2004 Special IssuePerceptual grouping and the interactions between visual cortical areas
Introduction
One of the extraordinary capabilities of the human visual system is its ability to rapidly select and group related elements in a complex visual scene. This capability serves to bring together information likely to belong to a common cause, such as the same contour, surface or object. Grouping also reflects a general function of cognitive systems in that it greatly simplifies the description by exploiting redundancy in the input pattern (Barlow, 1959). For example, the image of a set of parallel lines can be succinctly described as a single texture pattern (‘N repetitions of feature X’) without needing to specify each element within the pattern.
These pattern-processing capabilities appear to be reflected in the activities of neurons at various stages of the visual system. For example, the response of a neuron in V1 to a single bar oriented along a receptive field's preferred axis can be suppressed by parallel bars on the two sides or enhanced if orientations differ and a collinear bar can enhance the response (Kapadia et al., 2000, Knierim and van Essen, 1992). Such pattern context effects in V1 appear to be mediated by both local connections (Das & Gilbert, 1999) and by interactions with higher areas (Hupe et al., 1998).
Grouping local features that belong to an object is particularly interesting from a physiological perspective because object shape is believed to be represented in higher stages of the visual system beyond V1, so any influence of perceived shape on lower areas would require feedback. Feedback is generally thought of as a process where activity in lower areas is positively correlated with the activity occurring in higher areas. However, recent work on predictive coding models has suggested that feedback may operate to reduce activity. In these models, higher-stages of a network compete by projecting their predictions about the stimulus to lower stages, where they are then removed from incoming data. In these models, the activity of neurons in lower stages will decrease when neurons in higher stages can ‘explain’ a visual stimulus, but will increase when the top-down explanation is poor (Mumford, 1992, Rao and Ballard, 1999). Other mechanisms for reducing activity via feedback are also possible and are discussed below. We present results from our own research and those of others suggesting that feedback from high-level visual areas reduces average activity in lower areas in order to simplify the description of a visual image.
Section snippets
Feedback
Feedback projections are a prominent anatomical feature of the primate visual system (Felleman & Van Essen, 1991) and recent evidence suggests they play a critical role in visual perception (Pascual-Leone & Walsh, 2001 and see Bullier, 2001, Lamme and Roelfsema, 2000 for reviews). Nevertheless, the functional significance of these connections has been open to interpretation. There are two basic possibilities: feedback may act by modifying input-driven activity in an existing active neural
Experimental results
To examine the possible role of feedback during object perception, we conducted a series of fMRI experiments (Murray et al., 2002) using stimuli with features that could either be perceived as ungrouped elements or perceived as being grouped into a single perceptual ‘explanation’—specifically, a single shape or object. Our first experiment used random-dot structure-from-motion stimuli. In one condition (‘SFM’), random dot patterns were projected onto the surfaces of simple geometric shapes
Information processing functions of feedback
In Section 3, we discussed predictive coding and sharpening as possible explanations for the observed decrease in V1 activity as a function of shape perception. However, these ideas tell us little about how these mechanisms might be involved in solving the computational tasks of vision. Although the empirical basis for feedback between cortical areas is becoming increasingly well-established, understanding its role in information processing poses a major theoretical challenge. We can get some
Remaining questions
Our empirical results demonstrate that neuronal activity, even in V1, does not simply represent the signaling of features in a visual scene but is strongly influenced by high-level perceptions of object shape. Though these results, in combinations with other studies, offer a compelling example of the potential role for feedback processes in vision, there are still many unanswered questions. For example, having timing information about the relative changes in V1 and LOC is crucial to
Acknowledgements
Portions of this work were reported earlier in Murray et al. (2002) and at Human Brain Mapping (Shen, Kersten, and Ugurbil, 1999), ARVO (Kersten, Shen, Ugurbil, and Schrater, 1999) and Soc. Neurosci. (Murray Olshausen, and Woods, 2001) conferences. Supported by NIH R01 EY015261, NIH P41 RR08079, pre-doctoral NRSA MH-12791 and post-doctoral NRSA EY015342-01 (S.O.M.), NSF SBR-9631682 (D.K.), NIH MH-57921 (B.A.O.), NIH MH-41544 and VA Research Service (D.L.W.). We thank Peter Battaglia for helpful
References (79)
- et al.
Anatomical origins of the classical receptive field and modulatory surround field of single neurons in macaque visual cortical area V1
Progress in Brain Research
(2002) - et al.
Neuronal basis of contrast discrimination
Vision Research
(1999) Integrated model of visual processing
Brain Research. Brain Research Reviews
(2001)Learning and inference in the brain
Neural Networks
(2003)- et al.
View from the top: Hierarchies and reverse hierarchies in the visual system
Neuron
(2002) - et al.
Differences in perceived shape from shading correlate with activity in early visual areas
Current Biology
(1997) - et al.
Increased activity in human visual cortex during directed attention in the absence of visual stimulation
Neuron
(1999) - et al.
The distinct modes of vision offered by feedforward and recurrent processing
Trends in Neuroscience
(2000) The nature of illusory contour computation
Neuron
(2002)- et al.
The role of the primary visual cortex in higher level vision
Vision Research
(1998)
The cost of cortical computation
Current Biology
Neural response to perception of volume in the lateral occipital complex
Neuron
Diagnostic colors mediate scene recognition
Cognitive Psychology
Subjective contours—Bridging the gap between psychophysics and physiology
Trends in Neuroscience
Limited capacity of any realizable perceptual system is a sufficient reason for attentive behavior
Consciousness and Cognition
The predictive value of changes in effective connectivity for human learning
Science
ART2: Self-organization of stable category recognition codes for analog input patterns
Applied Optics
What's up in top-down processing?
Topography of contextual modulations mediated by short-range interactions in primary visual cortex
Nature
Neural mechanisms of selective visual attention
Annual Review of Neuroscience
Distributed hierarchical processing in the primate cerebral cortex
Cerebral Cortex
Analysis of functional MRI time-series
Human Brain Mapping
Neocognitron: A self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
Biological Cybernetics
Feedforward and feedback connections between areas V1 and V2 of the monkey have similar rapid conduction velocities
Journal of Neurophysiology
Elements of pattern theory
Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons
Nature
Computational modelling of visual attention
Nature Reviews. Neuroscience
Timecourse of neural signatures of object recognition
Journal of Vision
Spatial distribution of contextual interactions in primary visual cortex and in visual perception
Journal of Neurophysiology
Object perception as Bayesian inference
Annual Review of Psychology
Pattern inference theory: A probabilistic approach to vision
Bayesian models of object perception
Current Opinion in Neurobiology
Neuronal responses to static texture patterns in area V1 of the alert macaque monkey
Journal of Neurophysiology
Predicting the visual world: Silence is golden [news; comment]
Nature Neuroscience
The neurophysiology of figure-ground segregation in primary visual cortex
Journal of Neuroscience
Attention activates winner-take-all competition among visual filters
Nature Neuroscience
Hierarchical Bayesian inference in the visual cortex
Journal of the Optical Society of America. A. Optics, Image Science and Vision
Cited by (122)
Learning beyond sensations: How dreams organize neuronal representations
2024, Neuroscience and Biobehavioral ReviewsThe N400 in silico: A review of computational models
2022, Psychology of Learning and Motivation - Advances in Research and TheoryTopographic signatures of global object perception in human visual cortex
2020, NeuroImageCitation Excerpt :Building upon previous research involving the diamond stimulus (de-Wit et al., 2012), it is important to highlight that our results in lower visual cortex across all experiments seem to contradict suggestions of predictive coding theories that suppressive effects should be confined to cortical sites encoding the physical stimulus and accompanied by unchanged activity in the background region (e.g., Mumford, 1992; Murray et al., 2004; Rao and Ballard, 1999). They furthermore seem to conflict with alternative accounts, such as response sharpening (e.g., Kersten et al., 2004; Kersten and Yuille, 2003; Murray et al., 2004). Response sharpening accounts assume that predictive feedback from higher-tier areas sharpens diffuse responses in lower-tier areas (due to noise or ambiguity) by increasing activity matching the global interpretation of the bottom-up input and decreasing non-matching activity.
Fitting predictive coding to the neurophysiological data
2019, Brain Research