#termproject

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.

Prediction Error — How the Brain Learns

Prediction error is the difference between what the brain expects and what actually happens. Predictive coding theory proposes that the brain constantly updates its model of the world by minimizing these errors (Friston 2005).

When predictions match reality → processing becomes efficient. When predictions fail → the brain updates its internal model.

🔢 Bayesian Brain Model

The brain combines:

Priors (what it expects)

Likelihood (the current sensory input)

Posterior (updated understanding)

Perception = Prior × Evidence → Updated Belief

🌫 Why Illusions Cause Large Prediction Errors

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.