Using a Veto paradigm to investigate the decision models in explaining Libet-style experiments

The question of whether free will exists or not has intrigued philosophers for centuries. About 40 years ago, cognitive neuroscientists such as Benjamin Libet have joined the discussion by demonstrating that an ERP component, the readiness potential (RP), precedes the reported time of decision to act by a few hundred milliseconds. Libet et al. (1983) argued that our brains unconsciously prepare the movement before we experience any conscious intention, which led some free will skeptics (e.g., Ebert & Wegner, 2011) to argue that free will does not exist. While Libet’s interpretation of his findings initiated an intense philosophical debate, alternative interpretations have been put forward more recently (Bode et al., 2014, Brass et al., 2019, Schurger et al., 2012, Schurger et al., 2021). Integration to bound models (ITB) of Libet-style experiments suggest that we accumulate information until an intention threshold is reached, which triggers our experience of intention and execution of voluntary behaviors. The RP, from this perspective reflects the decision process itself rather than the consequence of an unconscious decision. To determine if the ITB model better predicts behavioral patterns in Libet-style experiments, we added a whether-component to the classical Libet task (the Veto Libet task) and compared the behavioral measures in the Veto Libet task with the Classical Libet task. We hypothesized that the signal accumulation in the Veto Libet task would be less steep than in the Classical Libet task, resulting in longer wait times and earlier self-reported intentions to act (i.e., the W). The result in general supported our hypotheses. In addition, these behavioral differences between the Classical Libet task and the Veto Libet task established valuable behavioral correlates for future investigations into the vetoing phenomenon. Finally, this study was also the first application of the Libet task in an online setting, and the behavioral parameters were highly comparable to the previous offline studies, further supporting the possibility of using the online platform to study arbitrary decision-making.