Editing Attention and Perception (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Simulating a Visual Search Task (Feature vs. Conjunction Search):'''
<syntaxhighlight lang="python">
import random
import time

def simulate_visual_search(n_items, search_type='feature'):
    """
    Simulates the reaction time logic of a visual search.
    'feature': Search for a red 'O' among green 'O's (Parallel).
    'conjunction': Search for a red 'O' among green 'O's and red 'X's (Serial).
    """
    # Feature search is independent of set size
    if search_type == 'feature':
        base_rt = 400 # ms
        noise = random.uniform(0, 20)
        return base_rt + noise
    
    # Conjunction search increases linearly with set size
    elif search_type == 'conjunction':
        base_rt = 400
        per_item_cost = 20 # ms per item scanned
        return base_rt + (n_items * per_item_cost) + random.uniform(0, 50)

# Compare set sizes 10 vs 50
print(f"Feature Search (10 items): {simulate_visual_search(10, 'feature'):.1f}ms")
print(f"Feature Search (50 items): {simulate_visual_search(50, 'feature'):.1f}ms")
print(f"Conjunction Search (10 items): {simulate_visual_search(10, 'conjunction'):.1f}ms")
print(f"Conjunction Search (50 items): {simulate_visual_search(50, 'conjunction'):.1f}ms")
# Feature search time is flat; conjunction search time scales with complexity.
</syntaxhighlight>

; Design Applications
: '''User Interface (UI) Design''' → Using Gestalt principles (grouping, contrast) to guide user attention.
: '''Aviation/Driving''' → Understanding the limits of divided attention to prevent "cognitive tunneling."
: '''Advertising''' → Using "bottom-up" salience (bright colors, movement) to capture reflexive attention.
: '''Gaming''' → Managing cognitive load so players don't miss critical cues (inattentional blindness).
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