Modern theories of behavioral control converge onto the idea that goal-directed/voluntary behaviors are intimately tied to the evaluation of resources. According to this idea, all moving animals select those actions that, based on a subjective value assigned to them, are most likely to lead to the greatest reward. These computations are at the root of behavioral flexibility. Of key relevance in the decision-making processes that underlie action selection are those stimuli bearing emotional contents. However, even though it is acknowledged that emotional information affects behavioral control, the exact way in which emotions impact on action and perception, including the underlying neural processes, is largely unknown. Research on such phenomena is clinically relevant, as impairments in the integration of emotional information with ongoing motor/cognitive processes are potential biomarkers of psychiatric and neurological disorders. Combining advanced methods in psychophysics, neurophysiology, neuropsychology, and neuroimaging, the present project aims to shed light on this issue from novel perspectives. Data obtained from our experiments will provide new information that will improve our understanding of the basic mechanisms of decision-making subserving voluntary behavior, perceptual processing outside voluntary control, and the clinical management of disorders in which the processing of emotional information is impaired.
The present project has the potentiality to impact social, medical, scientific, economic and technological fields.
Humans stand out in their ability to make sense of others' behavior and to establish appropriate social bonds with the other people sharing their environment. The ability to generate appropriate responses, especially in social contexts, often requires the integration of emotional information with ongoing cognitive processes. On the one hand, facial expressions have a key role in social cognition, as reading emotions on the other's face and assigning them appropriate values is fundamental for effective social interactions [1]. On the other hand, affective states induced by the environmental context also exert a deep influence on the way we interact with others, as they can deeply affect our overall tendency for being empathetic. Therefore, understanding how the interactions between emotion and cognition take place will represent a milestone toward the comprehension of pathologies affecting social cognition characterized by deep alterations of interpersonal relationships such as Parkinson's disease (PD), autism spectrum disorders, bipolar disorder anxiety, antisocial and sociopathic personality disorders. These pathologies have a high prevalence in industrialized countries and, therefore, they have an extremely high social and economic impact, which needs to be reduced by the discovery of better treatments. By combining behavioral, neuropsychological and electrophysiological data we will provide a core knowledge about decision making processes underlying voluntary behavior and on some basic mechanisms underlying pathologies in which the processing of emotional information is impaired opening the door to possible novel treatments. Among those electrophysiological data might turn out to be extremely useful for brain machine interfaces (BMI). BMI emerged as an attempt to restore motor function to paralyzed individuals, (e.g. after spinal cord injury), by decoding motor signals from cortical motor areas and directing them to external assistive devices, such as limb prostheses and exoskeletons. Notwithstanding some successes of the BMI approach there are still several limitations, first among others the guidance of prosthetic limbs is still far from approximating natural behaviors, as it requires large efforts required for learning, it has high error rates and slow response speed [2,3]. One weakness lies in the decoding of neural modulation subserving decision making. Decoding algorithms are almost always based on very simple, repetitive movements, rather far from the computation occurring in more realistic scenarios like those in which we have to flexibly integrate emotional information to produce or to cancel goal directed movements. Therefore, our findings have the potential to create new and more efficient templates on which BMI algorithm could be built.
All in all, this project is potentially relevant to priority 3 "Societal Challenges" of Horizon 2020, as our findings will contribute to "improve the lifelong health and wellbeing of all", decreasing the costs of the Health System at the same time.
In addition, this project will also allow relevant advances in brain research (priority 1 "Excellent Science" of Horizon 2020). As stated above, there is a need to overcome the confounding factors that limited previous studies, which is exactly what this project is about. Among other things, an outstanding result will be provided by mapping at different scales the neural processes of the amygdala. The comparison between signals coming from single units and from small neuronal populations recorded at the same site at the same moment will allow us to assess whether the two signals convey different messages.
References
1. Davidson (1998) Psychophysiol 35:607-14
2. Baranauskas (2014) Front Syst Neurosci. 8:68
3. Mirabella (2012) Front Neuroeng. 5:20