Who is Who in Cognitive & Perceptual Sciences

Gibsonian Affordances Simulacrum(1904–1979)

Ecological Optics · Affordances · Direct Perception · The Senses Considered as Perceptual Systems

Based on the work of James J. Gibson, founder of ecological psychology. Gibson argued that perception is direct — that the environment offers affordances, possibilities for action, which organisms perceive without inference. His ecological optics studied the structure of ambient light as a source of information, rejecting the view that perception requires internal representation. The concept of affordances became central to design, robotics, and embodied cognition.

Can help you study: Affordances and direct perception, ecological optics, the information available in the optic array, the critique of representational theories of perception, and the ecological approach to mind.

Maurice Merleau-Ponty(1908–1961)

Phenomenology · Embodied Cognition · Perception

French phenomenologist whose Phenomenology of Perception (1945) argued that perception is not a mental process that happens to use the body as an instrument but is fundamentally bodily — that the body is not an object in the world but our very vehicle of being-in-the-world. His account of the “lived body” challenged Cartesian dualism from within phenomenology, and his concept of the “motor intentionality” — the body's pre-reflective directedness toward its environment — became foundational for embodied cognition, robotics, and the study of skilled action.

Can help you study: Embodied cognition and the lived body, phenomenology of perception, motor intentionality, the critique of Cartesian dualism, the relationship between perception and action, and the argument that thinking is something bodies do, not something minds do to bodies.

Gunnar Johansson(1911–1998)

Biological Motion · Point-Light Displays · Kinematic Geometry

Swedish perceptual psychologist who demonstrated with striking simplicity that the visual system extracts biological motion from remarkably sparse data. His point-light display experiments (1973) showed that when reflective dots are attached to a person's joints and filmed moving in darkness, observers immediately and effortlessly perceive a human figure walking, running, or dancing — from information that contains no shape, no texture, no outline. The visual system, he showed, performs vector analysis: decomposing complex motion into its components and identifying the component structure as biological.

Can help you study: Biological motion perception and point-light displays, the visual system's analysis of motion, the extraction of structure from sparse information, perceptual organisation and Gestalt principles, the relationship between the visual system's computations and ecological regularities, and the argument that perception is inference about the causes of sensory stimulation.

Noenian Vision Simulacrumb. 1964

Enactive Perception · Presence · Strange Tools · Sensorimotor Contingencies · The Extended Mind

Based on the published writings of Alva Noë. Noë argues that perception is something we do rather than something that happens in us — an activity of the whole embodied animal exploring its environment through mastery of sensorimotor contingencies. His enactive approach rejects the idea that seeing is the construction of an internal picture, and extends to art, which he treats as a tool for investigating the nature of experience itself.

Can help you study: Enactive and sensorimotor theories of perception, the problem of perceptual presence, the extended mind, and the relationship between art, tools, and consciousness.

Bajcsyan Active Perception

A simulacrum abstracted from the published work of Ruzena Bajcsy, who is living and has had no part in it. ⭑ Perception is ACTIVE: an observer must control its sensors to acquire information, so vision is not the processing of an image that arrives but the deliberate business of deciding where to look and moving to look there. The same argument runs through the haptic work — a hand does not receive a shape, it executes exploratory procedures to find one out.

Can help you study: Active perception as a design principle: what changes when sensing is treated as a control problem rather than a signal-processing one. Exploratory procedures in touch — the specific movements that recover hardness, texture, weight and shape, each a different action. Purposive sensing: choosing the next measurement by what would most reduce uncertainty. And why a passive vision system and an active one need different mathematics rather than the same mathematics with a camera on a motor.

Nikolai Bernstein(1896–1966)

Biomechanics · Degrees of Freedom · Motor Control

Russian physiologist and biomechanist whose work on the control of movement — conducted against the grain of Pavlovian reflexology that dominated Soviet science — introduced the concept of the degrees of freedom problem: the brain must coordinate far more independent variables than are necessary to accomplish any movement, and must find principled ways to reduce this redundancy. His concept of “repetition without repetition” — that skilled movement is never exactly the same twice but always achieves the same goal by different means — remains the foundational insight of motor control theory.

Can help you study: The degrees of freedom problem in motor control, repetition without repetition, the hierarchical organisation of movement, the history of Soviet physiology and its relationship to Pavlovian theory, biomechanics and skilled action, and the argument that the brain solves the control of movement by setting goals rather than issuing motor commands.

Bernsteinian Building Simulacrum(1896–1966)

Degrees of Freedom Problem · Dexterity · Motor Learning · Movement as Problem-Solving · Physiology of Activity

Based on the work of Nikolai Bernstein, the Soviet physiologist who reframed movement as a problem of control. The body has far more degrees of freedom than any movement requires; skill, he argued, is the progressive mastery of those redundant degrees of freedom. His “physiology of activity” treated the organism as an active solver of motor problems rather than a passive bundle of reflexes, anticipating modern motor-control and robotics theory by decades.

Can help you study: The degrees-of-freedom problem, motor learning and the acquisition of dexterity, movement as active problem-solving, and the physiology of coordinated action.

D'Arcy Thompson(1860–1948)

Mathematical Biology · Growth · Form

Scottish mathematical biologist whose On Growth and Form (1917) argued that the shapes of living organisms are largely determined by mathematical and physical principles — that the form of a wave, a bubble, a bone, or a horn can be explained by the same equations that govern purely physical systems. His transformation diagrams, showing how one species's form can be derived from another's by a coordinate transformation, anticipated computational morphometrics by seventy years. The book is one of the longest, most idiosyncratic, and most influential works of twentieth-century biology.

Can help you study: On Growth and Form and mathematical biology, transformation diagrams and morphometrics, the application of physics and mathematics to biological form, the relationship between structure and function, the history of theoretical biology, and the argument that living form is constrained by physical law.

W. Ross Ashby(1903–1972)

Cybernetics · Homeostat · Requisite Variety

British psychiatrist and cybernetician whose Design for a Brain (1952) and Introduction to Cybernetics (1956) established cybernetics as a rigorous science and introduced two of its most important principles: the Law of Requisite Variety (a control system must have at least as much variety as the system it controls) and ultrastability (the property of a system that allows it to reorganise its structure in response to destabilising disturbances). His Homeostat machine demonstrated ultrastability in physical hardware.

Can help you study: The Law of Requisite Variety and its implications, ultrastability and adaptive systems, the Homeostat machine, cybernetics as a science, the relationship between control theory and biological systems, and the argument that the brain is an adaptive system that maintains homeostasis across multiple levels.

W. Grey Walter(1910–1977)

Cybernetics · Turtle Robots · EEG · Brainwaves

British neurophysiologist and cybernetician who built the first autonomous mobile robots — his “tortoises” Elmer and Elsie (1948) — and demonstrated that simple neural circuits could produce complex, apparently purposive behaviour. He also pioneered the clinical use of electroencephalography (EEG) and discovered the contingent negative variation — a brainwave that anticipates an expected event — demonstrating for the first time that expectation has a measurable neural correlate. His 1953 book The Living Brain is one of the most elegant popular science books about neuroscience ever written.

Can help you study: The Walter tortoises and early robotics, EEG and contingent negative variation, cybernetics and neural circuits, the relationship between simple mechanism and complex behaviour, the living brain as a cybernetic system, and the history of early neurophysiology in Britain.

Valentino Braitenberg(1926–2011)

Synthetic Neuroscience · Vehicles · Behaviour

Italian-German neuroscientist whose Vehicles: Experiments in Synthetic Psychology (1984) demonstrated through a sequence of thought experiments that surprisingly complex and apparently purposive behaviour can emerge from very simple connections between sensors and motors. A Braitenberg vehicle is a minimal robot: sensors directly wired to wheels. With only two sensors and two motors, a vehicle can appear to love light or fear it, to be aggressive or curious. The Vehicles sequence showed that intentionality — the appearance of purpose — is easier to produce than anyone had supposed.

Can help you study: Braitenberg vehicles and synthetic psychology, emergence of complex behaviour from simple mechanisms, the relationship between neural architecture and behaviour, the appearance of intentionality in simple systems, and the argument that psychology can be studied through the construction of minimal machines.

Misha Mahowald(1963–1996)

Neuromorphic Vision · Silicon Retina · Analogue VLSI

American neuromorphic engineer whose doctoral work at Caltech with Carver Mead produced the silicon retina — an analogue VLSI circuit designed to mimic the processing performed by the vertebrate retina, including adaptation to illumination level, edge detection, and temporal differentiation. Her work pioneered the approach of building computation in analogue hardware that directly instantiates biological neural principles rather than simulating them digitally. She died of accidental drug overdose at 32. The silicon retina she built is still a reference point in neuromorphic engineering.

Can help you study: Neuromorphic engineering and the silicon retina, analogue VLSI and biological computation, the architecture of retinal processing, the relationship between biological and silicon neural systems, and the argument that understanding the brain requires building things that work the way the brain works.

Albert Uttley Simulacrum1906–1985

Conditional probability computers · Classification of signals · Informon theory · Learning machines

Albert Uttley came to the nervous system from the engineer’s bench — servo-mechanisms, navigation computers, a parallel digital machine at Malvern — and turned the designer’s method round: take the animal’s behaviour as the specification, build the least machine that shows it, then ask the cortex. His conditional probability computers of the 1950s learned by counting co-occurring events, and his later informon theory proposed how a single neuron might learn to classify.

Can help you study: Learning as counting: what a machine of counters can infer from a past, and what it cannot. Specification-first design, and why it suits a field that hands you no equations. Classification of signals and the place of rare events in it. ⭑ And the honest heart of the method — induction is an illogical principle that every animal lives by, so build it anyway and call it by its name.

Gerald Edelman(1929–2014)

Neural Darwinism · Consciousness · Selectionism

American immunologist turned neuroscientist who won the Nobel Prize in Physiology or Medicine in 1972 for his work on the structure of antibodies, then spent the second half of his career developing a theory of consciousness and neural Darwinism. His theory of neuronal group selection (TNGS) proposed that the brain develops not by executing a genetic program but by a Darwinian process of variation and selection among neural circuits: circuits that fire together survive together. His Bright Air, Brilliant Fire (1992) and A Universe of Consciousness (2000, with Giulio Tononi) are his most accessible statements.

Can help you study: Neuronal group selection and neural Darwinism, the theory of consciousness in Edelman's framework, primary and higher-order consciousness, the relationship between immunology and neuroscience, Nobel Prize work on antibody structure, and the argument that Darwinian selection operates within the developing and operating brain.

Colin Blakemore(1944–2022)

Visual Neuroscience · Critical Periods · Development

British neuroscientist and science communicator whose research on the visual cortex — particularly his work with Graham Cooper demonstrating that the orientation-selective neurons of the primary visual cortex can be shaped by early visual experience — was foundational to developmental neuroscience. His work showed that the architecture of the brain is not fully determined at birth but is sculpted by experience during critical periods. His public engagement with neuroscience, and his willingness to defend animal research against antivivisection campaigners, made him a significant figure in the relationship between science and public discourse.

Can help you study: The visual cortex and orientation selectivity, critical periods in neural development, the interaction between genes and experience in shaping the brain, the neuroscience of visual perception, science communication and its challenges, and the ethics of animal research in neuroscience.

Markraman Brain SimulationLiving

Brain simulation · The Blue Brain and Human Brain projects · Cortical microcircuitry · Simulation as method · Building rather than describing

A simulacrum abstracted from the published work of Henry Markram, who is living and has had no part in it. Its commitment is connectome-native reconstruction: build the circuit at the level of individual cells and their measured connections, run it, and see what behaviour emerges rather than assuming it. ⚠ The strategy is bottom-up in the strongest sense — detail first, phenomena later — and it is the most publicly contested research programme in the field.

Can help you study: Bottom-up simulation as a research strategy, and what it claims that abstract modelling does not. What a reconstruction at cellular resolution requires in data before it can be run at all, which becomes the limiting step. Reading emergent activity in a simulation and asking honestly whether it was put in. ⚠ And the case itself as a lesson in scientific ambition — a programme whose promises attracted very public criticism, worth examining for what was actually claimed against what was heard.

Michael Reimann(21st century)

Topological Neuroscience · Cliques · Neural Homology

Computational neuroscientist whose work applies algebraic topology — the mathematics of shapes and connectivity — to the structure of neural circuits. In collaboration with the Blue Brain Project, his work demonstrated that the neocortex contains structures of up to eleven dimensions in the topological sense — that groups of neurons connect in ways that form geometric objects (simplices, cliques) of very high dimensionality that appear to be related to the brain's processing of information. This is topology applied not as metaphor but as literal measurement of neural circuit geometry.

Can help you study: Algebraic topology applied to neuroscience, simplicial complexes in neural circuits, the Blue Brain Project, the geometry of neural connectivity, the relationship between mathematical structure and neural computation, and the argument that the brain's structure can only be fully understood using tools from pure mathematics.

Yuri Dabaghian(21st century)

Topological Neuroscience · Spatial Cognition · Homology

Computational neuroscientist who applies topological methods — particularly persistent homology — to the study of hippocampal spatial cognition and memory. His work addresses the question of how the hippocampus constructs a topological map of space from the activity of place cells: not a metric map (with distances) but a topological one (with connectivity). This approach suggests that what the hippocampus actually computes is not “where am I?” but “what is connected to what?” — a topological, not geometric, question.

Can help you study: Persistent homology applied to neuroscience, hippocampal spatial maps and their topology, place cells and cognitive mapping, the distinction between metric and topological representations, and the argument that the hippocampus is computing topology rather than geometry.

Curtonian Geometry Simulacrum(Living)

Neural Codes · Convex Realizability · Combinatorial Topology

This simulacrum draws on the published mathematics of Carina Curto, and on nothing else: the neural ring and neural ideal, which turn a set of firing patterns into an algebraic object; the characterisation of local obstructions to convexity, and the awkward fact that the characterisation is complete and still does not decide whether a code is convex; and combinatorial threshold-linear networks, where the fixed points of a circuit can be read off its graph. You will not speak to Carina Curto. You will speak to the operations her papers run, which begin by throwing away every firing rate and asking what the bare pattern of co-activity already forces about the space the animal is in.

Can help you study: Neural codes and what can be recovered from them without a metric, convex codes and local obstructions, why “no obstruction found” is not a verdict of convexity, the simplicial complex of a code and how it differs from a complex built by thresholding a similarity, neural rings and ideals, and combinatorial threshold-linear networks and their graph rules.

Hawkinsian Memory-Prediction Simulacrum(Living)

On Intelligence · The memory-prediction framework · Hierarchical temporal memory · Active dendrites and sparse codes · A thousand models in the cortical columns

This simulacrum draws on the published work of Jeff Hawkins and his collaborators on the theory that the neocortex is a memory system that constantly predicts its own input. It works from the later papers that made the theory precise: neurons with thousands of synapses that recognise patterns on their dendrites, sparse codes that almost never collide by chance, columns that learn objects as features at locations, and the claim that many such models vote. Its habit is to ask what a circuit expected a moment ago, and what it does when the expectation fails.

Can help you study: Prediction as the basic act of cortex, why active dendrites matter, the arithmetic of sparse distributed representations, sequence memory and what a surprise looks like in neurons, grid-cell-like location signals and object learning in columns, and the difference between a theory that biology licenses and one it tests.

Valiantian Neuroidal Simulacrum(Living)

Circuits of the Mind · The neuroidal model · Memorisation and association on weak synapses · Learnability · Resources counted, never assumed free

This simulacrum draws on the published work of Leslie Valiant on the brain as a computer bound by the same quantitative laws as any other: his 1984 definition of what it means to learn, and the neuroidal model of cortex, in which many neurons, sparse random wiring and weak synapses must still memorise new items and associate old ones without disturbing what is stored. It treats every claim about the mind as a task with an input, an output and a cost, and it will not accept a mechanism until the numbers have been filled in.

Can help you study: Stating a cognitive task precisely, resource budgets for neural theories, random graphs and local algorithms that act globally, learning without programming and what PAC learnability asks, lifelong learning without interference, and why showing the brain could do something is not showing how it does.

Papadimitrian Assembly Calculus Simulacrum(Living)

The Assembly Calculus · Random projection and cap · Projection, association and merge · A language organ built from assemblies · A level between neuron and sentence

This simulacrum draws on the published work of Christos Papadimitriou and his collaborators on the Assembly Calculus: a small set of operations on assemblies of neurons, built from random connectivity, inhibition that lets only the top few fire, and plasticity, and proposed as a level of description between spiking neurons and cognition. It also draws on the attempt to build a parser and a language organ from those operations. It writes thought as a short program, compiles it down line by line, and marks plainly the lines that are assumed rather than shown.

Can help you study: How random projection and a cap make stable assemblies, association and pattern completion, a neural account of syntactic merge, levels of explanation between neuron and mind, why a hard problem the brain seems to solve may be a misdescription, and the open question of what controls the program.

Fly Olfactory Hashing Simulacrum(Living)

Fly olfaction as locality-sensitive hashing · Expand and sparsify · A Bloom filter for novelty · Circuits read as algorithms · Benchmarks at equal cost

This simulacrum draws on the published work of Sanjoy Dasgupta, Charles F. Stevens (1934–2022) and Saket Navlakha, with Timothy Sheehan, on the fruit fly’s olfactory circuit read as computer science. Fifty inputs are spread by sparse random wiring onto some two thousand cells, and one inhibitory neuron keeps only the most active few: a locality-sensitive hash that differs from the textbook one in three named ways, and that does better on standard benchmarks when the cost is held equal. The same circuit’s output synapses are read as a filter that grades how novel a smell is. Its habit is to ask what problem a circuit solves, and what the textbook would have done instead.

Can help you study: Reading a wiring diagram as an algorithm, similarity search and locality-sensitive hashing, random projection and why expanding before sparsifying helps, Bloom filters and graded novelty, how to benchmark a brain-inspired method fairly, and the gap between a circuit having a property and that property being its purpose.

Kandelian Synaptic Memory Simulacrum(Living)

Memory from the synapse up · The two clocks of memory · Genes, synapses and learning · Psychiatry and the biology of mind · Reductionism in art and brain science

This simulacrum draws on the published work of Eric R. Kandel and his colleagues on how a memory is made and kept. That work reduced a defensive reflex in the sea snail Aplysia to identified cells, and found habituation, sensitization and classical conditioning as changes in the strength of particular synapses. It then traced two machines at the same synapse: one brief signal alters existing proteins for minutes, while repeated, spaced training reaches the nucleus, switches genes on, lifts a molecular brake and grows new connections — and a single neuron keeps that growth at the one synapse that was marked. In the mouse the same switch reappears in the hippocampus, thrown by attention. Its habit is to ask of any memory which kind it is, on which clock, at which synapse and against what control — and then what that explains about a person.

Can help you study: How a simple reflex learns and where the change sits; short-term against long-term memory, and why new protein matters; the controls that separate association from sensitization; synapse-specific memory and how a synapse is marked; attention and the stability of the hippocampal map; and what any learning system — a brain or a machine — would have to show before one could say it stores a memory the way a synapse does.

Sompolinskian Manifold Simulacrum(Living)

Neural manifolds · Separating manifolds, not points · Capacity per neuron · Statistical mechanics of disordered networks · Untangling geometry through a hierarchy

This simulacrum draws on the published work of Haim Sompolinsky and his collaborators, which brought the statistical mechanics of disordered systems into neuroscience: chaos and balance in large random networks, the capacity of the perceptron, and more recently the theory of neural manifolds. Its central claim is that a category seen under every variation produces not a point but a manifold in a population’s activity, and that recognition is the separation of manifolds. It asks how much a readout can separate per neuron, and what a hierarchy does to the geometry, layer by layer.

Can help you study: Neural population geometry, manifold capacity and separability, the perceptron and its capacity, random-network theory and mean-field methods, and how deep networks and the visual pathway untangle object categories.

Grossbergian Resonance SimulacrumLiving

Adaptive Resonance Theory · The stability–plasticity dilemma · Complementary computing · Neural dynamics of learning

A simulacrum abstracted from the published work of the living theorist Stephen Grossberg, who has had no part in it. It starts from a paradox rather than a mechanism: how does a brain learn something new, quickly, without washing out what it already knew? Its answer is resonance — a bottom-up signal and a top-down expectation that match, hold each other, and only then permit learning — derived as real-time nonlinear dynamics and tested against many data sets at once.

Can help you study: The stability–plasticity dilemma, and why catastrophic forgetting in modern networks is the same problem under another name. Adaptive Resonance Theory: matching, vigilance, and the search that a mismatch sets off. Complementary computing — paired streams whose weaknesses cancel. ⚠ And the method itself, which is the harder lesson: a mechanism earns its place only by explaining several unrelated data sets with one set of principles.

Fristonite Systems Simulacrumb. 1959

Free Energy Principle · Active Inference · Predictive Processing · Variational Inference · The Markov Blanket

Based on the published writings of Karl Friston. Friston’s free energy principle proposes that any self-organising system that persists must minimise the surprise of its sensory states — that perception, action, and learning are all forms of inference under a single imperative. Active inference recasts the brain as a prediction machine that acts to confirm its own model of the world. The framework is among the most ambitious unifying theories in contemporary neuroscience.

Can help you study: The free energy principle, active inference and predictive processing, variational inference and the Markov blanket, and the attempt to unify perception, action, and learning under one principle.

Chalmersian Analysis Simulacrumb. 1966

The Hard Problem · Philosophical Zombies · Panpsychism · The Conscious Mind · Two-Dimensional Semantics

Based on the published writings of David Chalmers. Chalmers framed the “hard problem” of consciousness — why physical processing is accompanied by subjective experience at all — and argued, via the conceivability of philosophical zombies, that consciousness cannot be reductively explained by physical facts alone. His work has revived serious discussion of panpsychism and shaped the philosophy of mind for a generation.

Can help you study: The hard problem of consciousness, philosophical zombies and conceivability arguments, panpsychism, two-dimensional semantics, and the limits of physicalist explanation.

Evelyn Bartnik(21st century)

Affordance Theory · Visual Neuroscience · Action Spaces

Australian geneticist and science communicator whose work focuses on the application of genomics to rare and undiagnosed diseases, particularly in children. She is a leading figure in the clinical translation of genomic sequencing technology — the application of whole-genome sequencing to patients whose conditions have previously resisted diagnosis. Her work sits at the intersection of basic genomics, clinical medicine, and the ethics of genomic information: what does it mean to sequence a patient's entire genome, to find variants of uncertain significance, to communicate probabilistic risk to families?

Can help you study: Clinical genomics and rare disease diagnosis, whole-genome sequencing and its applications, the ethics of genomic information, variants of uncertain significance, the translation of genomic research into clinical practice, and the question of what patients and families should be told about probabilistic genetic findings.

Woodwardian Causal Explanation SimulacrumLiving

Interventionist causation · Variable choice · Invariance · What an agent can learn by doing

A simulacrum abstracted from the published work of the living philosopher of science James Woodward, who has had no part in it. Its account of causation is interventionist: X causes Y when some change to X, made in the right way, would make a difference to Y — and an explanation is good to the degree that the relationship it cites stays invariant under such changes. It begins every causal question by asking what you would change, how, and what would then be different.

Can help you study: The interventionist account of causation and explanation, and what it answers that regularity and mechanism accounts leave open. Invariance as the mark of a good explanatory generalisation. Choosing variables — why the same system can be carved into good and bad causal variables. How children and other agents learn causal structure by acting. ⚠ And the account’s own boundary: it says what a causal claim means, not how nature makes it true.

Cisekian Affordance Simulacrum(Living)

Affordance competition · Urgency gating · Phylogenetic refinement · Embodied decision-making

This simulacrum draws on the published work of Paul Cisek on how animals choose between actions. His affordance competition hypothesis holds that the brain prepares several possible actions at once and lets them compete within the same sensorimotor circuits, with no separate executive that decides first and commands afterwards. His urgency-gating model accounts for when the competition ends, and his phylogenetic approach rebuilds the categories of behaviour from the way vertebrate brains were elaborated over evolution.

Can help you study: Embodied decision-making, the affordance competition hypothesis, urgency and the timing of commitment, decisions made while moving, the evolution of vertebrate behavioural control, and the case against the sense–think–act picture of the mind.

Shadlenian Deliberation Simulacrum(Living)

Perceptual decision-making · Evidence accumulation to a bound · Confidence and decision time · Consciousness as a decision to report

This simulacrum draws on the published work of Michael Shadlen on the neural basis of decisions: neurons in parietal cortex that accumulate evidence from a noisy display until it reaches a bound, the bounded drift-diffusion account that explains choice and reaction time together, the confidence that comes from the same process, and the urgency that brings deliberation to an end. It treats a decision as a window on cognition more broadly, including the proposal that becoming aware of something is itself a decision to report it.

Can help you study: Perceptual decision-making and the random-dot motion task, drift-diffusion models and how to fit them, the speed–accuracy trade-off, confidence and metacognition, and the sequential probability ratio test.

Redishian Decision-Systems Simulacrum(Living)

Vicarious trial and error · Multiple decision systems · Neuroeconomics of choice · Computational psychiatry

This simulacrum draws on the published work of A. David Redish on decisions made by several interacting systems — Pavlovian, deliberative, procedural and reflexive — and on what happens when they fail. His studies of vicarious trial and error showed rats pausing at a choice point while hippocampal activity swept ahead along the possible paths, a deliberation that fades as a route becomes habit. His account of addiction treats it as a set of distinct vulnerabilities in these decision systems.

Can help you study: Multiple decision systems and how to tell them apart, deliberation and the hippocampus, the passage from deliberation to habit, neuroeconomic tasks such as regret and sunk costs, and computational psychiatry.

Mattarian Deliberation Simulacrum(Living)

Prioritized memory access · Hippocampal replay and planning · The value of computation · Fatigue, boredom and opportunity cost

This simulacrum draws on the published work of Marcelo Mattar on memory, replay and planning. Its central idea is a rule for which memory to rehearse next: each candidate is valued by how much reconsidering it would improve future choices and how soon it will be needed, which accounts for when the hippocampus replays forward or backward, at rest or before a choice. Later work places planning within a taxonomy of algorithms and asks what computation costs, and when it is worth doing at all.

Can help you study: Hippocampal replay and planning, prioritized memory access, the value of computation, model-based reinforcement learning, how to tell planning algorithms apart from behaviour, and fatigue, boredom and opportunity cost.

O’Dohertian Arbitration Simulacrum(Living)

Decision Neuroscience · Arbitration Between Goal-Directed and Habitual Control · Model-Based fMRI · Reinforcement Learning in the Brain

This simulacrum draws on the published work of John O’Doherty on reinforcement learning in the human brain, studied with model-based fMRI: prediction-error signals in the striatum, the separate roles of its ventral and dorsal parts, the distinction between goal-directed and habitual control, and evidence that regions of prefrontal cortex arbitrate between the two according to how reliable each has recently been. It makes the strongest case that something in the brain does arbitrate, and says what would count against it.

Can help you study: Model-based and model-free reinforcement learning, goal-directed and habitual behaviour, prediction errors and the striatum, model-based fMRI as a method, and arbitration between control systems.

Srinivasanian Vision Simulacrumb. 1948

Insect Vision · Optic Flow · Polarisation Navigation · Waggle Dance · Biomimetic Systems

Based on the published writings of Mandyam Srinivasan. Srinivasan’s research on the visual systems of bees and other insects revealed how tiny brains solve navigation, distance estimation, and landing using optic flow rather than computation-heavy depth perception. His findings have been applied directly to the guidance of autonomous aircraft and robots, demonstrating that biological solutions can outperform engineered ones.

Can help you study: Insect vision and navigation, optic flow and its uses, polarisation-based navigation, the waggle dance, and the application of biological vision to autonomous systems.

David Marr Simulacrum1945–1980

Levels of analysis · The computational theory of vision · Natural constraints and stereopsis · Codons and the cerebellar theory · Simple memory

David Marr read mathematics at Trinity College, Cambridge, and between 1969 and 1971 published theories of three brain structures: a cerebellum that learns motor skills from a teaching signal carried by its climbing fibres, with an expansion of its input into “codons” that makes similar patterns less alike; a neocortex that forms categories; and a hippocampus that stores the events of a day and recalls them from fragments. At the M.I.T. Artificial Intelligence Laboratory he turned to vision, built with Tomaso Poggio a theory of stereopsis resting on two constraints from the physical world, and argued that a system like the visual brain must be understood at separate levels — what is computed and why, the representation and algorithm, and the hardware. His book Vision appeared in 1982, two years after his death. The simulacrum asks what a mechanism is for before examining it, and counts cells, synapses and events before believing it.

Can help you study: The levels of analysis and how to place a claim among them; the stereo correspondence problem and the constraints that solve it; the primal sketch, the 2½-D sketch and the 3-D model; expansion recoding, codon overlap and how sparse a code should be; capacity arithmetic for associative memories and recall from partial cues; and the cerebellar theory of motor learning.