Seeing a face gaze at an object elicits rapid attention shifts toward the same object. We tested whether gaze cueing is predictive: do people shift their attention toward objects others are merely expected to look at?...
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Seeing a face gaze at an object elicits rapid attention shifts toward the same object. We tested whether gaze cueing is predictive: do people shift their attention toward objects others are merely expected to look at? Participants categorized objects while a face either looked at this object, at another object, or straight ahead. Unbeknownst to participants, one face would only look at drinks and the other at foods. We tested whether attention was drawn toward objects "favored" by a face even when currently looking straight ahead. Indeed, while gaze expectations initially had a disruptive effect, participants did shift attention to the faces' favored objects once learning had been established, as long as emotional expressions had indicated personal relevance of the object to the individual. These data support predictive models of social perception, which assume that predictions can drive perception and action, as if these stimuli were directly perceived.
Identifying familiar faces is a fundamentally important aspect of social perception that requires the ability to assign very different (ambient) images of a face to a common identity. The current consensus is that the...
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Identifying familiar faces is a fundamentally important aspect of social perception that requires the ability to assign very different (ambient) images of a face to a common identity. The current consensus is that the brain processes face identity at approximately 250-300 msec following stimulus onset, as indexed by the N250 event related potential. However, using two experiments we show compelling evidence that where experimental paradigms induce expectations about person identity, changes in famous face identity are in fact detected at an earlier latency corresponding to the face-sensitive N170. In Experiment 1, using a rapid periodic stimulation paradigm presenting highly variable ambient images, we demonstrate robust effects of low frequency, periodic face-identity changes in N170 amplitude. In Experiment 2, we added infrequent aperiodic identity changes to show that the N170 was larger to both infrequent periodic and infrequent aperiodic identity changes than to high frequency identities. Our use of ambient stimulus images makes it unlikely that these effects are due to adaptation of low-level stimulus features. In line with current ideas about predictive coding, we therefore suggest that when expectations about the identity of a face exist, the visual system is capable of detecting identity mismatches at a latency consistent with the N170. (C) 2016 Elsevier Ltd. All rights reserved.
Bayesian models are currently a dominant framework for describing human information processing. However, it is not clear yet how major tenets of this framework can be translated to brain processes. In this study, we a...
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Bayesian models are currently a dominant framework for describing human information processing. However, it is not clear yet how major tenets of this framework can be translated to brain processes. In this study, we addressed the neural underpinning of prior probability and its effect on anticipatory activity in category-specific areas. Before fMRI scanning, participants were trained in two behavioral sessions to learn the prior probability and correct order of visual events within a sequence. The events of each sequence included two different presentations of a geometric shape and one picture of either a house or a face, which appeared with either a high or a low likelihood. Each sequence was preceded by a cue that gave participants probabilistic information about which items to expect next. This allowed examining cue-related anticipatory modulation of activity as a function of prior probability in category-specific areas (fusiform face area and parahippocampal place area). Our findings show that activity in the fusiform face area was higher when faces had a higher prior probability. The finding of a difference between levels of expectations is consistent with graded, probabilistically modulated activity, but the data do not rule out the alternative explanation of a categorical neural response. Importantly, these differences were only visible during anticipation, and vanished at the time of stimulus presentation, calling for a functional distinction when considering the effects of prior probability. Finally, there were no anticipatory effects for houses in the parahippocampal place area, suggesting sensitivity to stimulus material when looking at effects of prediction.
High-resolution blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) at the sub-millimeter scale has become feasible with recent advances in MR technology. In principle, this would enable t...
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High-resolution blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) at the sub-millimeter scale has become feasible with recent advances in MR technology. In principle, this would enable the study of layered cortical circuits, one of the fundaments of cortical computation. However, the spatial layout of cortical blood supply may become an important confound at such high resolution. In particular, venous blood draining back to the cortical surface perpendicularly to the layered structure is expected to influence the measured responses in different layers. Here, we present an extension of a hemodynamic model commonly used for analyzing fMRI data (in dynamic causal models or biophysical network models) that accounts for such blood draining effects by coupling local hemodynamics across layers. We illustrate the properties of the model and its inversion by a series of simulations and show that it successfully captures layered fMRI data obtained during a simple visual experiment. We conclude that for future studies of the dynamics of layered neuronal circuits with high-resolution fMRI, it will be pivotal to include effects of blood draining, particularly when trying to infer on the layer-specific connections in cortex - a theme of key relevance for brain disorders like schizophrenia and for theories of brain function such as predictive coding. (C) 2015 Elsevier Inc. All rights reserved.
The mind and brain sciences began with consciousness as a central concern. But for much of the 20th century, ideological and methodological concerns relegated its empirical study to the margins. Since the 1990s, study...
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The mind and brain sciences began with consciousness as a central concern. But for much of the 20th century, ideological and methodological concerns relegated its empirical study to the margins. Since the 1990s, studying consciousness has regained a legitimacy and momentum befitting its status as the primary feature of our mental lives. Nowadays, consciousness science encompasses a rich interdisciplinary mixture drawing together philosophical, theoretical, computational, experimental, and clinical perspectives, with neuroscience its central discipline. Researchers have learned a great deal about the neural mechanisms underlying global states of consciousness, distinctions between conscious and unconscious perception, and self-consciousness. Further progress will depend on specifying closer explanatory mappings between (first-person subjective) phenomenological descriptions and (third-person objective) descriptions of (embodied and embedded) neuronal mechanisms. Such progress will help reframe our understanding of our place in nature and accelerate clinical approaches to a wide range of psychiatric and neurological disorders.
We introduce a compressible representation of 3D geometry (including its attributes, such as color texture) intermediate between polygonal meshes and point clouds called a polygon cloud. Polygon clouds, compared to po...
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ISBN:
(纸本)9781509041183
We introduce a compressible representation of 3D geometry (including its attributes, such as color texture) intermediate between polygonal meshes and point clouds called a polygon cloud. Polygon clouds, compared to polygonal meshes, are more robust to live capture noise and artifacts. Furthermore, dynamic polygon clouds, compared to dynamic point clouds, are easier to compress, if certain challenges are addressed. In this paper, we propose methods for compressing dynamic polygon clouds using transform coding of color and motion residuals. We find that, compared to static polygon clouds and a fortiori static point clouds, dynamic polygon clouds can improve color compression by up to 2-3 dB in fidelity, and can improve geometry compression up to a factor of 2-5 in bit rate.
Speech recognition is rapid, automatic and amazingly robust. How the brain is able to decode speech from noisy acoustic inputs is unknown. We show that the brain recognizes speech by integrating bottom-up acoustic sig...
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Speech recognition is rapid, automatic and amazingly robust. How the brain is able to decode speech from noisy acoustic inputs is unknown. We show that the brain recognizes speech by integrating bottom-up acoustic signals with top-down predictions. Subjects listened to intelligible normal and unintelligible fine structure speech that lacked the predictability of the temporal envelope and did not enable access to higher linguistic representations. Their top-down predictions were manipulated using priming. Activation for unintelligible fine structure speech was confined to primary auditory cortices, but propagated into posterior middle temporal areas when fine structure speech was made intelligible by top-down predictions. By contrast, normal speech engaged posterior middle temporal areas irrespective of subjects' predictions. Critically, when speech violated subjects' expectations, activation increases in anterior temporal gyri/sulci signalled a prediction error and the need for new semantic integration. In line with predictive coding, our findings compellingly demonstrate that top-down predictions determine whether and how the brain translates bottom-up acoustic inputs into intelligible speech. (C) 2015 Elsevier Inc. All rights reserved.
Some recent theoretical accounts in the cognitive sciences suggest that prediction is necessary to understand language. Here we evaluate this proposal. We consider arguments that prediction provides a unified theoreti...
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Some recent theoretical accounts in the cognitive sciences suggest that prediction is necessary to understand language. Here we evaluate this proposal. We consider arguments that prediction provides a unified theoretical principle of the human mind and that it pervades cortical function. We discuss whether evidence of human abilities to detect statistical regularities is necessarily evidence for predictive processing and evaluate suggestions that prediction is necessary for language learning. We point out that not all language users appear to predict language and that suboptimal input makes prediction often very challenging. Prediction, moreover, is strongly context-dependent and impeded by resource limitations. We also argue that it may be problematic that most experimental evidence for predictive language processing comes from prediction-encouraging experimental set-ups. We conclude that languages can be learned and understood in the absence of prediction. Claims that all language processing is predictive in nature are premature.
In primary visual cortex, a subset of neurons responds when a particular stimulus is encountered in a certain location in visual space. This activity can be modeled using a visual receptive field. In addition to visua...
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In primary visual cortex, a subset of neurons responds when a particular stimulus is encountered in a certain location in visual space. This activity can be modeled using a visual receptive field. In addition to visually driven activity, there are neurons in visual cortex that integrate visual and motor-related input to signal a mismatch between actual and predicted visual flow. Here we show that these mismatch neurons have receptive fields and signal a local mismatch between actual and predicted visual flow in restricted regions of visual space. These mismatch receptive fields are aligned to the retinotopic map of visual cortex and are similar in size to visual receptive fields. Thus, neurons with mismatch receptive fields signal local deviations of actual visual flow from visual flow predicted based on self-motion and could therefore underlie the detection of objects moving relative to the visual flow caused by self-motion.
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