Showing papers on "Surprise published in 2018"

Deconstructing monetary policy surprises: the role of information shocks

Abstract: Central bank announcements simultaneously convey information about monetary policy and the central bank's assessment of the economic outlook. This paper disentangles these two components and studies their effect on the economy using a structural vector autoregression estimated on both US and euro area data. It relies on the information inherent in high-frequency comovement of interest rates and stock prices around policy announcements: a surprise policy tightening raises interest rates and reduces stock prices, while the complementary positive central bank information shock raises both. These two shocks have intuitive and very different effects on the economy. Ignoring the central bank information shocks biases the inference on monetary policy non-neutrality. We make this point formally and offer an interpretation of the central bank information shock using a New Keynesian macroeconomic model with financial frictions.

Am I Self-Conscious? (Or Does Self-Organization Entail Self-Consciousness?).

Abstract: Is self-consciousness necessary for consciousness? The answer is yes. So there you have it-the answer is yes. This was my response to a question I was asked to address in a recent AEON piece (https://aeon.co/essays/consciousness-is-not-a-thing-but-a-process-of-inference). What follows is based upon the notes for that essay, with a special focus on self-organization, self-evidencing and self-modeling. I will try to substantiate my (polemic) answer from the perspective of a physicist. In brief, the argument goes as follows: if we want to talk about creatures, like ourselves, then we have to identify the characteristic behaviors they must exhibit. This is fairly easy to do by noting that living systems return to a set of attracting states time and time again. Mathematically, this implies the existence of a Lyapunov function that turns out to be model evidence (i.e., self-evidence) in Bayesian statistics or surprise (i.e., self-information) in information theory. This means that all biological processes can be construed as performing some form of inference, from evolution through to conscious processing. If this is the case, at what point do we invoke consciousness? The proposal on offer here is that the mind comes into being when self-evidencing has a temporal thickness or counterfactual depth, which grounds inferences about the consequences of my action. On this view, consciousness is nothing more than inference about my future; namely, the self-evidencing consequences of what I could do.

Negative News and Investor Trust: The Role of $Firm and #CEO Twitter Use

Abstract: We examine how CEOs can facilitate the development of investor trust that helps mitigate the effects of negative information. Results from an experiment show that investors trust the CEO more and are more willing to invest in the firm when the CEO communicates firm news followed by a negative earnings surprise through a personal Twitter account than when the news and surprise comes from the CEO via a website or from the firm's Investor Relations Twitter account or website. A follow‐up experiment shows that repeating the negative news does not incrementally affect investors who received the news from the CEO's Twitter account, but does further negatively impact investors who received the news via other disclosure mediums, especially those who received the news via the Investor Relations Twitter account. Our results have implications for firms and executives considering the costs and benefits of communicating with investors via Twitter.

Defining Creativity: Don't We Also Need to Define What Is Not Creative?

Abstract: I argue that any attempt to define creative ideas cannot fully succeed without also defining uncreative ideas. This argument begins by defining three parameters that characterize a potentially creative thought: the idea's initial probability (p), the final utility (u), and the creator's prior knowledge of that utility (v). The three parameters then lead to a three-criterion multiplicative definition of personal creativity, namely, c = (1 − p)u(1 − v), where the first factor indicates originality and the third factor surprise. Although creativity can only maximize as originality, utility, and surprise all approach unity, the same definition indicates that there are seven different ways that creativity can minimize. These alternatives were identified as (a) routine, reproductive, or habitual ideas, (b) fortuitous response bias, (c) irrational perseveration, (d) problem finding, (e) rational suppression, (f) irrational suppression, and (g) blissful ignorance. If the third parameter v is omitted, then the number of creative and noncreative outcomes reduces to just four, making creativity indistinguishable from irrational suppression. The alternative outcomes are then illustrated using the classic two-string problem. Besides providing a more finely differentiated conception of creativity failures, the definition has critical implications regarding the processes and procedures required to generate highly creative ideas.

A nice surprise? Predictive processing and the active pursuit of novelty

Abstract: Recent work in cognitive and computational neuroscience depicts human brains as devices that minimize prediction error signals: signals that encode the difference between actual and expected sensory stimulations. This raises a series of puzzles whose common theme concerns a potential misfit between this bedrock informationtheoretic vision and familiar facts about the attractions of the unexpected. We humans often seem to actively seek out surprising events, deliberately harvesting novel and exciting streams of sensory stimulation. Conversely, we often experience some wellexpected sensations as unpleasant and to-be-avoided. In this paper, I explore several core and variant forms of this puzzle, using them to display multiple interacting elements that together deliver a satisfying solution. That solution requires us to go beyond the discussion of simple information-theoretic imperatives (such as 'minimize long-term prediction error') and to recognize the essential role of species-specific prestructuring, epistemic foraging, and cultural practices in shaping the restless, curious, novelty-seeking human mind.

Balancing New against Old Information: The Role of Puzzlement Surprise in Learning

Abstract: Surprise describes a range of phenomena from unexpected events to behavioral responses. We propose a novel measure of surprise and use it for surprise-driven learning. Our surprise measure takes into account data likelihood as well as the degree of commitment to a belief via the entropy of the belief distribution. We find that surprise-minimizing learning dynamically adjusts the balance between new and old information without the need of knowledge about the temporal statistics of the environment. We apply our framework to a dynamic decision-making task and a maze exploration task. Our surprise-minimizing framework is suitable for learning in complex environments, even if the environment undergoes gradual or sudden changes, and it could eventually provide a framework to study the behavior of humans and animals as they encounter surprising events.

The use of computer games in classroom environment

Abstract: As part of the educational use of ICT, digital games can be learning tools, motivators and generators of curiosity and as a result an effective means of optimising student learning and performance in daily educational practice. The positive relationship between learning and students' engagement while using digital games has been confirmed by various independent studies over the years. Thus, the integration of digital games at different levels of education for teaching multiple learning objects comes as no surprise. Despite the many studies on the learning and motivational effects of digital games, teaching with digital games is not yet widespread in secondary education. Current research emphasises that most of these factors appear to stem from difficulties with the implementation of games in classroom settings. Problems with technology, the cost/expense of games/equipment, the lack of technical support are defined as some of the barriers to the addition of games and simulations in education.

Developing a Theory of Surprise from Travelers’ Extraordinary Food Experiences:

Abstract: The purpose of this research is to explore the extraordinary experiences of food tourists and to develop a theory of surprise in relation to a typology of food cultural capital. We draw on phenomenological interviews with 16 food tourists. We found that food tourists experienced surprise in different ways, depending on their food cultural capital. Food tourists who possessed a high level of cultural capital were surprised by the simplicity or complexity of the experience while those possessing a low level of cultural capital were surprised by the genuinity of the experience. Thus, we make an important theoretical contribution here as we learn that the resources food tourists possessed in the form of cultural capital conditioned the ways in which they conceived an extraordinary experience. More so, using the cultural capital perspective, we have also demonstrated the role of social context in contributing to creating an extraordinary experience.

Affective and cognitive validation of thoughts: An appraisal perspective on anger, disgust, surprise, and awe.

Abstract: Anger, disgust, surprise, and awe are multifaceted emotions. Both anger and disgust are associated with feeling unpleasant as well as experiencing a sense of confidence, whereas surprise and awe tend to be more pleasant emotions that are associated with doubt. Most prior work has examined how appraisals (confidence, pleasantness) lead people to experience different emotions or to experience different levels of intensity within the same emotion. Instead, the current research focused on the consequences (rather the antecedents) of appraisals of emotion, and it focuses specifically on the consequences for thought usage rather than the consequences for generating many or few thoughts. We show that when these four emotions are induced following thought generation, thoughts can be used either more or less with each emotion depending on whether the pleasantness/unpleasantness or confidence/doubt appraisal is made salient. In five experiments, it was predicted and found that anger and disgust following thought generation led to more thought use than surprise and awe when a confidence appraisal for the emotion was encouraged, but led to less thought use than surprise and awe when a pleasantness appraisal was made salient. The current studies are the first to reveal that different appraisals can lead to different (even opposite) outcomes on thought usage within the same experimental design. (PsycINFO Database Record (c) 2018 APA, all rights reserved)

Predicting one-year mortality in heart failure using the 'Surprise Question': a prospective pilot study.

Abstract: Background: The Surprise Question: ‘would you be surprised if this patient were to die within the next year?’ has been shown to predict mortality in patients with chronic kidney disease and cancer. This prospective study aimed to determine whether the Surprise Question could identify heart failure patients with a prognosis of less than 1 year, and whether the Surprise Question can be used by different healthcare professionals. Methods and results: Overall, 129 consecutive patients admitted with decompensated heart failure were included. Doctors and nurses were asked to provide a ‘surprised’ or ‘not surprised’ response to the Surprise Question for each patient. Patients were followed up until death or 1 year following study inclusion. The sensitivity, specificity, positive predictive value and negative predictive value of the Surprise Question were assessed. Cox regression was used to determine covariates significantly associated with survival. The Surprise Question showed excellent sensitivity (0.85) and negative predictive value (0.88) but only fair specificity (0.59) and positive predictive value (0.52) when asked of cardiologists. There were similar levels of accuracy between doctors and specialist nurses. The Surprise Question was significantly associated with all‐cause mortality in multivariate regression analysis (hazard ratio 2.8, 95% confidence interval 1.0–7.9, P = 0.046). Conclusion: This study demonstrates that the Surprise Question can identify heart failure patients within the last year of life. Despite over‐classification of patients into the ‘not surprised’ category, the Surprise Question identified nearly all patients who were within the last year of life, whilst also accurately identifying those unlikely to die.

Training Pilots for Unexpected Events: A Simulator Study on the Advantage of Unpredictable and Variable Scenarios.

Abstract: Objective: This study tested whether simulator-based training of pilot responses to unexpected or novel events can be improved by including unpredictability and variability in training scenarios. Background: Current regulations allow for highly predictable and invariable training, which may not be sufficient to prepare pilots for unexpected or novel situations in-flight. Training for surprise will become mandatory in the near future. Method: Using an aircraft model largely unfamiliar to the participants, one group of 10 pilots (the unpredictable and variable [U/V] group) practiced responses to controllability issues in a relatively U/V manner. A control group of another 10 pilots practiced the same failures in a highly predictable and invariable manner. After the practice, performance of all pilots was tested in a surprise scenario, in which the pilots had to apply the learned knowledge. To control for surprise habituation and familiarization with the controls, two control tests were included. Results: Whereas the U/V group required more time than the control group to identify failures during the practice, the results indicated superior understanding and performance in the U/V group as compared to the control group in the surprise test. There were no significant differences between the groups in surprise or performance in the control tests. Conclusion: Given the results, we conclude that organizing pilot training in a more U/V way improves transfer of training to unexpected situations in-flight. Application: The outcomes suggest that the inclusion of U/V simulator training scenarios is important when training pilots for unexpected situations.

NLP at IEST 2018: BiLSTM-Attention and LSTM-Attention via Soft Voting in Emotion Classification

Abstract: This paper describes our method that competed at WASSA2018 Implicit Emotion Shared Task. The goal of this task is to classify the emotions of excluded words in tweets into six different classes: sad, joy, disgust, surprise, anger and fear. For this, we examine a BiLSTM architecture with attention mechanism (BiLSTM-Attention) and a LSTM architecture with attention mechanism (LSTM-Attention), and try different dropout rates based on these two models. We then exploit an ensemble of these methods to give the final prediction which improves the model performance significantly compared with the baseline model. The proposed method achieves 7th position out of 30 teams and outperforms the baseline method by 12.5% in terms of macro F1.

Paradoxical Thinking as a Conflict-Resolution Intervention: Comparison to Alternative Interventions and Examination of Psychological Mechanisms:

Abstract: Conflict-resolution interventions based on the paradoxical thinking principles, that is, expressing amplified, exaggerated, or even absurd ideas that are congruent with the held conflict-supporting societal beliefs, have been shown to be an effective avenue of intervention, especially among individuals who are adamant in their views. However, the question as to why these interventions have been effective has remained unanswered. In the present research, we have examined possible underlying psychological mechanisms, focusing on identity threat, surprise, and general disagreement. In a small-scale lab study and a large-scale longitudinal study, we compared paradoxical thinking interventions with traditional interventions based on providing inconsistent information. The paradoxical thinking interventions led rightists to show more unfreezing of held conflict-supporting beliefs and openness to alternative information, whereas the inconsistency-based interventions tended to be more effective with the centrist participants. Both studies provide evidence that the effects were driven by identity threat, surprise, and lower levels of disagreement.

Detecting Genuine and Deliberate Displays of Surprise in Static and Dynamic Faces.

Abstract: People are good at recognizing emotions from facial expressions, but less accurate at determining the authenticity of such expressions. We investigated whether this depends upon the technique that senders use to produce deliberate expressions, and on decoders seeing these in a dynamic or static format. Senders were filmed as they experienced genuine surprise in response to a jack-in-the-box (Genuine). Other senders faked surprise with no preparation (Improvised) or after having first experienced genuine surprise themselves (Rehearsed). Decoders rated the genuineness and intensity of these expressions, and the confidence of their judgment. It was found that both expression type and presentation format impacted decoder perception and accurate discrimination. Genuine surprise achieved the highest ratings of genuineness, intensity, and judgmental confidence (dynamic only), and was fairly accurately discriminated from deliberate surprise expressions. In line with our predictions, Rehearsed expressions were perceived as more genuine (in dynamic presentation), whereas Improvised were seen as more intense (in static presentation). However, both were poorly discriminated as not being genuine. In general, dynamic stimuli improved authenticity discrimination accuracy and perceptual differences between expressions. While decoders could perceive subtle differences between different expressions (especially from dynamic displays), they were not adept at detecting if these were genuine or deliberate. We argue that senders are capable of producing genuine-looking expressions of surprise, enough to fool others as to their veracity.

Cities in global capitalism

Abstract: To some, it has come as a surprise that, besides the positive outcomes of the capitalist city – namely productivity and innovation – inequality within and between cities is an inevitable outcome of...

From Ressentiment to Resentment as a Tertiary Emotion

Abstract: Resentment is a noxious emotion that can exist in sublimated form as a result of being subjected to inferiorization, stigmazation, or violence. In its active form, resentment can be a forceful response to acts that have created unjustified and meaningless suffering. We consider sociomoral conceptualizations of resentment by Adam Smith, Hume, and Levinas. Nietzsche and Scheler developed the broader notion of ressentiment, a generalized form of resentment arising out of powerlessness and the experience of brutalization neither forgotten nor forgiven. Resentment is seen historically as a sentiment that is saturated with frustration, contempt, outrage, and malevolence. Marshall described oppositional class-consciousness as permeated with resentment and anger, but resentment also contains the basic emotions of surprise and disgust. Resentment is linked to the concept of relative deprivation. A partial classification of emotions is used to further analyze resentment as containing three secondary-level emotions: contempt (anger & disgust), shock (surprise & disgust), and outrage (surprise & anger). Thus, resentment is conceptualized as a tertiary-level emotion, containing three primary and three secondary emotions.

From Apprentice to President: The Role of Parasocial Connection in the Election of Donald Trump

Abstract: Data suggest that the outcome of the 2016 American presidential election was a surprise to most people. We conducted a study to examine the role parasocial bonds formed with Trump due to his appear...

Distributional learning is error-driven: the role of surprise in the acquisition of phonetic categories

Abstract: Abstract Much previous research on distributional learning and phonetic categorization assumes that categories are either faithful reproductions or parametric summaries of experienced frequency distributions, acquired through a Hebbian learning process in which every experience contributes equally to the category representation. We suggest that category representations may instead be formed via error-driven predictive learning. Rather than passively storing tagged category exemplars or updating parametric summaries of token counts, learners actively anticipate upcoming events and update their beliefs in proportion to how surprising/unexpected these events turn out to be. As a result, rare category members exert a disproportionate influence on the category representation. We present evidence for this hypothesis from a distributional learning experiment on acquiring a novel phonetic category, and show that the results are well described by a classic error-driven learning model (Rescorla, R. A. & A. R. Wagner. 1972. A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In A. H. Black & W. F. Prokasy (eds.), Classical conditioning II: Current research and theory, 64–99. New York, NY: Appleton-Century-Crofts).

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.

Pleasures of the Mind: What Makes Jokes and Insight Problems Enjoyable.

Abstract: In this paper, a parallel analysis of the enjoyment derived from humor and insight problem solving is presented, with reference to a “general” Theory of the Pleasures of the Mind (TPM) (Kubovy, 1999) rather than to “local” theories regarding what makes humor and insight problem solving enjoyable. The similarity of these two cognitive activities has already been discussed in previous literature in terms of the cognitive mechanisms which underpin getting a joke or having an insight experience in a problem solving task. The paper explores whether we can learn something new about the similarities and differences between humor and problem solving by means of an investigation of what makes them pleasurable. In the first part of the paper, the framework for this joint analysis is set. Two descriptive studies are then presented in which the participants were asked to report on their experiences relating to solving visuo-spatial insight problems (Study 1) or understanding cartoons (Study 2) in terms of whether they were enjoyable or otherwise. In both studies, the responses were analyzed with reference to a set of categories inspired by the TPM. The results of Study 1 demonstrate that finding the solution to a problem is associated with a positive evaluation and the most frequent explanations for this were reported as being Curiosity, Virtuosity and Violation of expectations. The results of Study 2 suggest that understanding a joke (Joy of Verification) and being surprised by it (Feeling of Surprise) were two essential conditions: when they were not present, the cartoons were perceived as not enjoyable. However, this was not enough to explain the motivations for the choice of the most enjoyable cartoons. Recognizing a Violation of expectations and experiencing a Diminishment in the cleverness or awareness initially attributed to the characters in the cartoon were the aspects which were most frequently indicated by the participants to explain why they enjoyed the joke. These findings are evaluated in the final discussion, together with their limitations and potential future developments.

Surprise Me If You Can: Serendipity in Health Information

Abstract: Our natural tendency to be curious is increasingly important now that we are exposed to vast amounts of information. We often cope with this overload by focusing on the familiar: information that matches our expectations. In this paper we present a framework for interactive serendipitous information discovery based on a computational model of surprise. This framework delivers information that users were not actively looking for, but which will be valuable to their unexpressed needs. We hypothesize that users will be surprised when presented with information that violates the expectations predicted by our model of them. This surprise model is balanced by a value component which ensures that the information is relevant to the user. Within this framework we have implemented two surprise models, one based on association mining and the other on topic modeling approaches. We evaluate these two models with thirty users in the context of online health news recommendation. Positive user feedback was obtained for both of the computational models of surprise compared to a baseline random method. This research contributes to the understanding of serendipity and how to "engineer" serendipity that is favored by users.

The Impact of Monetary Policy on the Exchange Rate: A Study Using Intraday Data

Abstract: We investigate the impact of monetary policy on the exchange rate using an event study with intraday data for four countries. Carefully selecting the sample periods ensures that the policy change is exogenous to the exchange rate. An unanticipated tightening of 25 basis points leads to a rapid appreciation of around 0.35 percent. We also show that the impact depends on how the surprise affects expectations of future monetary policy. If expectations of future policy are revised by the full amount of the surprise, then the impact on the exchange rate is larger (0.4 percent) than if the surprise only brings forward an anticipated change in policy (0.2 percent).