Recent studies of visual statistical learning (VSL) have demonstrated that statistical regularities in sequences of visual stimuli can be automatically extracted, even without intent or awareness. Despite much work on this topic, however, several fundamental questions remain about the nature of VSL. In particular, previous experiments have not explored the underlying units over which VSL operates. In a sequence of colored shapes, for example, does VSL operate over each feature dimension independently, or over multidimensional objects in which color and shape are bound together? The studies reported here demonstrate that VSL can be both object-based and feature-based, in systematic ways based on how different feature dimensions covary. For example, when each shape covaried perfectly with a particular color, VSL was object-based: Observers expressed robust VSL for colored-shape sub-sequences at test but failed when the test items consisted of monochromatic shapes or color patches. When shape and color pairs were partially decoupled during learning, however, VSL operated over features: Observers expressed robust VSL when the feature dimensions were tested separately. These results suggest that VSL is object-based, but that sensitivity to feature correlations in multidimensional sequences (possibly another form of VSL) may in turn help define what counts as an object.

To be published in Behavioral and Brain Sciences (in press)

Stimulus intensity is an important determinant for perception, learning and behaviour. We studied the effects of odorant concentration on classical conditioning involving odorants and odorant-mechanosensory compounds using the proboscis-extension reflex in the honeybee. Our results show that high concentrations of odorant (a) support better discrimination in a feature-positive task using rewarded odorant-mechanosensory compounds versus unrewarded mechanosensory stimuli, (b) have a stronger capacity to overshadow learning of a simultaneously trained mechanosensory stimulus, and (c) induce better memory consolidation. Furthermore, honeybees were trained discriminatively to two different concentrations of one odorant. Honeybees are not able to

The present study aimed to replicate an associative learning effect, overshadowing, both in the traditional laboratory conditions and over the internet. The experimental task required participants to predict an outcome based on the presence of several cues. When a cue that was always trained together with a second cue was presented on isolation at test, the expectancy of the outcome was impaired, which revealed overshadowing. This experimental task was performed by undergraduate students (N = 106) in the laboratory and by a different set of anonymous participants over the internet (N = 91). Similar levels of overshadowing were obtained in both locations. These similarities show that web-delivered experiments can be used as a complement of traditional experiments. © 2006 Elsevier B.V. All rights reserved.

Symbols enable people to organize and communicate about the world. However, the ways in which symbolic knowledge is learned and then represented in the mind are poorly understood. We present a formal analysis of symbolic learning—in particular, word learning—in terms of prediction and cue competition, and we consider two possible ways in which symbols might be learned: by learning to predict a label from the features of objects and events in the world, and by learning to predict features from a label. This analysis predicts significant differences in symbolic learning depending on the sequencing of objects and labels. We report a computational simulation and two human experiments that confirm these differences, revealing the existence of Feature-Label-Ordering effects in learning. Discrimination learning is facilitated when objects predict labels, but not when labels predict objects. Our results and analysis suggest that the semantic categories people use to understand and communicate about the world can only be learned if labels are predicted from objects. We discuss the implications of this for our understanding of the nature of language and symbolic thought, and in particular, for theories of reference.

Several studies have shown that predictive and causal judgments vary depending on whether the question used to assess the relationship between events is presented after each piece of information or only after all the available information has been observed. This effect could be understood by assuming that in the two cases people perceive that the test question requires that different sets of evidence be taken into account. This hypothesis is tested in the present experiments through contextual manipulations that take place at the time of training and at the time of test. Our results show that people use this contextual information to infer which set of events should be considered when making their subjective assessments. The results are at odds with current theoretical approaches, but it is possible to develop mechanisms that would allow these models to account for the observed evidence. Learning to predict future events from present events is one of the most powerful adaptive tools, since it allows an organism to find the necessary resources for survival and to avoid dangerous situations. Given its importance, this kind of predictive learning was the central focus of animal behavior research throughout the twentieth century. During the last decades, predictive learning has also become important in the area of human cognition, where it has given rise to a great amount of empirical and theoretical research. The vast amount of evidence provided by this research has sometimes turned out to be quite difficult to explain by the available theoretical approaches. Many variables usually neglected by theoretical models influence the process of human learning of predictive relations among events or the way in which humans use the acquired information. Among other things, it has been shown that the probe question used to assess participants ’ judgment (Matute,

Two experiments are reported that examine the role of temporal contiguity on judgments of contingency in a human analogue of the Pavlovian task. The data show that the effect of the actual delay on contingency judgment depends on the observers expectation regarding the delay. For a fixed contingency between the cue and the outcome, ratings of the contingency are higher when the actual delay is congruent with the observers expectation than when it is incongruent. We argue that our data can be understood within the context of the temporal coding hypothesis. There is considerable evidence of similarities between the operations that modulate the strength of conditioning in nonhuman animals and those that modulate the rating of the contingency between events by humans (see Allan, 1993). One of these similarities is the effect of temporal contiguity. It is well established in the animal literature that temporal contiguity is an important variable in both instrumental and Pavlovian conditioning (see Allan, Balsam, Church, & Terrace, 2002; Allan & Church, 2002). For example, increasing the delay between a response and reinforcement in an instrumental task decreases the rate of responding. Similarly, increasing the delay between a conditioned stimulus and an unconditioned stimulus in a Pavlovian task retards the acquisition of the conditioned response. The studies that have examined the effect of temporal contiguity on ratings of contingency have used human analogues of the animal instrumental procedure (e.g., Buehner & May,

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Abstract—The abstraction of patterns from data and the formation of categories is a hallmark of human cognitive ability. As such, it has been studied from many different perspectives by researchers, and these studies have led to several explanatory models. In this paper, we consider the inference of categorical representations for the purpose of producing taskspecific responses. Task-relevant responses require a knowledge repertoire that is organized to allow efficient access to useful information. We present a neurodynamical system that infers functionally coherent categories from semantic inputs (or concepts) presented sequentially in different contexts, and encodes them as attractors in a two-dimensional topological feature space. The resulting category representations can then act as pointers in a larger system for semantic cognition. The system allows controlled hierarchical organization and functional segregation of the inferred categories. I.

The concordance between performance and judgements of the causal effectiveness of an instrumental action suggests that such actions are mediated by causal knowledge. Although causal learning exhibits many associative phenomena—blocking, inhibitory or preventative learning, and super-learning—judgements of the causal status of a cue can be changed retrospectively as a result of learning episodes that do not directly involve the cue. In order to explain retrospective revaluation, a modi®ed associative theory is described in which the learning processes for retrieved cue representations are the opposite to those for presented cues, and this theory is evaluated by studies of the role of within-compound associations in retrospective revaluation and blocking. However, this modi®ed theory only applies when the within-compound association represents a contiguous rather than a causal cue relationship. Causal learning and representation is a fundamental form of cognition, if not the fundamental form. Without the capacity to learn about and represent the causal relationships between our actions and their consequences, the mind would be radically disconnected from the world. However detailed and rich our knowledge, however sophisticated and complex our inferences and planning, cognition would be impotent if our thoughts could not be