Frontal lobes and attention: Processes and networks, fractionation and integration

2006 ◽  
Vol 12 (2) ◽  
pp. 261-271 ◽  
Author(s):  
DONALD T. STUSS
Keyword(s):  
Frontal Lobes ◽  
Task Demands ◽  
Activation Process ◽  
Top Down ◽  
Supervisory System ◽  
Current Task ◽  
Frontal Processes ◽  
General Activation ◽  
Lesion Studies ◽  
The Brain

The frontal lobes (FL), are they a general adaptive global capacity processor, or a series of fractionated processes? Our lesion studies focusing on attention have demonstrated impairments in distinct processes due to pathology in different frontal regions, implying fractionation of the “supervisory system.” However, when task demands are manipulated, it becomes evident that the frontal lobes are not just a series of independent processes. Increased complexity of task demands elicits greater involvement of frontal regions along a fixed network related to a general activation process. For some task demands, one or more anatomically distinct frontal processes may be recruited. In other conditions, there is a bottom-up nonfrontal/frontal network, with impairment noted maximally for the lesser task demands in the nonfrontal automatic processing regions, and then as task demands change, increased involvement of different frontal (more “strategic”) regions, until it appears all frontal regions are involved. With other measures, the network is top-down, with impairment in the measure first noted in the frontal region and then, with changing task demands, involving a posterior region. Adaptability is not just a property of FL, it is the fluid recruitment of different processes anywhere in the brain as required by the current task. (JINS, 2006,12, 261–271.)

scholarly journals Distracted by previous reward: integrating selection history, current task demands and saliency in a computational model

2021 ◽  
Author(s):  
Neda Meibodi ◽  
Hossein Abbasi ◽  
Anna Schubö ◽  
Dominik Endres
Keyword(s):  
Reaction Time ◽  
Task Demands ◽  
Top Down ◽  
Data Set ◽  
Learning History ◽  
History Effects ◽  
Selection History ◽  
Priority Map ◽  
Current Task ◽  
Level Model

Attention can be biased by previous learning and experience. We present analgorithmic-level model of this bias in visual attention that predicts quantitatively howbottom-up, top-down and selection history compete to control attention. In the model,the output of saliency maps as bottom-up guidance interacts with a history map thatencodes learning effects and a top-down task control to prioritize visual features. Wetest the model on a reaction-time (RT) data set from the experiment presented in [1].The model accurately predicts parameters of reaction time distributions from anintegrated priority map that is comprised of an optimal, weighted combination ofseparate maps. Analysis of the weights confirms learning history effects on attentionguidance. The model is able to capture individual differences between participants.Moreover, we demonstrate that a model with a reduced set of maps performs worse,indicating that integrating history, saliency and task information are required for aquantitative description of human attention.

scholarly journals Top-Down Beta Oscillatory Signaling Conveys Behavioral Context to Primary Visual Cortex

2016 ◽  
Author(s):  
Craig G. Richter ◽  
Richard Coppola ◽  
Steven L. Bressler
Keyword(s):  
Visual Cortex ◽  
Granger Causality ◽  
Frequency Band ◽  
Sensory Information ◽  
Task Demands ◽  
Behavioral Context ◽  
Global Knowledge ◽  
Top Down ◽  
Current Task ◽  
Beta Frequency

AbstractTop-down modulation of sensory processing is a critical neural mechanism subserving a number of important cognitive roles. Principally, top-down influences appear to inform lower-order sensory systems of the current ‘task at hand’, and thus may convey behavioral context to these systems. Accumulating evidence indicates that top-down cortical influences are carried by directed interareal synchronization of oscillatory neuronal populations. An important question currently under investigation by a number of laboratories is whether the information conveyed by directed interareal synchronization depends on the frequency band in which it is conveyed. Recent results point to the beta frequency band as being particularly important for conveying task-related information. However, little is known about the nature of the information conveyed by top-down directed influences. To investigate the information content of top-down directed beta-frequency influences, we measured spectral Granger Causality using local field potentials recorded from microelectrodes chronically implanted in visual cortical areas V1, V4, and TEO, and then applied multivariate pattern analysis to the spatial patterns of top-down spectral Granger Causality in the visual cortex. We decoded behavioral context by discriminating patterns of top-down (V4/TEO → V1) beta-peak spectral Granger Causality for two different task rules governing the correct responses to visual stimuli. The results indicate that top-down directed influences in visual cortex are carried by beta oscillations, and differentiate current task demands even before visual stimulus processing. They suggest that top-down beta-frequency oscillatory processes may coordinate the processing of sensory information by conveying global knowledge states to early levels of the sensory cortical hierarchy independently of bottom-up stimulus-driven processing.

Gamma response of the brain: a multifunctional oscillation that represents bottom-up with top-down processing

2001 ◽  
Vol 39 (2-3) ◽  
pp. 137-150 ◽  
Author(s):  
S Karakaş ◽  
C Başar-Eroğlu ◽  
Ç Özesmi ◽  
H Kafadar ◽  
Ö.Ü Erzengin
Keyword(s):  
Top Down ◽  
Bottom Up ◽  
The Brain

Role of the frontal lobes in integrative activity of the brain inMacaca mulatta

1980 ◽  
Vol 10 (3) ◽  
pp. 265-270 ◽  
Author(s):  
A. S. Batuev ◽  
A. A. Pirogov ◽  
A. A. Orlov
Keyword(s):  
Frontal Lobes ◽  
Integrative Activity ◽  
The Brain

Drivers’ calibration in self-evaluated performance

2016 ◽  
Vol 60 (1) ◽  
pp. 1904-1904
Author(s):  
William J. Horrey ◽  
Mary F. Lesch ◽  
Yulan Liang
Keyword(s):  
Task Demands ◽  
Objective Measures ◽  
Current Task

Drivers tend to hold favorable or optimistic views of their skills and abilities (e.g., Horswill et al., 2004), which can lead to situations where over-confident drivers are ill-equipped (Gregersen, 1996). In addition to general self-evaluations of skills, drivers can also make erroneous estimates of their own performance and of current task demands, possibly leading to poor decisions or failures to adjust behavior to mitigate risk (e.g., Horrey et al., 2015). Gaps between perceptions, self-evaluations and objective measures have been likened to the notion of calibration.

scholarly journals EPITHELIAL SARCOMA OF THE NASAL PASSAGES INVADING THE FRONTAL LOBES OF THE BRAIN

1891 ◽  
Vol 101 (1) ◽  
pp. 91-92
Author(s):  
M. G. DURANTE
Keyword(s):  
Frontal Lobes ◽  
Nasal Passages ◽  
The Brain

scholarly journals Neuronal Correlates of Multiple Top–Down Signals during Covert Tracking of Moving Objects in Macaque Prefrontal Cortex

2012 ◽  
Vol 24 (10) ◽  
pp. 2043-2056 ◽  
Author(s):  
Ayano Matsushima ◽  
Masaki Tanaka
Keyword(s):  
Prefrontal Cortex ◽  
Object Tracking ◽  
Visual Processing ◽  
Moving Objects ◽  
Top Down ◽  
Selective Suppression ◽  
Internal Object ◽  
Object Segregation ◽  
Task Irrelevant ◽  
The Brain

Resistance to distraction is a key component of executive functions and is strongly linked to the prefrontal cortex. Recent evidence suggests that neural mechanisms exist for selective suppression of task-irrelevant information. However, neuronal signals related to selective suppression have not yet been identified, whereas nonselective surround suppression, which results from attentional enhancement for relevant stimuli, has been well documented. This study examined single neuron activities in the lateral PFC when monkeys covertly tracked one of randomly moving objects. Although many neurons responded to the target, we also found a group of neurons that exhibited a selective response to the distractor that was visually identical to the target. Because most neurons were insensitive to an additional distractor that explicitly differed in color from the target, the brain seemed to monitor the distractor only when necessary to maintain internal object segregation. Our results suggest that the lateral PFC might provide at least two top–down signals during covert object tracking: one for enhancement of visual processing for the target and the other for selective suppression of visual processing for the distractor. These signals might work together to discriminate objects, thereby regulating both the sensitivity and specificity of target choice during covert object tracking.

Orthonasal Smell

Neuroenology ◽  
2016 ◽  
pp. 88-97
Author(s):  
Gordon M. Shepherd
Keyword(s):  
Frontal Lobes ◽  
Olfactory Cortex ◽  
Conscious Perception ◽  
Sensory Representation ◽  
The Brain

The olfactory cortex is a pattern recognizer, changing the image from a sensory representation to an internal “sensory object”. In this state it represents individual components of the aroma molecules, but also qualities of the aroma as a merged object. The perception in the wine taster’s brain has both these qualities, challenging the ability to make fine distinctions. This conscious perception is believed to arise at the highest levels in the frontal lobes, where circuits enable the brain to be flexible in learning to prefer different wines.

Artificial Visual Attention Using Combinatorial Pyramids

2013 ◽  
pp. 437-455
Author(s):  
E. Antúnez ◽  
Y. Haxhimusa ◽  
R. Marfil ◽  
W. G. Kropatsch ◽  
A. Bandera
Keyword(s):  
Image Data ◽  
Recognition Algorithm ◽  
Top Down ◽  
Bottom Up ◽  
Geometrical Properties ◽  
Image Descriptors ◽  
Topological Features ◽  
Two Factors ◽  
Current Task ◽  
Computer Vision Systems

Computer vision systems have to deal with thousands, sometimes millions of pixel values from each frame, and the computational complexity of many problems related to the interpretation of image data is very high. The task becomes especially difficult if a system has to operate in real-time. Within the Combinatorial Pyramid framework, the proposed computational model of attention integrates bottom-up and top-down factors for attention. Neurophysiologic studies have shown that, in humans, these two factors are the main responsible ones to drive attention. Bottom-up factors emanate from the scene and focus attention on regions whose features are sufficiently discriminative with respect to the features of their surroundings. On the other hand, top-down factors are derived from cognitive issues, such as knowledge about the current task. Specifically, the authors only consider in this model the knowledge of a given target to drive attention to specific regions of the image. With respect to previous approaches, their model takes into consideration not only geometrical properties and appearance information, but also internal topological layout. Once the focus of attention has been fixed to a region of the scene, the model evaluates if the focus is correctly located over the desired target. This recognition algorithm considers topological features provided by the pre-attentive stage. Thus, attention and recognition are tied together, sharing the same image descriptors.

scholarly journals A Computational Role for Top–Down Modulation from Frontal Cortex in Infancy

2020 ◽  
Vol 32 (3) ◽  
pp. 508-514 ◽  
Author(s):  
Sagi Jaffe-Dax ◽  
Alex M. Boldin ◽  
Nathaniel D. Daw ◽  
Lauren L. Emberson
Keyword(s):  
Associative Learning ◽  
Preterm Infants ◽  
Prediction Error ◽  
Frontal Lobes ◽  
Full Term ◽  
Computational Learning ◽  
Top Down ◽  
Term Infants ◽  
Young Infants ◽  
Sensory Prediction

Recent findings have shown that full-term infants engage in top–down sensory prediction, and these predictions are impaired as a result of premature birth. Here, we use an associative learning model to uncover the neuroanatomical origins and computational nature of this top–down signal. Infants were exposed to a probabilistic audiovisual association. We find that both groups (full term, preterm) have a comparable stimulus-related response in sensory and frontal lobes and track prediction error in their frontal lobes. However, preterm infants differ from their full-term peers in weaker tracking of prediction error in sensory regions. We infer that top–down signals from the frontal lobe to the sensory regions carry information about prediction error. Using computational learning models and comparing neuroimaging results from full-term and preterm infants, we have uncovered the computational content of top–down signals in young infants when they are engaged in a probabilistic associative learning.

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