The Experts below are selected from a list of 44502 Experts worldwide ranked by ideXlab platform
Rufin Vanrullen - One of the best experts on this subject based on the ideXlab platform.
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the rhythms of predictive coding pre stimulus phase modulates the influence of Shape Perception on luminance judgments
Scientific Reports, 2017Co-Authors: Biao Han, Rufin VanrullenAbstract:Predictive coding is an influential model emphasizing interactions between feedforward and feedback signals. Here, we investigated the temporal dynamics of these interactions. Two gray disks with different versions of the same stimulus, one enabling predictive feedback (a 3D-Shape) and one impeding it (random-lines), were simultaneously presented on the left and right of fixation. Human subjects judged the luminance of the two disks while EEG was recorded. The choice of 3D-Shape or random-lines as the brighter disk was used to assess the influence of feedback signals on sensory processing in each trial (i.e., as a measure of post-stimulus predictive coding efficiency). Independently of the spatial response (left/right), we found that this choice fluctuated along with the pre-stimulus phase of two spontaneous oscillations: a ~5 Hz oscillation in contralateral frontal electrodes and a ~16 Hz oscillation in contralateral occipital electrodes. This pattern of results demonstrates that predictive coding is a rhythmic process, and suggests that it could take advantage of faster oscillations in low-level areas and slower oscillations in high-level areas.
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the rhythms of predictive coding pre stimulus phase modulates the influence of Shape Perception on luminance judgments
bioRxiv, 2016Co-Authors: Biao Han, Rufin VanrullenAbstract:Predictive coding is an influential model emphasizing interactions between feedforward and feedback signals. Here, we investigated its temporal dynamics. Two gray disks with different versions of the same stimulus, one enabling predictive feedback (a 3D-Shape) and one impeding it (random-lines), were simultaneously presented on the left and right of fixation. Human subjects judged the luminance of the two disks while EEG was recorded. Independently of the spatial response (left/right), we found that the choice of 3D-Shape or random-lines as the brighter disk (our measure of post-stimulus predictive coding efficiency on each trial) fluctuated along with the pre-stimulus phase of two spontaneous oscillations: a ~5Hz oscillation in contralateral frontal electrodes and a ~16Hz oscillation in contralateral occipital electrodes. This pattern of results demonstrates that predictive coding is a rhythmic process, and suggests that it could take advantage of faster oscillations in low-level areas and slower oscillations in high-level areas.
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Shape Perception enhances perceived contrast evidence for excitatory predictive feedback
Scientific Reports, 2016Co-Authors: Biao Han, Rufin VanrullenAbstract:Predictive coding theory suggests that predictable responses are “explained away” (i.e., reduced) by feedback. Experimental evidence for feedback inhibition, however, is inconsistent: most neuroimaging studies show reduced activity by predictive feedback, while neurophysiology indicates that most inter-areal cortical feedback is excitatory and targets excitatory neurons. In this study, we asked subjects to judge the luminance of two gray disks containing stimulus outlines: one enabling predictive feedback (a 3D-Shape) and one impeding it (random-lines). These outlines were comparable to those used in past neuroimaging studies. All 14 subjects consistently perceived the disk with a 3D-Shape stimulus brighter; thus, predictive feedback enhanced perceived contrast. Since early visual cortex activity at the population level has been shown to have a monotonic relationship with subjective contrast Perception, we speculate that the perceived contrast enhancement could reflect an increase in neuronal activity. In other words, predictive feedback may have had an excitatory influence on neuronal responses. Control experiments ruled out attention bias, local feature differences and response bias as alternate explanations.
Biao Han - One of the best experts on this subject based on the ideXlab platform.
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the rhythms of predictive coding pre stimulus phase modulates the influence of Shape Perception on luminance judgments
Scientific Reports, 2017Co-Authors: Biao Han, Rufin VanrullenAbstract:Predictive coding is an influential model emphasizing interactions between feedforward and feedback signals. Here, we investigated the temporal dynamics of these interactions. Two gray disks with different versions of the same stimulus, one enabling predictive feedback (a 3D-Shape) and one impeding it (random-lines), were simultaneously presented on the left and right of fixation. Human subjects judged the luminance of the two disks while EEG was recorded. The choice of 3D-Shape or random-lines as the brighter disk was used to assess the influence of feedback signals on sensory processing in each trial (i.e., as a measure of post-stimulus predictive coding efficiency). Independently of the spatial response (left/right), we found that this choice fluctuated along with the pre-stimulus phase of two spontaneous oscillations: a ~5 Hz oscillation in contralateral frontal electrodes and a ~16 Hz oscillation in contralateral occipital electrodes. This pattern of results demonstrates that predictive coding is a rhythmic process, and suggests that it could take advantage of faster oscillations in low-level areas and slower oscillations in high-level areas.
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the rhythms of predictive coding pre stimulus phase modulates the influence of Shape Perception on luminance judgments
bioRxiv, 2016Co-Authors: Biao Han, Rufin VanrullenAbstract:Predictive coding is an influential model emphasizing interactions between feedforward and feedback signals. Here, we investigated its temporal dynamics. Two gray disks with different versions of the same stimulus, one enabling predictive feedback (a 3D-Shape) and one impeding it (random-lines), were simultaneously presented on the left and right of fixation. Human subjects judged the luminance of the two disks while EEG was recorded. Independently of the spatial response (left/right), we found that the choice of 3D-Shape or random-lines as the brighter disk (our measure of post-stimulus predictive coding efficiency on each trial) fluctuated along with the pre-stimulus phase of two spontaneous oscillations: a ~5Hz oscillation in contralateral frontal electrodes and a ~16Hz oscillation in contralateral occipital electrodes. This pattern of results demonstrates that predictive coding is a rhythmic process, and suggests that it could take advantage of faster oscillations in low-level areas and slower oscillations in high-level areas.
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Shape Perception enhances perceived contrast evidence for excitatory predictive feedback
Scientific Reports, 2016Co-Authors: Biao Han, Rufin VanrullenAbstract:Predictive coding theory suggests that predictable responses are “explained away” (i.e., reduced) by feedback. Experimental evidence for feedback inhibition, however, is inconsistent: most neuroimaging studies show reduced activity by predictive feedback, while neurophysiology indicates that most inter-areal cortical feedback is excitatory and targets excitatory neurons. In this study, we asked subjects to judge the luminance of two gray disks containing stimulus outlines: one enabling predictive feedback (a 3D-Shape) and one impeding it (random-lines). These outlines were comparable to those used in past neuroimaging studies. All 14 subjects consistently perceived the disk with a 3D-Shape stimulus brighter; thus, predictive feedback enhanced perceived contrast. Since early visual cortex activity at the population level has been shown to have a monotonic relationship with subjective contrast Perception, we speculate that the perceived contrast enhancement could reflect an increase in neuronal activity. In other words, predictive feedback may have had an excitatory influence on neuronal responses. Control experiments ruled out attention bias, local feature differences and response bias as alternate explanations.
Stanislas Dehaene - One of the best experts on this subject based on the ideXlab platform.
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sensitivity to geometric Shape regularity in humans and baboons a putative signature of human singularity
Proceedings of the National Academy of Sciences of the United States of America, 2021Co-Authors: Mathias Sablemeyer, Joel Fagot, Serge Caparos, Timo Van Kerkoerle, Marie Amalric, Stanislas DehaeneAbstract:Among primates, humans are special in their ability to create and manipulate highly elaborate structures of language, mathematics, and music. Here we show that this sensitivity to abstract structure is already present in a much simpler domain: the visual Perception of regular geometric Shapes such as squares, rectangles, and parallelograms. We asked human subjects to detect an intruder Shape among six quadrilaterals. Although the intruder was always defined by an identical amount of displacement of a single vertex, the results revealed a geometric regularity effect: detection was considerably easier when either the base Shape or the intruder was a regular figure comprising right angles, parallelism, or symmetry rather than a more irregular Shape. This effect was replicated in several tasks and in all human populations tested, including uneducated Himba adults and French kindergartners. Baboons, however, showed no such geometric regularity effect, even after extensive training. Baboon behavior was captured by convolutional neural networks (CNNs), but neither CNNs nor a variational autoencoder captured the human geometric regularity effect. However, a symbolic model, based on exact properties of Euclidean geometry, closely fitted human behavior. Our results indicate that the human propensity for symbolic abstraction permeates even elementary Shape Perception. They suggest a putative signature of human singularity and provide a challenge for nonsymbolic models of human Shape Perception.
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sensitivity to geometric Shape regularity in humans and baboons a putative signature of human singularity
Proceedings of the Annual Meeting of the Cognitive Science Society, 2021Co-Authors: Mathias Sablemeyer, Joel Fagot, Serge Caparos, Timo Van Kerkoerle, Marie Amalric, Stanislas DehaeneAbstract:Author(s): Sable-Meyer, Mathias; Fagot, Joel; Caparos, Serge; van Kerkoerle, Timo; Amalric, Marie; Dehaene, Stanislas | Abstract: Among primates, humans are special in their ability to create and manipulate highly elaborate structures of language, mathematics or music. We show that this sensitivity is present in a much simpler domain: the visual Perception of geometric Shapes. We asked human subjects to detect an intruder Shape among six quadrilaterals. Although the intruder was defined by an identical amount of displacement of a single vertex, the results revealed a geometric regularity effect: detection was considerably easier with most regular Shapes. This effect was replicated in several tasks and in both uneducated adults and preschoolers. Baboons, however, showed no such geometric regularity effect even after extensive training. Baboon behavior was captured by convolutional neural networks (CNNs) but a symbolic model was needed to fit human behavior. Our results indicate that the human propensity for symbolic abstraction permeates even elementary Shape Perception and they suggest a new putative signature of human singularity.
Krishnankutty Sathian - One of the best experts on this subject based on the ideXlab platform.
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Object familiarity modulates the relationship between visual object imagery and haptic Shape Perception.
NeuroImage, 2009Co-Authors: Simon Lacey, Randall Stilla, Peter Flueckiger, Michael Lava, Krishnankutty SathianAbstract:Although visual cortical engagement in haptic Shape Perception is well established, its relationship with visual imagery remains controversial. We addressed this using functional magnetic resonance imaging during separate visual object imagery and haptic Shape Perception tasks. Two experiments were conducted. In the first experiment, the haptic Shape task employed unfamiliar, meaningless objects, whereas familiar objects were used in the second experiment. The activations evoked by visual object imagery overlapped more extensively, and their magnitudes were more correlated, with those evoked during haptic Shape Perception of familiar, compared to unfamiliar, objects. In the companion paper (Deshpande et al., this issue), we used task-specific functional and effective connectivity analyses to provide convergent evidence: these analyses showed that the neural networks underlying visual imagery were similar to those underlying haptic Shape Perception of familiar, but not unfamiliar, objects. We conclude that visual object imagery is more closely linked to haptic Shape Perception when objects are familiar, compared to when they are unfamiliar.
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Object familiarity modulates effective connectivity during haptic Shape Perception.
NeuroImage, 2009Co-Authors: Gopikrishna Deshpande, Randall Stilla, Simon Lacey, Krishnankutty SathianAbstract:In the preceding paper (Lacey, S., Flueckiger, P., Stilla, R., Lava, M., Sathian, K., 2009a. Object familiarity modulates involvement of visual imagery in haptic Shape Perception), we showed that the activations evoked by visual imagery overlapped more extensively, and their magnitudes were more correlated, with those evoked during haptic Shape Perception of familiar, compared to unfamiliar, objects. Here we used task-specific analyses of functional and effective connectivity to provide convergent evidence. These analyses showed that the visual imagery and familiar haptic Shape tasks activated similar networks, whereas the unfamiliar haptic Shape task activated a different network. Multivariate Granger causality analyses of effective connectivity, in both a conventional form and one purged of zero-lag correlations, showed that the visual imagery and familiar haptic Shape networks involved top-down paths from prefrontal cortex into the lateral occipital complex (LOC), whereas the unfamiliar haptic Shape network was characterized by bottom-up, somatosensory inputs into the LOC. We conclude that Shape representations in the LOC are flexibly accessible, either top-down or bottom-up, according to task demands, and that visual imagery is more involved in LOC activation during haptic Shape Perception when objects are familiar, compared to unfamiliar.
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Activity and effective connectivity of parietal and occipital cortical regions during haptic Shape Perception.
Neuropsychologia, 2006Co-Authors: Scott Peltier, Randall Stilla, Erica Mariola, Stephen M. Laconte, Krishnankutty SathianAbstract:It is now widely accepted that visual cortical areas are active during normal tactile Perception, but the underlying mechanisms are still not clear. The goal of the present study was to use functional magnetic resonance imaging (fMRI) to investigate the activity and effective connectivity of parietal and occipital cortical areas during haptic Shape Perception, with a view to potentially clarifying the role of top-down and bottom-up inputs into visual areas. Subjects underwent fMRI scanning while engaging in discrimination of haptic Shape or texture, and in separate runs, visual Shape or texture. Accuracy did not differ significantly between tasks. Haptic Shape-selective regions, identified on a contrast between the haptic Shape and texture conditions in individual subjects, were found bilaterally in the postcentral sulcus (PCS), multiple parts of the intraparietal sulcus (IPS) and the lateral occipital complex (LOC). The IPS and LOC foci tended to be Shape-selective in the visual modality as well. Structural equation modelling was used to study the effective connectivity among the haptic Shape-selective regions in the left hemisphere, contralateral to the stimulated hand. All possible models were tested for their fit to the correlations among the observed time-courses of activity. Two equivalent models emerged as the winners. These models, which were quite similar, were characterized by both bottom-up paths from the PCS to parts of the IPS, and top-down paths from the LOC and parts of the IPS to the PCS. We conclude that interactions between unisensory and multisensory cortical areas involve bidirectional information flow.
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Multisensory cortical processing of object Shape and its relation to mental imagery.
Cognitive affective & behavioral neuroscience, 2004Co-Authors: Minming Zhang, Valerie D. Weisser, Randall Stilla, S.c. Prather, Krishnankutty SathianAbstract:Here, we used functional magnetic resonance imaging to investigate the multisensory processing of object Shape in the human cerebral cortex and explored the role of mental imagery in such processing. Regions active bilaterally during both visual and haptic Shape Perception, relative to texture Perception in the respective modality, included parts of the superior parietal gyrus, the anterior intraparietal sulcus, and the lateral occipital complex. Of these bimodal regions, the lateral occipital complexes preferred visual over haptic stimuli, whereas the parietal areas preferred haptic over visual stimuli. Whereas most subjects reported little haptic imagery during visual Shape Perception, experiences of visual imagery during haptic Shape Perception were common. Across subjects, ratings of the vividness of visual imagery strongly predicted the amount of haptic Shape-selective activity in the right, but not in the left, lateral occipital complex. Thus, visual imagery appears to contribute to activation of some, but not all, visual cortical areas during haptic Perception.
Floris P. De Lange - One of the best experts on this subject based on the ideXlab platform.
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no evidence for altered up and downregulation of brain activity in visual cortex during illusory Shape Perception in autism
Cortex, 2019Co-Authors: Christian Utzerath, Peter Kok, Iris C Schmits, Jan K Buitelaar, Floris P. De LangeAbstract:Autism spectrum disorder (ASD) may be marked by an altered balance between sensory input and prior expectations. Because many illusions rely on integrating sensory input with prior information such as spatial context, individuals with ASD may therefore be less susceptible to visual illusions than typically developing (TD) individuals. Yet empirical evidence on the matter is rather divergent, varying depending on the type of illusion, study procedure, and population. Visual illusions lead to neural activity alterations in the visual system. In the so-called Kanizsa illusion, these are likely caused by top-down feedback to V1. Here we tested the hypothesis that a reduced susceptibility to illusions in ASD would manifest as diminished modulation of V1 activity by illusions, using functional magnetic resonance imaging (fMRI). We presented 22 adolescents with ASD and 22 age-, gender-, and intelligence-matched TD controls with displays that consisted of three circular inducers. These either formed an illusory triangle (Kanizsa illusion) or not. We identified regions in primary visual cortex (V1) that corresponded to (the visual field locations of) the illusory triangle and its inducers, and recorded their visual response. Previous research in healthy volunteers has shown a specific pattern of up- and down-regulation in regions of V1 that process the Shape and inducers, respectively. Here, we replicated this pattern of up- and downregulation in V1, in both the TD and ASD groups, with no differences between groups. This suggests that illusory Shape processing in primary visual cortex is equally present in ASD, suggesting unimpaired processing of spatial context.
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how do expectations Shape Perception
Trends in Cognitive Sciences, 2018Co-Authors: Floris P. De Lange, Micha Heilbron, Peter KokAbstract:Perception and perceptual decision-making are strongly facilitated by prior knowledge about the probabilistic structure of the world. While the computational benefits of using prior expectation in Perception are clear, there are myriad ways in which this computation can be realized. We review here recent advances in our understanding of the neural sources and targets of expectations in Perception. Furthermore, we discuss Bayesian theories of Perception that prescribe how an agent should integrate prior knowledge and sensory information, and investigate how current and future empirical data can inform and constrain computational frameworks that implement such probabilistic integration in Perception.
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Shape Perception simultaneously up and downregulates neural activity in the primary visual cortex
Current Biology, 2014Co-Authors: Peter Kok, Floris P. De LangeAbstract:Donders Institute for Brain, Cognition and Behaviour,Radboud University Nijmegen, Kapittelweg 29, 6525 ENNijmegen, the NetherlandsSummaryAnessentialpartofvisualPerceptionisthegroupingoflocalelements (such as edges and lines) into coherent Shapes.Previous studies have shown that this grouping processmodulates neural activity in the primary visual cortex (V1)that is signaling the local elements [1–4]. However, thenature of this modulation is controversial. Some studiesfind that Shape Perception reduces neural activity in V1 [2,5, 6], while others report increased V1 activity during ShapePerception [1, 3, 4, 7–10]. Neurocomputational theories thatcast Perception as a generative process [11–13] proposethat feedback connections carry predictions (i.e., the gener-ative model), while feedforward connections signal themismatch between top-down predictions and bottom-upinputs.Withinthisframework,theeffectoffeedbackonearlyvisual cortex may be either enhancing or suppressive, de-pendingonwhetherthefeedbacksignalismetbycongruentbottom-up input. Here, we tested this hypothesis by quanti-fying the spatial profile of neural activity in V1 during thePerception of illusory Shapes using population receptivefield mapping. We find that Shape Perception concurrentlyincreases neural activity in regions of V1 that have a recep-tive field on the Shape but do not receive bottom-up inputand suppresses activity in regions of V1 that receivebottom-upinputthatispredictedbytheShape.Theseeffectswere not modulated by task requirements. Together, thesefindingssuggestthatShapePerceptionchangeslower-ordersensory representations in a highly specific and automaticmanner, in line with theories that cast Perception in termsof hierarchical generative models.ResultsThe role of early visual regions during Shape Perception is illunderstood, with some studies reporting activity suppressiondue to grouping [2, 5, 6] while others report enhancement[1, 3, 4, 7–10]. According to theories that cast Perception intermsofhierarchicalgenerativemodels[11–13],neuralactivityinlower-ordersensoryregionsisdependentbothonwhetheritisdrivenbysensorystimulationandwhetherthisstimulationispredicted on the basis of top-down feedback signals. In thisframework, early visual neurons that do not receive anybottom-up input, but that are predicted to be active becausea Shape is inferred at their receptive field location, are ex-pected to show relatively enhanced neural activity [3, 4, 8, 9].On the other hand, early visual neurons that receive bottom-up input that is congruent with the Shape prediction are ex-pected to show a relatively suppressed response [2, 6, 14].Here, we directly test this framework within the context ofillusory Shape Perception.Illusory Shape Perception provides an ideal test bed, as theillusory Shape results in both unexpected absence of visualinput (at the location where the Shape is perceived but retinalinput is absent) and expected presence of visual input(at the location where the Shape provides an explanation forthe bottom-up input). We made use of the well-known illusory‘‘Kanizsa’’ Shapes [15], wherein circles with missing wedges(‘‘Pac-Man’’ inducers) are aligned such that they can inducethe Perception of an illusory figure (Figure 1A). Using fMRIand population receptive field mapping [16], we quantifiedthe spatial profile of neural activity in early visual cortex whilesubjects (n = 20) were presented with stimuli that either did(Figure 1A) or did not (Figure 1B) induce an illusory figure.Moreover, to examine whether effects of Shape Perceptionwere dependent on attention, we manipulated the focus ofsubjects’ attention. In half of the trials, subjects had to detectthe presence of an occasional illusory diamond (‘‘figure task’’;Figure 1C), placing their attentional focus on the location ofthe illusory Shapes. In the other half of the trials, subjectshadtodetecttwotargetletters(XandZ)inarapidlypresentedletter stream at fixation, drawing their attention away from theillusory Shapes (‘‘letter task’’).Below, we present the spatially specific responses to thesestimuliinearlyvisualcortexintwodifferentways.First,weesti-mated the population receptive field (pRF) [16] of every voxelin early visual cortex (see Figures S1A–S1C and SupplementalExperimental Procedures available online) and used thisinformation to transform the blood oxygen-level-dependent(BOLD) signal into the reference frame of subjects’ visual fieldof view (Figure S1D; Supplemental Experimental Procedures).Second, we selected groups of voxels based on the locationof their receptive field and averaged over the BOLD signalmeasured in such voxels. In this way, we obtained separateestimates of neural activity in regions of primary visual cortex(V1) corresponding to the area of the visual field where theillusory triangles were presented (‘‘figure region’’) and regionscorresponding to the Pac-Man inducers (‘‘inducer region’’;seeSupplementalExperimentalProceduresfordetailsofvoxelselection).Theseanalysisstrategiesarecomplementary:whilethe first method allows for a characterization and visualizationof neural activity concurrently for all parts of visual space, thesecond approach is more standard and more easily allowsfor statistical quantification of the experimental effects.Reconstruction of Neural Response to Illusory FiguresWe reconstructed the neural response evoked by illusory fig-ures (Figure 1A), compared to control stimuli with the samelow-level features but that did not induce an illusory figure(Figure 1B). The results showed a striking spatial dissociation(Figure 2A): neural activity for regions of V1 that correspondedto the illusory figure (but not the Pac-Man inducers; figure re-gion) was enhanced when an illusory triangle was present,compared to when the inducers did not form an illusory figure(Figure3A;p<0.001).Inotherwords,theseV1regionsshowedan increased response to the illusory figures, despite the
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how prediction errors Shape Perception attention and motivation
Frontiers in Psychology, 2012Co-Authors: Peter Kok, Hanneke Den E M Ouden, Floris P. De LangeAbstract:Prediction errors are a central notion in theoretical models of reinforcement learning, perceptual inference, decision-making and cognition, and prediction error signals have been reported across a wide range of brain regions and experimental paradigms. Here, we will make an attempt to see the forest for the trees, considering the commonalities and differences of reported prediction errors signals in light of recent suggestions that the computation of prediction errors forms a fundamental mode of brain function. We discuss where different types of prediction errors are encoded, how they are generated, and the different functional roles they fulfil. We suggest that while encoding of prediction errors is a common computation across brain regions, the content and function of these error signals can be very different, and are determined by the afferent and efferent connections within the neural circuitry in which they arise.
