How active perception and attractor dynamics shape perceptual categorization: A computational model

Nicola Catenacci Volpi, Jean Charles Quinton, Giovanni Pezzulo

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    4 Citations (Scopus)
    31 Downloads (Pure)


    We propose a computational model of perceptual categorization that fuses elements of grounded and sensorimotor theories of cognition with dynamic models of decision-making. We assume that category information consists in anticipated patterns of agent–environment interactions that can be elicited through overt or covert (simulated) eye movements, object manipulation, etc. This information is firstly encoded when category information is acquired, and then re-enacted during perceptual categorization. The perceptual categorization consists in a dynamic competition between attractors that encode the sensorimotor patterns typical of each category; action prediction success counts as ‘‘evidence’’ for a given category and contributes to falling into the corresponding attractor. The evidence accumulation process is guided by an active perception loop, and the active exploration of objects (e.g., visual exploration) aims at eliciting expected sensorimotor patterns that count as evidence for the object category. We present a computational model incorporating these elements and describing action prediction, active perception, and attractor dynamics as key elements of perceptual categorizations. We test the model in three simulated perceptual categorization tasks, and we discuss its relevance for grounded and sensorimotor theories of cognition.
    Original languageEnglish
    Pages (from-to)1-16
    Number of pages16
    JournalNeural Networks
    Early online date23 Jul 2014
    Publication statusE-pub ahead of print - 23 Jul 2014


    • Hopfield networks
    • Perceptual categorization
    • Prediction
    • Active vision
    • Dynamic choice


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