Online research in philosophy

Arguments for feature creation receive support from studies of young infants forming category representations from perceptual experience. A challenge for Schyns et al. will be to determine how a feature creation system might interface with a perceptual system that appears constrained to follow organizational principles that specify how edge segments should be grouped into functional units of coherent object representations.

Physiological evidence predicts a model of concept categorisation that evolves through direct interaction with object feature selection. The requirement stated by Schyns et al. for feature plasticity is supported, but important caveats raise a question about the level at which feature identification can occur. Visual attribute selection for feature creation is likely to be directed by top-down and attentional processes.

Attributing the creation of new features to categorization requirements implies that the exemplars displayed are correctly assigned to their category. This constraint limits the scope of Schyns et al.'s proposal to supervised learning. We present data suggesting that this constraint is unwarranted and we argue that feature creation is better thought of as a byproduct of the attentional, on-line processing of incoming information.

We address two major limitations of Schyns et al. First, we clarify their concept of “features” by postulating several levels for processing. The composition of the feature set at each level determines the set at the next higher level, following simple structural guidelines. Second, we show that our proposed framework reconciles feature-creation and fixed-feature approaches.

According to one productive and influential approach to cognition, categorization, object recognition, and higher level cognitive processes operate on a set of fixed features, which are the output of lower level perceptual processes. In many situations, however, it is the higher level cognitive process being executed that influences the lower level features that are created. Rather than viewing the repertoire of features as being fixed by low-level processes, we present a theory in which people create features to subserve the representation and categorization of objects. Two types of category learning should be distinguished. Fixed space category learning occurs when new categorizations are representable with the available feature set. Flexible space category learning occurs when new categorizations cannot be represented with the features available. Whether fixed or flexible, learning depends on the featural contrasts and similarities between the new category to be represented and the individual's existing concepts. Fixed feature approaches face one of two problems with tasks that call for new features: If the fixed features are fairly high level and directly useful for categorization, then they will not be flexible enough to represent all objects that might be relevant for a new task. If the fixed features are small, subsymbolic fragments (such as pixels), then regularities at the level of the functional features required to accomplish categorizations will not be captured by these primitives. We present evidence of flexible perceptual changes arising from category learning and theoretical arguments for the importance of this flexibility. We describe conditions that promote feature creation and argue against interpreting them in terms of fixed features. Finally, we discuss the implications of functional features for object categorization, conceptual development, chunking, constructive induction, and formal models of dimensionality reduction. Key Words: concept learning; conceptual development; features; perceptual learning; stimulus encoding.

Dawson's provocative comment makes three connected points: (1) to be falsifiable, theories that assume flexible features must constrain their feature creation and mechanisms, (2) the explanatory power of such functional theories is rooted in the properties of their underlying physical mechanisms, and (3) to derive the relevant constraints of feature creation from these mechanisms, it is critical to avoid the scope slip. We will argue here that even though we agree with (1) and (2), (3) confuses two different levels of analysis of computational systems: the functional identification and the physical implementation of relevant constraints.

Schyns, Goldstone & Thibaut's argument is evaluated from a developmental perspective. Theoretically, feature creation is not necessarily problematic; this view derives from the assumption of innate content (primitive feature sets). Alternative assumptions (e.g., Piaget's theory) are possible. Preschool children readily search for novel features in response to task demands. This is compatible with functionalist approaches, but not the rationalist ones criticized by the authors.

Schyns, Goldstone & Thibaut present reasonable arguments for feature creation in category learning. We argue, however, that they do not provide unequivocal evidence either for the necessity or for the occurrence of feature creation. In an effort to sharpen the debate, we take the stand that a fixed feature approach is to be preferred in the absence of compelling evidence.

The origin of features from nonfeatural information is a problem that should concern all theories of object categorization and recognition, not just the flexible feature approach. In contrast to the idea that new features must originate from combinations of simpler fixed features, we argue that holistic features can be created from a direct imprinting on the visual medium. Furthermore, featural descriptions can emerge from processes that by themselves do not operate on feature detectors. Once acquired, features can be decomposed into component features if required by other categorizations. We therefore argue that it is not necessary to separate holistic and componential approaches to representations, because the latter is a development of the former. The requirements for representational flexibility outstrip the performance of any existing computational models, but specific mechanisms of feature creation are discussed and evaluated. Challenges for feature creation mechanisms are discussed together with the constraints (perceptual, statistical, functional, and task) they will need to satisfy.

Schyns et al. argue that flexibility in categorisation implies “feature creation.” We argue that this notion is flawed, that flexibility can be explained by combinations over fixed feature sets, and that feature creation would in any case fail to explain categorisation. We suggest that flexibility in categorisation is due to pragmatic factors influencing feature combination, rendering feature creation unnecessary.