#predictivecoding

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Week 12: Functional Divisions of Mental Simulation

🎯 Learning Goal

Explain the functional divisions of “David’s Integrated Neural System for Mental Simulation.”

📚 Core Concepts

Professor Havas’ Integrated Neural System for Mental Simulation breaks down how the brain simulates actions and outcomes using distinct but interconnected systems:

• Perception–Action Cycle:

Every perception leads to an action, and every action leads to a new perception. This cycle allows the brain to continuously update its internal model of the world.

Motor Cortex Hierarchy:

SMA (Supplementary Motor Area): Forms intentions and abstract action plans.

PM (Premotor Cortex): Specifies and selects action sequences.

M1 (Primary Motor Cortex): Executes fine motor movements.

“Actions are planned with respect to a goal… and unfold through a hierarchy of motor areas.” — Week 12 Motor Cognition Lecture

• Mirror Neuron System:

Observing others’ actions activates the same motor areas in the observer’s brain, enabling simulation, empathy, and social understanding.

Internal vs. External Information Streams:

External: Affordances — objects automatically suggest possible actions (e.g., a chair affords sitting).

Internal: Goals, memories, and emotional states influence whether or how we act.

🔬 Evidence from Research

• Lecture Insight:

The brain is a prediction machine. It learns the structure of the world from the bottom up and uses top-down projections to simulate what will happen next.

“The brain’s predictions are corrected by the world… and the brain learns from its mistakes.” — Week 12 Motor Cognition Lecture

• Saygin et al. (2004):

Even simplified point-light animations of human motion activate the premotor cortex, showing that the brain simulates actions even when visual input is minimal.

“These stimuli can also recruit action observation networks… characterized by motion cues alone.” — Saygin et al., 2004

• TED Talk (Biological Motion):

Point-light walkers reveal how we extract rich social and emotional information from motion alone — gender, emotion, even personality traits.

“There’s lots of information we can retrieve from the way someone moves.” — Biomotion Lab

🧩 Integration & Synthesis

This system supports predictive coding: the brain constantly simulates what will happen next based on past experience. The SMA, PM, and M1 form a functional hierarchy that allows us to mentally rehearse actions before we perform them.

The mirror neuron system allows us to simulate others’ actions, which supports empathy, language comprehension, and social cognition. This is why we flinch when we see someone else get hurt — our motor system simulates their experience.

Example: Seeing a cup of coffee activates motor plans for grasping — even if we don’t act. Example: A mug with a handle affords grasping — its shape and orientation suggest how to interact with it. But whether we actually reach for it depends on internal factors like thirst, memory of its contents, or social context (e.g., “Is this my mug?”). Example: Watching someone kick a ball activates our own motor cortex as if we were kicking.

🎨 Visuals

“The motor system is organized from intention (SMA) to execution (M1).”

“Perception and action form a continuous loop of simulation and feedback.”

“Even minimal motion cues activate motor simulation areas.”

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Week 11: Motor Cognition and Mental Simulation

🎯 Learning Goal

Describe three distinct cognitive roles for different motor areas of the brain.

📚 Core Concepts

Motor cognition isn’t just about movement — it’s the foundation of how we think, plan, and simulate actions. The three key motor areas and their cognitive roles are:

Supplementary Motor Area (SMA):

Responsible for intention formation — setting up abstract action plans.

Example: Deciding to go to the store or study for an exam.

Premotor Cortex (PM):

Handles specification and selection of action sequences.

Example: Choosing how to reach for a cup — with your left or right hand, from which angle, etc.

Primary Motor Cortex (M1):

Executes fine motor movements by directly controlling muscles.

Executes fine motor movements by sending signals to muscles.

These divisions show how motor cognition is not “primitive” but central to planning, simulating, and executing complex actions.

🔬 Evidence from Research

“The brain really seems to be some kind of a prediction-making machine.”

— Motor Cognition Lecture

• Lecture Insight:

The perception–action cycle shows that every perception leads to an action, and every action leads to a new perception. This loop is central to how we simulate actions before doing them.

• Saygin et al. (2004):

Using fMRI, researchers found that even point-light biological motion (just dots moving like a person) activates the premotor cortex, showing that our motor system helps us interpret and simulate others’ actions — even when visual information is minimal.

“Saygin et al. (2004): Using fMRI, researchers found that even point-light biological motion (just dots moving like a person) activates the premotor cortex, showing that our motor system helps us interpret and simulate others’ actions — even when visual information is minimal.

“Point-light biological motion activates human premotor cortex.” — Saygin et al., 2004

“These stimuli can also recruit action observation networks, although they are very simplified and characterize actions by motion cues alone.” — Saygin et al., 2004

• Daniel Wolpert’s TED Talk: Wolpert argues that “we have a brain for one reason and one reason only — to produce adaptable and complex movement.” This supports the idea that cognition evolved primarily to serve motor control.

"Brains evolved not to think or feel, but to move." - Daniel Wolpert, TED Talk (2011)

🧩 Integration & Synthesis

Motor cognition supports predictive coding — the brain constantly generates hypotheses about what will happen next based on past experiences. SMA, PM, and M1 form a hierarchy that allows simulation at multiple levels: planning, sequencing, and execution.

This also connects to mirror neurons: when we observe someone else performing an action, our own motor areas activate as if we were doing it ourselves. This underlies empathy, social learning, and language comprehension.

Example: Seeing a coffee cup activates motor plans for grasping — even before we touch it.

Example: Watching someone reach for a cup activates our own motor cortex, helping us understand their intention.

This supports the predictive coding model introduced in Week 2 — the brain uses prior experience to simulate and anticipate action outcomes.

“The brain forms internal models based on past experiences and uses them to predict incoming sensory input. These predictions guide both perception and action.” — Biology Insights, 2025 biologyinsig...

🎨 Visuals

The motor cortex is organized hierarchically, from abstract planning (SMA) to execution (M1).

Every perception leads to an action, and every action leads to a new perception — forming a continuous simulation loop.

Even simplified motion cues like point-light walkers activate premotor cortex — showing how the brain simulates action

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Deep Dive: Cognition & Perception — Project Overview

Deep Dive: Cognition & Perception — Project Overview

This Tumblr serves as my Deep Dive term project for PSYCH 351. The central theme is predictive coding — the idea that the brain is a prediction machine that constantly anticipates what it will see, hear, and experience, and then updates its beliefs when those predictions are wrong.

The posts on this blog are organized into several connected sections:

Predictive Coding — Core Concepts Introduction to the predictive brain framework and why perception is more than just bottom-up input.

Memory & Expectation — Predicting What Comes Next How prior experience and stored knowledge guide perception and interpretation.

Visual Illusions — When the Brain’s Predictions Beat Reality Classic illusions (checker-shadow, hollow mask, Ponzo) that reveal the gap between the retinal image and what we perceive.

Auditory Illusions — Hearing What We Expect Examples like the McGurk Effect and phoneme restoration that show prediction in hearing.

Prediction Error & the Bayesian Brain How mismatches between expectation and input drive learning and model updating.

Attention, Expectation & Perceptual Errors Inattentional blindness, change blindness, and how selective attention shapes what we notice.

Motor Prediction — The Brain’s Internal Simulator How the brain predicts the consequences of our movements and why actions feel smooth and automatic.

Reflection — Seeing the Mind Seeing Final reflections on what this project taught me about perception, prediction, and my own thinking.

Together, these posts aim to show that perception is not a simple recording of reality, but an active process where the brain constantly predicts, tests, and revises its model of the world.

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Motor Prediction — The Brain’s Internal Simulator

Motor Prediction — The Brain’s Internal Simulator

The brain predicts the consequences of movement before the movement happens. This allows actions to feel smooth, controlled, and automatic.

The cerebellum builds “forward models” that simulate the expected outcome of a movement. When reality matches prediction, movement feels effortless. When prediction fails, movements feel clumsy or inaccurate.

⚡ Examples

Why tickling yourself doesn’t work (the brain predicts the sensation).

Why athletes anticipate ball direction before it moves.

How motor learning uses prediction error to refine skills.

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Reflection — Seeing the Mind Seeing

Working through the Deep Dive project changed the way I think about perception. I used to imagine the brain as a processor that responded to the world moment-by-moment. Now I understand perception as a prediction cycle, shaped by experience, memory, and expectation. The brain constantly tests its assumptions, corrects its errors, and updates its understanding of reality.

This project helped me appreciate how much of what we “see” is actually generated internally. Our experiences influence our expectations, and those expectations guide attention, interpretation, and behavior. Predictive processing explains why illusions trick us, why we sometimes miss important details, and why we react automatically in familiar situations.

Overall, this Deep Dive made cognitive psychology more personal: it showed me how my own mind interprets the world, and how awareness of these predictive mechanisms can help reduce bias, improve learning, and make perception more intentional.

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Real-World Applications — Cognitive Prediction in Daily Life

Predictive processing influences everyday behavior in subtle but powerful ways. For example, expectations shape what we hear in conversations, making speech comprehension faster but also prone to misunderstanding. In visual perception, the brain fills in details we never consciously notice, allowing us to move efficiently through complex environments.

Prediction also plays a role in emotional and social situations. The brain anticipates others’ reactions, making social interactions smoother — but also leading to anxiety when predictions are negative or uncertain. In learning, prediction errors help refine skills as the brain continually adjusts its internal model based on feedback (Fenn & Hambrick 2011).

Even habits operate through predictive loops: the brain expects certain outcomes and triggers behaviors automatically. By understanding how these predictions work, we can become more aware of how perception, memory, and action interact to shape real-world decisions.

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Memory & Expectation — Predicting What Comes Next

Memory plays a central role in predictive processing. The brain uses stored experiences to anticipate patterns, fill in missing information, and guide perception. Instead of reacting to the present moment from scratch, we interpret the world by referencing prior knowledge — what we have seen, heard, and learned before (Rao & Ballard 1999).

This helps explain why two people can view the same event and perceive it differently: different memories create different expectations. It also clarifies why familiar environments feel easier to navigate — the brain can predict what will happen with greater accuracy.

Prediction is most visible in ambiguous situations. When sensory information is weak or noisy, the brain leans heavily on memory to “guess” what’s happening. These guesses feel automatic, but they shape everything from reading text quickly to recognizing faces to responding to potential threats. Understanding these memory-based predictions helps explain errors such as false memories, biases, and perceptual distortions.

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Auditory Illusions — When Hearing Deceives the Brain

Auditory Illusions — Hearing What We Expect

Auditory Illusions — Hearing What We Expect

Just like vision, hearing is shaped heavily by prediction. Auditory illusions show that the brain fills in missing sound, corrects noise, and interprets speech using expectation.

🗣 1. McGurk Effect

When the sound “ba” is paired with a video of lips saying “fa,” people hear “fa.” Vision rewrites hearing.

Why it matters: the brain predicts speech using multi-sensory integration, not sound alone.

🔊 2. Phoneme Restoration Effect

A missing sound in a word is “filled in” by the brain even though the sound never occurred.

Example: “It was found that the *cough* eel was on the axle.” You hear “wheel,” but the “w” never existed.

🎵 3. Shepard Tone (the “infinite” pitch)

A tone seems to rise forever, but never actually gets higher.

This illusion shows how the brain predicts musical structure and continuity.