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Where's Waldo? How perceptual, cognitive, and emotional brain processes cooperate during learning to categorize and find desired objects in a cluttered scene.

Chang HC, Grossberg S, Cao Y - Front Integr Neurosci (2014)

Bottom Line: What stream cognitive-emotional learning processes enable the focusing of motivated attention upon the invariant object categories of desired objects.A volitional signal can convert these primes into top-down activations that can, in turn, prime What stream view- and positionally-specific categories.These processes describe interactions among brain regions that include visual cortex, parietal cortex, inferotemporal cortex, prefrontal cortex (PFC), amygdala, basal ganglia (BG), and superior colliculus (SC).

View Article: PubMed Central - PubMed

Affiliation: Graduate Program in Cognitive and Neural Systems, Department of Mathematics, Center for Adaptive Systems, Center for Computational Neuroscience and Neural Technology, Boston University Boston, MA, USA.

ABSTRACT
The Where's Waldo problem concerns how individuals can rapidly learn to search a scene to detect, attend, recognize, and look at a valued target object in it. This article develops the ARTSCAN Search neural model to clarify how brain mechanisms across the What and Where cortical streams are coordinated to solve the Where's Waldo problem. The What stream learns positionally-invariant object representations, whereas the Where stream controls positionally-selective spatial and action representations. The model overcomes deficiencies of these computationally complementary properties through What and Where stream interactions. Where stream processes of spatial attention and predictive eye movement control modulate What stream processes whereby multiple view- and positionally-specific object categories are learned and associatively linked to view- and positionally-invariant object categories through bottom-up and attentive top-down interactions. Gain fields control the coordinate transformations that enable spatial attention and predictive eye movements to carry out this role. What stream cognitive-emotional learning processes enable the focusing of motivated attention upon the invariant object categories of desired objects. What stream cognitive names or motivational drives can prime a view- and positionally-invariant object category of a desired target object. A volitional signal can convert these primes into top-down activations that can, in turn, prime What stream view- and positionally-specific categories. When it also receives bottom-up activation from a target, such a positionally-specific category can cause an attentional shift in the Where stream to the positional representation of the target, and an eye movement can then be elicited to foveate it. These processes describe interactions among brain regions that include visual cortex, parietal cortex, inferotemporal cortex, prefrontal cortex (PFC), amygdala, basal ganglia (BG), and superior colliculus (SC).

No MeSH data available.


Related in: MedlinePlus

Model simulations of view-invariant object category learning, after ten reinforcement simulation trials. Figure 8B presents the scenic input for the simulation. The attentional shrouds competitively form around objects in the Where stream and the winner shroud carries out view-invariant object category learning in the What stream. The persistence of a shroud controls the eye movements on the salient features on the object surface, thereby generating a sequence of views to that are encoded by view-specific categories which are, in turn, associated with the view-invariant object category. The collapse of an active shroud triggers a reset signal which shuts off the corresponding layers, including the spatial attention map, object surface, view category integrator, and view-invariant object category, to enable an attentional shift to another object. (A) Sum of the neural activities of each shroud. Each line indicates the total activities of the shroud that is activated by the corresponding object. Blue line: soccer ball; red line: cellphone; green line: motorcycle. (B) Object category reset signals. A reset is triggered at time = 1.25, 2.6, and 3.95 when collapse of the shroud reaches the threshold ε for triggering a reset signal in Equation (A55). (C) Habituative gate of reset signal. The depletion of the habituative neurotransmitter in Equation (A57) causes the reset signal in Equation (A42) to collapse after its transient burst and then to replenish through time to enable future resets to occur. (D) Eye movement traces of the simulated scene. The figures show only the central regions of the simulated scene. The initial eye fixation is located at the center of the scene and each square indicates an eye fixation on the object surfaces. (E) View-specific category activities in corresponding regions. Different colored lines indicate that each category activates for a short time and gets reset after the saccadic eye movement occurs. (1) Region 13 activation corresponding to the foveal views. (2) Region 14 activation corresponding to the extra-foveal view after the first object is learned. (3) Region 6 activation corresponding to the extra-foveal view after the second object is learned. (F) View category integrator activities in the corresponding regions. Different colored lines indicate integrators’ persistent activities that are inhibited when they receive a reset signal. (G) Reinforcing inputs are presented to value categories when the view-invariant object categories are active. (H) Invariant object category activities. The activation of the first object category corresponds to learning the cellphone; activation of the third object category corresponds to learning the motorcycle. (I) Value category activities corresponding to the activations of invariant object categories. (J) Object-value category activities driven by activations of invariant object categories. (K) Name category activities.
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Figure 10: Model simulations of view-invariant object category learning, after ten reinforcement simulation trials. Figure 8B presents the scenic input for the simulation. The attentional shrouds competitively form around objects in the Where stream and the winner shroud carries out view-invariant object category learning in the What stream. The persistence of a shroud controls the eye movements on the salient features on the object surface, thereby generating a sequence of views to that are encoded by view-specific categories which are, in turn, associated with the view-invariant object category. The collapse of an active shroud triggers a reset signal which shuts off the corresponding layers, including the spatial attention map, object surface, view category integrator, and view-invariant object category, to enable an attentional shift to another object. (A) Sum of the neural activities of each shroud. Each line indicates the total activities of the shroud that is activated by the corresponding object. Blue line: soccer ball; red line: cellphone; green line: motorcycle. (B) Object category reset signals. A reset is triggered at time = 1.25, 2.6, and 3.95 when collapse of the shroud reaches the threshold ε for triggering a reset signal in Equation (A55). (C) Habituative gate of reset signal. The depletion of the habituative neurotransmitter in Equation (A57) causes the reset signal in Equation (A42) to collapse after its transient burst and then to replenish through time to enable future resets to occur. (D) Eye movement traces of the simulated scene. The figures show only the central regions of the simulated scene. The initial eye fixation is located at the center of the scene and each square indicates an eye fixation on the object surfaces. (E) View-specific category activities in corresponding regions. Different colored lines indicate that each category activates for a short time and gets reset after the saccadic eye movement occurs. (1) Region 13 activation corresponding to the foveal views. (2) Region 14 activation corresponding to the extra-foveal view after the first object is learned. (3) Region 6 activation corresponding to the extra-foveal view after the second object is learned. (F) View category integrator activities in the corresponding regions. Different colored lines indicate integrators’ persistent activities that are inhibited when they receive a reset signal. (G) Reinforcing inputs are presented to value categories when the view-invariant object categories are active. (H) Invariant object category activities. The activation of the first object category corresponds to learning the cellphone; activation of the third object category corresponds to learning the motorcycle. (I) Value category activities corresponding to the activations of invariant object categories. (J) Object-value category activities driven by activations of invariant object categories. (K) Name category activities.

Mentions: Figure 10 details the results of view-invariant object category learning of these three objects during reinforcement learning trials. Within a simulation trial, three successive formations and collapses of attentional shrouds in the Where stream (Figure 10A) support learning of three object categories in the What stream. About 24 views are generated (three objects by approximately eight eye movements) leading to learning of the corresponding view-specific categories and activation of the corresponding view category integrator neurons which, in turn, are associated with three view-invariant object category neurons.


Where's Waldo? How perceptual, cognitive, and emotional brain processes cooperate during learning to categorize and find desired objects in a cluttered scene.

Chang HC, Grossberg S, Cao Y - Front Integr Neurosci (2014)

Model simulations of view-invariant object category learning, after ten reinforcement simulation trials. Figure 8B presents the scenic input for the simulation. The attentional shrouds competitively form around objects in the Where stream and the winner shroud carries out view-invariant object category learning in the What stream. The persistence of a shroud controls the eye movements on the salient features on the object surface, thereby generating a sequence of views to that are encoded by view-specific categories which are, in turn, associated with the view-invariant object category. The collapse of an active shroud triggers a reset signal which shuts off the corresponding layers, including the spatial attention map, object surface, view category integrator, and view-invariant object category, to enable an attentional shift to another object. (A) Sum of the neural activities of each shroud. Each line indicates the total activities of the shroud that is activated by the corresponding object. Blue line: soccer ball; red line: cellphone; green line: motorcycle. (B) Object category reset signals. A reset is triggered at time = 1.25, 2.6, and 3.95 when collapse of the shroud reaches the threshold ε for triggering a reset signal in Equation (A55). (C) Habituative gate of reset signal. The depletion of the habituative neurotransmitter in Equation (A57) causes the reset signal in Equation (A42) to collapse after its transient burst and then to replenish through time to enable future resets to occur. (D) Eye movement traces of the simulated scene. The figures show only the central regions of the simulated scene. The initial eye fixation is located at the center of the scene and each square indicates an eye fixation on the object surfaces. (E) View-specific category activities in corresponding regions. Different colored lines indicate that each category activates for a short time and gets reset after the saccadic eye movement occurs. (1) Region 13 activation corresponding to the foveal views. (2) Region 14 activation corresponding to the extra-foveal view after the first object is learned. (3) Region 6 activation corresponding to the extra-foveal view after the second object is learned. (F) View category integrator activities in the corresponding regions. Different colored lines indicate integrators’ persistent activities that are inhibited when they receive a reset signal. (G) Reinforcing inputs are presented to value categories when the view-invariant object categories are active. (H) Invariant object category activities. The activation of the first object category corresponds to learning the cellphone; activation of the third object category corresponds to learning the motorcycle. (I) Value category activities corresponding to the activations of invariant object categories. (J) Object-value category activities driven by activations of invariant object categories. (K) Name category activities.
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Figure 10: Model simulations of view-invariant object category learning, after ten reinforcement simulation trials. Figure 8B presents the scenic input for the simulation. The attentional shrouds competitively form around objects in the Where stream and the winner shroud carries out view-invariant object category learning in the What stream. The persistence of a shroud controls the eye movements on the salient features on the object surface, thereby generating a sequence of views to that are encoded by view-specific categories which are, in turn, associated with the view-invariant object category. The collapse of an active shroud triggers a reset signal which shuts off the corresponding layers, including the spatial attention map, object surface, view category integrator, and view-invariant object category, to enable an attentional shift to another object. (A) Sum of the neural activities of each shroud. Each line indicates the total activities of the shroud that is activated by the corresponding object. Blue line: soccer ball; red line: cellphone; green line: motorcycle. (B) Object category reset signals. A reset is triggered at time = 1.25, 2.6, and 3.95 when collapse of the shroud reaches the threshold ε for triggering a reset signal in Equation (A55). (C) Habituative gate of reset signal. The depletion of the habituative neurotransmitter in Equation (A57) causes the reset signal in Equation (A42) to collapse after its transient burst and then to replenish through time to enable future resets to occur. (D) Eye movement traces of the simulated scene. The figures show only the central regions of the simulated scene. The initial eye fixation is located at the center of the scene and each square indicates an eye fixation on the object surfaces. (E) View-specific category activities in corresponding regions. Different colored lines indicate that each category activates for a short time and gets reset after the saccadic eye movement occurs. (1) Region 13 activation corresponding to the foveal views. (2) Region 14 activation corresponding to the extra-foveal view after the first object is learned. (3) Region 6 activation corresponding to the extra-foveal view after the second object is learned. (F) View category integrator activities in the corresponding regions. Different colored lines indicate integrators’ persistent activities that are inhibited when they receive a reset signal. (G) Reinforcing inputs are presented to value categories when the view-invariant object categories are active. (H) Invariant object category activities. The activation of the first object category corresponds to learning the cellphone; activation of the third object category corresponds to learning the motorcycle. (I) Value category activities corresponding to the activations of invariant object categories. (J) Object-value category activities driven by activations of invariant object categories. (K) Name category activities.
Mentions: Figure 10 details the results of view-invariant object category learning of these three objects during reinforcement learning trials. Within a simulation trial, three successive formations and collapses of attentional shrouds in the Where stream (Figure 10A) support learning of three object categories in the What stream. About 24 views are generated (three objects by approximately eight eye movements) leading to learning of the corresponding view-specific categories and activation of the corresponding view category integrator neurons which, in turn, are associated with three view-invariant object category neurons.

Bottom Line: What stream cognitive-emotional learning processes enable the focusing of motivated attention upon the invariant object categories of desired objects.A volitional signal can convert these primes into top-down activations that can, in turn, prime What stream view- and positionally-specific categories.These processes describe interactions among brain regions that include visual cortex, parietal cortex, inferotemporal cortex, prefrontal cortex (PFC), amygdala, basal ganglia (BG), and superior colliculus (SC).

View Article: PubMed Central - PubMed

Affiliation: Graduate Program in Cognitive and Neural Systems, Department of Mathematics, Center for Adaptive Systems, Center for Computational Neuroscience and Neural Technology, Boston University Boston, MA, USA.

ABSTRACT
The Where's Waldo problem concerns how individuals can rapidly learn to search a scene to detect, attend, recognize, and look at a valued target object in it. This article develops the ARTSCAN Search neural model to clarify how brain mechanisms across the What and Where cortical streams are coordinated to solve the Where's Waldo problem. The What stream learns positionally-invariant object representations, whereas the Where stream controls positionally-selective spatial and action representations. The model overcomes deficiencies of these computationally complementary properties through What and Where stream interactions. Where stream processes of spatial attention and predictive eye movement control modulate What stream processes whereby multiple view- and positionally-specific object categories are learned and associatively linked to view- and positionally-invariant object categories through bottom-up and attentive top-down interactions. Gain fields control the coordinate transformations that enable spatial attention and predictive eye movements to carry out this role. What stream cognitive-emotional learning processes enable the focusing of motivated attention upon the invariant object categories of desired objects. What stream cognitive names or motivational drives can prime a view- and positionally-invariant object category of a desired target object. A volitional signal can convert these primes into top-down activations that can, in turn, prime What stream view- and positionally-specific categories. When it also receives bottom-up activation from a target, such a positionally-specific category can cause an attentional shift in the Where stream to the positional representation of the target, and an eye movement can then be elicited to foveate it. These processes describe interactions among brain regions that include visual cortex, parietal cortex, inferotemporal cortex, prefrontal cortex (PFC), amygdala, basal ganglia (BG), and superior colliculus (SC).

No MeSH data available.


Related in: MedlinePlus