Acquiring contextualized concepts: a connectionist approach

Conceptual knowledge is acquired through recurrent experiences, by extracting statistical regularities at different levels of granularity. At a fine level, patterns of feature co-occurrence are categorized into objects. At a coarser level, patterns of concept co-occurrence are categorized into contexts. We present and test CONCAT, a connectionist model that simultaneously learns to categorize objects and contexts. The model contains two hierarchically organized CALM modules (Murre, Phaf, & Wolters, 1992). The first module, the Object Module, forms object representations based on co-occurrences between features. These representations are used as input for the second module, the Context Module, which categorizes contexts based on object co-occurrences. Feedback connections from the Context Module to the Object Module send activation from the active context to those objects that frequently occur within this context. We demonstrate that context feedback contributes to the successful categorization of objects, especially when bottom-up feature information is degraded or ambiguous.     

Syntactic structure and artificial grammar learning: the learnability of embedded hierarchical structures

Embedded hierarchical structures, such as "the rat the cat ate was brown", constitute a core generative property of a natural language theory. Several recent studies have reported learning of hierarchical embeddings in artificial grammar learning (AGL) tasks, and described the functional specificity of Broca's area for processing such structures. In two experiments, we investigated whether alternative strategies can explain the learning success in these studies. We trained participants on hierarchical sequences, and found no evidence for the learning of hierarchical embeddings in test situations identical to those from other studies in the literature. Instead, participants appeared to solve the task by exploiting surface distinctions between legal and illegal sequences, and applying strategies such as counting or repetition detection. We suggest alternative interpretations for the observed activation of Broca's area, in terms of the application of calculation rules or of a differential role of working memory. We claim that the learnability of hierarchical embeddings in AGL tasks remains to be demonstrated.     

A statistical model for discriminating between subliminal and near-liminal performance

The concept of a psychophysical threshold is foundational in perceptual psychology. In practice, thresholds are operationalized as stimulus values that lead to a fairly high level of performance such as .75 or .707 in two-choice tasks. These operationalizations are not useful for assessing subliminality—the state in which a stimulus is so weak that performance is at chance. We present a hierarchical Bayesian model of performance that incorporates a threshold that divides subliminal from near-liminal performance. The model provides a convenient means to measure at-chance thresholds and therefore is useful for testing theories of subliminal priming. The hierarchical nature of the model is critical for efficient analysis as strength is pooled across people and stimulus values. A comparison to Rasch psychometric models is provided.     

Functional hemispheric differences for the categorization of global and local information in naturalistic stimuli

Up to now functional hemispheric asymmetries for global/local processing have mainly been investigated with hierarchical letters as stimuli. In the present study, three experiments were conducted to examine whether corresponding visual-field (VF) effects can also be obtained with more naturalistic stimuli. To this end, images of animals with a pattern placed on their body were displayed as stimuli. The task for the global level and for the local level was to categorize the animals and the patterns, respectively. As a result, VF-effects were also found for these stimuli and tasks. It is concluded that the hemispheric differences observed for hierarchical letters also hold for naturalistic stimuli.     

Developmental changes in the processing of hierarchical shapes continue into adolescence

The present study was designed to trace the normal development of local and global processing of hierarchical visual forms. We presented pairs of hierarchical shapes to children and adults and asked them to indicate whether the two shapes were the same or different at either the global or the local level. In Experiments 1 (6-year-olds, 10-year-olds, adults) and 2 (10-year-olds, 14-year-olds, adults), we presented stimuli centrally. All age groups responded faster on global trials than local trials (global precedence effect), but the bias was stronger in children and diminished to the adult level between 10 and 14 years of age. In Experiment 3 (10-year-olds, 14-year-olds, adults), we presented stimuli in the left or right visual field so that they were transmitted first to the contralateral hemisphere. All age groups responded faster on local trials when stimuli were presented in the right visual field (left hemisphere); reaction times on global trials were independent of visual field. The results of Experiment 3 suggest that by 10 years of age the hemispheres have adult-like specialization for the processing of hierarchical shapes, at least when attention is directed to the global versus local level. Nevertheless, their greater bias in Experiments 1 and 2 suggests that 10-year-olds are less able than adults to modulate attention to the output from local versus global channels-perhaps because they are less able to ignore distractors and perhaps because the cerebral hemispheres are less able to engage in parallel processing.     

Auditory attention to frequency and time: an analogy to visual local-global stimuli

Two priming experiments demonstrated exogenous attentional persistence to the fundamental auditory dimensions of frequency (Experiment 1) and time (Experiment 2). In a divided-attention task, participants responded to an independent dimension, the identification of three-tone sequence patterns, for both prime and probe stimuli. The stimuli were specifically designed to parallel the local-global hierarchical letter stimuli of [Navon D. (1977). Forest before trees: The precedence of global features in visual perception. Cognitive Psychology, 9, 353-383] and the task was designed to parallel subsequent work in visual attention using Navon stimuli [Robertson, L. C. (1996). Attentional persistence for features of hierarchical patterns. Journal of Experimental Psychology: General, 125, 227-249; Ward, L. M. (1982). Determinants of attention to local and global features of visual forms. Journal of Experimental Psychology: Human Perception and Performance, 8, 562-581]. The results are discussed in terms of previous work in auditory attention and previous approaches to auditory local-global processing.     

Learning the form of causal relationships using hierarchical bayesian models

People learn quickly when reasoning about causal relationships, making inferences from limited data and avoiding spurious inferences. Efficient learning depends on abstract knowledge, which is often domain or context specific, and much of it must be learned. While such knowledge effects are well documented, little is known about exactly how we acquire knowledge that constrains learning. This work focuses on knowledge of the functional form of causal relationships; there are many kinds of relationships that can apply between causes and their effects, and knowledge of the form such a relationship takes is important in order to quickly identify the real causes of an observed effect. We developed a hierarchical Bayesian model of the acquisition of knowledge of the functional form of causal relationships and tested it in five experimental studies, considering disjunctive and conjunctive relationships, failure rates, and cross-domain effects. The Bayesian model accurately predicted human judgments and outperformed several alternative models.     

Uniqueness of real-valued hierarchical classes models

Two novel uniqueness theorems are derived for the family of hierarchical classes (HICLAS) models, a family of structural decomposition models for N-way N-mode data that imply simultaneous hierarchically organized classifications of all modes involved in the data. The theorems generalize earlier results on binary HICLAS models to the integer- and real-valued cases. In addition, they allow for a shorter and insightful proof of a result on Boolean matrix invertibility that goes back to earlier work of Luce (1952) and Rutherford (1963).     

Probability judgment in hierarchical learning: a conflict between predictiveness and coherence

Why are people's judgments incoherent under probability formats? Research in an associative learning paradigm suggests that after structured learning participants give judgments based on predictiveness rather than normative probability. This is because people's learning mechanisms attune to statistical contingencies in the environment, and they use these learned associations as a basis for subsequent probability judgments. We introduced a hierarchical structure into a simulated medical diagnosis task, setting up a conflict between predictiveness and coherence. Thus, a target symptom was more predictive of a subordinate disease than of its superordinate category, even though the latter included the former. Under a probability format participants tended to violate coherence and make ratings in line with predictiveness; under a frequency format they were more normative. These results are difficult to explain within a unitary model of inference, whether associative or frequency-based. In the light of this, and other findings in the judgment and learning literature, a dual-component model is proposed.