuitive biological knowledge and that environmental richness and relevant experience contribute to the salience and availability of ecological knowledge, and thereby, conceptual flexibility in biological thinking. More generally, they highlight important linkages between domain-specific knowledge and domain-general cognitive abilities. (shrink)

Do properties of a word's features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge lengths computed using various distance measures. Feature distinctiveness was computed as a distance measure, showing how far an object in a network is from other objects based on (...) shared and non-shared features. Feature distinctiveness predicted order of acquisition across all measures: Words that were further away from other words in the network space were learned earlier. The best distance measures were based only on non-shared features and did not include shared features. This indicates that shared features may play less of a role in early word learning than non-shared features. In addition, the strongest effects were found for visual form and surface features. Cluster analysis further revealed that this effect is a localized effect in the object feature space, where objects' distances from their cluster centroid were inversely correlated with their age of acquisition. Together, these results suggest a role for feature distinctiveness in early word learning. (shrink)

Children accrue experiences with buoyancy on a daily basis, yet research paints a mixed picture of children’s buoyancy knowledge. Whereas children’s predictions and explanations of the floating and the sinking of common objects are often based on a single feature (e.g., mass or facts), children’s predictions of novel cubes reveal solution strategies based on mass and volume integrations. Correspondingly, category learning theory suggests that categories (e.g., floaters vs. sinkers) are easier to identify when items mainly vary from one another in (...) the relevant defining features. For example, a set of cubes only varies in mass and volume and hence density, thereby being able to highlight the deterministic role of density when placed in water. Here we asked how item variation during hands-on exploration affects children’s subsequent predictions and explanations of buoyancy. Kindergarteners and first-, second-, and third-grade children individually explored either a set of 10 systematically varied cubes (i.e., systematic condition; n = 95) or a set of 10 common objects (i.e., non-systematic condition; n = 96) in a water basin. Next, the children predicted the buoyancy of five new cubes and five new common objects one at a time. Subsequently, the children explained their predictions one subset at a time. The children in the systematic condition were more accurate in their predictions of the test cubes than the children in the non-systematic condition. Latent class regression analyses identified three cube prediction solution strategies. The children in the systematic condition were more likely to use a strategy in which buoyancy decisions were made based on an accurate integration of mass and volume, while the children in the non-systematic condition were more likely to use a strategy in which mass was given more predictive load than volume. A third strategy was characterized by guessing. Latent class analyses of the children’s explanations revealed different explanation strategies, each appealing to several features, but as hypothesized, no clear condition differences were found. The findings indicate that even 5 min of exploration with systematically varied cubes can already help children use an advanced buoyancy prediction strategy. This provides evidence in favor of using category learning theory to inform early science education design. (shrink)

El estudio del desarrollo cognitivo ha estado y todavía sigue estando enfrentado a dos problemas fundamentales, vinculados entre sí, y para los que no se ha encontrado una respuesta satisfactoria. El primero de estos problemas se refiere a una distinción terminológica y los correspondientes contenidos semánticos - qué rasgos o características permiten distinguir a los perceptos (esquemas perceptivos o categorías perceptivas) de los conceptos propiamente dichos. El segundo problema, que depende de esa distinción, consiste en determinar de qué modo y (...) en qué momento del desarrollo cognitivo se produce el paso o transformación de las categorías perceptivas en conceptos. Este trabajo proporciona una respuesta clara y precisa a esas dos cuestiones. (shrink)