def extract_percept_descriptions(context_text):
   tokens = [t.text.lower() for t in tokenize(context_text) if t.text.isalpha()]
   lemmas = [morph.parse(w)[0].normal_form for w in tokens]
    
    percept_positions = [i for i, l in enumerate(lemmas) if l in percept_lemmas]
    
    descriptions = {'visual': [], 'auditory': [], 'emotional': []}
    
        for pos in percept_positions:
        start = max(0, pos - 5)
        end = min(len(lemmas), pos + 6)
        window_lemmas = lemmas[start:end]
        
        for l in window_lemmas:
            if l in visual_lemmas:
                descriptions['visual'].append(l)
            elif l in auditory_lemmas:
                descriptions['auditory'].append(l)
            elif l in emotional_lemmas:
                descriptions['emotional'].append(l)