Cortical dynamics of contextually cued attentive visual learning and search: Spatial and object evidence accumulation.

Abstract

How do humans use predictive contextual information to facilitate visual search? How are consistently paired scenic objects and positions learned and used to more efficiently guide search in familiar scenes? For example, a certain combination of objects can define a context for a kitchen and trigger a more efficient search for a typical object, such as a sink, in that context. A neural model, ARTSCENE Search, is developed to illustrate the neural mechanisms of such memory-based contextual learning and guidance, and to explain challenging behavioral data on positive/negative, spatial/object, and local/distant global cueing effects during visual search. The model proposes how global scene layout at a first glance rapidly forms a hypothesis about the target location. This hypothesis is then incrementally refined by enhancing target-like objects in space as a scene is scanned with saccadic eye movements. The model clarifies the functional roles of neuroanatomical, neurophysiological, and neuroimaging data in visual search for a desired goal object. In particular, the model simulates the interactive dynamics of spatial and object contextual cueing in the cortical What and Where streams starting from early visual areas through medial temporal lobe to prefrontal cortex. After learning, model dorsolateral prefrontal c ortical cells (area 46) prime possible target locations in posterior parietal cortex based on goalmodulated percepts of spatial scene gist represented in parahippocampal cortex, whereas model ventral prefrontal cortical cells (area 47/12) prime possible target object representations in inferior temporal cortex based on the history of viewed objects represented in perirhinal cortex. The model hereby predicts how the cortical What and Where streams cooperate during scene perception, learning, and memory to accumulate evidence over time to drive efficient visual search of familiar scenes.3

Cybernetics of the Mind: Learning Individual's Perceptions Autonomously

“…Our approach is different from the existing studies on cognitive neuroscience where the learning is considered at a neuron and brain elements (visual cortex, hypothalamus, etc.) level [10], [11]. It is also significantly different from the large body of literature on perception in a psychological context where the studies are qualitative and descriptive [1]- [4].…”

“…However, it is very often (especially in adults) of unsupervised type of learning in the broader sense that no "external teacher" or external feedback of stimulus may be present and still we can continue to learn autonomously and dynamically evolve our own understanding of the world [14]. This hypothesis has strong links with the ART concept by Carpenter and Grossberg in regards to the threshold and arousal when new information is presented [11].…”

Processing real-world imagery with FACADE-based approaches

“…In the original formulas, as indicated in Eqs. (14) and (15), one can deduce that the spatial competition of the hyper-complex cell occurs among the neighboring hyper-complex cells (hence, the competition being called a "short-range" competition). A hyper-complex cell interacts with its neighboring hypercomplex cells in an isotropic circular range, which we call the competition domain of a hyper-complex.…”

“…Some of these models have provided useful contributions to neuroscience, and a few have even had an impact on physiologists. In terms of applicability to constructing an HVS featured visual information processing system, several models have been more successful and are better known than others, such as the HMAX model by Poggio et al [3][4][5][6], the VisNet model by Rolls et al [7][8][9][10], and the series of visual perception models by Grossberg et al [11][12][13][14], including the so-called FACADE framework, which is the main topic of this paper.…”

Inverse Bilateral Filter for Saliency

“…An approach in [22] states that the units of attention are often various kinds of visual objects. Interactive dynamics of object and spatial contextual cueing and attention in the cortical are presented in [23] [24]. A model of proto-objects that eventually guides a saliency mechanism is defined in [25] and applied on a humanoid robot.…”