Action film

Summary

Action film is a film genre in which the protagonist is thrust into a series of events that typically involve violence and physical feats. The genre tends to feature a mostly resourceful hero struggling against incredible odds, which include life-threatening situations, a dangerous villain, or a pursuit which usually concludes in victory for the hero. Advancements in (CGI) have made it cheaper and easier to create action sequences and other visual effects that required the efforts of professional stunt crews in the past. However, reactions to action films containing significant amounts of CGI have been mixed, as some films use CGI to create unrealistic, highly unbelievable events. While action has long been a recurring component in films, the "action film" genre began to develop in the 1970s along with the increase of stunts and special effects. This genre is closely associated with the thriller and adventure genres and may also contain elements of drama and spy fiction. Screenwrite

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When humans or animals perform an action that led to a desired outcome, they show a tendency to repeat it. The mechanisms underlying learning from past experience and adapting future behavior are still not fully understood. In this thesis, I study how humans learn from sparse and delayed reward during multi-step tasks. Learning a sequence of multiple decisions, from a reward obtained only at the end of the sequence, requires a mechanism to link earlier actions to later reward. The theory of reinforce- ment learning suggests an algorithmic solution to this problem, namely, to keep a decaying memory of the state-action history. Such memories are called eligibility traces. They bridge the temporal delay between the moment an action is taken and a subsequent reward. We ask whether humans make use of eligibility traces when learning a sequential decision making task. The difficulty in answering this question is that different competing algorithmic solu- tions make similar predictions about behavior. Only during a few initial trials, learning with eligibility traces is qualitatively different from other algorithms. Here, I implemented a novel learning task with an experimental manipulation that allowed us to guide participants through a controlled sequence of states. With this hidden manipulation, we were able to isolate the specific trials in which the competing models are distinguishable. Behavioral data as well as simultaneously recorded pupil dilation revealed effects compatible with eligibility traces, but not with simpler models. Furthermore, the trial-by-trial reward prediction errors were correlated with pupil dilation and EEG measurements. Our experimental data show effects of eligibility traces in behavior and pupil data, after a single experience of state-action associations, which has not been studied before in a multi-step task. We view our results in the light of one-shot learning and as a signature of a learning mechanism present both in temporal difference and one-shot learning.

EPFL2018

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Action-oriented, transformative, and transdisciplinary approaches are increasingly heralded as promising tools that enable researchers not only to produce new knowledge about a situation, but also to actively engage in tackling it; however, such approaches raise critical questions about the methodological implications and conflicts involved in pursuing multiple objectives concurrently. This article seeks to advance this debate by examining the methodological implications of pursuing both knowledge production-oriented (epistemic) and action-oriented (transformative) objectives in urgent and uncertain situations. It asks how far a transformative mixed methods research design can leverage the potential of research to achieve multiple objectives in times of crisis. This methodological inquiry is based on a transformative mixed methods study on housing conditions and well-being which was initiated during the first COVID-19 lockdown in Switzerland. The study was composed of a country-wide survey, mobile crowdsourcing, interviews and Citizen Think Tanks. The results of this methodological reflection illustrate how this research design made it possible to (i) explore the effects of the crisis on domestic living spaces, (ii) engage with the crisis in a joint research space, and (iii) stimulate empowerment through reflection and mutual learning. These insights suggest the multidimensional orientation of the transformative mixed methods approach is appropriate for acting upon urgent crises. However, it challenges core methodological values and research constellations including (i) tackling unequal engagement opportunities, (ii) navigating social and epistemic control, and (iii) paying attention to situatedness and positionality. The article concludes that, in anticipation of future crises, favorable conditions for multi-targeted, collaborative research need to be fostered, both on the institutional and on personal levels. These conditions should enable fast and adequate team formation, as well as reflection and negotiation of positionalities, and divergent interests and objectives, of both scientists and citizens.

2022

Evidence for eligibility traces in human learning

Wulfram Gerstner, Michael Herzog, Marco Philipp Lehmann, Vasiliki Liakoni, Kerstin Preuschoff, He Xu

Whether we prepare a coffee or navigate to a shop: in many tasks we make multiple decisions before reaching a goal. Learning such state-action sequences from sparse reward raises the problem of credit-assignment: which actions out of a long sequence should be reinforced? One solution provided by reinforcement learning (RL) theory is the eligibility trace (ET); a decaying memory of the state-action history. Here we investigate behaviorally and neurally whether humans utilize an ET when learning a multi-step decision making task. We implemented three versions of a novel task using visual, acoustic, and spatial cues. Eleven subjects performed all three conditions while we recorded their pupil diameter. We considered model-based and model-free (with and without ET) algorithms to explain human learning. Using the Akaike Information Criterion (AIC) we find that model-free learning with ET explains the human behavior best in all three conditions. Cross-validation confirms this behavioral result. We then compare pupil dilation in early and late learning and observe differences that are consistent with an ET contribution. In particular, we find significant changes in pupil response to non-goal states after just a single reward in all three experimental conditions. In this research we introduce a novel paradigm to study the ET in human learning in a multi-step sequential decision making task. The analysis of the behavioral and pupil data provides evidence that humans utilize an eligibility trace to solve the credit-assignment problem when learning from sparse and delayed reward.

arXiv2017

EPFL2018

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2022