25 results on '"Ince, Robin A.A."'
Search Results
2. Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data
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Combrisson, Etienne, Allegra, Michele, Basanisi, Ruggero, Ince, Robin A.A., Giordano, Bruno L., Bastin, Julien, and Brovelli, Andrea
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- 2022
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3. Within-participant statistics for cognitive science
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Ince, Robin A.A., Kay, Jim W., and Schyns, Philippe G.
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- 2022
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4. Timing of brain entrainment to the speech envelope during speaking, listening and self-listening
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Pérez, Alejandro, Davis, Matthew H., Ince, Robin A.A., Zhang, Hanna, Fu, Zhanao, Lamarca, Melanie, Lambon Ralph, Matthew A., and Monahan, Philip J.
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- 2022
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5. Neurocomputational mechanisms underlying cross-modal associations and their influence on perceptual decisions
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Bolam, Joshua, Boyle, Stephanie C., Ince, Robin A.A., and Delis, Ioannis
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- 2022
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6. Grounding deep neural network predictions of human categorization behavior in understandable functional features: The case of face identity
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Daube, Christoph, Xu, Tian, Zhan, Jiayu, Webb, Andrew, Ince, Robin A.A., Garrod, Oliver G.B., and Schyns, Philippe G.
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- 2021
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7. Disentangling presentation and processing times in the brain
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Caplette, Laurent, Ince, Robin A.A., Jerbi, Karim, and Gosselin, Frédéric
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- 2020
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8. Cultural facial expressions dynamically convey emotion category and intensity information
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Chen, Chaona, Messinger, Daniel S., Chen, Cheng, Yan, Hongmei, Duan, Yaocong, Ince, Robin A.A., Garrod, Oliver G.B., Schyns, Philippe G., and Jack, Rachael E.
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- 2024
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9. Auditory cortical delta-entrainment interacts with oscillatory power in multiple fronto-parietal networks
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Keitel, Anne, Ince, Robin A.A., Gross, Joachim, and Kayser, Christoph
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- 2017
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10. Strength of predicted information content in the brain biases decision behavior
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Yan, Yuening, Zhan, Jiayu, Garrod, Oliver, Cui, Xuan, Ince, Robin A.A., and Schyns, Philippe G.
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- 2023
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11. Characterising cognitive heterogeneity in individuals at clinical high-risk for psychosis: a cluster analysis with clinical and functional outcome prediction
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Haining, Kate, Gajwani, Ruchika, Gross, Joachim, Gumley, Andrew I., Ince, Robin A.A., Lawrie, Stephen M., Schultze-Lutter, Frauke, Schwannauer, Matthias, and Uhlhaas, Peter J.
- Abstract
Schizophrenia is characterised by cognitive impairments that are already present during early stages, including in the clinical high-risk for psychosis (CHR-P) state and first-episode psychosis (FEP). Moreover, data suggest the presence of distinct cognitive subtypes during early-stage psychosis, with evidence for spared vs. impaired cognitive profiles that may be differentially associated with symptomatic and functional outcomes. Using cluster analysis, we sought to determine whether cognitive subgroups were associated with clinical and functional outcomes in CHR-P individuals. Data were available for 146 CHR-P participants of whom 122 completed a 6- and/or 12-month follow-up; 15 FEP participants; 47 participants not fulfilling CHR-P criteria (CHR-Ns); and 53 healthy controls (HCs). We performed hierarchical cluster analysis on principal components derived from neurocognitive and social cognitive measures. Within the CHR-P group, clusters were compared on clinical and functional variables and examined for associations with global functioning, persistent attenuated psychotic symptoms and transition to psychosis. Two discrete cognitive subgroups emerged across all participants: 45.9% of CHR-P individuals were cognitively impaired compared to 93.3% of FEP, 29.8% of CHR-N and 30.2% of HC participants. Cognitively impaired CHR-P participants also had significantly poorer functioning at baseline and follow-up than their cognitively spared counterparts. Specifically, cluster membership predicted functional but not clinical outcome. Our findings support the existence of distinct cognitive subgroups in CHR-P individuals that are associated with functional outcomes, with implications for early intervention and the understanding of underlying developmental processes.
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- 2022
12. A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula
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Ince, Robin A.A., Giordano, Bruno L., Kayser, Christoph, Rousselet, Guillaume A., Gross, Joachim, and Schyns, Philippe G.
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- 2017
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13. Facial expressions elicit multiplexed perceptions of emotion categories and dimensions
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Liu, Meng, Duan, Yaocong, Ince, Robin A.A., Chen, Chaona, Garrod, Oliver G.B., Schyns, Philippe G., and Jack, Rachael E.
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- 2022
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14. Information-theoretic methods for studying population codes
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Ince, Robin A.A., Senatore, Riccardo, Arabzadeh, Ehsan, Montani, Fernando, Diamond, Mathew E., and Panzeri, Stefano
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- 2010
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15. Cross-participant prediction of vigilance stages through the combined use of wPLI and wSMI EEG functional connectivity metrics
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Imperatori, Laura Sophie, Cataldi, Jacinthe, Betta, Monica, Ricciardi, Emiliano, Ince, Robin A.A., Siclari, Francesca, and Bernardi, Giulio
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Functional connectivity (FC) metrics describe brain inter-regional interactions and may complement information provided by common power-based analyses. Here we investigated whether the FC-metrics weighted Phase Lag Index (wPLI) and weighted Symbolic Mutual Information (wSMI) may unveil functional differences across four stages of vigilance – wakefulness (W), NREM-N2, NREM-N3 and REM sleep – with respect to each other and to power-based features. Moreover, we explored their possible contribution in identifying differences between stages characterized by distinct levels of consciousness (REM+W vs. N2+N3) or sensory disconnection (REM vs. W). Overnight sleep and resting-state wakefulness recordings from 24 healthy participants (27±6yrs, 13F) were analysed to extract power and FC-based features in six classical frequency bands. Cross-validated linear discriminant analyses (LDA) were applied to investigate the ability of extracted features to discriminate i) the four vigilance stages, ii) W+REM vs. N2+N3, and iii) W vs. REM. For the four-way vigilance stages classification, combining features based on power and both connectivity metrics significantly increased accuracy relative to considering only power, wPLI or wSMI features. Delta-power and connectivity (0.5-4Hz) represented the most relevant features for all the tested classifications, in line with a possible involvement of slow waves in consciousness and sensory disconnection. Sigma-FC, but not sigma-power (12-16Hz), was found to strongly contribute to the differentiation between states characterized by higher (W+REM) and lower (N2+N3) probabilities of conscious experiences. Finally, alpha-FC resulted as the most relevant FC-feature for distinguishing among wakefulness and REM sleep and may thus reflect the level of disconnection from the external environment.
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- 2021
16. Quantitatively Comparing Predictive Models with the Partial Information Decomposition
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Daube, Christoph, Giordano, Bruno, Schyns, Philippe G., and Ince, Robin A.A.
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There is increasing focus in cognitive and computational neuroscience on the use of encoding and decoding models to gain insight into cognitive processing. Frequently, encoding models are fit to a number of different features sets, and the out-of-sample predictive performance of the resulting models is compared. However, to gain the maximum benefit from this modelling, we need to go beyond simply ranking model performance in terms of absolute predictive power. We also need to directly compare and relate the predictions between models, to gain insight into which models are predicting common vs unique aspects of the neural response. The Partial Information Decomposition (PID) provides a principled theoretical framework to address this question, as it decomposes the total predictive performance of two models into redundant (overlapping), unique, and synergistic parts. We show that like classical information theoretic quantities, variance decomposition approaches conflate synergy and redundancy and so could provide a misleading view of the unique predictive power of a model. We also suggest how the use of encoding models and PID can help interpret decoding models.
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- 2019
17. Simple acoustic features can explain phoneme-based predictions of cortical responses to speech
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Daube, Christoph, Ince, Robin A.A., and Gross, Joachim
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When we listen to speech, we have to make sense of a waveform of sound pressure. Hierarchical models of speech perception assume that, to extract semantic meaning, the signal is transformed into unknown, intermediate neuronal representations. Traditionally, studies of such intermediate representations are guided by linguistically defined concepts, such as phonemes. Here, we argue that in order to arrive at an unbiased understanding of the neuronal responses to speech, we should focus instead on representations obtained directly from the stimulus. We illustrate our view with a data-driven, information theoretic analysis of a dataset of 24 young, healthy humans who listened to a 1 h narrative while their magnetoencephalogram (MEG) was recorded. We find that two recent results, the improved performance of an encoding model in which annotated linguistic and acoustic features were combined and the decoding of phoneme subgroups from phoneme-locked responses, can be explained by an encoding model that is based entirely on acoustic features. These acoustic features capitalize on acoustic edges and outperform Gabor-filtered spectrograms, which can explicitly describe the spectrotemporal characteristics of individual phonemes. By replicating our results in publicly available electroencephalography (EEG) data, we conclude that models of brain responses based on linguistic features can serve as excellent benchmarks. However, we believe that in order to further our understanding of human cortical responses to speech, we should also explore low-level and parsimonious explanations for apparent high-level phenomena.
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- 2019
18. Exact Partial Information Decompositions for Gaussian Systems Based on Dependency Constraints
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Kay, Jim W. and Ince, Robin A.A.
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FOS: Computer and information sciences ,maximum entropy ,Computer science ,Computer Science - Information Theory ,Gaussian ,General Physics and Astronomy ,FOS: Physical sciences ,lcsh:Astrophysics ,Multivariate normal distribution ,Machine Learning (stat.ML) ,unique information ,Quantitative Biology - Quantitative Methods ,01 natural sciences ,Article ,010305 fluids & plasmas ,03 medical and health sciences ,symbols.namesake ,0302 clinical medicine ,Statistics - Machine Learning ,lcsh:QB460-466 ,0103 physical sciences ,Applied mathematics ,dependency constraints ,Graphical model ,lcsh:Science ,mutual information ,Condensed Matter - Statistical Mechanics ,Quantitative Methods (q-bio.QM) ,partial information decomposition ,Statistical Mechanics (cond-mat.stat-mech) ,Principle of maximum entropy ,Information Theory (cs.IT) ,Univariate ,Probability and statistics ,Mutual information ,Gaussian graphical models ,lcsh:QC1-999 ,Physics - Data Analysis, Statistics and Probability ,FOS: Biological sciences ,symbols ,lcsh:Q ,Marginal distribution ,lcsh:Physics ,030217 neurology & neurosurgery ,Data Analysis, Statistics and Probability (physics.data-an) - Abstract
The Partial Information Decomposition (PID) [arXiv:1004.2515] provides a theoretical framework to characterize and quantify the structure of multivariate information sharing. A new method (Idep) has recently been proposed for computing a two-predictor PID over discrete spaces. [arXiv:1709.06653] A lattice of maximum entropy probability models is constructed based on marginal dependency constraints, and the unique information that a particular predictor has about the target is defined as the minimum increase in joint predictor-target mutual information when that particular predictor-target marginal dependency is constrained. Here, we apply the Idep approach to Gaussian systems, for which the marginally constrained maximum entropy models are Gaussian graphical models. Closed form solutions for the Idep PID are derived for both univariate and multivariate Gaussian systems. Numerical and graphical illustrations are provided, together with practical and theoretical comparisons of the Idep PID with the minimum mutual information PID (Immi). [arXiv:1411.2832] In particular, it is proved that the Immi method generally produces larger estimates of redundancy and synergy than does the Idep method. In discussion of the practical examples, the PIDs are complemented by the use of deviance tests for the comparison of Gaussian graphical models., Comment: 39 pages, 9 figures, 9 tables
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- 2018
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19. Tracing the Flow of Perceptual Features in an Algorithmic Brain Network
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Ince, Robin A.A., Van Rijsbergen, Nicola J., Thut, Gregor, Rousselet, Guillaume A., Gross, Joachim, Panzeri, Stefano, and Schyns, Philippe G.
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Brain Mapping ,Cognition ,Quantitative Biology::Neurons and Cognition ,Models, Neurological ,Brain ,Humans ,Perception ,Nerve Net ,Article ,Algorithms - Abstract
The model of the brain as an information processing machine is a profound hypothesis in which neuroscience, psychology and theory of computation are now deeply rooted. Modern neuroscience aims to model the brain as a network of densely interconnected functional nodes. However, to model the dynamic information processing mechanisms of perception and cognition, it is imperative to understand brain networks at an algorithmic level--i.e. as the information flow that network nodes code and communicate. Here, using innovative methods (Directed Feature Information), we reconstructed examples of possible algorithmic brain networks that code and communicate the specific features underlying two distinct perceptions of the same ambiguous picture. In each observer, we identified a network architecture comprising one occipito-temporal hub where the features underlying both perceptual decisions dynamically converge. Our focus on detailed information flow represents an important step towards a new brain algorithmics to model the mechanisms of perception and cognition.
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- 2015
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20. Eye coding mechanisms in early human face event-related potentials
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Rousselet, Guillaume A., Ince, Robin A.A., van Rijsbergen, Nicola J., and Schyns, Philippe G.
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body regions ,genetic structures ,behavioral disciplines and activities - Abstract
In humans, the N170 event-related potential (ERP) is an integrated measure of cortical activity that varies in amplitude and latency across trials. Researchers often conjecture that N170 variations reflect cortical mechanisms of stimulus coding for recognition. Here, to settle the conjecture and understand cortical information processing mechanisms, we unraveled the coding function of N170 latency and amplitude variations in possibly the simplest socially important natural visual task: face detection. On each experimental trial, 16 observers saw face and noise pictures sparsely sampled with small Gaussian apertures. Reverse-correlation methods coupled with information theory revealed that the presence of the eye specifically covaries with behavioral and neural measurements: the left eye strongly modulates reaction times and lateral electrodes represent mainly the presence of the contralateral eye during the rising part of the N170, with maximum sensitivity before the N170 peak. Furthermore, single-trial N170 latencies code more about the presence of the contralateral eye than N170 amplitudes and early latencies are associated with faster reaction times. The absence of these effects in control images that did not contain a face refutes alternative accounts based on retinal biases or allocation of attention to the eye location on the face. We conclude that the rising part of the N170, roughly 120–170 ms post-stimulus, is a critical time-window in human face processing mechanisms, reflecting predominantly, in a face detection task, the encoding of a single feature: the contralateral eye.
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- 2014
21. Frontal Top-Down Signals Increase Coupling of Auditory Low-Frequency Oscillations to Continuous Speech in Human Listeners.
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Park, Hyojin, Ince, Robin A.A., Schyns, Philippe G., Thut, Gregor, and Gross, Joachim
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SPEECH perception , *INTELLIGIBILITY of speech , *AUDITORY cortex , *MAGNETOENCEPHALOGRAPHY , *FUNCTIONAL magnetic resonance imaging - Abstract
Summary Humans show a remarkable ability to understand continuous speech even under adverse listening conditions. This ability critically relies on dynamically updated predictions of incoming sensory information, but exactly how top-down predictions improve speech processing is still unclear. Brain oscillations are a likely mechanism for these top-down predictions [ 1, 2 ]. Quasi-rhythmic components in speech are known to entrain low-frequency oscillations in auditory areas [ 3, 4 ], and this entrainment increases with intelligibility [ 5 ]. We hypothesize that top-down signals from frontal brain areas causally modulate the phase of brain oscillations in auditory cortex. We use magnetoencephalography (MEG) to monitor brain oscillations in 22 participants during continuous speech perception. We characterize prominent spectral components of speech-brain coupling in auditory cortex and use causal connectivity analysis (transfer entropy) to identify the top-down signals driving this coupling more strongly during intelligible speech than during unintelligible speech. We report three main findings. First, frontal and motor cortices significantly modulate the phase of speech-coupled low-frequency oscillations in auditory cortex, and this effect depends on intelligibility of speech. Second, top-down signals are significantly stronger for left auditory cortex than for right auditory cortex. Third, speech-auditory cortex coupling is enhanced as a function of stronger top-down signals. Together, our results suggest that low-frequency brain oscillations play a role in implementing predictive top-down control during continuous speech perception and that top-down control is largely directed at left auditory cortex. This suggests a close relationship between (left-lateralized) speech production areas and the implementation of top-down control in continuous speech perception. [ABSTRACT FROM AUTHOR]
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- 2015
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22. A novel test to determine the significance of neural selectivity to single and multiple potentially correlated stimulus features
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Ince, Robin A.A., Mazzoni, Alberto, Bartels, Andreas, Logothetis, Nikos K., and Panzeri, Stefano
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STIMULUS & response (Biology) , *STOCHASTIC analysis , *RANDOM variables , *BIOLOGY experiments , *VISUAL fields , *STATISTICAL power analysis - Abstract
Abstract: Mutual information is a principled non-linear measure of dependence between stochastic variables, which is widely used to study the selectivity of neural responses to external stimuli. Here we define and develop a set of novel statistical independence tests based on mutual information, which quantify the significance of neural selectivity to either single features or to multiple, potentially correlated stimulus features like those often present in naturalistic stimuli. If the values of different features are correlated during stimulus presentation, it is difficult to establish if one feature is genuinely encoded by the response, or if it instead appears to be encoded only as a side effect of its correlation with another genuinely represented feature. Our tests provide a way to disambiguate between these two possibilities. We use realistic simulations of neural responses tuned to one or more correlated stimulus features to investigate how limited sampling bias correction procedures affect the statistical power of such independence tests, and we characterize the regimes in which the distribution of information values under the null hypothesis can be approximated by simple distributions (Chi-square or Gaussian). Finally, we apply these tests to experimental data to determine the significance of tuning of the band limited power (BLP) of the gamma [30–100Hz] frequency range of the primary visual cortical local field potential to multiple correlated features during presentation of naturalistic movies. We show that gamma BLP carries significant, genuine information about orientation, space contrast and time contrast, despite the strong correlations between these features. [Copyright &y& Elsevier]
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- 2012
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23. Frontal Top-Down Signals Increase Coupling of Auditory Low-Frequency Oscillations to Continuous Speech in Human Listeners
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Park, Hyojin, Ince, Robin A.A., Schyns, Philippe G., Thut, Gregor, and Gross, Joachim
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Agricultural and Biological Sciences(all) ,Biochemistry, Genetics and Molecular Biology(all) ,otorhinolaryngologic diseases - Abstract
SummaryHumans show a remarkable ability to understand continuous speech even under adverse listening conditions. This ability critically relies on dynamically updated predictions of incoming sensory information, but exactly how top-down predictions improve speech processing is still unclear. Brain oscillations are a likely mechanism for these top-down predictions [1, 2]. Quasi-rhythmic components in speech are known to entrain low-frequency oscillations in auditory areas [3, 4], and this entrainment increases with intelligibility [5]. We hypothesize that top-down signals from frontal brain areas causally modulate the phase of brain oscillations in auditory cortex. We use magnetoencephalography (MEG) to monitor brain oscillations in 22 participants during continuous speech perception. We characterize prominent spectral components of speech-brain coupling in auditory cortex and use causal connectivity analysis (transfer entropy) to identify the top-down signals driving this coupling more strongly during intelligible speech than during unintelligible speech. We report three main findings. First, frontal and motor cortices significantly modulate the phase of speech-coupled low-frequency oscillations in auditory cortex, and this effect depends on intelligibility of speech. Second, top-down signals are significantly stronger for left auditory cortex than for right auditory cortex. Third, speech-auditory cortex coupling is enhanced as a function of stronger top-down signals. Together, our results suggest that low-frequency brain oscillations play a role in implementing predictive top-down control during continuous speech perception and that top-down control is largely directed at left auditory cortex. This suggests a close relationship between (left-lateralized) speech production areas and the implementation of top-down control in continuous speech perception.
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24. Dynamic Construction of Reduced Representations in the Brain for Perceptual Decision Behavior.
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Zhan, Jiayu, Ince, Robin A.A., van Rijsbergen, Nicola, and Schyns, Philippe G.
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TEMPORAL lobe , *OCCIPITAL lobe , *VISUAL perception , *INFORMATION theory , *MAGNETOENCEPHALOGRAPHY - Abstract
Summary Over the past decade, extensive studies of the brain regions that support face, object, and scene recognition suggest that these regions have a hierarchically organized architecture that spans the occipital and temporal lobes [ 1–14 ], where visual categorizations unfold over the first 250 ms of processing [ 15–19 ]. This same architecture is flexibly involved in multiple tasks that require task-specific representations—e.g. categorizing the same object as "a car" or "a Porsche." While we partly understand where and when these categorizations happen in the occipito-ventral pathway, the next challenge is to unravel how these categorizations happen. That is, how does high-dimensional input collapse in the occipito-ventral pathway to become low dimensional representations that guide behavior? To address this, we investigated what information the brain processes in a visual perception task and visualized the dynamic representation of this information in brain activity. To do so, we developed stimulus information representation (SIR), an information theoretic framework, to tease apart stimulus information that supports behavior from that which does not. We then tracked the dynamic representations of both in magneto-encephalographic (MEG) activity. Using SIR, we demonstrate that a rapid (∼170 ms) reduction of behaviorally irrelevant information occurs in the occipital cortex and that representations of the information that supports distinct behaviors are constructed in the right fusiform gyrus (rFG). Our results thus highlight how SIR can be used to investigate the component processes of the brain by considering interactions between three variables (stimulus information, brain activity, behavior), rather than just two, as is the current norm. Highlights • We show how the brain reduces high-dimensional input into decision representations • Occipital cortex reduces features irrelevant for behavior ∼170 ms post stimulus • Past 170 ms, fusiform gyrus combines the features supporting behavioral decisions In a decision task, Zhan et al. visualize within a new information theoretic framework the dynamic representation of visual information in brain activity. They demonstrate rapid reduction of behaviorally irrelevant information in the occipital cortex and a combination of the features that supports distinct decisions in the right fusiform gyrus. [ABSTRACT FROM AUTHOR]
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- 2019
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25. Modeling individual preferences reveals that face beauty is not universally perceived across cultures.
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Zhan, Jiayu, Liu, Meng, Garrod, Oliver G.B., Daube, Christoph, Ince, Robin A.A., Jack, Rachael E., and Schyns, Philippe G.
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SEXUAL dimorphism , *FACE , *PSYCHOBIOLOGY , *WESTERN civilization , *EAST Asians , *PERSONAL beauty , *SOCIAL perception - Abstract
Facial attractiveness confers considerable advantages in social interactions, 1,2 with preferences likely reflecting psychobiological mechanisms shaped by natural selection. Theories of universal beauty propose that attractive faces comprise features that are closer to the population average 3 while optimizing sexual dimorphism. 4 However, emerging evidence questions this model as an accurate representation of facial attractiveness, 5–7 including representing the diversity of beauty preferences within and across cultures. 8–12 Here, we demonstrate that Western Europeans (WEs) and East Asians (EAs) evaluate facial beauty using culture-specific features, contradicting theories of universality. With a data-driven method, we modeled, at both the individual and group levels, the attractive face features of young females (25 years old) in two matched groups each of 40 young male WE and EA participants. Specifically, we generated a broad range of same- and other-ethnicity female faces with naturally varying shapes and complexions. Participants rated each on attractiveness. We then reverse correlated the face features that drive perception of attractiveness in each participant. From these individual face models, we reconstructed a facial attractiveness representation space that explains preference variations. We show that facial attractiveness is distinct both from averageness and from sexual dimorphism in both cultures. Finally, we disentangled attractive face features into those shared across cultures, culture specific, and specific to individual participants, thereby revealing their diversity. Our results have direct theoretical and methodological impact for representing diversity in social perception and for the design of culturally and ethnically sensitive socially interactive digital agents. • We modeled individual preferences for attractive faces in two cultures • Attractive face features differ from the face average and sexual dimorphism • Instead, culture and individual preferences shape attractive face features • Attractive face features from a culture are used to judge other-ethnicity faces Zhan et al. refute theories of universal beauty, showing that Western and Eastern cultures and individual preferences shape attractive face features. Individual preference models show that attractive features differ from the average and sexual dimorphism to form a space that cultural members use to perceive face attractiveness in other ethnicities. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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