Feeling with a probe may seem unusual, but in fact, it is commonplace. For example, when people scrape the bottom of a pot with a spoon or write with a pencil on grained paper, the perceptual impression of a textured surface arises from the vibrations transmitted to the fingers that hold the implement. As Katz (1925Katz ( /1989 noted, people have a rich impression of the surface, not of the vibrations themselves.A theoretical account of this perceptual process must take into account three general components: (1) the physics of the probe-tip/surface interaction and the transmission of vibrations through the probe shaft, (2) the filtering imposed by the skin and the responses of the mechanoreceptors, and (3) higher order factors that might alter roughness perception, such as active versus passive control of exploration and, correspondingly, the role of efferent commands. In the present experiments, we examined the effects of variables that are related to the first of these influences. The manipulated variables are of two types. One pertains to the geometric properties of the probe and the textured surface, including the diameter and shape of the probe and the spacing and shape of the elements that form the surface. The other pertains to the nature of exploration, including the speed with which the probe is passed over the plate and the posture of the hand holding the probe. Before considering the potential effects of these factors, we first will review theories of direct texture perception with the bare finger. Texture Perception Via the Bare FingerA substantial amount of research on haptic perception has been directed at understanding how people perceive the property of roughness as they explore a textured surface with the bare skin. The model that has resulted from this research was characterized by Klatzky and Lederman (1999) as spatial intensive in nature, because it assumes that processing begins with a representation of the surface as a spatial pressure map and culminates with a unidimensional estimate of roughness magnitude.This model of roughness perception is based on studies that systematically manipulated the geometric properties of textured surfaces and the nature of exploration, while measuring behavioral and neurophysiological responses. Psychophysical research has led to an understanding of how texture perception varies with the interaction between skin and surface, whereas neurophysiological research has indicated the underlying peripheral and central neural computations. Using psychophysical methods, Taylor and Lederman (1975) found that the perceived roughness of a surface was directly related to the total area of skin that was instantaneously deformed from a baseline resting position while in contact with a surface 613Copyright 2003 Psychonomic Society, Inc.This research was partially supported by the National Science and Engineering Research Council of Canada. Data from Experiment 3 were previously reported in Lederman, Klatzky, Hamilton, and Grindley (2000) and were adapted with permission for...

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Understanding speech in the presence of background sound can be challenging for older adults. Speech comprehension in noise appears to depend on working memory and executive-control processes (e.g., Heald and Nusbaum, 2014), and their augmentation through training may have rehabilitative potential for age-related hearing loss. We examined the efficacy of adaptive working-memory training (Cogmed; Klingberg et al., 2002) in 24 older adults, assessing generalization to other working-memory tasks (near-transfer) and to other cognitive domains (far-transfer) using a cognitive test battery, including the Reading Span test, sensitive to working memory (e.g., Daneman and Carpenter, 1980). We also assessed far transfer to speech-in-noise performance, including a closed-set sentence task (Kidd et al., 2008). To examine the effect of cognitive training on benefit obtained from semantic context, we also assessed transfer to open-set sentences; half were semantically coherent (high-context) and half were semantically anomalous (low-context). Subjects completed 25 sessions (0.5–1 h each; 5 sessions/week) of both adaptive working memory training and placebo training over 10 weeks in a crossover design. Subjects' scores on the adaptive working-memory training tasks improved as a result of training. However, training did not transfer to other working memory tasks, nor to tasks recruiting other cognitive domains. We did not observe any training-related improvement in speech-in-noise performance. Measures of working memory correlated with the intelligibility of low-context, but not high-context, sentences, suggesting that sentence context may reduce the load on working memory. The Reading Span test significantly correlated only with a test of visual episodic memory, suggesting that the Reading Span test is not a pure-test of working memory, as is commonly assumed.

This report tests the hypothesis that individual humpback whales (Megaptera novaeangliae) can be recognized and discriminated from other individuals on the basis of the ’’cry’’ vocalization. Multivariate discriminant analyses, based on six measurable variables, were used to examine intra- and interindividual differences in the ’’cries’’ from several individuals. Cries between themes of one song are very different, whereas those between songs of one individual are similar. Results show a high degree of discriminating power between individual animals. Thus, signature information is potentially available from ’’cries’’ within songs, despite common song formats for all calling animals on a given bank. The statistical techniques used will be of great value in animal vocalization studies.

If humans can detect the wealth of tactile and haptic information potentially available in live facial expressions of emotion (FEEs), they should be capable of haptically recognizing the six universal expressions of emotion (anger, disgust, fear, happiness, sadness, and surprise) at levels well above chance. We tested this hypothesis in the experiments reported here. With minimal training, subjects' overall mean accuracy was 51% for static FEEs (Experiment 1) and 74% for dynamic FEEs (Experiment 2). All FEEs except static fear were successfully recognized above the chance level of 16.7%. Complementing these findings, overall confidence and information transmission were higher for dynamic than for corresponding static faces. Our performance measures (accuracy and confidence ratings, plus response latency in Experiment 2 only) confirmed that happiness, sadness, and surprise were all highly recognizable, and anger, disgust, and fear less so.