The Unexpected Unexpected and the Expected Unexpected: How People's Conception of the Unexpected is Not That Unexpected
The answers people give when asked to 'think of the unexpected' for everyday event scenarios appear to be more expected than unexpected. There are expected unexpected outcomes that closely adhere to the given information in a scenario, based on familiar disruptions and common plan-failures. There are also unexpected unexpected outcomes that are more inventive, that depart from given information, adding new concepts/actions. However, people seem to tend to conceive of the unexpected as the former more than the latter. Study 1 tests these proposals by analysing the object-concepts people mention in their reports of the unexpected and the agreement between their answers. Study 2 shows that object-choices are weakly influenced by recency, the order of sentences in the scenario. The implications of these results for ideas in philosophy, psychology and computing is discussed
Code (1)
Tasks
PhilosophySimilar Papers 제목 키워드 기반
Latent Unexpected Recommendations
Unexpected recommender system constitutes an important tool to tackle the problem of filter bubbles and user boredom, which aims at providing unexpected and satisfying recommendations to target users at the same time. Pr…
Recommendation SystemsLatent Unexpected and Useful Recommendation
Providing unexpected recommendations is an important task for recommender systems. To do this, we need to start from the expectations of users and deviate from these expectations when recommending items. Previously propo…
Recommendation SystemsPURS: Personalized Unexpected Recommender System for Improving User Satisfaction
Classical recommender system methods typically face the filter bubble problem when users only receive recommendations of their familiar items, making them bored and dissatisfied. To address the filter bubble problem, une…
Recommendation SystemsRobust Recommendation with Implicit Feedback via Eliminating the Effects of Unexpected Behaviors
In the implicit feedback recommendation, incorporating short-term preference into recommender systems has attracted increasing attention in recent years. However, unexpected behaviors in historical interactions like clic…
Recommendation SystemsBayesian Language Model based on Mixture of Segmental Contexts for Spontaneous Utterances with Unexpected Words
This paper describes a Bayesian language model for predicting spontaneous utterances. People sometimes say unexpected words, such as fillers or hesitations, that cause the miss-prediction of words in normal N-gram models…
Automatic Speech Recognition (ASR)Language ModelingLanguage ModellingSpeech Recognition