Amygdala and hippocampal activity during acquisition and extinction of human fear conditioning

Previous functional magnetic resonance imaging (fMRI) studies have characterized brain systems involved in conditional response acquisition during Pavlovian fear conditioning. However, the functional neuroanatomy underlying the extinction of human conditional fear remains largely undetermined. The present study used fMRI to examine brain activity during acquisition and extinction of fear conditioning. During the acquisition phase, participants were either exposed to light (CS) presentations that signaled a brief electrical stimulation (paired group) or received light presentations that did not serve as a warning signal (control group). During the extinction phase, half of the paired group subjects continued to receive the same treatment, whereas the remainder received light alone. Control subjects also received light alone during the extinction phase. Changes in metabolic activity within the amygdala and hippocampus support the involvement of these regions in each of the procedural phases of fear conditioning. Hippocampal activity developed during acquisition of the fear response. Amygdala activity increased whenever experimental contingencies were altered, suggesting that this region is involved in processing changes in environmental relationships. The present data show learning-related amygdala and hippocampal activity during human Pavlovian fear conditioning and suggest that the amygdala is particularly important for forming new associations as relationships between stimuli change.     

Wais-III matrix reasoning: instruction effects on task perception and performance

Instructions for the Matrix Reasoning subtest of the WAIS-III do not communicate to examinees that the subtest is untimed. The present study examined what percentage of participants (34 women, 26 men, M age = 20.1; 55 Caucasian, 3 African American, 2 Hispanic) made the assumption that Matrix Reasoning was timed, and its effect on examinees' scores. 55% of participants receiving standard instructions retrospectively reported assuming the subtest was timed, and those who did not assume Matrix Reasoning was timed scored significantly higher than participants who did. Participants receiving additional instructions that clarified the untimed nature of Matrix Reasoning scored significantly higher than those receiving standard instructions who believed the subtest was timed.     

Probabilistic constraint satisfaction at the lexical/phonetic interface: evidence for gradient effects of within-category VOT on lexical access

Research in speech perception has been dominated by a search for invariant properties of the signal that correlate with lexical and sublexical categories. We argue that this search for invariance has led researchers to ignore the perceptual consequences of systematic variation within such categories and that sensitivity to this variation may provide an important source of information for integrating information over time in speech perception. Data from a study manipulating VOT continua in words using an eye-movement paradigm indicate that lexical access shows graded sensitivity to within-category variation in VOT and that this sensitivity has a duration sufficient to be useful for information integration. These data support a model in which the perceptual system integrates information from multiple sources and from the surrounding temporal context using probabilistic cue-weighting mechanisms.     

A causal-model theory of conceptual representation and categorization

This article presents a theory of categorization that accounts for the effects of causal knowledge that relates the features of categories. According to causal-model theory, people explicitly represent the probabilistic causal mechanisms that link category features and classify objects by evaluating whether they were likely to have been generated by those mechanisms. In 3 experiments, participants were taught causal knowledge that related the features of a novel category. Causal-model theory provided a good quantitative account of the effect of this knowledge on the importance of both individual features and interfeature correlations to classification. By enabling precise model fits and interpretable parameter estimates, causal-model theory helps place the theory-based approach to conceptual representation on equal footing with the well-known similarity-based approaches.     

A knowledge-resonance (KRES) model of category learning

This article introduces a connectionist model of category learning that takes into account the prior knowledge that people bring to new learning situations. In contrast to connectionist learning models that assume a feedforward network and learn by the delta rule or backpropagation, this model, the knowledge-resonance model, or KRES, employs a recurrent network with bidirectional symmetric connection whose weights are updated according to a contrastive Hebbian learning rule. We demonstrate that when prior knowledge is represented in the network, KRES accounts for a considerable range of empirical results regarding the effects of prior knowledge on category learning, including (1) the accelerated learning that occurs in the presence of knowledge, (2) the better learning in the presence of knowledge of category features that are not related to prior knowledge, (3) the reinterpretation of features with ambiguous interpretations in light of error-corrective feedback, and (4) the unlearning of prior knowledge when that knowledge is inappropriate in the context of a particular category.     

How causal knowledge affects classification: A generative theory of categorization

Several theories have been proposed regarding how causal relations among features of objects affect how those objects are classified. The assumptions of these theories were tested in 3 experiments that manipulated the causal knowledge associated with novel categories. There were 3 results. The 1st was a multiple cause effect in which a feature's importance increases with its number of causes. The 2nd was a coherence effect in which good category members are those whose features jointly corroborate the category's causal knowledge. These 2 effects can be accounted for by assuming that good category members are those likely to be generated by a category's causal laws. The 3rd result was a primary cause effect, in which primary causes are more important to category membership. This effect can also be explained by a generative account with an additional assumption: that categories often are perceived to have hidden generative causes.     

Age-related changes in event-cued visual and auditory prospective memory proper

We rely upon prospective memory proper (ProMP) to bring back to awareness previously formed plans and intentions at the right place and time, and to enable us to act upon those plans and intentions. To examine age-related changes in ProMP, younger and older participants made decisions about simple stimuli (ongoing task) and at the same time were required to respond to a ProM cue, either a picture (visually cued ProM test) or a sound (auditorily cued ProM test), embedded in a simultaneously presented series of similar stimuli (either pictures or sounds). The cue display size or loudness increased across trials until a response was made. The cue size and cue loudness at the time of response indexed ProMP. The main results showed that both visual and auditory ProMP declined with age, and that such declines were mediated by age declines in sensory functions (visual acuity and hearing level), processing resources, working memory, intelligence, and ongoing task resource allocation.     

Feature inference and the causal structure of categories

The purpose of this article was to establish how theoretical category knowledge-specifically, knowledge of the causal relations that link the features of categories-supports the ability to infer the presence of unobserved features. Our experiments were designed to test proposals that causal knowledge is represented psychologically as Bayesian networks. In five experiments we found that Bayes' nets generally predicted participants' feature inferences quite well. However, we also observed a pervasive violation of one of the defining principles of Bayes' nets-the causal Markov condition-because the presence of characteristic features invariably led participants to infer yet another characteristic feature. We argue that this effect arises from a domain-general bias to assume the presence of underlying mechanisms associated with the category. Specifically, people take an exemplar to be a "well functioning" category member when it has most or all of the category's characteristic features, and thus are likely to infer a characteristic value on an unobserved dimension.     

Fusions and Ordinary Physical Objects

In “Tropes and Ordinary Physical Objects”, Kris McDaniel argues that ordinary physical objects are fusions of monadic and polyadic tropes. McDaniel calls his view “TOPO”—for “Theory of Ordinary Physical Objects”. He argues that we should accept TOPO because of the philosophical work that it allows us to do. Among other things, TOPO is supposed to allow endurantists to reply to Mark Heller’s argument for perdurantism. But, we argue in this paper, TOPO does not help endurantists do that; indeed, we argue that anyone who accepts TOPO should reject endurantism.     

Thirty-something categorization results explained: selective attention, eyetracking, and models of category learning

An eyetracking study testing D. L. Medin and M. M. Schaffer's (1978) 5-4 category structure was conducted. Over 30 studies have shown that the exemplar-based generalized context model (GCM) usually provides a better quantitative account of 5-4 learning data as compared with the prototype model. However, J. D. Smith and J. P. Minda (2000) argued that the GCM is a psychologically implausible account of 5-4 learning because it implies suboptimal attention weights. To test this claim, the authors recorded undergraduates' eye movements while the students learned the 5-4 category structure. Eye fixations matched the attention weights estimated by the GCM but not those of the prototype model. This result confirms that the GCM is a realistic model of the processes involved in learning the 5-4 structure and that learners do not always optimize attention, as commonly supposed. The conditions under which learners are likely to optimize attention during category learning are discussed.