Flipping a coin in your head without monitoring outcomes? Comments on predicting free choices and a demo program

Mistaking opposition for autonomy: psychophysical studies on detecting choice bias

Do people know when they act freely and autonomously versus when their actions are influenced? While the human aspiration to freedom is widespread, little research has investigated how people perceive whether their choices are biased. Here, we explored how actions congruent or incongruent with suggestions are perceived as influenced or free. Across three experiments, participants saw directional stimuli cueing left or right manual responses. They were instructed to follow the cue's suggestion, oppose it or ignore it entirely to make a 'free' choice. We found that we could bias participants' 'free responses' towards adherence or opposition, by making one instruction more frequent than the other. Strikingly, participants consistently reported feeling less influenced by cues to which they responded incongruently, even when response habits effectively biased them towards such opposition behaviour. This effect was so compelling that cues that were frequently presented with the Oppose instruction became systematically judged as having less influence on behaviour, artificially increasing the sense of freedom of choice. Taken together, these findings demonstrate that acting contrarian distorts the perception of autonomy. Crucially, we demonstrate the existence of a novel illusion of freedom evoked by trained opposition. Our results have important implications for understanding mechanisms of persuasion.

Instruction effects on randomness in sequence generation

Randomness is a fundamental property of human behavior. It occurs both in the form of intrinsic random variability, say when repetitions of a task yield slightly different behavioral outcomes, or in the form of explicit randomness, say when a person tries to avoid being predicted in a game of rock, paper and scissors. Randomness has frequently been studied using random sequence generation tasks (RSG). A key finding has been that humans are poor at deliberately producing random behavior. At the same time, it has been shown that people might be better randomizers if randomness is only an implicit (rather than an explicit) requirement of the task. We therefore hypothesized that randomization performance might vary with the exact instructions with which randomness is elicited. To test this, we acquired data from a large online sample (n = 388), where every participant made 1,000 binary choices based on one of the following instructions: choose either randomly, freely, irregularly, according to an imaginary coin toss or perform a perceptual guessing task. Our results show significant differences in randomness between the conditions as quantified by conditional entropy and estimated Markov order. The randomization scores were highest in the conditions where people were asked to be irregular or mentally simulate a random event (coin toss) thus yielding recommendations for future studies on randomization behavior.

Decoding the contents and strength of imagery before volitional engagement

Is it possible to predict the freely chosen content of voluntary imagery from prior neural signals? Here we show that the content and strength of future voluntary imagery can be decoded from activity patterns in visual and frontal areas well before participants engage in voluntary imagery. Participants freely chose which of two images to imagine. Using functional magnetic resonance (fMRI) and multi-voxel pattern analysis, we decoded imagery content as far as 11 seconds before the voluntary decision, in visual, frontal and subcortical areas. Decoding in visual areas in addition to perception-imagery generalization suggested that predictive patterns correspond to visual representations. Importantly, activity patterns in the primary visual cortex (V1) from before the decision, predicted future imagery vividness. Our results suggest that the contents and strength of mental imagery are influenced by sensory-like neural representations that emerge spontaneously before volition.

Proactive Recruitment of Frontoparietal and Salience Networks for Voluntary Decisions

There is evidence that neural patterns are predictive of voluntary decisions, but findings come from paradigms that have typically required participants to make arbitrary choices decisions in highly abstract experimental tasks. It remains to be seen whether proactive neural activity reflects upcoming choices for individuals performing decisions in more complex, dynamic, scenarios. In this functional Magnetic Resonance Imaging (fMRI) study, we investigated proactive neural activity for voluntary decisions compared with instructed decisions in a virtual environment, which more closely mimicked a real-world decision. Using partial least squares (PLS) analysis, we found that the frontoparietal and salience networks were associated with voluntary choice selection from a time at which decisions were abstract and preceded external stimuli. Using multi-voxel pattern analysis (MVPA), we showed that participants’ choices, which were decodable from motor and visual cortices, could be predicted with lower accuracy for voluntary decisions than for instructed decisions. This corresponded to eye-tracking data showing that participants made a greater number of fixations to alternative options during voluntary choices, which might have resulted in less stable choice representations. These findings suggest that voluntary decisions engage proactive choice selection, and that upcoming choices are encoded in neural representations even while individuals continue to consider their options in the environment.

Free Will and Neuroscience: From Explaining Freedom Away to New Ways of Operationalizing and Measuring It

The concept of free will is hard to define, but crucial to both individual and social life. For centuries people have wondered how freedom is possible in a world ruled by physical determinism; however, reflections on free will have been confined to philosophy until half a century ago, when the topic was also addressed by neuroscience. The first relevant, and now well-known, strand of research on the brain correlates of free will was that pioneered by Libet et al. (1983), which focused on the allegedly unconscious intentions taking place in decisions regarded as free and voluntary. Libet’s interpretation of the so-called readiness potential (RP) seems to favor a sort of deflation of freedom (Soon et al., 2008). However, recent studies seem to point to a different interpretation of the RP, namely that the apparent build-up of the brain activity preceding subjectively spontaneous voluntary movements (SVM) may reflect the ebb and flow of the background neuronal noise, which is triggered by many factors (Schurger et al., 2016). This interpretation seems to bridge the gap between the neuroscientific perspective on free will and the intuitive, commonsensical view of it (Roskies, 2010b), but many problems remain to be solved and other theoretical paths can be hypothesized. The article therefore, proposes to start from an operationalizable concept of free will (Lavazza and Inglese, 2015) to find a connection between higher order descriptions (useful for practical life) and neural bases. This new way to conceptualize free will should be linked to the idea of “capacity”: that is, the availability of a repertoire of general skills that can be manifested and used without moment by moment conscious control. The capacity index, which is also able to take into account the differences of time scales in decisions, includes reasons-responsiveness and is related to internal control, understood as the agent’s ownership of the mechanisms that trigger the relevant behavior. Cognitive abilities, needed for one to have capacity, might be firstly operationalized as a set of neuropsychological tests, which can be used to operationalize and measure specific executive functions, as they are strongly linked to the concept of control. Subsequently, a free will index would allow for the search of the underlying neural correlates of the capacity exhibited by people and the limits in capacity exhibited by each individual.