[chapter, section]s of Grossberg 2021

[Chapter, section]s of Stephen Grossberg 2021 "Conscious Mind, Resonant Brain"

"... ??insert a quote, saying, quip?? ..."

Introduction
Conscious mind, resonant brain: Table of Contents
Conscious mind, resonant brain: sub-section list

Introduction

The following Tables of Conttnts, one just listing the chapters, and the other listing section headings, do not substitute for the book's index of terms, nor for reading through the countless references provided in the book. They are simply an aide to [tracking, notes] while reading, and for limited extraction of related ideas of interest.

I retyped content DIRECTLY from [Grossberg 2021 "Conscious Mind, Resonant brain"], which sounds insane as normal document tools do this automatically, and this is probably available on Grossberg's webPages or elsewhere. Still, it was important for me to do this to foce myself to go step-by-step through the book, rather than lazily skimming through it which is mostly "in one eye and out the mind?" for me. Also, it was extremely important for me to see all of the gaps in my own awareness of Grossberg's work and thinking, in spote of having bought previous books, and reading numberous papers of Grossberg and his colleagues. I was shocked at how much I had missed, which is perhaps not surprising given all of the other areas of reserch in neural networks. Mostly, I was missing the overall scope of his work, and the higher-level concepts.

Nothing can substitute for creating computer systems of his concepts, and applying that to real data. I won't be able to do that generally as it was already a daunting task just to go through the section titles for all chapters. However, I do hope to do that in a limited sense for the driving questions for this webSubSite.

I started by doing a detailed reading of the first two chapters, THEN composing the list of section titles. But this would take too long before posting this webPage in a reasonable form, and I needed this during my reading. So I then simply typed out the section titles for the remainder of the book.

Because this was retyped manual, without a spell check (lazy, eh?), there will be [error, omission]s.

The list of [[chapter, section], figure, table, other]s always starts with the page number. [figure, table] lists then follow with [fig, tbl] and its number (repectively), while others includes a grep searchlist, then the name of the person whose idea is noted.

A [figure, table] often has two list items:

"title" taken often from the figure or table. Can be used for an image file name.
"caption" taken directly from the figure caption or table description

Notation

p370 Chapter 11	means (Grossberg 2021) page 370, Chapter 11
p002sec Illusion and reality	means (Grossberg 2021) page 2, section Illusion and reality
p013fig01.09	means (Grossberg 2021) page 13, Figure 1.09 (1.9 as in book)
p030tbl01.02	means (Grossberg 2021) page 30, Table 1.02 (1.2 as in book)
p111c2h0.5	means (Grossberg 2021) page 111, column 2, height from top as fraction of page height
\|\| text...	Are notes in addition to [figure, table] captions, mostly comprised of text within the image, but also including quotes of text in the book. Rarely, it includes comments by Howell preceded by "Howell". The latter are distinct from "readers notes" (see, for example : reader Howell notes).
p044 Howell: grepStr 'conscious'	means a comment by reader Howell, extracted using the grep string shown, referring to page 44 in (Grossberg 2021)

Full links are provided, such that the html code, when pasted to emails or other html files, will still work. This facilitates use by readers for their own documents.

Conscious mind, resonant brain: Table of Contents

page ix -

p00I PrefacePreface - Biological intelligence in sickness, health, and technology
p001 Chapter 1 Overview - From Complementary Computing and Adaptive Resonance to conscious awareness
p050 Chapter 2 How a brain makes a mind - Physics and psychology split as brain theories were born
p086 Chapter 3 How a brain sees: Constructing reality - Visual reality as illusions that explain how we see art
p122 Chapter 4 How a brain sees: Neural mechanisms - From boundary completion and surface flling-in to figure-ground perception
p184 Chapter 5 Learning to attend, recognize, and predict the world - From vigilant conscious awareness to autism, amnesia, and Alzheimer's disease
p250 Chapter 6 Conscious seeing and invariant recognition - Complementary cortical streams coordinate attention for seeing and recognition
p280 Chapter 7 How do we see a changing world? - How vision regulates object and scene persistence
p289 Chapter 8 How we see and recognize object motion - Visual form and motion perception obey complementary laws
p337 Chapter 9 Target tracking, navigation, and decision-making - Visual tracking and navigation obey complementary laws
p353 Chapter 10 Laminar computing by cerebral cortex - Towards a unified theory of biologucal and artificial intelligence
p370 Chapter 11 How we see the world in depth - From 3D vision to how 2D pictures induce 3D percepts
p404 Chapter 12From seeing and reaching to hearing and speaking - Circular reaction, streaming, working memory, chunking, and number
p480 Chapter 13 From knowing to feeling - How emotion regulates motivation, attention, decision, and action
p517 Chapter 14 How prefrontal cortex works - Cognitive working memory, planning, and emotion conjointly achieved valued goals
p539 Chapter 15 Adaptively timed learning - How timed motivation regulates conscious learning and memory consolidation
p572 Chapter 16 Learning maps to navigate space - From grid, place, and time cells to autonomous mobile agents
p618 Chapter 17 A universal development code - Mental measurements embody universal laws of cell biology and physics

Credits p641
References p667
Index p715

Conscious mind, resonant brain: sub-section list

Re-typing the sub-section titles helped me greatly to [remember, refer to] the concepts in context, and may help other readers as well because these sub-section lists do not appear in the book. They also provides a perspective of the [breadth, depth] of Grossberg's concepts. A simple text search of this webPage may help you to browse around Grossberg's concept-space.
Many topics in the sub-section titles below do not address consciousness, but almost all contribute to the theory of conciousness that emerges.

I will have to read the book several times to get this all to sink in, notwithstanding 3 decades of reading a small subset of Grossberg's [book, paper]s. Ultimately, though, only working with code and data goes far enough. My re-tying of this material will have errors.

Preface

p000IXsec Biological intelligence in sickness, health, and technology
p000IXsec Our brains are not digital computers!
p000Xsec A new paradigm for understanding mind and brain: Autonomous adaptive intelligence
p000Xsec Is the brain just a "bag of tricks"?
p000XIsec Mind-body problem: Brain theories assemble laws and modules into modal architectures
p000XIsec Why so many books about consciousness?
p000XIIsec The varieties of brain resonances: All conscious states are resonant states
p000XIIsec From brain science to mental disorders, irrational decisions, and the human condition
p000XIIIsec From brains to autonomously intelligent technologies that include Adaptive Resonance
p000XIVsec From Laminar Computing to neuromorphic chips
p000XVIIsec Cognitive impenetrability and a theoretical method for penetrating it
p000XVIIIsec New computational paradigms: Laminar Computing and Complementary Computing
p000XIXsec All the chapters strive to be self-contained and mutually independent: Chapter topics
p000XXsec The unifying perspective of autonomous adaptive intelligence
p000XXIsec Building from mind to morals, cellular organisms, and the physical world around us
p000XXIsec Thank you! (acknowledgements)

Chapter 1 - Overview

From Complementary Computing and Adaptive Resonance to conscious awareness

p001sec Expectation, imagination, creativity, and illusion
p001sec Seeing, recognizing, and consciousness
p002sec Illusion and reality
p003sec Art and movies are seen with boundaries and surfaces
p003sec From Helmholtz and Kanizsa to adaptive resonance: unlikely and bistable percepts
p005sec Opaque vs transparent: geometry vs contrast
p005sec Why do not all occluding objects look transparent? conscious seeing vs recognition
p005sec Fast learning, slow forgetting, exploration, and culture
p006sec Balance, complementary computing, and the stability-plasticity dilemma
p006sec Learning, expectation, and attention
p006sec Balancing expected vs unexpected events: top-down vs bottom-up attention
p007sec Autonomous adaptation in real time to a changing world
p007sec Noise-saturation dilemma: balancing cooperation and competition
p008sec Short-term, working, long-term memory
p009sec Volition, rehearsal, and inhibition-of-return
p009sec Cooperative-competitive dynamics: stable economic markets and the invisible hand
p011sec Free markets?
p011sec Cooperative and competitive dynamics: democracy, totalitarianism, and socialism
p012sec Sigmoid signals enable noise suppression without winner-take-all choice
p012sec Cooperative-competitive-learning dynamics: ART in the brain and technology
p013sec Cognition and emotion: male or female?
p014sec Causality, superstition, and gambling: classical conditioning and prediction
p015sec Predicting what happens next in a changing world: blocking and inblocking
p015sec The value of being wrong
p015sec The disconfirming moment: on the cusp between past and future
p016sec Predictive mismatch, nonspecific arousal, and memory search
p017sec Unexpected non-occurences, emotional rebounds, and relaxation responses
p017sec Do we know what we like? peak shift and behavioural contrast
p018sec The noise-saturation dilemma and short-term memory normalisation
p019sec Opponent processing, forgetting, obseession, and exploration
p020sec Partial reward: irrationality, gambling, creativity, and unrequited love
p021sec Learned helplessness in the lab and society
p022sec Secondary conditioning and advertising
p022sec Gated dipole opponent processing: arousal, habituation, and competition
p023sec [Short, medium, long]-term memory
p024sec Drives, satiety, and starving for love
p026sec Golden mean, inverted U, and two types of emotional depression
p027sec Affective neuroscience, mental disorders, and uppers that bring you down
p028sec Two types of learning: perceiving and knowing vs moving and navigating
p028sec What and Where cortical processing streams and consciousness
p029sec Two revolutionary paradigms: [complementary, laminar] computing
1. p029sec Complementaty processing streams for perception/cognitio and space/action
2. p030sec Our bilateral brains use crebral dominance to do complimentary computing: Split brains
3. p031sec Laminar neocortical circuits to represent higher-order biological intelligence
p032sec Complementary computing vs independent modules
p033sec Hierarchical resolution of uncertainty and complementary consistency
p033sec Differences between physical and brain measurements: self-organisation
p034sec Universal designs for self-organizing measurement and prediction systems
p034sec Is the conscious present in the past?
p034sec Phonemic restoration and stable learning about a changing world
p036sec Learned expectations again! Resonance between item chunks and list chunks
p038sec How does object attention work? ART matching rule
p038sec All conscious states are resonant states
p039sec The emergence of self, the longing for unity, and the appeal of monotheism
p039sec Invariant learning, expectation, and cognitive-emotional resonance in early religious beliefs
p040sec Conscious seeing, hearing, feeling, and knowing: linking conciousness to action
p040sec Why did consciousness evolve?
p041sec Self-stabilizing memories and the challenge of learning without bias through life
p041sec The road to ART: Helmholtz, James, and Gregory
p042sec The varieties of resonant experiences during seeing, hearing, feeling, and knowing
p042sec All conscious states are resonant states, but some resonant states are not conscious
p043sec Towards solving the 'Hard Problem' of consciousness: a philosophical third rail?
p044sec To what extent can any scientific theory clarify the 'Hard Problem'?
p044sec A linking hypothesis between resonant brain dynamics and the conscious mind
p045sec Some facts that support the CLEARS predictions
p046sec Equations, modules, and modal architectures
including: Some other approaches to understanding consciousness (Grossberg 2021 p47c1h0.5)

Credibility from non-[bio, psycho]logical applications of Grossberg's ART

This is a special list extracted from a page in the book...

p013sec airplane design
p013sec medical database diagnosis and prediction
p013sec remote sensing and geospatial mapping and classification
p013sec multidimensional data fusion
p013sec classification of data from artificial sensors with high dynamic noise and dynamic range
(synthetic aperture radar, laser radar, multi-spectral infra-red, night vision)
p013sec speaker-normalized speech recognition
p013sec automatic rule extraction and hierachical knowledge discovery
p013sec machine vision and image understanding
p013sec mobile robot controllers
p013sec satellite remote sensing image classification
p013sec sonar classification
p013sec musical analysis
p013sec electrocardiogram wave recognition
p013sec prediction of protein folding secondary structure
p013sec strength prediction for concrete mixes
p013sec tool failure monitoring
p013sec chemical analysis from ultraviolet and infrared spectra
p013sec design of electromagnetic systems
p013sec face recognition
p013sec familiarity discrimination
p013sec power transmission line losses
As stated in [Grossberg 2021 p13c1h1.0] : "... This range of applications is possible because ART models embody general-purpose properties that are needed to solve the stability-plasticity dilemma in many different types of environments. In all these applications, insights about cooperative-competitive dynamics also play a critical role. ..."
add content of subSection "Multiple applications of ART to large-scale problems in engineering and technology"

Chapter 2 - How a brain makes a mind

Physics and psychology split as brain theories were born

p050sec A historical watershed: When pysicists were also neuroscientists
p052sec The three N's: Nonlinear, Nonlocal, and Nonstationary
p053sec A century of controversy as major scientific revolution is born
p054sec The astonishing hypothesis
p054sec Brains are designed to control behavioural success
p055sec Keeping the forest, trees, and leaves all in view
p055sec Why differential equations? The case of phonemic restoration
p056sec From behavior to brain: Autonomous adaptation to a changing world
p057sec Why does the brain use neural networks?
p058sec First love: Serial learning, events that go "backwards in time", and the middle is a muddle
p059sec Asymmetry between past and future: How the occurence of "nothing" influences learning
p060sec What is the functional stimulus: Items or sequence chunks?
p061sec From serial learning to neural networks: Discovering the dynamics of STM and LTM
p062sec Dissociating STM and LTM: Bilateral hippocampectomy in HM
p062sec On the shoulders of giants: The functional units of STM and LTM are spacial patterns
p064sec Three sources of neural network research: Binary, Linear, and Continuous nonlinear
p064sec Binary models
1. p064sec McCulloch-Pitts model and the digital computer
2. p065sec Perceptron
3. p065sec From perceptrons to back propagation and Deep Learning
p066sec Linear
1. p066sec ADELINE and MADELINE
2. p067sec Brain-State-in-a-Box
3. p067sec From Moore-Penrose psudo-inverse to Self-Organizing Maps
p067sec Continuous nonlinear
1. p067sec Hartline-Ratliff equation for the horseshoe crab retina
2. p068sec Hodgkin-Huxley equations for the giant sqid axon
3. p068sec Linking brain to mind: The units that govern behavioural success are distributed patterns
p069sec Why does the brain use nonlinear multiple-scale feedback networks?
p070sec A gedanken experiment for solving the noise-saturation dilemma in cellular tissues
1. p070sec Cooperation and comptition are universal in biology, including in brain networks
2. p070sec What is a cell: Computing with cellular patterns
3. p070sec Noise-saturation dilemma: Pattern processing by cell networks without noise of saturation
4. p071sec Computing ratios: Brightness constancy and contrast as two sides of a coin
5. p072sec A thought experiment for solving the noise-saturation dilemma in cellular networks
6. p074sec On-center off-surround anatomies can compute inpute ratios
7. p074sec Shunting on-center off-surround networks solve the noise saturation dilemma
8. p074sec Infinite dynamic range, automatic gain control, limited capacity, and real-time probabilities
9. p075sec Deriving the membrane equations of neurophysiology from a thought experiment
p075sec Towards a univeral developmental code using mass action cooperative-competitive dynamics
1. p076sec Weber law, adaptation, and shift property
2. p076sec Shift property and shunting on-center off-surround network in the mudpuppy retina
3. p077sec Intracelular adaptation withing photoreceptors creates a Weber law acting in time
4. p077sec From silent inhibition to hyperpolarization and adaptation levels
5. p078sec Adaptation levels require a broken symmetry between excitation and inhibition
6. p079sec Informational noise suppression and bottom-up/top-down pattern matching
7. p079sec How does informational noise suppression arise during development? Opposites attrract?
8. p080sec From a global off-surround to a distance-dependent one that preserves noise suppression
9. p081sec Informational noise suppresion implies contour detection in a multiple network
p081sec How does one evolve a computational brain? My theoretical method
p083sec Embedding, unlumping, and corresponding principles
p083sec A never-ending controversy?
p084sec From brain theories to technological applications

Chapter 3 - How a brain sees: Constructing reality

Visual reality as illusions that explain how we see art

p086sec Why do we bother to see?
p088sec Conscousness without qualia: Why do we bother to see?
p088sec All boundaries are invisible: From Kanizsa squares to figure-ground separation
p090sec Brighter objects look closer: Proximity-contrast covariance
p091sec Living with your blind spot
p092sec Jiggling eyes and occluded objects: Beware moving dinosaurs!
p093sec How illusions may represent reality
p094sec Boundaries contain filling-in of feature contours within surfaces
p095sec How do we distinguish the "real" from the "illusory"?
p095sec Neon color spreading
p096sec Complementary computation of boundaries and surfaces
p097sec Complementary processing streams in visual cortex
p097sec Multiple-scale symmetry-breaking generates complementary streams during development
p098sec Special-purpose modules or general-purpose modal systems?
p099sec The architecture is the algorithm
p099sec A new geometry: Cooperation and competition across space and orientation
p100sec From fuzzy to sharp groupings: Hierarchical resolution of uncertainty
p101sec Groupings of shading and texture, breaking camouflage, and 3D percepts of 2D pictures
1. p101sec Recognition is facilitated by invisible boundary groupings
2. p101sec Emergent boundary orientations obey Gestalt rules: Diagonals from horizontals and verticals
3. p102sec Perceiving the forest before the treees: Typography portraits
4. p104sec 3D curved shapes from filling-in within multiple-scale invisible boundary webs
5. p105sec Boundary web support percepts of Impressionist paitings
6. p105sec From Seurat to SAR
7. p106sec From the Optical Society of America to working with MIT Lincoln Laboratories
p106sec Did Matisse know that "all boundaries are invisible"?
p108sec Boundary contrast, assimilation, chiaroscuro, and the watercolor illusion
1. p108sec Color assimilation due to filling-in across weakened boundaries in the watercolor illusion
2. p109sec Multiple-scale boundary webs and the depthful percepts of chiaroscuro and trompe l'oeil
p109sec Cape Cod School of Art: Hawthorne and Hensche and plein air painting
p110sec Claude Monet and Impressionist painting: Seeing the forest and the leaves
p111sec How humans see paintings and paintings illuminate how humans see
p111sec Boundary webs, self-luminosity, glare, double brilliant illusion, and gloss
p112sec Lightness anchoring and Ross Bleckner's self-luminous paintings
1. p112sec Self-luminosity in paintings
2. p113sec 'Blurred highest luminance as white' and 'lightness anchoring'
p114sec Boundary webs and color field paintings: Jules Olitski's paintings that "hang like a cloud"
p115sec Gene Davis's lines: From color assimilation to depth and surface-shroud resonances
p116sec Perspective, T-junctions, end-gaps, and the Mona Lisa
p117sec Frank Stella: Occlusions, amodal completions, kineticism, and spatial attention shifts
p118sec Back to Monet: Gist, multiple-scale attentional shrouds, and scene recognition

Chapter 4 - How a brain sees: Neural mechanisms

From boundary completion and surface flling-in to figure-ground perception

p122sec Boundaries are barriers to surface filling-in
p123sec Visible effect of an invisible cause: Boundaries are also filling-in generators
p124sec Craik-O'Brien-Cornsweet effect: Now you see it, now you don't
p124sec Brightness perception
p124sec Discounting the illuminant
p125sec Brightness constancy
p127sec When competition is good: Making analog computation possible
p127sec Hierarchical resolution: Discounting the illuminant, feature contours, and surface filling-in
p130sec Brightness constancy, contrast, and assimilation
p133sec Contrast constancy
p133sec Catching filling-in "on the fly"
p134sec Why is neon color-spreading an important probe of how we consciously see?
p135sec Why resonance?
p135sec Can philosophers help us out?
p136sec How are boundaries formed? Evidence from filling-in
p136sec Balancing competition with cooperation during boundary formation
p137sec How does the boundary system work? From brains to the chips of future devices
p138sec FACADE theory vs. Naive Realism
p139sec Receptive fields detect oriented local contrasts: Avoiding edge detectors implies uncertainty
p139sec Simple cells and half-wave rectification
p140sec Complex cells pool opposite contrast polarities: Amodal boundary detectors
p142sec Glass patterns differentiate short-range cooperation from long-range cooperation
p143sec Orientation certainty implies positional uncertainty
1. p143sec Positon-Orientation Uncertainty Principle
p144sec A perceptual disaster: Uncontrolled filling-in
p145sec Every line end is an illusion! A second hierarchical resolution of uncertainty
p145sec Roman typeface letter fonts supply their own end cuts using serifs
p146sec Computing with patterns, not pixels
p146sec Short-range competition across space and orientation
p147sec Making an end cut by hierarchical resolution of simple cell uncertainty
p147sec Neurophysiological data support end cut predictions
p148sec End cuts during neon color spreading?
p149sec How does long-range cooperative grouping occur?
p150sec Bipole cells initiate boundary completion
p152sec Cooperative-competitive feedback selects the final grouping
p152sec Consciousness again!
p153sec All roads lead to a double filter and grouping network
p153sec Living with uncertainty until there is enough information to make a choice
p154sec A brain without Bayes
p155sec Emergent features in texture segregation
p156sec Explaining texture data with the double filter
p158sec Spatial impenetrability of occluding objects
p158sec Spatial impenetrability enables T-junctions to trigger figure-ground separation
p159sec Graffiti artists and Mooney faces
p160sec Hyperacuity and spatial localization
p161sec Inverted-U in illusory contour strength: Cooperation and competition
p162sec Analogue coherence and the laminar cortical architecture of visual cortex
p163sec Laminar models of vision, speech, and cognition: A universal design for intelligence?
p164sec Filling-in and the Koffka-Benussi ring
p165sec The Kanizsa-Minguzzi ring
p167sec Seeing the world in depth: From 2D picture to 3D percept
p168sec Boundaries are filling-in generators and filling-in barriers: Double opponent competition
p169sec Tissue contrast and double-opponent color processing: Lines are thick!
p170sec Only closed boundaries can lead to visible surface percepts
p171sec How are DaVinci stereopsis surfaces that are monocularly viewed seen in depth?
1. p171sec Sparse binocular boundaries to continuous surfaces in depth: Alletropia
p172sec How are closed and open boundaries created in depth-selective boundary representations?
p173sec Surface contours realize complementary consistency and begin figure-ground separation
p174sec Boundary pruning at farther depths enables figure-ground separation
p175sec Proximity-luminance covariance: Why do brighter Kanizsa squares look closer?
p177sec V2 and V4: Recognizing occluded objects, seeing unoccluded surfaces, and DaVinci again
1. p177sec Amodal completed boundaries and filled-in surface represntations in V2
2. p177sec Why are not all occluders transparent?
3. p178sec Modal surface representations in V4 and surface-shroud resonances between V4 and PPC
4. p178sec The critical importance of boundary enrichment in allowing opaque occluders to be seen
p179sec An evolutionary design tension between requirements of recognition and reaching
p180sec V2 and V4: Explaining when surfaces look opaque and transparent
p183sec Principled incremental theorizing using the Method of Minimal Anatomies
p183sec What's next?

Chapter 5 - Learning to attend, recognize, and predict the world

From vigilant conscious awareness to autism, amnesia, and Alzheimer's disease

p184sec This chapter will discuss how we
1. p184sec rapidly learn to categorize and recognize so many objects in the world
2. p184sec remember this information as well as we do over a period years
3. p184sec learn to expect and anticipate events that may occur in familiar situations
4. p184sec pay attention to events that are of particular interest to us
5. p184sec become conscious of these events
6. p184sec balance between expected and unexpected events, and orient to unexpected events
7. p184sec engage in fantasy activities such as visual imagery, internalized speech, and planning
8. p184sec learn language quickly and consciously hear completed speech sounds in noise
9. p184sec hallucinate during mental disorders
p185sec Perceiving vs. recognizing
p185sec Patterns vs. symbols
p185sec A thought experiment about category learning
p185sec Stability-plasticity dilemma: Fast learning and stable memory of recognition categories
1. p186sec Many-to-one and one-to-many maps
2. p186sec Learning concrete and abstract knowledge: The need for fast stable learning on the fly
p187sec Unsupervised and supervised learning of many-to-one and one-to-many maps
1. p188sec One-to-many map learning and catastrophic forgetting
p188sec HM, hippocampus, and amnesia
p189sec The predictive brain: Intention, attention, and consciousness
p190sec Computational properties of the ART Matching Rule
1. p190sec Botton-up automatic activation
2. p190sec Top-down priming
3. p190sec Match
4. p190sec Mismatch
p190sec ART matching: Expectation and attention during brightness perception
p193sec Neurobiological data for ART matching by corticogeniculate feedback
p194sec Neurobiological data for ART matching in visual, auditory, and somatosensory cortex
p195sec Excitatory matching vs. suppressive matching
p195sec Mathematical form of the ART Matching Rule
p196sec From automatic to task-selective attentional control
1. p196sec Bottom-up automatic attention?
2. p196sec Top-down task-selective attention and memory search for new categories
p196sec We learn, therefore we can imagine, think, plan, and hallucinate
1. p197sec Why are auditory hallucinations more prevalent than visual ones?
p198sec Volitional signals that convert primes to driving activities across multiple modalities
p198sec Category learning transforms distributed features into category choices
p198sec Instar bottom-up category learning and outstar top-down expectation learning
1. p199sec Category learning by INSTARS
2. p200sec Spatial and spatiotemporal pattern learning by iut-STARS
3. p201sec Joining competitive choice to INSTAR learning: Competitive learning
p202sec Competitive decisions: Contrast enhancement and normalization during category learning
p204sec Gated steepest descent combines Hebbian and anti-Hebbian learning
p204sec Competitive learning and self-organizing maps: On the road to adaptive rsonance
1. p204sec Nice properties of competitive learning in sparse environments
p205sec How does ART stabilize learning?
1. p205sec Hypothesis testing using top-down learned expectations
2. p206sec How do initial expectations match every possible learned pattern?
p206sec How do we learn anything new? Match learning requires mismatch reset and search
p206sec ART cycle of hypothesis testing and category learning
1. p206sec A self-organizing production system with creative properties
2. p208sec ART links synchronous oscillations to attention and learning
p208sec Complementary attentional and orienting systems
p208sec Sufficient Reason and novelty-sensitive nonspecfic arousal: Novel events are arousing!
p209sec Medium-term memory: Habituative transmitter gates in nonstationary hypothesis testing
p210sec How self-normalizing total activity enables search
p210sec Category networks are gated dipole fields: Probablistic hypothesis testing and prediction
p210sec Feature-category resonances and complementary PN and N200 event-related potentials
p212sec A thought experiment that leads to ART
1. p212sec How can a coding error be corrected if no individual cell knows that it has occurred?
2. p212sec Symbols cannot detect errors
3. p213sec Why learning of top-down expectations is necessary
4. p213sec Complementary information is computed at distributed and symbolic levels
5. p214sec Nonspecific arousal as an error-correcting reset event
6. p215sec Parallel processing of expected and unexpected events in attentional and orienting systems
7. p215sec Mismatch in the attentional system activates nonspecific arousal from the orienting system
8. p216sec Recurrent double-opponent networks help to discover most predictive categories
p216sec Resonances of distributed features and compressed categories solves symbol grounding
p217sec How is the generality of knowledge controlled? Exemplars vs. prototypes
p218sec Both concrete and abstract categories emerge from minimax learning
p219sec Mismatch-mediated memory search and resonance together stabilize learned memories
p219sec Novelty, vigilence control, and task-selective learning of concrete and abstract categories
p219sec Vigilence is computed in the orienting system
p220sec Balancing between the remembered past and the anticipated future
p221sec Minimax learning via match tracking: Learning the most general predictive categories
1. p222sec Vigilence control depends upon complementary computing
2. p222sec Multiple applications of ART to large-scale problems in engineering and technology
p223sec Learning resonant hierarchies of grandmother cohorts
p224sec Vigilence control by match tracking during learning of the alphabet
p225sec Catastrophic forgetting without the top-down ART Matching Rule
p226sec Statistical hypothesis testing and decision making in a nonstationary world
1. p226sec The importance of fast learning under both supervised and unsupervised conditions
2. p227sec Why can deterministic laws describe noisy interacting neurons?
3. p227sec ART direct access solves the local minimum problem
p227sec Memory consolidation, amnesia, and lesions of amygdala, hippocampus, and neocortex
p228sec Learning of fuzzy IF-THEN rules by a self-organizing ART production system
1. p228sec ART provides autonomous soluutions for Explanable AI
p229sec Additional brain data about attentive category learning and orienting search
p229sec Gamma and beta oscillations during attentive resonance and mismatch reset
1. p229sec SMART: A laminar cortical hierarchy of spiking neurons
p230sec Beta oscillations in the deeper layers of visual cortex
1. p232sec Does varying the amount of novelty change the amount of beta?
2. p233sec Do superficial and deeper layers synchronize and de-synchronize during resonance and reset?
p233sec Beta oscillations during spatial attention shifts in the frontal eye fields
p233sec Beta oscillations during the learning of hippocampal place fields in novel environments
1. p234sec Inverted-U in beta power through time during hippocampal place cell learning
p234sec The gamma-beta dichotomy described in terms of encoding and default states
p234sec Gamma and beta oscillations regulate working memory dynamics
p235sec Cholinergic modulation of category learning via nucleus basalis vigilence control
p236sec Tonic vs. phasic vigilance control: How vigilance may vary with the task at hand
1. p236sec Vigilance varies with the difficulty of a visual discernation
p237sec Vigilence diseases: Autism, media temporal amnesia, and Alzheimer's disease
1. p237sec High vigilance and hyperspecific category learning in autism
2. p237sec Low vigilance in corticohippocampal dynamics during medial temporal amnesia
p240sec Converting algebraic exemplar models into dynamical ART prototype models
p241sec Explaining human categorization data with ART: Learning rules-plus-exceptions
1. p242sec Self-supervised ARTMAP: Learning on our own after leaving school
p242sec Is there a collapse of tonic and phasic vigilance control during Alzheimer's disease?
p244sec Relating sleep and Alzheimer disease pathology
1. p245sec Self-normalizing laminar cortical circuits balance excitation and inhibition
2. p245sec Self-normalizing inhibition during boundary completion
3. p246sec Self-normalizing inhibition during attentional priming with the ART Matching Rule
4. p247sec Recurrent off-surround normalizes cell responses to converging sources
5. p247sec From grouping and attention during waking to Up and Down states during slaw wave sleep
6. p248sec Slow wave sleep can disrupt and be disrupted by Alzheimer's disease: A vicious circle
7. p248sec Can novelty-seeking behaviours lower the chance of developing Alzheimer's?
8. p248sec Sleep disturbances in autism and schizophrenia: Due to vigilance abnormalities?
p249sec Many kinds of psychological and neurobiological data have been explained by ART

Chapter 6 - Conscious seeing and invariant recognition

Complementary cortical streams coordinate attention for seeing and attention

p250sec From learning specific categories to learning invariant categories
1. p250sec Autonomous learning during free scanning of a scene with eye movements
2. p251sec Do cortical streams even exist? How interacting streams can obscure this basic fact
3. p251sec Coordinated category learning between the posterior and anterior inferotemporal cortex
p251sec Surface-shroud resonances are generated between V4 and PPC
p252sec V2 and V4: Recognizing occluded objects, seeing unoccluded surfaces, and transparency
p253sec Solving the view-to-object binding problem during free scanning of a scene (what, where)
p256sec Invariant object category learning is regulated by a surface-shroud resonance
p256sec Explaining data about visual neglect: Coordinates, competition, grouping, and action
1. p256sec Visual neglect
2. p257sec Head-centered shroud coexists with retinotopic surface qualia
3. p257sec Competition for spatial attention across parietal cortex
4. p257sec Preserved figure-ground segmentation during neglect
5. p258sec Unconscious processing of neglected object identity
6. p259sec A link between visual neglect and motor planning deficits: Seeing to reach
7. p259sec Visual agnosia
8. p259sec IPL lesions lead to deficits in sustained visual attention
p259sec Explaining data about visual crowding and situational awareness
1. p261sec Towards a unified explanation of data about crowding, visual search, and neglect
p262sec Explaining data about change blindness and motion-induced blindness: No map?
p264sec Explaining many data with the same model mechanisms
p264sec Shrouds have complex internal structure that influences properties of visual search
p264sec From View- and Position-Specific categories to View- and Position-Invariant categories
1. p264sec Conscious visual qualia are computed in retinotopic coordinates
2. p265sec Visual imagery: Basal ganglia volition converts top-down modulation to driving inputs
p265sec From complementary consistency to figure-ground separation: Surface contours
p286sec The main problem: Why inhibiting view categories does not inhit invariant categories
1. p267sec Attentional shroud inhibits reset of invariant object category during object search
p267sec Human and monkey data support shroud reset properties: Explanations and predictions
p268sec A surface-shroud resonance enables the eyes to explore multiple object views
1. p268sec Exploring the same object for awhile using attentional pointers to the next saccadic target
2. p269sec Transforming between vision and action occurs after figure-ground separation:V3A!
p269sec Predictive remapping: Gain fields maintain shroud stability
p270sec Both retinotopic and spatial coordinates are needed during active vision
p270sec Learning view-, position-, and size-invariant object categories: Persistent IT cells
1. p271sec Target swapping data: Why catastrophic forgetting of invariant categories does not happen
2. p272sec Prediction: Vary the ISI in a combined shroud and swapping experiment
3. p273sec Explaining the tradeoff between object selectivity and tolerance
p274sec Perception stability: Binocular fusion during eye movements
p276sec Two types of perceptual stability cooperate during active conscious vision
p278sec Seeing and knowing: Synchronous surface-shroud and feature-category resonances
p278sec Agnosia: Parietal attention and intention control seeing and reaching without knowing
p279sec Where's Waldo search: From where-to-what to what-to where interactions

Chapter 7 - How do we see a changing world?

How vision regulates object and scene persistence

p289sec From perceptions of static to changing visual forms
p281sec Visual persistence and boundary reset. For example, visual persistence:
1. p282sec decreases with stimulus duration real contours
2. p282sec decreases with stimulus luminance of real contours
3. p282sec decreases with stimulus to a test stimulus after adaptation to a stimulus of similar orientation
4. p282sec increases to a test stimulus after adaptation to a stimulus of perpendicular orientation
5. p282sec increases with the interstimulus interval between a test stimulus and a subsequent masking stimulus
6. p282sec increases with the interstimulus interval betwween a test stimulus and a subsequent masking stimulus
7. p282sec is longer for illusory contoiurs than for real contours
8. p282sec increases before decreasing with stimulus duration in response to illusory contours
p282sec Gated dipoles and boundary reset
p284sec Persistence of real contours
p285sec Persistance of illusory contours
p286sec Persistence after oriented adapting stimuli
p287sec Persistence and spatial competition

Chapter 8 - How we see and recognize object motion

Visual form and motion perception obey complementary laws

p289sec Why does the brain need separate form and motion streams?
p290sec Complementary computing of orientation and direction: Long-range directional filter
p292sec Negative aftereffects of orientation and direction
p292sec Motion perception in the forest primeval
p293sec Feature tracking and the short-range directional filter
p294sec The Aperature Problem, barberpole illusion, and global motion capture
p205sec From motion capture to long-range apparent motion
p296sec Introduction to apparent motion
p296sec How apparent speed varies with flash ISI, distance, and luminance
p297sec The Motion ESP Problem: Tracking a variable-speed target behind occluding clutter
p298sec Apparent motion of illusory contours and formotion complementarity
p299sec Explaining variable-speed apparent motion and continuous tracking
p299sec G-waves of apparent motion and shifting spatial attention
p301sec Motion ESP: Variable speed and multi-scale synchrony of apparent motion trajectories
p302sec Korte's Laws and Ternus motion as examples of formotion complementarity
p305sec Motion direction filter and the Motion BCS model
p306sec Ternus motion: Transient cells and G-waves
p308sec Spatial attention shifts and tracking by the Where cortical stream
p309sec Solving the Aperture Problem: Global capture of object direction and speed
p309sec From ambiguous local motion to correct object motion: Integration and segmentation
p310sec Simultaneous estimation of an object's motion direction and speed
p311sec Speed selectivity from interactions among multiple scales of directional cells
p313sec Explaining properties of cells in cortical area MT
p313sec Solving the Aperture Problem: Cycling bottom-up motion grouping and top-down priming
1. p314sec A single feedback loop controls motion capture and attentional priming of motion direction
2. p314sec Complimentary motion streams for motion capture and visually-based systems
3. p315sec ART again: Choosing an object's motion direction and speed
4. p315sec ART again: Dynamically stabilizing learned directional cells also solves the aperture problem
p316sec Stable learning unifies preattentive motion capture and attentive directional priming
p316sec Data supporting the MT-MSTv prediction
p317sec Line motion illusion: Attentive or preattentive?
p318sec Pyschological and neurophysiological data supporting feedback during motion capture
p319sec ART Matching Rule explains induced motion
p320sec Barberpole motion
p321sec Motion transparency, opponent directions, rebounds, and asymmetry between near and far
p323sec Chopstick illusion and feedback between V1, MT, and MST
p325sec How do these results help to explain conscious peerception?
p326sec From moving dots and bars to moving people
p326sec Object reference frames and motion vector decomposition via a directional peak shift
1. p327sec Gaussian algebra: Peak shifts and G-waves
p327sec Johannson dot motions relative to a moving frame
p330sec Duncker motion: Cycloid motion or rotation around a wheel?
p331sec Converting motion into action during perceptual decision-making
1. p331sec From solving the aperture problem to motion-based probablistic decision-making
2. p333sec Probabilistic motion-based decision-making in monkeys
3. p333sec MODE simulations of saccadic accuracy and RT, and of LIP neural responses
4. p334sec How can sets of moving dots have an aperture problem while individual dots do not?
5. p335sec Are decisions by our brains Bayesian?

Chapter 9 - Target tracking, navigation, and decision-making

Visual tracking and navigation obey complementary laws

p337sec Target tracking and optic flow navigation use complementary cortical streams
p338sec Optic flow and heading
p340sec Compensating for eye rotation with a corollary discharge
p341sec Additive processing by MSTd to compute heading
p342sec Reconciling space-variance with position variance in MSTd
p342sec How the brain reconciles space-variance with position invariance: A subtle tradeoff
p344sec From heading to STARS and ViSTARS
p345sec Smooth pursuit of a moving target
p346sec How tracking continues after the eyes catch up: Predictive SPEMs
p348sec Goal approach and obstacle avoidance via attractor-repeller control
p349sec Steering with Gaussian peak shifts
p350sec Solving the aperture problem for heading in natural scenes using the ART Matching Rule

Chapter 10 - Laminar computing by cerebral cortex

Towards a unified theory of biological and artificial intelligence

p353sec Why does the entire cerebral cortex use variations of a canonical laminar circuit?
p355sec Where preattentive and attentive processes meet
1. p355sec Perceptual grouping and attention: Interactions, similarities, and differences
2. p356sec Three basic properties of Laminar Computing
  1. p356sec self-stabilizing development and learning
  2. Seamless fusion of
    1. p356sec pre-attentive automatic bottom-up processing
    2. p356sec attentive task-selective top-down processing
  3. p356sec Analog coherence: solution of the binding problem for perceptual grouping without loss of analog sensitivity
3. p356sec Laminar Computing unifies oposing design constraints
  1. best properties of :
    1. p356sec Fast feedforward processing when data are unambiguous
    2. p356sec Slower feedback chooses among ambiguous alternatives
    3. p356sec Goes beyond Bayesian models!
  2. p357sec Analog and Digital: Analog coherence combines the stability of digital with the sensitivity of analog
  3. p357sec Preattentive and attentive learning: Reconciles the differences, eg Helmholtz and Kanizsa
p357sec Infant development and adult learning use similar laws: A universal development code
p358sec Escaping an infinite regress: The Attention-Preattention interface problem
1. p358sec A preattentive grouping is its own attentional prime
2. p358sec Intracortical but interlaminar feedback also carries out the ART Matching Rule
p358sec Laminar mechanisms of preattentive perceptual grouping
1. p358sec Analog sensitivity to bottom-up sensory inputs
2. p359sec Layer 6-to-4 on-center is modulatory
3. p360sec Bipole boundary grouping: Balancing excitation and inhibition during development
4. p361sec Bipole grouping: Balanced excitation and inhibition and total inhibitory normalization
5. p361sec Folded feedback and analog coherence
6. p362sec Self-similar hierarchical boundary processing
p363sec Laminar mechanisms of attention, development, and learning
1. p363sec Top-down feedback from V1 to LGN
2. p363sec Folded feedback from layer 6 of V2 to layer 4 of V1
3. p363sec Layer 6-to-4 excitatory signals are modulatory: Inhibition learns to balance excitation
4. p364sec Propagating task-selective attentional primes through the entire cortical hierarchy
5. p365sec ART Matching Rule in multiple cortical modalities
6. p365sec Two botton-up input sources to layer 4
7. p365sec A preattentive grouping is its own attentional prime, revisited
p366sec A unified view of developmental, neurophysiological, and perceptual processes and data
1. p366sec From cortical development in infants to adults grouping and attention
2. p366sec When attention is not needed to learn: Perceptual learning without awareness
3. p369sec From non-laminar models of 2D vision to laminar cortical models of 3D vision
4. p369sec Towards a synthesis of biological and artificial intelligence

Chapter 11 - How we see the world in depth

From 3D vision to how 2D pictures induce 3D percepts

p373sec Correspondence Problem: How our brains know which left and right eye features to fuse
p373sec Contrast-specific binocular vision vs. contrast-invariant boundary perception
p374sec Contrast constraint on binocular fusion
p375sec 3D perceptal grouping circuit also includes a disparity filter that eliminates false matches
p376sec How does the monocular information contribute to depth perception?
p376sec Explaining challenging 3D percepts using interactions between a few simple mechanisms
p377sec Explaining DaVinci stereopsis percepts without invoking a biological optics hypothesis
p380sec Surface-to-boundary surface contours and fixation plane bias contribute to DaVinci percepts
p381sec Venetian blind effect and Panum's limiting case
p383sec Unique binocular matching vs. disparity filter selection
p383sec From dichoptic masking to Panum's limiting case
p384sec Panum's limiting case as an example of dichoptic masking
p385sec 3D surface and figure-ground percepts from Julesz stereograms
p386sec Dense Random-Dot-Stereograms (RDS)
p387sec Sparse RDS
p388sec Dense RDS that induce perceptual completion of partially occluded objects
p388sec Multiple-scale, depth-selective boundary groupings determine perceived depth
p390sec Size-disparity correlation: How do multiple scales get converted into multiple depths?
p391sec Explaining why a shaded ellipse does not look flat: Scale-to-depth and depth-to-scale maps
p392sec Simultaneous fusion and rivalry percepts from viewing Kulikowski and Kaufman sterograms
p394sec How to explain both binocular rivalry and stable 3D vision in a unified way?
p393sec Why binocular rivalry percepts involve many parts of the visual cortex
p395sec How three basic properties of boundary grouping generate binocular rivalry
p396sec Stimulus rivalry vs. eye rivalry?
p398sec Explaining stimulus rivalry and eye rivalry data in a unified way: Habituation again!
p400sec Explaining stable and bistable percepts of slanted surfaces in depth: Necker cube
p402sec 3D boundary and surface representations of natural scenes

Chapter 12 - From seeing and reaching to hearing and speaking

Circular reaction, streaming, working memory, chunking, and number

p404sec What and where/how streams for audition
1. p404sec Ventral sound-to-menaing vs. dorsal sound-to-action: Complementary invariants
2. p405sec Circular reactions in vision and audition: Babbling to reach and to speak
3. p406sec Vector integration to endpoint control of arm movement trajectories
4. p408sec The three S's of movement control: Synergy, Synchrony, and Speed
5. p408sec Variant and invariant properties of arm movement trajectories under size and speed changes
6. p411sec From VITE to VAM: How a circular reaction drives mismatch learning to calibrate the model
7. p413sec From Platonic plans to Newtonian force and obstacle compensation
p414sec Motor-equivalent reaching and tool use
p417sec From motor-equivalent reaching to motor-quivalent speech production and coarticulation
1. p417sec From eating to speaking
p419sec Auditory neglect and speech production deficits
p419sec Stream-shroud resonances for conscious hearing of auditory qualia
1. p419sec Auditory scene analysis: Tracking sound sources through noise and overlapping frequencies
2. p419sec Auditory continuity illision and spectral-pitch-and-timbre resonances
3. p420sec SPINET and ARTSTREAM: Resonant dynamics doing auditory streaming
4. p422sec From SPINET processing of sound spectra to ARTSTREAM creation of multiple streams
p423sec How multiple auditory streams are formed: Strip maps and spectral-pitch resonances
1. p424sec Some simulated auditory streaming data
2. p426sec Generalizing AIRSTREAM to include timbre and voice categories: From item to list chunks
3. p427sec Stream-shroud resonances for conscious perception of auditory objects in streams
p427sec From streaming to speech: Learning to imitate speech requires speaker normalization
1. p427sec From circular reactions to language imitation: Speaker normalization and imitative maps
2. p429sec From imitative map normalization to the Motor Theory of Speech Perception
3. p429sec NormNet: A shared design for auditory streaming and speaker normalization
4. p430sec Strip maps and assymetric competition for auditory streaming and speaker normalization
5. p431sec Strip maps as a general principle of brain design
p431sec Where do numbers come from? Motion and space processing in parietal cortex
1. p431sec Strip maps and asymmetric competition for numerical estimation
2. p432sec Strip maps and numerical language categories in place-value number systems
3. p434sec Zeno's Dichotomy Paradox: Infinitely many steps to get anywhere means you never get there
4. p434sec What-to-Where interactions in formotion tracking, Where's Waldo search, and synesthesia
5. p434sec Formotion tracking
6. p434sec Where's Waldo search
7. p435sec Synesthesia
8. p435sec Mechanistic unifications of psychologically diverse
p435sec From streaming to speech: How working memory and list chunking nets are designed
1. p435sec Short-term storage of item chunk sequences in working memory and learning of list chunks
2. p436sec Why is storage of temporal order information so imperfect? Bowing during free recall
3. p438sec All working memories obey the LTM Invariance Principle and Normalization Rule
4. p439sec Item and order working memories are recurrent shunting on-center off-surround networks
5. p440sec Stable chunking implies primacy, recency, or bowed gradients in working memory
6. p440sec Primacy gradient
7. p440sec Recency gradient
8. p440sec Bowed gradient
9. p440sec From primacy to bowed gradient
10. p440sec An algebraic working memory model explains primacy, recency, and bowed gradients
11. p441sec Psychological and neurophysiological data support predicted working memory properties
12. p443sec LIST PARSE: A laminar model of variable-length sequences
13. p444sec A universal design for linguistic, spatial, and motor working memories
14. p446sec The magical numbers seven and four: Transient and immediate memory spans
p448sec Learning chunks of variable lengths and sequences of repeated words
1. p448sec Why is the Magical Number Seven magical?
2. p448sec A Masking Field working memory chunks lists of variable length
3. p449sec Temporal Chunking Problem: Learning words of variable length
4. p449sec Self-similar growth and length-sensitive competition solve the Temporal Chunking Problem
5. p450sec Self-similarity clarifies the Magical Number Seven and the word superiority effect
6. p450sec All familiar letters activate item chunks and list chunks
p451sec From streaming to speech: Item-list resonances for recognition of speech and language
1. p451sec Top-down attentive matching during speech and laguage using the ART Matching Rule
2. p451sec Phonemic restoration: How the future can influence what is consciously heard in the past
3. p452sec Conscious speech is a resonant wave: Coherently grouping the units of speech and language
4. p452sec When is a "gray chip" a "great ship"?
5. p453sec Resonant transfer from "gray" to "great": Longer silence intervals allow greater habituation
6. p455sec Phonemic restoration in a laminar cortical model of speech perception
p457sec Item-Order-Rank working memories: From numerical to positional coding
1. p457sec Storing sequences of repeated items in working memory
2. p458sec From parietal numerical representations to prefrontal Item-Order-Rank working memory
p459sec An Item-Order-Rank Masking Field hierarchy chunks lists of repeated words
p459sec lisTELOS: Storing, learning, planning, and performing eye movement sequences
1. p460sec Spatial Item-Order-Rank working memory stores sequences of saccadic target positions
2. p461sec How does the brain know before it knows? Balancing reactive vs. planned movements
3. p462sec Choosing reactive and planned movements using frontal-parietal resonances
p463sec From TELOS to lisTELOS: Basal ganglia coordination of multiple brain processes
1. p464sec Three basal ganglia gates regulate saccadic sequence storage, choice, and performance
p466sec Learning and storage of speaker-invariant and rate-invariant working memories
1. p466sec From speaker normalization to rate normalization: Transients and two kinds of gain control
2. p466sec Transient and sustained processing streams in audition and speech
3. p467sec PHONET: Asymmetric T-to-S gain control within syllable rate invariance
4. p468sec ARTPHONE: Rate-sensitive gain control creates rate-invariant working memories
5. p470sec Category boundary shifts due to gain control by average speech rate
6. p470sec Resonant fusion and reset during speech
7. p470sec Why don't sounds last forever? Habituative collapse via activity-dependent transmitter gates
8. p471sec Varying the integration rate to preserve rate-invariant speech codes
9. p471sec Data supporting MTM by activity-dependent habituative gates
10. p472sec How can continuous sound be heard during 125 milliseconds of silence?
p472sec Adaptive resonance in lexical decision tasks: Error rate vs. reaction time data
1. p473sec Semantic vs. visual similarity
2. p473sec Auditory-visual interactions are needed to model semantic relatedness
3. p474sec Explaining chunk data from the tachistoscopic condition using ART
4. p474sec Explaining data from the reaction time condition using ART: List item error trade-off
p476sec Adaptive resonance in word frequency and related tasks
p477sec From invariant working memory storage to volitionally-variant percepts and productions
1. p477sec Back to LIST PARSE: Volitionally-controlled variable-rate sequential performance
p478sec From learning of invariant lists to rhythmic intonated performance
1. p478sec Factorization of order and rythm
2. p479sec Independence of lyrics and tunes
p479sec From speaking to singing

Chapter 13 - From knowing to feeling

How emotion regulates motivation, attention, decision, and action

p480sec Cognitive-emotional dynamics: Beyond Chomsky and Skinner
1. p480sec Resonating object, value, and object-value categories: The "feeling of what happens"
2. p481sec Separate and antagonistic historic movements studied cognition and emotion
p482sec Reinforcement learning: Conditional reinforcer and incentive motivational learning
1. p482sec Classical conditioning
2. p482sec Learning to become a conditioned reinforcer and source if incentive motivation
3. p483sec Anatomical substrates of object, value, and object-value categories
4. p483sec Cognitive-emotoponal resonance and attentional blocking
p484sec CogEM: Cognitive-Emotional-Motor model of conditioning
1. p484sec Classical conditioning and non-stationary prediction
2. p485sec Learning causality: ISI inverted-U, secondary conditioning, and attentional blocking
3. p485sec Classical conditioning shows that we are minimal adaptive predictors
4. p486sec Blocking follows when the inverted-U is combined with secondary conditioning
5. p487sec How CogEM explains attentional blocking
6. p489sec A polyvalent orbitofrontal processing stage helps CogEM to work properly
7. p489sec Interactions between sensory cortices, amygdala, and orbitofrontal cortex
8. p490sec CogEM type circuits are an ancient design!
p490sec Universal evolutionary constraints: The synchronization and persistence problems
1. p490sec The turkey-lover fiasco: Feedforward interactions cannot prevent affective short cicuits
p491sec A thought experiment about cognitive-emotional interactions that leads to CogEM
1. p491sec Starting in the middle: Outstar learning and stimulus samplin
2. p491sec Learning with variable CS-US delays is possible!
3. p492sec US-activated nonspecific arousal at polyvalent sensory cells
4. p492sec Conditioned reinforcer learning: CS-activated conditioned arousal at drive representations
5. p493sec Polyvalent object-value cells control conditioned actions
6. p494sec Internal drive inputs: Eating when hungry
7. p495sec Conditioned incentive motivation
p495sec Homologous sensory and drive representations: Evolutionary precursors?
p495sec An ancient design: Avalanche circuits for sequence learning
1. p495sec What is the smallest network that can learn an arbitrary act?
2. p496sec Ritualistic learning of a movie as a series of still pictures
3. p496sec Why do we need big brains?
4. p496sec Avalanche cells are polyvalent cells where specific and nonspecific arousal signals converge
5. p497sec Arousal-modulated avalanches: A conserved design from crustacea to songbirds
6. p497sec From avalanches to emotion and cognition
p498sec How gated dipole opponent processes work: Learning of opponent emotions
1. p499sec Fear, relief, and their antagonistic rebounds
p500sec Chemical transmitters as unbiased transducers
1. p500sec A thought experiment about unbiased transmisson leads to antagonistic rebounds
2. p500sec Transmitter acculmulation and release: Infinity does not exist in biology!
3. p500sec Unbiased transduction, medium-term memory, depressing synapses, and dynamic synapses
4. p501sec A minor mathematic miracle: Transmitter gating and the importance of brain nonlinearity
5. p501sec Fast signaling and slow habituation lead to overshoot, habituation, and undershoot
6. p502sec Antagonistic rebounds to phasic cue decrements: Frustration and the relaxation response
7. p504sec When food is frustrating and fear causes relief
8. p504sec Graded rebounds to shock reductions in operant reinforcement
9. p505sec Antagonsistic rebound to tonic arousal increment: Novel events are arousing!
10. p505sec Parallel hypothesis testing and memory search in response to unexpected events
p506sec Three cognitive and emotional processes during learning about a reinforcer
p507sec Inverted-U and arousal: Golden Mean, depression, autism, schizophrenia, and ADHD
p508sec READ circuit: REcurrent Associative Dipole
1. p508sec Feedback enables secondary inhibitory conditioning
2. p508sec A sleight of hand: Secondary inhibitory conditioning requires recurrent connections
3. p508sec Stable motivational baseline and rapid motivational switching
4. p509sec Life-long affective learning without associative saturation or passive forgetting
5. p509sec Informational noise suppression prevents LTM saturation
6. p509sec Dissociating LTM read-out and read-in: Backpropagating dendritic teaching signals
7. p510sec Opponent extinction leads to learning of normalized net activity
8. p510sec Stable memories and mismatch-mediated active forgetting over the lifespan
9. p511sec Reconsolitdation and retraining clinical symptoms during disorders like PTSD
p512sec Why do conditioned excitors extinguish but conditioned inhibitors do not?
1. p512sec A conditioned excitor extinguishes
2. p512sec A conditioned onhibitor does not extinguish
3. p513sec Extinction due to disconfirmation of a learned expectation
4. p513sec There is no one "engram": Recognition learning is disctinct from reinforcement learning
5. p514sec A disconfirmed expectation is not an explicit teacher!
p514sec The feeling of what happens, core consciousness, dual competitionm and survival circuits
1. p515sec Breakdowns during mental disorders: Theory of Mind, autism, and schizophrenia
2. p515sec From survival circuits to ARTSCAN Search, pART, and the Where's Waldo problem
3. p516sec Conscious vs. non-conscious emotions

Chapter 14 - How prefrontal cortex works

Cognitive working memory, planning, and emotion conjointly achieved valued goals

p517sec Towards a unified theoretical understanding of prefrontal cortex and its functions
1. p517sec Functional roles of orbitofrontal, ventrolateral, and dorsolateral prefrontal cortex
p518sec Predictive Adaptive Resonance Theory: A unified neural theory of prefrontal cortex
1. p519sec Updating a predicted outcome's desirability and availability
p520sec Cognitive-emotional dynamics and the orbitofrontal cortex
1. p520sec Reinforcement learning, motivated attention, resonance, and directed action
2. p521sec Category learning and memory consolidation: Effects of lesions
3. p521sec Polyvalent constraints and competition interact to choose the most valued options
4. p522sec Orbitofrontal coding of desirability as probed by selective satiation
5. p522sec How do we learn preferences for specific foods?
p523sec MOTIVATOR: Amygdala and basal ganglia dynamics during conditioning
1. p525sec Learning value categories for specific foods and effects of their removal
2. p526sec Two mismatch-mediated mechanisms for shifting from unsuccessful behaviors
3. p526sec Opponent processing by gated dipoles in object and value categories
4. p527sec Affective antagonistic rebounds and reversal learning
p527sec Working memory, chunking, and reinforcement in prefrontal cortex and related areas
1. p527sec VLPFC lesions cause a deficit in learning probablistic stimulus-outcome associations
2. p528sec Some classical data about sequential dependencies influencing future choices
3. p528sec Cognitive working memory and list chunks in VLPFC
4. p528sec List chunks snd reinforcement interact in a probablistic choice environment
5. p529sec DLPFC credit assignment by selective and sustained working memory storage
6. p529sec Two processors regulate whether items will be stored in working memory
7. p530sec Visual search: Efficient vs. inefficient, bottom-up vs. top-down
8. p531sec Object and spatial contexts and reinforcement influence predictive choices
9. p532sec Perirhinal and parahippocampal cortices store object and spatial contexts
10. p534sec RTs in behavioural data and simulations about object and spatial searches
11. p535sec Where-To-What and What-to-Where interactions learn and search for objects
12. p535sec What working memory filtering and activation of Where target positions
13. p537sec Synchronization of multiple cortical regions for feature-based attention
14. p537sec How do we feel when we heara DOG vs. DOG EATS DOG?
15. p537sec Concluding remarks

Chapter 15 - Adaptively timed learning

How timed motivation regulates conscious learning and memory consolidation

p539sec How do learn When to act and Where we are?
1. p540sec Conditioing and consciousness: Trace conditioning, hippocampus, and "time cells"
2. p544sec How is an adaptively timed response learned by a spectral timing circuit?
3. p545sec Is eposodic learning and memory necessary to consciously experience trace conditioning?
4. p545sec Neural relativity: A homology between spectral spacing and spectral timing
5. p546sec Balancing exploratory and consummatory behaviours: Functional role of Weber Law
6. p547sec Distinguishing expected vs. unexpected disconfirmations, or non-occurrences, of reward
7. p547sec How the orienting system stays inhibited during an expected disconfirmation
8. p548sec Timing paradox paradox: Combining inhibition of orienting responses with adaptively timed responses
9. p548sec Why is there a Weber law?
p549sec START: Spectrally Timed Adaptive Resonance Theory
1. p549sec Coordinating adaptively timed learning in the hippocampus and cerebellun
2. p550sec Achieving three fundamental behavioural competences
  1. p550sec Fast motivated attention
  2. p550sec Adaptively timed responding
  3. p550sec Adaptively timed diration of motivate4d attention and inhibition of orienting responses
3. p551sec Adaptively timed learning by the metabotropic glutamate receptor system
p552sec nSTART: neurotrophic Spectrally Timed Adaptive Resonance Theory
1. p552sec Hippocampal, amygdala, and orbitofrontal lesions affect memory consolidation
2. p553sec Paradoxical memory consolidation data follow three obvious behavioural facts
3. p553sec Why do early hippocampal lesions interfere with memory consolidation but late ones do not?
4. p554sec BDNF in learning and memory consolidation
p555sec Two kinds of hippocampally-mediated memory consolidation
p556sec Failure of adaptive timing in autistic individuals
1. p556sec Interactions of multiple imbalanced brain regions during autism
2. p557sec How to break an environmentally-mediated vicious circle that perpetuates symtoms?
3. Fragile X syndrom: Adaptive timing, trace conditioning, and mGluR 558
4. p558sec Adaptive timing of reward expectation by the basal ganglia substantia nigra pars compacta
5. p562sec A calcium spectrum in HeLa cancer cells, Xenopus oocytes, and cardiac myocytes
6. p562sec Basal ganglia gating of all behaviors
7. p562sec Basal ganglia gating during normal and autistic repetitive behaviors
8. p564sec Repetitive behaviors in normal individuals: Motor gaits and sccade staircases
9. p566sec Repetitive behaviors in restricted environments: Tonic motor drive
10. p566sec Hypothalamic circadian oscillator arouses appetitive drive representations
11. p566sec Circadian and appetitive hypothalamic circuit homologs
12. p567sec Transfer from one operant activity to another in restricted environments
13. p568sec Prolonged gate opening and involuntary repetitive movements in autism
14. p569sec D1 and D2 receptors and nucleus accumbens for direct and indirect pathway control
p569sec Imitation learning and social cognition
1. p570sec The critical importance of joint attention in imitation learning
2. p570sec From intrapersonal circular reactions to interpersonal circular reactions: Mirror neurons

Chapter 16 - Learning maps to navigate space

From grid, place, and time cells to autonomous mobile agents

p572sec How do we know where we are in space?
p573sec Place cells and the hippocampus as a cognitive map
1. p573sec Some remaining unanswered questions: What was missing?
p575sec An emerging unified theory of how grid cells and place cells are learned
1. p575sec Both visual and path integration cues are used to navigate the world
p575sec SOVEREIGN: Balancing reactive and planned behaviors during spatial navigation
1. p576sec From locally ignorant components and circuits to emergence of intelligent adaptive functions
2. p577sec Homologous circuits for looking/reaching and spatial navigation
3. p577sec Learning a labeled graph of angles and distances during route navigation
p578sec Grid cells and place cells are spatial categories in a huerarchy of self-organizing maps
p579sec Spatial navigation uses a self-stabilizing ART spatial category learning system
p579sec How do grid cells and place cells arise through development and learning?
p580sec Why are there hexagonal grid cell receptive fields?
p581sec Converting angular and linear velocity into a representation of a position: Stripe cells
p583sec GRIDSmap: From stripe cells to grid cells
1. p584sec How many oriented stripe cells are needed to learn hexagonal grids?
2. p585sec How do hippocampal place cells learn to represent much larger spaces than grid cells can?
p586sec The trigonometry of space revisited: Coding the most frequent and energetic coactivations
1. p587sec An hierarchy of identical SOMs for coordinated learning of grid cells and place cells
2. p587sec Place cell learning without grid cells: Why are grid cells needed?
p589sec Learning a dorsoventral gradient of grids and oscillation frequencies: Neural relativity
p590sec From Spectral Timing to Spectral Spacing: Solving the scale selection priblem
1. p591sec Does a calcium gradient enable mGluR to generate all rate spectra?
p592sec Data and model simulations of grid cell properties along the dorsoventral axis of MEC
p593sec Development of grid cell modules
p593sec Homologous processing of angular and linear velocity path integration inputs
p594sec Stable place cell learning, memory, and attention
p600sec An ART spatial category learning system: The hippocampus IS a cognitive map!
p600sec The hippocampus is MORE than a cognitive map: Three interacting learning processes
1. p600sec Mismatch mediated learning and memory consolidation during recognition learning
2. p600sec Adaptively timed learning and memory consolidation during reinforcement learning
3. p601sec Spatial navigation, neural relativity, and episodic learning and memory
p601sec Beta and gamma oscialltions in hippocampus and cortical areas V1, V2, and V4
p601sec Grid cell realignment and place cell remapping
p602sec When hippocampal feedback fails: Grid cell spatial periodicity and firing rate collapse
p604sec Effects on grid cells of inactivating medial septum and the theta rythm
1. p604sec Acetocholine modulates vigilance control of cognitive, motor, and spatial category learning
2. p606sec Grid cells without oscillation interference
p606sec Three types of grid cell models
1. p606sec SOM models
2. p608sec Oscillatory interference models
3. p608sec Continuous attractor models
4. p609sec Continuous attractor with top-down uniform positive hippocampal-to-entorhinal feedback
p609sec Intrinsic theta rythm from a dissociation between associative read-out and read-in
1. p609sec Competitive choices in STM
2. p610sec From READ circuits to SOM theta rythm: Dissociating associative read-out from read-in
3. p610sec SOM theta rythm dissociates read-out from read-in with backpropagating teaching signals
4. p611sec Why a rythm at all?
5. p611sec Why phase precession of the theta rythm?
p612sec SOVEREIGN's cognitive-emotional architecture for reactive and planned navigation
1. p612sec Parallel What and Where streams for visual categorization and directional movement
2. p613sec Decomposing navigational movements into sequences of angles turned and linear distances
3. p613sec Parallel What and Where streams for working memory, planning, and voting
4. p614sec Parallel READ circuits for reward, motivation, and cognitive-emotional interactions
5. p614sec Simulating exploration and learned navigation in a virtual reality environment
p614sec Towards a synthesis of declarative and episodic memory
1. p616sec From SOVEREIGN to true autonomous adaptive mobile intelligence

Chapter 17 - A universal development code

Mental measurements embody universal laws of cell biology and physics

p618sec Resonant conscious experiences of perceiving, feeling, and knowing
1. p618sec A universal computational code for mental life: Life's moments cohere in a sense of self
2. p619sec Why we become conscious: Hierarchical resolution of complementarity and uncertainty
3. p619sec Consciousness and the brain-environment loops that drive brain self-organization
p619sec Complementarity of the laws for perception/cognition/emotion and action
1. p620sec Learning causes and realizing valued goals through action and error
2. p620sec "Use your head!" to predict the most valued outcomes
3. p621sec Are all values relative? Wherefore moraloty?
4. p621sec Symmetry-breaking of positive and negative in cognition and emotion: A reason for hope
5. p621sec Symmetry-breaking between approach and avoidance in emotion and action
6. p621sec Positive emotions facilitate positive sustainable motivations and empathy
7. p622sec Relief can motivate many sustained positive behaviors: Relaxation response and creativity
8. p622sec The Dark Side: Learned helplessness, self-punitive behaviors, and fetishes
9. p624sec Living and planning for A Rush?
10. p624sec Symmetry breaking, adpative resonance, and believing in God
p625sec A universal development code for biology: Computing with cellular patterns
1. p625sec Coopeeration and competition among species and among cells
2. p625sec Self-organizing pattern transformations within and between cells: STM, MTM, and LTM
3. p626sec Brain as a universal self-organising measurement system
4. p627sec Reaction-diffusion models in development: The great Alan Turing
5. p627sec Regeneration of Hydra's head
6. p628sec Guerer-Meinhardt reaction-diffusion model of development
7. p629sec Comparing reaction-diffusion and recurrent shunting network models of development
8. p630sec The analogy between regulation and adaptation: Form invariance when size changes
9. p631sec Blastula to gastrula in the sea urchin
10. p622sec How gastrula initiation uses STM and LTM laws
11. p632sec Invoking cellular STM and LTM dynamics to explain sycytium formation
12. p633sec An LTM cascade of directed growth followed by learned synaptic tuning
13. p633sec Biochemical memory and the folds of Rhodnius
14. p634sec Slime mold aggregation and slug motion
p635sec A universal measurement device of the world and in the world: Brains adapt to physics
1. p636sec Complementarity, uncertainty, and resonance in brains and the world
2. p637sec Microscopic reversibility, macroscopic irreversibility, and arrow in time: Serial learning
3. p637sec Dimensions in space and time?
4. p638sec Wave-particle duality vs. resonant choce and synchronic oscillations
p639sec A final thought: Rebalancing what we know about ourselves and about the external world
1. p639sec Web-based curricula to reach millions of students and adults around the world