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Xu H, Zhou J, Shen M. Hierarchical Constraints on the Distribution of Attention in Dynamic Displays. Behav Sci (Basel) 2024; 14:401. [PMID: 38785892 PMCID: PMC11117499 DOI: 10.3390/bs14050401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 04/23/2024] [Accepted: 05/09/2024] [Indexed: 05/25/2024] Open
Abstract
Human vision is remarkably good at recovering the latent hierarchical structure of dynamic scenes. Here, we explore how visual attention operates with this hierarchical motion representation. The way in which attention responds to surface physical features has been extensively explored. However, we know little about how the distribution of attention can be distorted by the latent hierarchical structure. To explore this topic, we conducted two experiments to investigate the relationship between minimal graph distance (MGD), one key factor in hierarchical representation, and attentional distribution. In Experiment 1, we constructed three hierarchical structures consisting of two moving objects with different MGDs. In Experiment 2, we generated three moving objects from one hierarchy to eliminate the influence of different structures. Attention was probed by the classic congruent-incongruent cueing paradigm. Our results show that the cueing effect is significantly smaller when the MGD between two objects is shorter, which suggests that attention is not evenly distributed across multiple moving objects but distorted by their latent hierarchical structure. As neither the latent structure nor the graph distance was part of the explicit task, our results also imply that both the construction of hierarchical representation and the attention to that representation are spontaneous and automatic.
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Affiliation(s)
- Haokui Xu
- Department of Psychology and Behavior Sciences, Zhejiang University, Hangzhou 310023, China;
| | | | - Mowei Shen
- Department of Psychology and Behavior Sciences, Zhejiang University, Hangzhou 310023, China;
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2
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Shaw S, Kilpatrick ZP. Representing stimulus motion with waves in adaptive neural fields. J Comput Neurosci 2024; 52:145-164. [PMID: 38607466 DOI: 10.1007/s10827-024-00869-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/29/2024] [Accepted: 03/07/2024] [Indexed: 04/13/2024]
Abstract
Traveling waves of neural activity emerge in cortical networks both spontaneously and in response to stimuli. The spatiotemporal structure of waves can indicate the information they encode and the physiological processes that sustain them. Here, we investigate the stimulus-response relationships of traveling waves emerging in adaptive neural fields as a model of visual motion processing. Neural field equations model the activity of cortical tissue as a continuum excitable medium, and adaptive processes provide negative feedback, generating localized activity patterns. Synaptic connectivity in our model is described by an integral kernel that weakens dynamically due to activity-dependent synaptic depression, leading to marginally stable traveling fronts (with attenuated backs) or pulses of a fixed speed. Our analysis quantifies how weak stimuli shift the relative position of these waves over time, characterized by a wave response function we obtain perturbatively. Persistent and continuously visible stimuli model moving visual objects. Intermittent flashes that hop across visual space can produce the experience of smooth apparent visual motion. Entrainment of waves to both kinds of moving stimuli are well characterized by our theory and numerical simulations, providing a mechanistic description of the perception of visual motion.
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Affiliation(s)
- Sage Shaw
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, USA
| | - Zachary P Kilpatrick
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, USA.
- Institute for Cognitive Sciences, University of Colorado Boulder, Boulder, CO, USA.
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3
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Yang YH, Fukiage T, Sun Z, Nishida S. Psychophysical measurement of perceived motion flow of naturalistic scenes. iScience 2023; 26:108307. [PMID: 38025782 PMCID: PMC10679809 DOI: 10.1016/j.isci.2023.108307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 08/09/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023] Open
Abstract
The neural and computational mechanisms underlying visual motion perception have been extensively investigated over several decades, but little attempt has been made to measure and analyze, how human observers perceive the map of motion vectors, or optical flow, in complex naturalistic scenes. Here, we developed a psychophysical method to assess human-perceived motion flows using local vector matching and a flash probe. The estimated perceived flow for naturalistic movies agreed with the physically correct flow (ground truth) at many points, but also showed consistent deviations from the ground truth (flow illusions) at other points. Comparisons with the predictions of various computational models, including cutting-edge computer vision algorithms and coordinate transformation models, indicated that some flow illusions are attributable to lower-level factors such as spatiotemporal pooling and signal loss, while others reflect higher-level computations, including vector decomposition. Our study demonstrates a promising data-driven psychophysical paradigm for an advanced understanding of visual motion perception.
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Affiliation(s)
- Yung-Hao Yang
- Cognitive Informatics Laboratory, Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan
| | - Taiki Fukiage
- Human Information Science Laboratory, NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation, 3-1, Morinosato-Wakamiya, Atsugi, Kanagawa 243-0198, Japan
| | - Zitang Sun
- Cognitive Informatics Laboratory, Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan
| | - Shin’ya Nishida
- Cognitive Informatics Laboratory, Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan
- Human Information Science Laboratory, NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation, 3-1, Morinosato-Wakamiya, Atsugi, Kanagawa 243-0198, Japan
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4
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Shaw S, Kilpatrick ZP. Representing stimulus motion with waves in adaptive neural fields. ARXIV 2023:arXiv:2312.06100v1. [PMID: 38168459 PMCID: PMC10760209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Traveling waves of neural activity emerge in cortical networks both spontaneously and in response to stimuli. The spatiotemporal structure of waves can indicate the information they encode and the physiological processes that sustain them. Here, we investigate the stimulus-response relationships of traveling waves emerging in adaptive neural fields as a model of visual motion processing. Neural field equations model the activity of cortical tissue as a continuum excitable medium, and adaptive processes provide negative feedback, generating localized activity patterns. Synaptic connectivity in our model is described by an integral kernel that weakens dynamically due to activity-dependent synaptic depression, leading to marginally stable traveling fronts (with attenuated backs) or pulses of a fixed speed. Our analysis quantifies how weak stimuli shift the relative position of these waves over time, characterized by a wave response function we obtain perturbatively. Persistent and continuously visible stimuli model moving visual objects. Intermittent flashes that hop across visual space can produce the experience of smooth apparent visual motion. Entrainment of waves to both kinds of moving stimuli are well characterized by our theory and numerical simulations, providing a mechanistic description of the perception of visual motion.
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Affiliation(s)
- Sage Shaw
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, USA
| | - Zachary P Kilpatrick
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, USA
- Institute for Cognitive Sciences, University of Colorado Boulder, Boulder, CO, USA
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5
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Shivkumar S, DeAngelis GC, Haefner RM. Hierarchical motion perception as causal inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.18.567582. [PMID: 38014023 PMCID: PMC10680834 DOI: 10.1101/2023.11.18.567582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Since motion can only be defined relative to a reference frame, which reference frame guides perception? A century of psychophysical studies has produced conflicting evidence: retinotopic, egocentric, world-centric, or even object-centric. We introduce a hierarchical Bayesian model mapping retinal velocities to perceived velocities. Our model mirrors the structure in the world, in which visual elements move within causally connected reference frames. Friction renders velocities in these reference frames mostly stationary, formalized by an additional delta component (at zero) in the prior. Inverting this model automatically segments visual inputs into groups, groups into supergroups, etc. and "perceives" motion in the appropriate reference frame. Critical model predictions are supported by two new experiments, and fitting our model to the data allows us to infer the subjective set of reference frames used by individual observers. Our model provides a quantitative normative justification for key Gestalt principles providing inspiration for building better models of visual processing in general.
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Affiliation(s)
- Sabyasachi Shivkumar
- Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627, USA
- Zuckerman Mind Brain Behavior Institute, Columbia University, NY 10027, USA
| | - Gregory C DeAngelis
- Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627, USA
- Center for Visual Science, University of Rochester, Rochester, NY 14627, USA
| | - Ralf M Haefner
- Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627, USA
- Center for Visual Science, University of Rochester, Rochester, NY 14627, USA
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Ruesseler M, Weber LA, Marshall TR, O'Reilly J, Hunt LT. Quantifying decision-making in dynamic, continuously evolving environments. eLife 2023; 12:e82823. [PMID: 37883173 PMCID: PMC10602589 DOI: 10.7554/elife.82823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 10/13/2023] [Indexed: 10/27/2023] Open
Abstract
During perceptual decision-making tasks, centroparietal electroencephalographic (EEG) potentials report an evidence accumulation-to-bound process that is time locked to trial onset. However, decisions in real-world environments are rarely confined to discrete trials; they instead unfold continuously, with accumulation of time-varying evidence being recency-weighted towards its immediate past. The neural mechanisms supporting recency-weighted continuous decision-making remain unclear. Here, we use a novel continuous task design to study how the centroparietal positivity (CPP) adapts to different environments that place different constraints on evidence accumulation. We show that adaptations in evidence weighting to these different environments are reflected in changes in the CPP. The CPP becomes more sensitive to fluctuations in sensory evidence when large shifts in evidence are less frequent, and the potential is primarily sensitive to fluctuations in decision-relevant (not decision-irrelevant) sensory input. A complementary triphasic component over occipito-parietal cortex encodes the sum of recently accumulated sensory evidence, and its magnitude covaries with parameters describing how different individuals integrate sensory evidence over time. A computational model based on leaky evidence accumulation suggests that these findings can be accounted for by a shift in decision threshold between different environments, which is also reflected in the magnitude of pre-decision EEG activity. Our findings reveal how adaptations in EEG responses reflect flexibility in evidence accumulation to the statistics of dynamic sensory environments.
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Affiliation(s)
- Maria Ruesseler
- Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford Centre for Human Brain Activity (OHBA) University Department of Psychiatry Warneford HospitalOxfordUnited Kingdom
| | - Lilian Aline Weber
- Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford Centre for Human Brain Activity (OHBA) University Department of Psychiatry Warneford HospitalOxfordUnited Kingdom
- Department of Experimental Psychology, University of Oxford, Anna Watts Building, Radcliffe Observatory QuarterOxfordUnited Kingdom
| | - Tom Rhys Marshall
- Department of Experimental Psychology, University of Oxford, Anna Watts Building, Radcliffe Observatory QuarterOxfordUnited Kingdom
- Centre for Human Brain Health, University of BirminghamBirminghamUnited Kingdom
| | - Jill O'Reilly
- Department of Experimental Psychology, University of Oxford, Anna Watts Building, Radcliffe Observatory QuarterOxfordUnited Kingdom
| | - Laurence Tudor Hunt
- Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford Centre for Human Brain Activity (OHBA) University Department of Psychiatry Warneford HospitalOxfordUnited Kingdom
- Department of Experimental Psychology, University of Oxford, Anna Watts Building, Radcliffe Observatory QuarterOxfordUnited Kingdom
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Noel JP, Bill J, Ding H, Vastola J, DeAngelis GC, Angelaki DE, Drugowitsch J. Causal inference during closed-loop navigation: parsing of self- and object-motion. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220344. [PMID: 37545300 PMCID: PMC10404925 DOI: 10.1098/rstb.2022.0344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 06/20/2023] [Indexed: 08/08/2023] Open
Abstract
A key computation in building adaptive internal models of the external world is to ascribe sensory signals to their likely cause(s), a process of causal inference (CI). CI is well studied within the framework of two-alternative forced-choice tasks, but less well understood within the cadre of naturalistic action-perception loops. Here, we examine the process of disambiguating retinal motion caused by self- and/or object-motion during closed-loop navigation. First, we derive a normative account specifying how observers ought to intercept hidden and moving targets given their belief about (i) whether retinal motion was caused by the target moving, and (ii) if so, with what velocity. Next, in line with the modelling results, we show that humans report targets as stationary and steer towards their initial rather than final position more often when they are themselves moving, suggesting a putative misattribution of object-motion to the self. Further, we predict that observers should misattribute retinal motion more often: (i) during passive rather than active self-motion (given the lack of an efference copy informing self-motion estimates in the former), and (ii) when targets are presented eccentrically rather than centrally (given that lateral self-motion flow vectors are larger at eccentric locations during forward self-motion). Results support both of these predictions. Lastly, analysis of eye movements show that, while initial saccades toward targets were largely accurate regardless of the self-motion condition, subsequent gaze pursuit was modulated by target velocity during object-only motion, but not during concurrent object- and self-motion. These results demonstrate CI within action-perception loops, and suggest a protracted temporal unfolding of the computations characterizing CI. This article is part of the theme issue 'Decision and control processes in multisensory perception'.
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Affiliation(s)
- Jean-Paul Noel
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - Johannes Bill
- Department of Neurobiology, Harvard University, Boston, MA 02115, USA
- Department of Psychology, Harvard University, Boston, MA 02115, USA
| | - Haoran Ding
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - John Vastola
- Department of Neurobiology, Harvard University, Boston, MA 02115, USA
| | - Gregory C. DeAngelis
- Department of Brain and Cognitive Sciences, Center for Visual Science, University of Rochester, Rochester, NY 14611, USA
| | - Dora E. Angelaki
- Center for Neural Science, New York University, New York, NY 10003, USA
- Tandon School of Engineering, New York University, New York, NY 10003, USA
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard University, Boston, MA 02115, USA
- Center for Brain Science, Harvard University, Boston, MA 02115, USA
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