Pub Date : 2021-06-25DOI: 10.1093/oso/9780190070557.003.0016
S. Grossberg
This chapter explains how humans and other animals learn to learn to navigate in space. Both reaching and route-based navigation use difference vector computations. Route navigation learns a labeled graph of angles and distances moved. Spatial navigation requires neurons to learn navigable spaces that can be many meters in size. This is again accomplished by a spectrum of cells. Such spectral spacing supports learning of medial entorhinal grid cells and hippocampal place cells. The model responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales, and place cells with one or more firing fields, that match neurophysiological data about their development in juvenile rats. Both grid and place cells develop in a hierarchy of self-organizing maps by detecting, learning and remembering the most frequent and energetic co-occurrences of their inputs. Model parsimonious properties include: similar ring attractor mechanisms process linear and angular path integration inputs that drive map learning; the same self-organizing map mechanisms can learn both grid cell and place cell receptive fields; and the learning of the dorsoventral organization of multiple grid cell modules through medial entorhinal cortex to hippocampus uses a gradient of rates that is homologous to a rate gradient that drives adaptively timed learning at multiple rates through lateral entorhinal cortex to hippocampus (‘neural relativity’). The model clarifies how top-down hippocampal-to-entorhinal ART attentional mechanisms stabilize map learning, simulates how hippocampal, septal, or acetylcholine inactivation disrupts grid cells, and explains data about theta, beta and gamma oscillations.
{"title":"Learning Maps to Navigate Space","authors":"S. Grossberg","doi":"10.1093/oso/9780190070557.003.0016","DOIUrl":"https://doi.org/10.1093/oso/9780190070557.003.0016","url":null,"abstract":"This chapter explains how humans and other animals learn to learn to navigate in space. Both reaching and route-based navigation use difference vector computations. Route navigation learns a labeled graph of angles and distances moved. Spatial navigation requires neurons to learn navigable spaces that can be many meters in size. This is again accomplished by a spectrum of cells. Such spectral spacing supports learning of medial entorhinal grid cells and hippocampal place cells. The model responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales, and place cells with one or more firing fields, that match neurophysiological data about their development in juvenile rats. Both grid and place cells develop in a hierarchy of self-organizing maps by detecting, learning and remembering the most frequent and energetic co-occurrences of their inputs. Model parsimonious properties include: similar ring attractor mechanisms process linear and angular path integration inputs that drive map learning; the same self-organizing map mechanisms can learn both grid cell and place cell receptive fields; and the learning of the dorsoventral organization of multiple grid cell modules through medial entorhinal cortex to hippocampus uses a gradient of rates that is homologous to a rate gradient that drives adaptively timed learning at multiple rates through lateral entorhinal cortex to hippocampus (‘neural relativity’). The model clarifies how top-down hippocampal-to-entorhinal ART attentional mechanisms stabilize map learning, simulates how hippocampal, septal, or acetylcholine inactivation disrupts grid cells, and explains data about theta, beta and gamma oscillations.","PeriodicalId":370230,"journal":{"name":"Conscious Mind, Resonant Brain","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129517925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-25DOI: 10.1093/oso/9780190070557.003.0014
S. Grossberg
This chapter describes a unified theory of how the prefrontal cortex interacts with multiple brain regions to carry out the higher cognitive, emotional, and decision-making processes that define human intelligence, while also controlling actions to achieve valued goals. This predictive Adaptive Resonance Theory, or pART, model builds upon the foundation in earlier chapters. Prefrontal functions are often called executive functions. Executive functions regulate flexible and adaptive behaviors, notably in novel situations, while suppressing actions that are no longer appropriate, notably reflexive responses to current sensory inputs. Working memory is particularly involved in contextually appropriate behaviors. Prefrontal properties of desirability, availability, credit assignment, category learning, and feature-based attention are explained. These properties arise through interactions of orbitofrontal, ventrolateral prefrontal, and dorsolateral prefrontal cortices with inferotemporal cortex, perirhinal cortex, parahippocampal cortices; ventral bank of the principal sulcus, ventral prearcuate gyrus, frontal eye fields, hippocampus, amygdala, basal ganglia, hypothalamus, and visual cortical areas V1, V2, V3A, V4, MT, MST, LIP, and PPC. Model explanations include how the value of visual objects and events is computed, which objects and events cause desired consequences and which may be ignored as predictively irrelevant, and how to plan and act to realize these consequences, including how to selectively filter expected vs. unexpected events, leading to movements towards, and conscious perception of, expected events. Modeled processes include reinforcement learning and incentive motivational learning; object and spatial working memory dynamics; and category learning, including the learning of object categories, value categories, object-value categories, and sequence categories, or list chunks.
{"title":"How Prefrontal Cortex Works","authors":"S. Grossberg","doi":"10.1093/oso/9780190070557.003.0014","DOIUrl":"https://doi.org/10.1093/oso/9780190070557.003.0014","url":null,"abstract":"This chapter describes a unified theory of how the prefrontal cortex interacts with multiple brain regions to carry out the higher cognitive, emotional, and decision-making processes that define human intelligence, while also controlling actions to achieve valued goals. This predictive Adaptive Resonance Theory, or pART, model builds upon the foundation in earlier chapters. Prefrontal functions are often called executive functions. Executive functions regulate flexible and adaptive behaviors, notably in novel situations, while suppressing actions that are no longer appropriate, notably reflexive responses to current sensory inputs. Working memory is particularly involved in contextually appropriate behaviors. Prefrontal properties of desirability, availability, credit assignment, category learning, and feature-based attention are explained. These properties arise through interactions of orbitofrontal, ventrolateral prefrontal, and dorsolateral prefrontal cortices with inferotemporal cortex, perirhinal cortex, parahippocampal cortices; ventral bank of the principal sulcus, ventral prearcuate gyrus, frontal eye fields, hippocampus, amygdala, basal ganglia, hypothalamus, and visual cortical areas V1, V2, V3A, V4, MT, MST, LIP, and PPC. Model explanations include how the value of visual objects and events is computed, which objects and events cause desired consequences and which may be ignored as predictively irrelevant, and how to plan and act to realize these consequences, including how to selectively filter expected vs. unexpected events, leading to movements towards, and conscious perception of, expected events. Modeled processes include reinforcement learning and incentive motivational learning; object and spatial working memory dynamics; and category learning, including the learning of object categories, value categories, object-value categories, and sequence categories, or list chunks.","PeriodicalId":370230,"journal":{"name":"Conscious Mind, Resonant Brain","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124036630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-25DOI: 10.1093/oso/9780190070557.003.0011
S. Grossberg
This chapter explains how 3D vision and figure-ground perception occur in our brains. It shows how the 2D boundary and surface processes that are described in earlier chapters naturally generalize to 3D via both the FACADE (Form-And-Color-And-DEpth) theory of 3D vision and figure-ground perception, and the 3D LAMINART model that generalizes the laminar cortical circuits of Chapter 10 to 3D and naturally embodies and generalizes FACADE. Contrast-specific binocular fusion and contrast-invariant boundary formation are explained in terms of identified cells in specific layers of cortical areas V1 and V2. The correspondence problem is solved using a disparity filter that eliminates false binocular matches in layer 2/3 of V2, while it chooses the 3D binocular boundary grouping that is best supported by scenic cues. The critical role of monocular boundary information in figure-ground perception is explained and used to simulate DaVinci stereopsis percepts, along with surface-to-boundary surface contour signals and a fixation plane bias due to life-long experiences with fixated scenic features. Simulated data include the Venetian blind effect, Panum’s limiting case, dichoptic masking, 3D Craik-O’Brien-Cornsweet effect, Julesz random dot stereograms, 3D percepts of 2D pictures of shaded ellipses and discrete textures, simultaneous fusion and rivalry percepts when viewing Kulikowski and Kaufman stereograms, stimulus rivalry and eye rivalry, and bistable percepts of slanted surfaces, including the Necker cube. The size-disparity correlation enables signals from multiple scales to cooperate and compete to generate boundary representations at multiple depths. 3D percepts of natural scenes from stereograms are also simulated with these circuits.
{"title":"How We See the World in Depth","authors":"S. Grossberg","doi":"10.1093/oso/9780190070557.003.0011","DOIUrl":"https://doi.org/10.1093/oso/9780190070557.003.0011","url":null,"abstract":"This chapter explains how 3D vision and figure-ground perception occur in our brains. It shows how the 2D boundary and surface processes that are described in earlier chapters naturally generalize to 3D via both the FACADE (Form-And-Color-And-DEpth) theory of 3D vision and figure-ground perception, and the 3D LAMINART model that generalizes the laminar cortical circuits of Chapter 10 to 3D and naturally embodies and generalizes FACADE. Contrast-specific binocular fusion and contrast-invariant boundary formation are explained in terms of identified cells in specific layers of cortical areas V1 and V2. The correspondence problem is solved using a disparity filter that eliminates false binocular matches in layer 2/3 of V2, while it chooses the 3D binocular boundary grouping that is best supported by scenic cues. The critical role of monocular boundary information in figure-ground perception is explained and used to simulate DaVinci stereopsis percepts, along with surface-to-boundary surface contour signals and a fixation plane bias due to life-long experiences with fixated scenic features. Simulated data include the Venetian blind effect, Panum’s limiting case, dichoptic masking, 3D Craik-O’Brien-Cornsweet effect, Julesz random dot stereograms, 3D percepts of 2D pictures of shaded ellipses and discrete textures, simultaneous fusion and rivalry percepts when viewing Kulikowski and Kaufman stereograms, stimulus rivalry and eye rivalry, and bistable percepts of slanted surfaces, including the Necker cube. The size-disparity correlation enables signals from multiple scales to cooperate and compete to generate boundary representations at multiple depths. 3D percepts of natural scenes from stereograms are also simulated with these circuits.","PeriodicalId":370230,"journal":{"name":"Conscious Mind, Resonant Brain","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125032139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-25DOI: 10.1093/oso/9780190070557.003.0009
S. Grossberg
This chapter explains why and how tracking of objects moving relative to an observer, and visual optic flow navigation of an observer relative to the world, are controlled by complementary cortical streams through MT--MSTv and MT+-MSTd, respectively. Target tracking uses subtractive processing of visual signals to extract an object’s bounding contours as they move relative to a background. Navigation by optic flow uses additive processing of an entire scene to derive properties such as an observer’s heading, or self-motion direction, as it moves through the scene. The chapter explains how the aperture problem for computing heading in natural scenes is solved in MT+-MSTd using a hierarchy of processing stages that is homologous to the one that solves the aperture problem for computing motion direction in MT--MSTv. Both use feedback which obeys the ART Matching Rule to select final perceptual representations and choices. Compensation for eye movements using corollary discharge, or efference copy, signals enables an accurate heading direction to be computed. Neurophysiological data about heading direction are quantitatively simulated. Log polar processing by the cortical magnification factor simplifies computation of motion direction. This space-variant processing is maximally position invariant due to the cortical choice of network parameters. How smooth pursuit occurs, and is maintained during accurate tracking, is explained. Goal approach and obstacle avoidance are explained by attractor-repeller networks. Gaussian peak shifts control steering to a goal, as well as peak shift and behavioral contrast during operant conditioning, and vector decomposition during the relative motion of object parts.
{"title":"Target Tracking, Navigation, and Decision-Making","authors":"S. Grossberg","doi":"10.1093/oso/9780190070557.003.0009","DOIUrl":"https://doi.org/10.1093/oso/9780190070557.003.0009","url":null,"abstract":"This chapter explains why and how tracking of objects moving relative to an observer, and visual optic flow navigation of an observer relative to the world, are controlled by complementary cortical streams through MT--MSTv and MT+-MSTd, respectively. Target tracking uses subtractive processing of visual signals to extract an object’s bounding contours as they move relative to a background. Navigation by optic flow uses additive processing of an entire scene to derive properties such as an observer’s heading, or self-motion direction, as it moves through the scene. The chapter explains how the aperture problem for computing heading in natural scenes is solved in MT+-MSTd using a hierarchy of processing stages that is homologous to the one that solves the aperture problem for computing motion direction in MT--MSTv. Both use feedback which obeys the ART Matching Rule to select final perceptual representations and choices. Compensation for eye movements using corollary discharge, or efference copy, signals enables an accurate heading direction to be computed. Neurophysiological data about heading direction are quantitatively simulated. Log polar processing by the cortical magnification factor simplifies computation of motion direction. This space-variant processing is maximally position invariant due to the cortical choice of network parameters. How smooth pursuit occurs, and is maintained during accurate tracking, is explained. Goal approach and obstacle avoidance are explained by attractor-repeller networks. Gaussian peak shifts control steering to a goal, as well as peak shift and behavioral contrast during operant conditioning, and vector decomposition during the relative motion of object parts.","PeriodicalId":370230,"journal":{"name":"Conscious Mind, Resonant Brain","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114256563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-25DOI: 10.1093/oso/9780190070557.003.0003
S. Grossberg
The distinction between seeing and knowing, and why our brains even bother to see, are discussed using vivid perceptual examples, including image features without visible qualia that can nonetheless be consciously recognized, The work of Helmholtz and Kanizsa exemplify these issues, including examples of the paradoxical facts that “all boundaries are invisible”, and that brighter objects look closer. Why we do not see the big holes in, and occluders of, our retinas that block light from reaching our photoreceptors is explained, leading to the realization that essentially all percepts are visual illusions. Why they often look real is also explained. The computationally complementary properties of boundary completion and surface filling-in are introduced and their unifying explanatory power is illustrated, including that “all conscious qualia are surface percepts”. Neon color spreading provides a vivid example, as do self-luminous, glary, and glossy percepts. How brains embody general-purpose self-organizing architectures for solving modal problems, more general than AI algorithms, but less general than digital computers, is described. New concepts and mechanisms of such architectures are explained, including hierarchical resolution of uncertainty. Examples from the visual arts and technology are described to illustrate them, including paintings of Baer, Banksy, Bleckner, da Vinci, Gene Davis, Hawthorne, Hensche, Matisse, Monet, Olitski, Seurat, and Stella. Paintings by different artists and artistic schools instinctively emphasize some brain processes over others. These choices exemplify their artistic styles. The role of perspective, T-junctions, and end gaps are used to explain how 2D pictures can induce percepts of 3D scenes.
{"title":"How a Brain Sees: Constructing Reality","authors":"S. Grossberg","doi":"10.1093/oso/9780190070557.003.0003","DOIUrl":"https://doi.org/10.1093/oso/9780190070557.003.0003","url":null,"abstract":"The distinction between seeing and knowing, and why our brains even bother to see, are discussed using vivid perceptual examples, including image features without visible qualia that can nonetheless be consciously recognized, The work of Helmholtz and Kanizsa exemplify these issues, including examples of the paradoxical facts that “all boundaries are invisible”, and that brighter objects look closer. Why we do not see the big holes in, and occluders of, our retinas that block light from reaching our photoreceptors is explained, leading to the realization that essentially all percepts are visual illusions. Why they often look real is also explained. The computationally complementary properties of boundary completion and surface filling-in are introduced and their unifying explanatory power is illustrated, including that “all conscious qualia are surface percepts”. Neon color spreading provides a vivid example, as do self-luminous, glary, and glossy percepts. How brains embody general-purpose self-organizing architectures for solving modal problems, more general than AI algorithms, but less general than digital computers, is described. New concepts and mechanisms of such architectures are explained, including hierarchical resolution of uncertainty. Examples from the visual arts and technology are described to illustrate them, including paintings of Baer, Banksy, Bleckner, da Vinci, Gene Davis, Hawthorne, Hensche, Matisse, Monet, Olitski, Seurat, and Stella. Paintings by different artists and artistic schools instinctively emphasize some brain processes over others. These choices exemplify their artistic styles. The role of perspective, T-junctions, and end gaps are used to explain how 2D pictures can induce percepts of 3D scenes.","PeriodicalId":370230,"journal":{"name":"Conscious Mind, Resonant Brain","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134069296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-25DOI: 10.1093/oso/9780190070557.003.0010
S. Grossberg
The cerebral cortex computes the highest forms of biological intelligence in all sensory and cognitive modalities. Neocortical cells are organized into circuits that form six cortical layers in all cortical areas that carry out perception and cognition. Variations in cell properties within these layers and their connections have been used to classify the cerebral cortex into more than fifty divisions, or areas, to which distinct functions have been attributed. Why the cortex has a laminar organization for the control of behavior has, however, remained a mystery until recently. Also mysterious has been how variations on this ubiquitous laminar cortical design can give rise to so many different types of intelligent behavior. This chapter explains how Laminar Computing contributes to biological intelligence, and how layered circuits of neocortical cells support all the various kinds of higher-order biological intelligence, including vision, language, and cognition, using variations of the same canonical laminar circuit. This canonical circuit can be used in general-purpose VLSI chips that can be specialized to carry out different kinds of biological intelligence, and seamlessly joined together to control autonomous adaptive algorithms and mobile robots. These circuits show how preattentive automatic bottom-up processing and attentive task-selective top-down processing are joined together in the deeper cortical layers to form a decision interface. Here, bottom-up and top-down constraints cooperate and compete to generate the best decisions, by combining properties of fast feedforward and feedback processing, analog and digital computing, and preattentive and attentive learning, including laminar ART properties such as analog coherence.
{"title":"Laminar Computing by Cerebral Cortex","authors":"S. Grossberg","doi":"10.1093/oso/9780190070557.003.0010","DOIUrl":"https://doi.org/10.1093/oso/9780190070557.003.0010","url":null,"abstract":"The cerebral cortex computes the highest forms of biological intelligence in all sensory and cognitive modalities. Neocortical cells are organized into circuits that form six cortical layers in all cortical areas that carry out perception and cognition. Variations in cell properties within these layers and their connections have been used to classify the cerebral cortex into more than fifty divisions, or areas, to which distinct functions have been attributed. Why the cortex has a laminar organization for the control of behavior has, however, remained a mystery until recently. Also mysterious has been how variations on this ubiquitous laminar cortical design can give rise to so many different types of intelligent behavior. This chapter explains how Laminar Computing contributes to biological intelligence, and how layered circuits of neocortical cells support all the various kinds of higher-order biological intelligence, including vision, language, and cognition, using variations of the same canonical laminar circuit. This canonical circuit can be used in general-purpose VLSI chips that can be specialized to carry out different kinds of biological intelligence, and seamlessly joined together to control autonomous adaptive algorithms and mobile robots. These circuits show how preattentive automatic bottom-up processing and attentive task-selective top-down processing are joined together in the deeper cortical layers to form a decision interface. Here, bottom-up and top-down constraints cooperate and compete to generate the best decisions, by combining properties of fast feedforward and feedback processing, analog and digital computing, and preattentive and attentive learning, including laminar ART properties such as analog coherence.","PeriodicalId":370230,"journal":{"name":"Conscious Mind, Resonant Brain","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126135134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-25DOI: 10.1093/oso/9780190070557.003.0001
S. Grossberg
An overview is provided of multiple book themes. A critical one is explaining how and where conscious states of seeing, hearing, feeling, and knowing arise in our minds, why they are needed to choose effective actions, yet how unconscious states also critically influence behavior. Other themes include learning, expectation, attention, imagination, and creativity; differences between illusion and reality, and between conscious seeing and recognizing, as embodied within surface-shroud resonances and feature-category resonances, respectively; roles of visual boundaries and surfaces in understanding visual art, movies, and TV; different legacies of Helmholtz and Kanizsa towards understanding vision; how stable opaque percepts and bistable transparent percepts are explained by the same laws; how solving the stability-plasticity dilemma enables brains to learn quickly without catastrophically forgetting previously learned but still useful knowledge; how we correct errors, explore novel experiences, and develop individual selves and cumulative cultural accomplishments; how expected vs. unexpected events are regulated by interacting top-down and bottom-up processes, leading to either adaptive resonances that support fast and stable new learning, or hypothesis testing whereby to learn about novel experiences; how variations of the same cooperative and competitive processes shape intelligence in species, cellular tissues, economic markets, and political systems; how short-term memory, medium-term memory, and long-term memory regulate adaptation to changing environments on different time scales; how processes whereby we learn what events are causal also support irrational, superstitious, obsessional, self-punitive, and antisocial behaviors; how relaxation responses arise; and how future acoustic contexts can disambiguate conscious percepts of past auditory and speech sequences that are occluded by noise or multiple speakers.
{"title":"Overview","authors":"S. Grossberg","doi":"10.1093/oso/9780190070557.003.0001","DOIUrl":"https://doi.org/10.1093/oso/9780190070557.003.0001","url":null,"abstract":"An overview is provided of multiple book themes. A critical one is explaining how and where conscious states of seeing, hearing, feeling, and knowing arise in our minds, why they are needed to choose effective actions, yet how unconscious states also critically influence behavior. Other themes include learning, expectation, attention, imagination, and creativity; differences between illusion and reality, and between conscious seeing and recognizing, as embodied within surface-shroud resonances and feature-category resonances, respectively; roles of visual boundaries and surfaces in understanding visual art, movies, and TV; different legacies of Helmholtz and Kanizsa towards understanding vision; how stable opaque percepts and bistable transparent percepts are explained by the same laws; how solving the stability-plasticity dilemma enables brains to learn quickly without catastrophically forgetting previously learned but still useful knowledge; how we correct errors, explore novel experiences, and develop individual selves and cumulative cultural accomplishments; how expected vs. unexpected events are regulated by interacting top-down and bottom-up processes, leading to either adaptive resonances that support fast and stable new learning, or hypothesis testing whereby to learn about novel experiences; how variations of the same cooperative and competitive processes shape intelligence in species, cellular tissues, economic markets, and political systems; how short-term memory, medium-term memory, and long-term memory regulate adaptation to changing environments on different time scales; how processes whereby we learn what events are causal also support irrational, superstitious, obsessional, self-punitive, and antisocial behaviors; how relaxation responses arise; and how future acoustic contexts can disambiguate conscious percepts of past auditory and speech sequences that are occluded by noise or multiple speakers.","PeriodicalId":370230,"journal":{"name":"Conscious Mind, Resonant Brain","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122442806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-25DOI: 10.1093/oso/9780190070557.003.0012
S. Grossberg
This far-ranging chapter provides unified explanations of data about audition, speech, and language, and the general cognitive processes that they specialize. The ventral What stream and dorsal Where cortical stream in vision have analogous ventral sound-to-meaning and dorsal sound-to-action streams in audition. Circular reactions for learning to reach using vision are homologous to circular reactions for learning to speak using audition. VITE circuits control arm movement properties of synergy, synchrony, and speed. Volitional basal ganglia GO signals choose which limb to move and how fast it moves. VAM models use a circular reaction to calibrate VITE circuit signals. VITE is joined with the FLETE model to compensate for variable loads, unexpected perturbations, and obstacles. Properties of cells in cortical areas 4 and 5, spinal cord, and cerebellum are quantitatively simulated. Motor equivalent reaching using clamped joints or tools arises from circular reactions that learn representations of space around an actor. Homologous circuits model motor-equivalent speech production, including coarticulation. Stream-shroud resonances play the role for audition that surface-shroud resonances play in vision. They support auditory consciousness and speech production. Strip maps and spectral-pitch resonances cooperate to solve the cocktail party problem whereby humans track voices of speakers in noisy environments with multiple sources. Auditory streaming and speaker normalization use networks with similar designs. Item-Order-Rank working memories and Masking Field networks temporarily store sequences of events while categorizing them into list chunks. Analog numerical representations and place-value number systems emerge from phylogenetically earlier Where and What stream spatial and categorical processes.
{"title":"From Seeing and Reaching to Hearing and Speaking","authors":"S. Grossberg","doi":"10.1093/oso/9780190070557.003.0012","DOIUrl":"https://doi.org/10.1093/oso/9780190070557.003.0012","url":null,"abstract":"This far-ranging chapter provides unified explanations of data about audition, speech, and language, and the general cognitive processes that they specialize. The ventral What stream and dorsal Where cortical stream in vision have analogous ventral sound-to-meaning and dorsal sound-to-action streams in audition. Circular reactions for learning to reach using vision are homologous to circular reactions for learning to speak using audition. VITE circuits control arm movement properties of synergy, synchrony, and speed. Volitional basal ganglia GO signals choose which limb to move and how fast it moves. VAM models use a circular reaction to calibrate VITE circuit signals. VITE is joined with the FLETE model to compensate for variable loads, unexpected perturbations, and obstacles. Properties of cells in cortical areas 4 and 5, spinal cord, and cerebellum are quantitatively simulated. Motor equivalent reaching using clamped joints or tools arises from circular reactions that learn representations of space around an actor. Homologous circuits model motor-equivalent speech production, including coarticulation. Stream-shroud resonances play the role for audition that surface-shroud resonances play in vision. They support auditory consciousness and speech production. Strip maps and spectral-pitch resonances cooperate to solve the cocktail party problem whereby humans track voices of speakers in noisy environments with multiple sources. Auditory streaming and speaker normalization use networks with similar designs. Item-Order-Rank working memories and Masking Field networks temporarily store sequences of events while categorizing them into list chunks. Analog numerical representations and place-value number systems emerge from phylogenetically earlier Where and What stream spatial and categorical processes.","PeriodicalId":370230,"journal":{"name":"Conscious Mind, Resonant Brain","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123537264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-25DOI: 10.1093/oso/9780190070557.003.0017
S. Grossberg
This final chapter discusses far-ranging implications of the discoveries that this book describes, including lessons about how to live more fulfilling lives, how perplexing aspects of the human condition arise, and how ethical value systems and religious beliefs are sustained. Principles of our brains’ self-organizing measurement process generalize to all cellular biological organisms, and are shaped by the physical world with which our brains ceaselessly communicate and adapt. In particular, our brains’ complementary computing, uncertainty principles, and resonance have analogs in the laws of the physical world that has shaped them. A universal computational code for mental life enables a lifetime of experiences to cohere in an emerging sense of self. Complementary computing and hierarchical resolution of uncertainty require conscious states to select effective actions, and thus actively engage us in the ceaseless brain-environment perception-cognition-emotion-action feedback loop that drives brain self-organization to adapt to a changing world. Actions that lead to errors can be corrected using cognitive and cognitive-emotional processes to discover a better understanding of environmental causes and the physical laws that shape them. Symmetry-breaking between approach and avoidance outcomes in cognition and emotion provides a biological basis for morality and religion, with positive emotions facilitating sustainable motivations and empathy, while also causing negative experiences like learned helplessness, self-punitive behaviors, fetishes, and the motivations to commit evil acts. A universal developmental code uses similar STM and LTM laws for brain development, adult learning, gastrulation, organ size increases that preserve tissue form, Hydra regeneration, slime mold aggregation, and Rhodnius cuticles.
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Pub Date : 2021-06-25DOI: 10.1093/oso/9780190070557.003.0013
S. Grossberg
Visual and auditory processes represent sensory information, but do not evaluate its importance for survival or success. Interactions between perceptual/cognitive and evaluative reinforcement/emotional/motivational mechanisms accomplish this. Cognitive-emotional resonances support conscious feelings, knowing their source, and controlling motivation and responses to acquire valued goals. Also explained is how emotions may affect behavior without being conscious, and how learning adaptively times actions to achieve desired goals. Breakdowns in cognitive-emotional resonances can cause symptoms of mental disorders such as depression, autism, schizophrenia, and ADHD, including explanations of how affective meanings fail to organize behavior when this happens. Historic trends in the understanding of cognition and emotion are summarized, including work of Chomsky and Skinner. Brain circuits of conditioned reinforcer learning and incentive motivational learning are modeled, including the inverted-U in conditioning as a function of interstimulus interval, secondary conditioning, and attentional blocking and unblocking. How humans and animals act as minimal adaptive predictors is explained using the CogEM model’s interactions between sensory cortices, amygdala, and orbitofrontal cortex. Cognitive-emotional properties solve phylogenetically ancient Synchronization and Persistence Problems using circuits that are conserved between mollusks and humans. Avalanche command circuits for learning arbitrary sequences of sensory-motor acts, dating back to crustacea, increase their sensitivity to environmental feedback as they morph over phylogeny into mammalian cognitive and emotional circuits. Antagonistic rebounds drive affective extinction. READ circuits model how life-long learning occurs without associative saturation or passive forgetting. Affective memories of opponent emotions like fear vs. relief can then persist until they are disconfirmed by environmental feedback.
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