Surprise

About: Surprise is a research topic. Over the lifetime, 4371 publications have been published within this topic receiving 99386 citations.

Cultural Specific Effects on the Recognition of Basic Emotions: A Study on Italian Subjects

Abstract: The present work reports the results of perceptual experiments aimed to investigate if some of the basic emotions are perceptually privileged and if the cultural environment and the perceptual mode play a role in this preference. To this aim, Italian subjects were requested to assess emotional stimuli extracted from Italian and American English movies in the single (either video or audio alone) and the combined audio/video mode. Results showed that anger, fear, and sadness are better perceived than surprise, happiness in both the cultural environments (irony instead strongly depend on the language), that emotional information is affected by the communication mode and that language plays a role in assessing emotional information. Implications for the implementation of emotionally colored interactive systems are discussed.

Surprise, Surprise: The Role of Surprising Numerical Feedback in Belief Change

Abstract: What percentage of U.S. residents is incarcerated? If you now learned the amount and it surprised you, would it be more memorable than if it were not surprising? Our past research documented conceptual changes related to policy issues when one receives a single, critical number. In the present study, Experiment 1 uses a Numerically-Driven Inferencing (NDI) paradigm method in which participants estimated policyrelevant quantities, learned the true quantities, and rated their surprise regarding that feedback. When asked to recall the quantities either eight or 84 days post-feedback, participants improved the most over their original estimates on items that surprised them the most. In Experiment 2, we found that a measure of prospective surprise (“shock”; Ranney, Cheng, Nelson, & Garcia de Osuna, 2001)—derived from an interval in which participants believed the number fell, and participants’ confidence that the number fell in that interval— reliably predicted retrospective surprise ratings like those in Experiment 1. We conclude that surprise is a rather stable construct about which people have considerable metacognition. Future work in this area may suggest how leaders, voters, and consumers can best employ their emotional responses to numbers and enhance cognitive strategies that help shape effective policies.

Memory for a single object has differently variable precisions for relevant and irrelevant features.

Abstract: Working memory is a limited resource. To further characterize its limitations, it is vital to understand exactly what is encoded about a visual object beyond the "relevant" features probed in a particular task. We measured the memory quality of a task-irrelevant feature of an attended object by coupling a delayed estimation task with a surprise test. Participants were presented with a single colored arrow and were asked to retrieve just its color for the first half of the experiment before unexpectedly being asked to report its direction. Mixture modeling of the data revealed that participants had highly variable precision on the surprise test, indicating a coarse-grained memory for the irrelevant feature. Following the surprise test, all participants could precisely recall the arrow's direction; however, this improvement in direction memory came at a cost in precision for color memory even though only a single object was being remembered. We attribute these findings to varying levels of attention to different features during memory encoding.

Emotion recognition for semi-autonomous vehicles framework

Abstract: The human being in his blessed curiosity has always wondered how to make machines feel, and, at the same time how a machine can detect emotions. Perhaps some of the tasks that cannot be replaced by machines are the ability of human beings to feel emotions. In the last year, this hypothesis is increasingly questioned by scientists who have done work that seeks to understand the phenomena of brain functioning using the state of the art in instrumentation, sensors, and signal processing. Today, the world scientists have powerful machine learning methods developed to challenge this issue.The field of emotion detection is gaining significance as the technology advances, and particularly due to the current developments in machine learning, the Internet of Things, industry 4.0 and Autonomous Vehicles. Machines will need to be equipped with the capacity to monitor the state of the human user and to change their behaviour in response. Machine learning offers a route to this and should be able to make use of data collected from questionnaires, facial expression scans, and physiological signals such as electroencephalograms (EEG), electrocardiograms, and galvanic skin response. In this study, an approach was proposed to identify the emotional state of a subject from the collected data in the elicited emotion experiments. An algorithm using EEG data was developed, using the power spectral density of the frequency cerebral bands (alpha, beta, theta, and gamma) as features for classifier training. A K Nearest Neighbors algorithm using Euclidian distance was used to predict the emotional state of the subject. This article proposes a novel approach for emotion recognition that not only depends on images of the face, as in the previous literature, but also on the physiological data. The algorithm was able to recognize nine different emotions (Neutral, Anger, Disgust, Fear, Joy, Sadness, Surprise, Amusement, and Anxiety), nine valence positions, and nine positions on arousal axes. Using the data from only 14 EEG electrodes, an accuracy of approximately 97% was achieved. An approach has been developed for evaluating the state of mind of an driver in the context of a semi-autonomous vehicle context, for example. However, the system has a much wider range of potential applications, from the design of products to the evaluation of the user experience.