Traditional and new principles of perceptual grouping

Perceptual grouping refers to the process of determining which regions and parts of the visual scene belong together as parts of higher order perceptual units such as objects or patterns. In the early 20th century, Gestalt psychologists identified a set of classic grouping principles which specified how some image features lead to grouping between elements given that all other factors were held constant. Modern vision scientists have expanded this list to cover a wide range of image features but have also expanded the importance of learning and other non-image factors. Unlike early Gestalt accounts which were based largely on visual demonstrations, modern theories are often explicitly quantitative and involve detailed models of how various image features modulate grouping. Work has also been done to understand the rules by which different grouping principles integrate to form a final percept. This chapter gives an overview of the classic principles, modern developments in understanding them, and new principles and the evidence for them. There is also discussion of some of the larger theoretical issues about grouping such as at what stage of visual processing it occurs and what types of neural mechanisms may implement grouping principles

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Neural Models of Motion Integration, Segmentation, and Probablistic Decision-Making

When brain mechanism carry out motion integration and segmentation processes that compute unambiguous global motion percepts from ambiguous local motion signals? Consider, for example, a deer running at variable speeds behind forest cover. The forest cover is an occluder that creates apertures through which fragments of the deer's motion signals are intermittently experienced. The brain coherently groups these fragments into a trackable percept of the deer in its trajectory. Form and motion processes are needed to accomplish this using feedforward and feedback interactions both within and across cortical processing streams. All the cortical areas V1, V2, MT, and MST are involved in these interactions. Figure-ground processes in the form stream through V2, such as the seperation of occluding boundaries of the forest cover from the boundaries of the deer, select the motion signals which determine global object motion percepts in the motion stream through MT. Sparse, but unambiguous, feauture tracking signals are amplified before they propogate across position and are intergrated with far more numerous ambiguous motion signals. Figure-ground and integration processes together determine the global percept. A neural model predicts the processing stages that embody these form and motion interactions. Model concepts and data are summarized about motion grouping across apertures in response to a wide variety of displays, and probabilistic decision making in parietal cortex in response to random dot displays.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Laminar Cortical Dynamics of Visual Form and Motion Interactions During Coherent Object Motion Perception

How do visual form and motion processes cooperate to compute object motion when each process separately is insufficient? A 3D FORMOTION model specifies how 3D boundary representations, which separate figures from backgrounds within cortical area V2, capture motion signals at the appropriate depths in MT; how motion signals in MT disambiguate boundaries in V2 via MT-to-Vl-to-V2 feedback; how sparse feature tracking signals are amplified; and how a spatially anisotropic motion grouping process propagates across perceptual space via MT-MST feedback to integrate feature-tracking and ambiguous motion signals to determine a global object motion percept. Simulated data include: the degree of motion coherence of rotating shapes observed through apertures, the coherent vs. element motion percepts separated in depth during the chopsticks illusion, and the rigid vs. non-rigid appearance of rotating ellipses.Air Force Office of Scientific Research (F49620-01-1-0397); National Geospatial-Intelligence Agency (NMA201-01-1-2016); National Science Foundation (BCS-02-35398, SBE-0354378); Office of Naval Research (N00014-95-1-0409, N00014-01-1-0624

Binocular fusion and invariant category learning due to predictive remapping during scanning of a depthful scene with eye movements

How does the brain maintain stable fusion of 3D scenes when the eyes move? Every eye movement causes each retinal position to process a different set of scenic features, and thus the brain needs to binocularly fuse new combinations of features at each position after an eye movement. Despite these breaks in retinotopic fusion due to each movement, previously fused representations of a scene in depth often appear stable. The 3D ARTSCAN neural model proposes how the brain does this by unifying concepts about how multiple cortical areas in the What and Where cortical streams interact to coordinate processes of 3D boundary and surface perception, spatial attention, invariant object category learning, predictive remapping, eye movement control, and learned coordinate transformations. The model explains data from single neuron and psychophysical studies of covert visual attention shifts prior to eye movements. The model further clarifies how perceptual, attentional, and cognitive interactions among multiple brain regions (LGN, V1, V2, V3A, V4, MT, MST, PPC, LIP, ITp, ITa, SC) may accomplish predictive remapping as part of the process whereby view-invariant object categories are learned. These results build upon earlier neural models of 3D vision and figure-ground separation and the learning of invariant object categories as the eyes freely scan a scene. A key process concerns how an object's surface representation generates a form-fitting distribution of spatial attention, or attentional shroud, in parietal cortex that helps maintain the stability of multiple perceptual and cognitive processes. Predictive eye movement signals maintain the stability of the shroud, as well as of binocularly fused perceptual boundaries and surface representations.Published versio

View-Invariant Object Category Learning, Recognition, and Search: How Spatial and Object Attention Are Coordinated Using Surface-Based Attentional Shrouds

Air Force Office of Scientific Research (F49620-01-1-0397); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

On Control Systems of the Brain: A Study of Their Connections, Activations, and Interactions

Implementation of daily functions in humans crucially relies on both the bottom-up moment-to- moment processing of relevant input and output information as well as the top-down controls that instantiate and regulate goal-directed strategies. The current dissertation focuses on different systems of brain regions related to task control. We are interested in investigating, in detail, some of the basic activity patterns that different control systems carry during simple tasks, and how differences in activity patterns may shed new insight onto the distinctions among the systems\u27 functional roles. In addition, carefully coordinated interactions between brain regions specialized for control-related activity and regions specialized for bottom-up information processing are essential for humans to adeptly undertake various goal-directed tasks. Hence, another goal is to explore how the relationships among regions related to control and regions related to processing will change as result of top-down control signals during tasks. In Chapter 2, we applied the graph theory method of link communities onto the brain\u27s resting-state intrinsic connectivity structure to identify possible points of interactions among the previously defined functional systems, including various control systems. In Chapter 3, we conducted a meta-analysis of tasks to examine the distinct functional characteristics of control systems in task activation. Using a data-driven clustering analysis, we identified two distinct trial-related response profiles that divided the regions of control systems into a right frontoparietal and cinguloopercular cluster, which may be engaged in fine-tuning task parameters and evaluating performance, and a left frontoparietal and dorsal attention cluster, which may be involved in timely updates of trial-wise parameters as well as information processing. In Chapter 4, we explored the changes in functional relationships among selected systems during individual trials of a goal-direct task and found the presence of complex and dynamic relationships that suggest changes among the various functional systems across a trial reflect both continuous as well as momentary effects of top-down signals. Collectively, the studies presented here both contributed to as well as challenged previous frameworks of task control in an effort to build better understanding of the basic organization and interactions among the brain\u27s functional systems

Pre-activation negativity (PrAN) : A neural index of predictive strength of phonological cues

We propose that a recently discovered event-related potential (ERP) component—the pre-activation negativity (PrAN)—indexes the predictive strength of phonological cues, including segments, word tones, and sentence-level tones. Specifically, we argue that PrAN is a reflection of the brain’s anticipation of upcoming speech (segments, morphemes, words, and syntactic structures). Findings from a long series of neurolinguistic studies indicate that the effect can be divided into two time windows with different possible brain sources. Between 136 and 200 ms from stimulus onset, it indexes activity mainly in the primary and secondary auditory cortices, reflecting disinhibition of neurons sensitive to the expected acoustic signal, as indicated by the brain regions’ response to predictive certainty rather than sound salience. After ~200 ms, PrAN is related to activity in Broca’s area, possibly reflecting inhibition of irrelevant segments, morphemes, words, and syntactic structures

Boundary contour based surface representation.

We receive most information about our surrounding space and objects through the eyes. To reconstruct the 3D space and objects in the visual system from the 2D retinal images, surface representation must be a critical intermediate stage in the visual processing stream. It is hypothesized in the dissertation that the visual system represents textured surface by a border-to-interior strategy: boundary contours would be encoded first and then border-ownership assignments would be resolved. This process would solve the related problems such as figure-ground segregation, surface depth relationship, occlusion, transparency, etc. As a result, the boundary contours of the surfaces would be well defined and then the visual system could register the local features in different domains with the boundary contours, gradually from the adjacent areas of the boundary contours to the interior of the surfaces. To testify this hypothesis in the current proposal, a monocular boundary contour (MBC) paradigm is adapted from earlier studies by Ooi and He (2005, 2006). In Chapter 1, the boundary-contour-based hypothesis, with the MBC paradigm, is used to re-address a decade-long debate about binocular vision: whether (and how) binocular integration and inhibition coexist. In Chapter 2–5, the MBC-induced binocular suppression is systematically investigated, especially in Chapter 3 where the cortical speed of the hypothesized border-to-interior spreading is quantitatively estimated. In the end, the rules how the surface fragments are integrated to a global representation is further studied in Chapter 6 and 7, especially focusing on the role of luminance and color contrast polarities