scholarly journals Age-Related Changes in the Neural Dynamics of Bottom-Up and Top-Down Processing During Visual Object Recognition: An Electrophysiological Investigation

2019 ◽  
Author(s):  
Leslie Y. Lai ◽  
Romy Frömer ◽  
Elena K. Festa ◽  
William C. Heindel
Keyword(s):  
Object Recognition ◽  
Visual Processing ◽  
Semantic Integration ◽  
Object Identification ◽  
Neural Dynamics ◽  
Visual Object ◽  
Elderly Adults ◽  
Top Down ◽  
Bottom Up ◽  
Increased Sensitivity

ABSTRACTWhen recognizing objects in our environments, we rely on both what we see and what we know. While elderly adults have been found to display increased sensitivity to top-down influences of contextual information during object recognition, the locus of this increased sensitivity remains unresolved. To address this issue, we examined the effects of aging on the neural dynamics of bottom-up and top-down visual processing during rapid object recognition. Specific EEG ERP components indexing bottom-up and top-down processes along the visual processing stream were assessed while systematically manipulating the degree of object ambiguity and scene context congruity. An increase in early attentional feedback mechanisms (as indexed by N1) as well as a functional reallocation of executive attentional resources (as indexed by P200) prior to object identification were observed in elderly adults, while post-perceptual semantic integration (as indexed by N400) remained intact. These findings suggest that compromised bottom-up perceptual processing of visual input in healthy aging leads to an increased involvement of top-down processes to resolve greater perceptual ambiguity during object recognition.

Visual Attention With Cognitive Aging

2019 ◽  
Author(s):  
David J. Madden ◽  
Zachary A. Monge
Keyword(s):  
Visual Attention ◽  
Visual Processing ◽  
Cognitive Aging ◽  
Brain Regions ◽  
Object Identification ◽  
Features Selection ◽  
Top Down ◽  
Bottom Up ◽  
Attentional Functioning ◽  
Age Related

Age-related decline occurs in several aspects of fluid, speed-dependent cognition, particularly those related to attention. Empirical research on visual attention has determined that attention-related effects occur across a range of information processing components, including the sensory registration of features, selection of information from working memory, controlling motor responses, and coordinating multiple perceptual and cognitive tasks. Thus, attention is a multifaceted construct that is relevant at virtually all stages of object identification. A fundamental theme of attentional functioning is the interaction between the bottom-up salience of visual features and top-down allocation of processing based on the observer’s goals. An underlying age-related slowing is prominent throughout visual processing stages, which in turn contributes to age-related decline in some aspects of attention, such as the inhibition of irrelevant information and the coordination of multiple tasks. However, some age-related preservation of attentional functioning is also evident, particularly the top-down allocation of attention. Neuroimaging research has identified networks of frontal and parietal brain regions relevant for top-down and bottom-up attentional processing. Disconnection among these networks contributes to an age-related decline in attention, but preservation and perhaps even increased patterns of functional brain activation and connectivity also contribute to preserved attentional functioning.

COMBINED MODEL-BASED 3D OBJECT RECOGNITION

2005 ◽  
Vol 19 (07) ◽  
pp. 839-852 ◽  
Author(s):  
SUNGHO KIM ◽  
GIJEONG JANG ◽  
WANG-HEON LEE ◽  
IN SO KWEON
Keyword(s):  
Object Recognition ◽  
Object Identification ◽  
Feature Binding ◽  
Human Vision ◽  
3D Object Recognition ◽  
Top Down ◽  
Combined Model ◽  
Bottom Up ◽  
3D Object ◽  
Model Based

This paper presents a combined model-based 3D object recognition method motivated by the robust properties of human vision. The human visual system (HVS) is very efficient and robust in identifying and grabbing objects, in part because of its properties of visual attention, contrast mechanism, feature binding, multiresolution and part-based representation. In addition, the HVS combines bottom-up and top-down information effectively using combined model representation. We propose a method for integrating these aspects under a Monte Carlo method. In this scheme, object recognition is regarded as a parameter optimization problem. The bottom-up process initializes parameters, and the top-down process optimizes them. Experimental results show that the proposed recognition model is feasible for 3D object identification and pose estimation.

A Cortical Mechanism for Triggering Top-Down Facilitation in Visual Object Recognition

2003 ◽  
Vol 15 (4) ◽  
pp. 600-609 ◽  
Author(s):  
Moshe Bar
Keyword(s):  
Object Recognition ◽  
Visual Recognition ◽  
Temporal Cortex ◽  
Input Image ◽  
Initial Guess ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Top Down ◽  
Bottom Up ◽  
Level Information

The majority of the research related to visual recognition has so far focused on bottom-up analysis, where the input is processed in a cascade of cortical regions that analyze increasingly complex information. Gradually more studies emphasize the role of top-down facilitation in cortical analysis, but it remains something of a mystery how such processing would be initiated. After all, top-down facilitation implies that high-level information is activated earlier than some relevant lower-level information. Building on previous studies, I propose a specific mechanism for the activation of top-down facilitation during visual object recognition. The gist of this hypothesis is that a partially analyzed version of the input image (i.e., a blurred image) is projected rapidly from early visual areas directly to the prefrontal cortex (PFC). This coarse representation activates in the PFC expectations about the most likely interpretations of the input image, which are then back-projected as an “initial guess” to the temporal cortex to be integrated with the bottom-up analysis. The top-down process facilitates recognition by substantially limiting the number of object representations that need to be considered. Furthermore, such a rapid mechanism may provide critical information when a quick response is necessary.

Strategies for Object Identification and Confidence: Influence of Appearance, Amount of Context, and Familiarity

1992 ◽  
Vol 75 (3) ◽  
pp. 739-745
Author(s):  
Robert E. Kleine ◽  
Jerome B. Kernan
Keyword(s):  
Experimental Study ◽  
Visual Processing ◽  
Physical Appearance ◽  
Object Identification ◽  
Top Down ◽  
Bottom Up ◽  
The Everyday

In an experimental study of the everyday, what-is-this? perceptual encounter, the strategies subjects used to identify a focal object were influenced by the object's physical appearance, the amount of context in which it was displayed, and subjects' familiarity with the object. These factors also affected subjects' confidence in the accuracy of their identifications. The results appear to support the contention that visual processing is a middle-out rather than either a top-down or bottom-up sequence.

scholarly journals The order of visual processing: “Top-down,” “bottom-up,” or “middle-out”

1979 ◽  
Vol 25 (3) ◽  
pp. 225-231 ◽  
Author(s):  
R. A. Kinchla ◽  
J. M. Wolfe
Keyword(s):  
Visual Processing ◽  
Top Down ◽  
Bottom Up

Top-Down Predictions Determine Perceptions

2017 ◽  
Author(s):  
Martin V. Butz ◽  
Esther F. Kutter
Keyword(s):  
Graphical Models ◽  
Information Integration ◽  
Visual Processing ◽  
Visual Information ◽  
Generative Models ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Top Down ◽  
Bottom Up ◽  
The Brain

While bottom-up visual processing is important, the brain integrates this information with top-down, generative expectations from very early on in the visual processing hierarchy. Indeed, our brain should not be viewed as a classification system, but rather as a generative system, which perceives something by integrating sensory evidence with the available, learned, predictive knowledge about that thing. The involved generative models continuously produce expectations over time, across space, and from abstracted encodings to more concrete encodings. Bayesian information processing is the key to understand how information integration must work computationally – at least in approximation – also in the brain. Bayesian networks in the form of graphical models allow the modularization of information and the factorization of interactions, which can strongly improve the efficiency of generative models. The resulting generative models essentially produce state estimations in the form of probability densities, which are very well-suited to integrate multiple sources of information, including top-down and bottom-up ones. A hierarchical neural visual processing architecture illustrates this point even further. Finally, some well-known visual illusions are shown and the perceptions are explained by means of generative, information integrating, perceptual processes, which in all cases combine top-down prior knowledge and expectations about objects and environments with the available, bottom-up visual information.

Brain activity related to integrative processes in visual object recognition: bottom-up integration and the modulatory influence of stored knowledge

2002 ◽  
Vol 40 (8) ◽  
pp. 1254-1267 ◽  
Author(s):  
C Gerlach ◽  
C.T Aaside ◽  
G.W Humphreys ◽  
A Gade ◽  
O.B Paulson ◽  
...  
Keyword(s):  
Object Recognition ◽  
Brain Activity ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Bottom Up ◽  
Integrative Processes
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