Microgravity induces overconfidence in perceptual decision-making

Does gravity affect decision-making? This question comes into sharp focus as plans for interplanetary human space missions solidify. In the framework of Bayesian brain theories, gravity encapsulates a strong prior, anchoring agents to a reference frame via the vestibular system, informing their decisions and possibly their integration of uncertainty. What happens when such a strong prior is altered? We address this question using a self-motion estimation task in a space analog environment under conditions of altered gravity. Two participants were cast as remote drone operators orbiting Mars in a virtual reality environment on board a parabolic flight, where both hyper- and microgravity conditions were induced. From a first-person perspective, participants viewed a drone exiting a cave and had to first predict a collision and then provide a confidence estimate of their response. We evoked uncertainty in the task by manipulating the motion's trajectory angle. Post-decision subjective confidence reports were negatively predicted by stimulus uncertainty, as expected. Uncertainty alone did not impact overt behavioral responses (performance, choice) differentially across gravity conditions. However microgravity predicted higher subjective confidence, especially in interaction with stimulus uncertainty. These results suggest that variables relating to uncertainty affect decision-making distinctly in microgravity, highlighting the possible need for automatized, compensatory mechanisms when considering human factors in space research.Comment: 12 pages, 10 figure

Belief as a Wise Wager:the Neural Representation of Uncertainty, Surprise and Confidence Across Cognitive and Perceptual Domains

From the moment we wake up in the morning to the day's ebb when we settle in to sleep, we are bound to the task of decision-making. Some of these decisions barely register in our consciousness, if at all, while others, less shy, take a more prominent place at our mind's table. Regardless of the importance or difficulty of the decision, few, if any, are made with perfect information: being such a small of part of a large system, we can only know so much. Further, the system itself sends us noisy information for us to encode and decode as best we can. How do we do this? How do we continuously and, for the most part successfully, resolve uncertainty in order to survive and even flourish? We propose to define uncertainty in decision-making as a computational process, in line with information-processing theories of neural mechanisms. To that end, we investigate the neural correlates of uncertainty processing using functional magnetic resonance imaging (fMRI) in humans within a predictive coding framework. The field has already produced considerable evidence showing that decisions are made with the aim of maximizing utility, a process involving the dopaminergic reward system. We turn our focus to the uncertainty surrounding predictions and their concomitant errors by conducting a two-part fMRI experiment on 23 subjects. In the first session, we elicited objective, cognitive (financial) uncertainty in a gambling task. In the second session, we exposed individuals to subjective, perceptual uncertainty, in the form of visual illusions. Our fMRI results, modeled by computational definitions of surprise, confidence and information, show that 1) the brain employs computational principles to resolve uncertainty; 2) certain regions are consistently implicated in processing said uncertainty, notably insular cortex regions, across modalities (cognitive and perceptual), be it of a subjective or objective nature. These findings support the notion that the brain is an active inference machine, a paradigm within which further aspects of cognition can be investigated

Leyla Loued-Khenissi's Quick Files

The Quick Files feature was discontinued and it’s files were migrated into this Project on March 11, 2022. The file URL’s will still resolve properly, and the Quick Files logs are available in the Project’s Recent Activity

Apathy and noradrenaline; silent partners to mild cognitive impairment in parkinsons's disease?

The search for PD-MCI biomarkers has employed an array of neuroimaging techniques, but still yields divergent findings. This may be due in part to MCI's broad definition, encompassing heterogeneous cognitive domains, only some of which are affected in Parkinson's disease. Most domains falling under the MCI umbrella include fronto-dependent executive functions, whereas others, notably learning, rely on the basal ganglia. Given the deterioration of the nigrostriatal dopaminergic system in Parkinson's disease, it has been the prime target of PD-MCI investigation. By testing well defined cognitive deficits in Parkinson's disease, distinct functions can be attributed to specific neural systems, overcoming conflicting results on PD-MCI. Apart from dopamine, other systems such as the neurovascular or noradrenergic systems are affected in Parkinson's disease. These factors may be at the basis of specific facets of PD-MCI for which dopaminergic involvement has not been conclusive. Finally, the impact of both dopaminergic and noradrenergic deficiency on motivational states in Parkinson's disease is examined in light of a plausible link between apathy and cognitive deficits

Information Theoretic Characterization of Uncertainty Distinguishes Surprise From Accuracy Signals in the Brain

Uncertainty presents a problem for both human and machine decision-making. While utility maximization has traditionally been viewed as the motive force behind choice behavior, it has been theorized that uncertainty minimization may supersede reward motivation. Beyond reward, decisions are guided by belief, i.e., confidence-weighted expectations. Evidence challenging a belief evokes surprise, which signals a deviation from expectation (stimulus-bound surprise) but also provides an information gain. To support the theory that uncertainty minimization is an essential drive for the brain, we probe the neural trace of uncertainty-related decision variables, namely confidence, surprise, and information gain, in a discrete decision with a deterministic outcome. Confidence and surprise were elicited with a gambling task administered in a functional magnetic resonance imaging experiment, where agents start with a uniform probability distribution, transition to a non-uniform probabilistic state, and end in a fully certain state. After controlling for reward expectation, we find confidence, taken as the negative entropy of a trial, correlates with a response in the hippocampus and temporal lobe. Stimulus-bound surprise, taken as Shannon information, correlates with responses in the insula and striatum. In addition, we also find a neural response to a measure of information gain captured by a confidence error, a quantity we dub accuracy. BOLD responses to accuracy were found in the cerebellum and precuneus, after controlling for reward prediction errors and stimulus-bound surprise at the same time point. Our results suggest that, even absent an overt need for learning, the human brain expends energy on information gain and uncertainty minimization

Echoes of the Abhidamma in the Component Process Model of Emotion

The empirical study of emotion is a comparatively young endeavor, long left to the wayside in favor of more tangible psychological phenomena. In the past few decades however several theories have emerged, examining emotion in the context of reason or cognition, accelerating a cultural shift in how we view the phenomenon. This development, in part facilitated by technological advances in neuroscience, has nudged emotions out of their empirical rut. But emotions themselves have been with us all along, embedded in our makeup, molding our consciousness and our interactions with people and the world. While research on emotions is relatively new in empirical domains, they have long been studied elsewhere, notably in the Buddhist Abhidhamma. The Buddhist study of mental phenomena encapsulated in the Abhidhamma is not merely descriptive but a systematic, hierarchical classification of the experiential, including emotion, grounded in theory. These mental factors bear a likeness to components of modern, process theories of emotion. In this paper, the similarities between the Buddhist theory of mental factors and the component process model of emotion will be highlighted as an example of likeness between Buddhist and modern Western psychologies. This comparative exercise serves a broader aim to identify the Abhidhamma as a potential repository of theories from which modern-day empirical hypotheses can be derived

Gambling on Others’ Health: Risky Pro-social Decision-Making in the Era of Covid19

How does cost and uncertainty shape an ordinary person’s action towards a stranger’s wellbeing? During the Covid19 pandemic, individuals were asked to perform costly actions to reduce harm to strangers, even while the general population, including authorities and experts, grappled with the uncertainty surrounding the novel virus. Many researches have examined health decision-making by experts, but the study of lay, non-expert, individuals decision-making on a stranger’s health has been left to the wayside, as ordinary citizens are usually not tasked with such decisions. We sought to capture a snapshot of this specific choice behavior by administering two surveys to the general population in the early days of the Covid-19 pandemic. We presented respondents with hypothetical diseases of variable severity affecting either oneself, a beloved person or a stranger. Participants had to choose between treatments who could either lead to a certain mild improvement (sure option) or cure entirely the effected person at a given probability (risky option). Respondents preferred risky options overall, but their risk-seeking attitude decreased progressively the higher the expected severity of the disease. This pattern was observed regardless of the identity of recipient. Instead, distinctions between targets emerged when decisions were conditioned on treatment cost, with participants preferring cheaper options for strangers. Overall, these findings provide a descriptive model of individual risky decision-making for others; and inform on the limits of what can be asked of an individual in service to a stranger. Loued-Khenissi, L., & Corradi-Dell'Acqua, C. (2020). Gambling on Others’ Health: Risky Pro-social Decision-Making in the Era of Covid19. PsyArXiv, doi: 110.31234/osf.io/qrbz

An Overview of Functional Magnetic Resonance Imaging Techniques for Organizational Research

Functional magnetic resonance imaging is a galvanizing tool for behavioral scientists. It provides a means by which to see what the brain does while a person thinks, acts, or perceives, without invasive procedures. In this, fMRI affords us a relatively easy manner by which to peek under the hood of behavior and into the brain. Characterizing behavior with a neural correlate allows us to support or discard theoretical assumptions about the brain and behavior, to identify markers for individual and group differences. The increasing popularity of fMRI is facilitated by the apparent ease of data acquisition and analysis. This comes at a price: low signal-to-noise ratios, limitations in experimental design, and the difficulty in correctly applying and interpreting statistical tests are just a few of the pitfalls that have brought into question the reliability and validity of published fMRI data. Here, we aim to provide a general overview of the method, with an emphasis on fMRI and its analysis. Our goal is to provide the novice user with a comprehensive framework to get started on designing an imaging experiment in humans

Anterior insula reflects surprise in value-based decision-making and perception

The brain has been theorized to employ inferential processes to overcome the problem of uncertainty. Inference is thought to underlie neural processes, including in disparate domains such as value-based decision-making and perception. Value-based decision-making commonly involves deliberation, a time-consuming process that requires conscious consideration of decision variables. Perception, by contrast, is thought to be automatic and effortless. Both processes may call on a general neural system to resolve for uncertainty however. We addressed this question by directly comparing uncertainty signals in visual perception and an economic task using fMRI. We presented the same individuals with different versions of a bi-stable figure (Necker’s cube) and with a gambling task during fMRI acquisition. We experimentally varied uncertainty, either on perceptual state or financial outcome. We found that inferential errors indexed by a formal account of surprise in the gambling task yielded BOLD responses in the anterior insula, in line with earlier findings. Moreover, we found perceptual uncertainty and surprise in the Necker Cube task yielded similar responses in the anterior insula. These results suggest that uncertainty, irrespective of domain, correlates to a common brain region, the anterior insula. These findings provide empirical evidence that the brain interacts with its environment through inferential processes