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Related Experiment Video

Updated: May 21, 2026

A Naturalistic Setup for Presenting Real People and Live Actions in Experimental Psychology and Cognitive Neuroscience Studies
07:43

A Naturalistic Setup for Presenting Real People and Live Actions in Experimental Psychology and Cognitive Neuroscience Studies

Published on: August 4, 2023

Proactive action preparation: seeing action preparation as a continuous and proactive process.

Giovanni Pezzulo1, Dimitri Ognibene

  • 1Istituto di Linguistica Computazionale Antonio Zampolli, CNR, Via Giuseppe Moruzzi, Pisa, Italy.

Motor Control
|May 31, 2012
PubMed
Summary
This summary is machine-generated.

Action preparation involves intertwined processes, challenging serial models. A parallel model suggests proactive preparation, using prior knowledge before decisions, is advantageous, as shown in a simulated tennis task.

Related Experiment Videos

Last Updated: May 21, 2026

A Naturalistic Setup for Presenting Real People and Live Actions in Experimental Psychology and Cognitive Neuroscience Studies
07:43

A Naturalistic Setup for Presenting Real People and Live Actions in Experimental Psychology and Cognitive Neuroscience Studies

Published on: August 4, 2023

Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Motor Control

Background:

  • Traditional serial models of goal-directed action propose distinct phases of preparation and control.
  • Recent evidence suggests intertwined representational and neural processes challenge the serial model.
  • A parallel model of action specification and selection offers an alternative framework.

Purpose of the Study:

  • To elucidate action preparation processes from conceptual and computational perspectives.
  • To contrast serial and parallel models of action preparation.
  • To investigate the concept and computational implementation of proactive action preparation.

Main Methods:

  • Review of traditional serial models of action preparation.
  • Analysis of evidence for intertwined subprocesses in action preparation.
  • Development and simulation of a computational model for proactive action preparation.

Main Results:

  • The serial model's validity is undermined by evidence of intertwined subprocesses.
  • A parallel model better explains action specification and selection.
  • Proactive action preparation, utilizing prior knowledge before decisions, offers advantages.
  • Computational implementation demonstrated benefits in a simulated task.

Conclusions:

  • Action preparation is better described by a parallel rather than a serial model.
  • Proactive action preparation, leveraging prior knowledge, can occur before explicit decisions.
  • Computational modeling supports the advantages of proactive action preparation in optimizing action selection.