Flipping a coin in your head without monitoring outcomes? Comments on predicting free choices and a demo program (original) (raw)
Related papers
2021
According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refining these predictions through error signals. Despite extensive research investigating the neural bases of this theory, to date no previous study has systematically attempted to define the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We first use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in taskdriven attention and execution. In sum, we find that: (i) predictive processing seems to occur more in certain brain regions than others, when considering different sensory modalities at a time; (ii) there is no evidence, at the network level, for a distinction between error and prediction processing. According to the theory of predictive coding (PC) 1-5 , our brain constantly attempts to model the probability of its own future states, with the goal of minimizing uncertainty 4. More specifically, the brain is considered a hierarchically organized system where, at each level of processing, higher layers try to predict the latent causes of the sensory input coming from lower layers 6,7. Thus, neurons at higher levels encode predictions about the upcoming signal, which is continuously compared with the effective signal received from lower levels. Through this comparison, the brain either reinforces existing predictions or it updates them, if these do not match the incoming signal 8. When predictions are violated, a prediction error signal 5,9,10 is fed back to the neurons encoding predictions. These recursive loops of predictions and error signals ultimately allow the individual to maintain up-to-date representations about its own internal states 11 and the surrounding external stimuli. Over the past two decades, PC theory has received extensive support from a vast range of theoretical and experimental studies, both in relation to primary sensory processes 5,12-14 and higher level cognitive processes 15,16 , such as decision making and naturalistic speech comprehension 14,17,18. Moreover, evidence has been obtained with a variety of methods, mostly with functional magnetic resonance imaging (fMRI), but also electroencephalography 19-21 , computational simulations 22 , transcranial magnetic stimulation 23 , and physiological recordings of single neurons (for a review, see 24). Since 1999, when Rao and Ballard published their seminal simulation work on predictive coding in the visual cortex 5 , there has been a proliferation of attempts to implement PC in the human brain. Initially, it was argued that predictive processing occurs at the cellular level 25 , where the activity of neural populations is modulated by higher-order predictions and units signalling precision of those predictions. According to Bastos and colleagues 26 , PC is a typical property of the human cerebral neocortex because its structure suits a hierarchical signal exchange between cortical layers. In particular, error signals seem to be computed in the granular layers (especially layer IV), while predictions would be encoded in layers II and III 26. These mechanisms have been identified in a large set of brain areas, including the primary sensory and motor cortices, motor association cortices, dorsal and ventral prefrontal cortices, parietal cortex, anterior cingulate cortex, insula, hippocampus, amygdala, basal ganglia, thalamus, hypothalamus, cerebellum and the superior colliculus 27,28. However, in all these regions, neuronal
bioRxiv (Cold Spring Harbor Laboratory), 2023
The default mode network (DMN) is a collection of brain regions including midline frontal and parietal structures, medial and lateral temporal lobes, and lateral parietal cortex. Although there is evidence that the network can be subdivided into at least two subcomponents, the network reliably exhibits highly correlated activity both at rest and during task performance. Current understanding regarding the function of the DMN rests on a large body of research indicating that activity in the network decreases during task epochs of experimental paradigms relative to inter-trial intervals. A seeming contradiction arises when the experimental paradigm includes tasks involving autobiographical memory, thinking about one's self, planning for the future, or social cognition. In such cases, the DMN's activity increases and is correlated with attentional networks. Some have therefore concluded that the DMN supports advanced human cognitive abilities such as interoceptive processing and theory of mind. This conclusion may be called into question by evidence of correlated activity in homologous brain regions in other, even non-primate, species. Thus, there are contradictory findings related to the function of the DMN that have been difficult to integrate into a coherent theory regarding its function. Using data from the Human Connectome Project, we explore the temporal dynamics of activity in different regions of the DMN in relation to stimulus presentation. We show that generally the dorsal portion of the network exhibits only a transient initial decrease in activity at the start of trials that increases over trial duration. The ventral component often has more similarity in its time course to that of task-activated areas. We propose that task-associated ramping dynamics in the network are incompatible with a task-negative view of the DMN and propose the dorsal and ventral sub-components of network may rather work together to support bottom-up salience detection and subsequent top-down voluntary action. In this context, we re-interpret the body of anatomical and neurophysiological experimental evidence, arguing that this interpretation can accommodate the seeming contradictions regarding DMN function in the extant literature. .
Predicting free choices for abstract intentions
Proceedings of the National Academy of Sciences of the United States of America, 2013
Unconscious neural activity has been repeatedly shown to precede and potentially even influence subsequent free decisions. However, to date, such findings have been mostly restricted to simple motor choices, and despite considerable debate, there is no evidence that the outcome of more complex free decisions can be predicted from prior brain signals. Here, we show that the outcome of a free decision to either add or subtract numbers can already be decoded from neural activity in medial prefrontal and parietal cortex 4 s before the participant reports they are consciously making their choice. These choice-predictive signals co-occurred with the so-called default mode brain activity pattern that was still dominant at the time when the choice-predictive signals occurred. Our results suggest that unconscious preparation of free choices is not restricted to motor preparation. Instead, decisions at multiple scales of abstraction evolve from the dynamics of preceding brain activity. free will | Libet | self-paced T he subjective experience that our voluntary actions are initiated in the conscious mind has been challenged by the finding that the human brain may already start shaping spontaneous decisions even before they enter into conscious awareness (1, 2). Specifically, the human brain can start preparing spontaneous movements up to several seconds before a person believes themselves to be consciously making a decision to move (1-3).
The Journal of …, 2010
Prior knowledge of the probabilities concerning decision alternatives facilitates the selection of more likely alternatives to the disadvantage of others. The neural basis of prior probability (PP) integration into the decision-making process and associated preparatory processes is, however, still essentially unknown. Furthermore, trial-to-trial fluctuations in PP processing have not been considered thus far. In a previous study, we found that the amplitude of the contingent negative variation (CNV) in a precueing task is sensitive to PP information (Scheibe et al., 2009). We investigated brain regions with a parametric relationship between neural activity and PP and those regions involved in PP processing on a trial-to-trial basis in simultaneously recorded electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data. Conventional fMRI analysis focusing on the information content of the probability precue revealed increasing activation of the posterior medial frontal cortex with increasing PP, supporting its putative role in updating action values. EEG-informed fMRI analysis relating single-trial CNV amplitudes to the hemodynamic signal addressed trial-to-trial fluctuations in PP processing. We identified a set of regions mainly consisting of frontal, parietal, and striatal regions that represents unspecific response preparation on a trial-to-trial basis. A subset of these regions, namely, the dorsolateral prefrontal cortex, the inferior frontal gyrus, and the inferior parietal lobule, showed activations that exclusively represented the contributions of PP to the trial-to-trial fluctuations of the CNV.
IEEE Reviews in Biomedical Engineering, 2000
Advances in neural signal and image acquisition as well as in multivariate signal processing and machine learning are enabling a richer and more rigorous understanding of the neural basis of human decision-making. Decision-making is essentially characterized behaviorally by the variability of the decision across individual trials-e.g., error and response time distributions. To infer the neural processes that govern decision-making requires identifying neural correlates of such trial-to-trial behavioral variability. In this paper, we review efforts that utilize signal processing and machine learning to enable single-trial analysis of neural signals acquired while subjects perform simple decision-making tasks. Our focus is on neuroimaging data collected noninvasively via electroencephalograpy (EEG) and functional magnetic resonance imaging (fMRI). We review the specific framework for extracting decision-relevant neural components from the neuroimaging data, the goal being to analyze the trial-to-trial variability of the neural signal along these component directions and to relate them to elements of the decision-making process. We review results for perceptual decision-making and discrimination tasks, including paradigms in which EEG variability is used to inform an fMRI analysis. We discuss how single-trial analysis reveals aspects of the underlying decision-making networks that are unobservable using traditional trial-averaging methods.
Scientific Reports
According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refining these predictions through error signals. Despite extensive research investigating the neural bases of this theory, to date no previous study has systematically attempted to define the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We first use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in t...
Abstract neural choice signals during action-linked decisions
2020
Humans can make abstract choices independent of motor actions. However, in laboratory tasks, choices are typically reported with an associated action. Consequentially, knowledge about the neural representation of abstract choices is sparse, and choices are often thought to evolve as motor intentions. Here, we show that in the human brain, perceptual choices are represented in an abstract, motor-independent manner, even when they are directly linked to an action. We measured MEG signals while participants made choices with known or unknown motor response mapping. Using multivariate decoding, we quantified stimulus, perceptual choice and motor response information with distinct cortical distributions. Choice representations were invariant to whether the response mapping was known during stimulus presentation, and they occupied distinct representational spaces from both stimulus and motor signals. Furthermore, their strength predicted decision confidence and accuracy, as expected from ...
Neuron, 2013
In the study of decision making, emphasis is placed on different forms of perceptual integration, while the influence of other factors, such as memory, is ignored. In addition, it is believed that the information underlying decision making is carried in the rate of the neuronal response, while its variability is considered unspecific. Here we studied the influence of recent experience on motor decision making by analyzing the activity of neurons in the dorsal premotor area of two monkeys performing a countermanding arm task. We observe that the across-trial variability of the neural response strongly correlates with trial history-dependent changes in reaction time. Using a theoretical model of decision making, we show that a trial history-monitoring signal can explain the observed behavioral and neural modulation. Our study reveals that, in the neural processes that culminate in motor plan maturation, the evidence provided by perception and memory is reflected in mean rate and variance respectively.