Editing Memory Learning (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Simulating the Rescorla-Wagner Model of Classical Conditioning:'''
<syntaxhighlight lang="python">
def simulate_conditioning(n_trials, alpha, beta, lambda_val):
    """
    Rescorla-Wagner model: V_next = V + alpha * beta * (lambda - V)
    V: Current associative strength
    alpha: Salience of CS (stimulus)
    beta: Salience of US (reward/unconditioned stimulus)
    lambda: Maximum possible associative strength
    """
    v = 0
    history = []
    for _ in range(n_trials):
        delta_v = alpha * beta * (lambda_val - v)
        v += delta_v
        history.append(v)
    return history

# Simulate learning progress
# High salience (fast learning)
fast_learner = simulate_conditioning(20, 0.5, 0.5, 1.0)
# Low salience (slow learning)
slow_learner = simulate_conditioning(20, 0.1, 0.5, 1.0)

print(f"Fast Learner after 5 trials: {fast_learner[4]:.2f}")
print(f"Slow Learner after 5 trials: {slow_learner[4]:.2f}")
# The result follows a negatively accelerated learning curve.
</syntaxhighlight>

; Strategies for Better Learning
: '''Spaced Repetition''' → Reviewing material at increasing intervals to combat the "forgetting curve."
: '''Retrieval Practice''' → Actively testing yourself rather than passively re-reading.
: '''Elaboration''' → Connecting new info to existing knowledge (semantic encoding).
: '''Interleaving''' → Mixing different topics or skills during a single study session.
: '''Dual Coding''' → Using both verbal and visual information to encode concepts.
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