Perceptual beliefs that categorize objects can be justified by demonstrating basic properties (eg shapes) of the objects. In these justifications, perceptual justifiers have different contents to the beliefs they justify. I argue that the justifications are not inferential. Subjects are unlikely to have bodies of beliefs adequate to inferentially justify the beliefs they actually form on the strength of their object recognition abilities, especially when recognition depends on stimulus-dependent retrieval of visual memories. Instead, I argue, the justifications exploit a partial (...) awareness that subjects have of states and processes involved in object recognition. As such, they show that subjects have a degree of internal access to the principles of externalistic perceptual warrant, and to the features of perceptual states that give them justificatory force on externalistic accounts. The justifications themselves are evidential, but in order for them to have any justificatory force, they have to be placed in an externalistic framework for perceptual justification. I conclude that this form of justification shows that subjects have a degree of insight into why their perceptual experiences justify their beliefs. (shrink)

Traditionally, theories of concepts in psychology assume that concepts are a single, uniform kind of mental representation. But no single kind of representation can explain all of the empirical data for which concepts are responsible. I argue that the assumption that concepts are uniformly the same kind of mental structure is responsible for these theories’ shortcomings, and outline a pluralist theory of concepts that rejects this assumption. On pluralism, concepts should be thought of as being constituted by multiple representational kinds, (...) with the particular kind of concept used on an occasion being determined by the context. I argue that endorsing pluralism does not lead to eliminativism about concepts as an object of scientific interest. (shrink)

From the viewpoint of pattern recognition and computational learning, mirror neurons form an interesting multimodal representation that links action perception and planning. While it seems unlikely that all details of such representations are specified by the genetic code, robust learning of such complex representations likely requires an appropriate interplay between plasticity, generalization, and anatomical constraints of the underlying neural architecture.

Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner. Using equivalences between models found in human categorization and (...) machine learning research, we explain how these semi-supervised techniques can be applied to human learning. A series of experiments are described which show that semi-supervised learning models prove useful for explaining human behavior when exposed to both labeled and unlabeled data. We then discuss some machine learning models that do not have familiar human categorization counterparts. Finally, we discuss some challenges yet to be addressed in the use of semi-supervised models for modeling human categorization. (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)

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)

Visualizing and mental imagery are thought to be cognitive states by all sides of the imagery debate. Yet the phenomenology of those states has distinctly visual ingredients. This has potential consequences for the hypothesis that vision is cognitively impenetrable, the ability of visual processes to ground perceptual warrant and justification, and the distinction between cognitive and perceptual phenomenology. I explore those consequences by describing two forms of visual ambiguity that involve visualizing: the ability to visually experience a picture surface as (...) flat after it has caused volumetric nonconceptual contents, and the ability to use a surface initially perceived as flat to visualize three-dimensional scenes. In both cases, the visual processes which extract viewer-centered volumetric shapes have to rely solely on monocular depth cues in the absence of parallax and stereopsis. Those processes can be cognitively penetrated by acts of visualizing, including ones that draw on conceptual information about kinds. However, the penetrability of the visual processes does not weaken their ability to provide perceptual warrant and justification for beliefs. The reason is that picture perceptions—whether they are stimulus -driven or based on acts of visualizing—are different to object perceptions both phenomenologically and in terms of their functional roles as states. Thus, although the penetrability of the visual processes does mean that subjects can have visual experiences with contradictory contents, perceptual belief is adopted at most towards one set of contents, and questions of warrant and justification are raised only for those contents. A rule-proving exception is provided by trompe-l’oeils. (shrink)

The late vision theorist David Marr identified three levels of explanation that he argued needed to be addressed in order to understand vision : (i) the psychological, functional or computational level of processes; (ii) the physical or neurological which is the level of explanation employed by Zeki; and (iii) the algorithmic – the level of implementation. For Zeki’s purpose of drawing upon vision-theory in order to better understand art and aesthetics, there is no need to focus on the third level. (...) I would argue however that in order to draw upon brain science to understand art, the first two levels of explanation are necessary. (shrink)

This paper investigates a the cognitive foundations of a pragmatic account of line drawings. It sets to highlight those features of line drawings that make them, as opposed to other types of visual representations, particularly conducive to communication. It is argued that representational and artifactual properties of drawings must be investigated together in order to understand the peculiarities of drawings as communicative tools.

This paper comments on an article by V.S. Ramachandran and William Hirstein (JCS,1999) in which they purport to be identifying the neurological principles of beauty. I draw attention to the way the problem of beauty is construed in the philosophical literature by Mary Mothersill (1984) and Immanuel Kant (Critique of Judgment). I argue that Ramachandran and Hirsteins' principles do not address the problem of beauty because they do not differentiate between the experience of beauty and other closely related phenomena. I (...) then show how one might address the problem of beauty within the framework that they have adopted. (shrink)