Daniel E. Callan

Bio: Daniel E. Callan is an academic researcher from National Institute of Information and Communications Technology. The author has contributed to research in topics: Speech perception & Brain activity and meditation. The author has an hindex of 28, co-authored 59 publications receiving 3176 citations. Previous affiliations of Daniel E. Callan include Institut supérieur de l'aéronautique et de l'espace & University of Wisconsin-Madison.

Visual Prosody and Speech Intelligibility Head Movement Improves Auditory Speech Perception

Abstract: People naturally move their heads when they speak, and our study shows that this rhythmic head motion conveys linguistic information. Three-dimensional head and face motion and the acoustics of a talker producing Japanese sentences were recorded and analyzed. The head movement correlated strongly with the pitch (fundamental frequency) and amplitude of the talker's voice. In a perception study, Japanese subjects viewed realistic talking-head animations based on these movement recordings in a speech-in-noise task. The animations allowed the head motion to be manipulated without changing other characteristics of the visual or acoustic speech. Subjects correctly identified more syllables when natural head motion was present in the animation than when it was eliminated or distorted. These results suggest that nonverbal gestures such as head movements play a more direct role in the perception of speech than previously known.

Neural processes underlying perceptual enhancement by visual speech gestures.

Abstract: This fMRI study explores brain regions involved with perceptual enhancement afforded by observation of visual speech gesture information. Subjects passively identified words presented in the following conditions: audio-only, audiovisual, audio-only with noise, audiovisual with noise, and visual only. The brain may use concordant audio and visual information to enhance perception by integrating the information in a converging multisensory site. Consistent with response properties of multisensory integration sites, enhanced activity in middle and superior temporal gyrus/sulcus was greatest when concordant audiovisual stimuli were presented with acoustic noise. Activity found in brain regions involved with planning and execution of speech production in response to visual speech presented with degraded or absent auditory stimulation, is consistent with the use of an additional pathway through which speech perception is facilitated by a process of internally simulating the intended speech act of the observed speaker.

Early language acquisition: cracking the speech code

Abstract: Infants learn language with remarkable speed, but how they do it remains a mystery. New data show that infants use computational strategies to detect the statistical and prosodic patterns in language input, and that this leads to the discovery of phonemes and words. Social interaction with another human being affects speech learning in a way that resembles communicative learning in songbirds. The brain's commitment to the statistical and prosodic patterns that are experienced early in life might help to explain the long-standing puzzle of why infants are better language learners than adults. Successful learning by infants, as well as constraints on that learning, are changing theories of language acquisition.

α-Band Electroencephalographic Activity over Occipital Cortex Indexes Visuospatial Attention Bias and Predicts Visual Target Detection

Abstract: Covertly directing visual attention toward a spatial location in the absence of visual stimulation enhances future visual processing at the attended position. The neuronal correlates of these attention shifts involve modulation of neuronal "baseline" activity in early visual areas, presumably through top-down control from higher-order attentional systems. We used electroencephalography to study the largely unknown relationship between these neuronal modulations and behavioral outcome in an attention orienting paradigm. Covert visuospatial attention shifts to either a left or right peripheral position in the absence of visual stimulation resulted in differential modulations of oscillatory alpha-band (8-14 Hz) activity over left versus right posterior sites. These changes were driven by varying degrees of alpha-decreases being maximal contralateral to the attended position. When expressed as a lateralization index, these alpha-changes differed significantly between attention conditions, with negative values (alpha_right < alpha_left) indexing leftward and more positive values (alpha_left < or = alpha_right) indexing rightward attention. Moreover, this index appeared deterministic for processing of forthcoming visual targets. Collapsed over trials, there was an advantage for left target processing in accordance with an overall negative bias in alpha-index values. Across trials, left targets were detected most rapidly when preceded by negative index values. Detection of right targets was fastest in trials with most positive values. Our data indicate that collateral modulations of posterior alpha-activity, the momentary bias of visuospatial attention, and imminent visual processing are linked. They suggest that the momentary direction of attention, predicting spatial biases in imminent visual processing, can be estimated from a lateralization index of posterior alpha-activity.

Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach.

Abstract: The authors present a computational neural-network model of how the hippocampus and medial temporal lobe cortex (MTLC) contribute to recognition memory. The hippocampal component contributes by recalling studied details. The MTLC component cannot support recall, but one can extract a scalar familiarity signal from MTLC that tracks how well a test item matches studied items. The authors present simulations that establish key differences in the operating characteristics of the hippocampal-recall and MTLC-familiarity signals and identify several manipulations (e.g., target–lure similarity, interference) that differentially affect the 2 signals. They also use the model to address the stochastic relationship between recall and familiarity and the effects of partial versus complete hippocampal lesions on recognition. Memory can be subdivided according to functional categories (e.g., declarative vs. procedural memory; Cohen & Eichenbaum, 1993; Squire, 1992b) and according to neural structures (e.g., hippocampally dependent vs. nonhippocampally dependent forms of memory). Various attempts have been made to align these functional and neural levels of analysis; for example, Squire (1992b) and others have argued that declarative memory depends on the medial temporal lobe whereas procedural memory depends on other cortical and subcortical structures. Recently, we and our colleagues have set forth a computationally explicit theory of how hippocampus and neocortex contribute to learning and memory (the complementary-learning-systems model; McClelland, McNaughton, & O’Reilly, 1995; O’Reilly & Rudy, 2001). In this article, we advance the complementary-learning-systems model by using it to provide a comprehensive treatment of recognitionmemory performance. In this introductory section, we describe two questions that have proved challenging for math-modeling and cognitive-neuroscience approaches to recognition, respectively: In the math-modeling literature, there has been considerable controversy regarding how to characterize the contribution of recall (vs. familiarity) to recognition memory; in the cognitive-neuroscience literature, researchers have debated how the hippocampus (vs. surrounding cortical regions) contributes to recognition. Then, we show how our modeling approach, which is jointly constrained by behavioral and neuroscientific data, can help resolve these controversies.