Resolution Of Confusion Research Articles

Image Interpretation Confusion Resolution by Collaboration

Visual scene understanding can benefit from inputs provided by multiple participants with their different perspectives, and a distributed version of a modified Waltz filtering enriched with modern AI inferences can potentially help accuracy and speed trade-offs by exploiting the simultaneous perspectives and logic. Speed is improved by the contribution of the implicit parallelisation in processing. Accuracy improvements are expected from updating constraints with novel and more powerful inferences that the participants can apply. Automatically understanding scenes is a highly relevant problem. Multiple robots communicate with one another to classify shapes of edges of an object. Local reasoning can reduce communication latency.

Can Strategic Behaviour Facilitate Confusion Resolution? The Interplay Between Confusion and Metacognitive Strategies in Betty’s Brain

Confusion may benefit learning when it is resolved or partially resolved. Metacognitive strategies (MS) may help learners to resolve confusion when it occurs during learning and problem solving. This study examined the relationship between confusion and MS that students evoked in Betty’s Brain, a computer-based learning-by-modelling environment where elementary and middle school students learn science by building causal maps. Participants were sixth graders. Emotion data were collected from real-time observations by trained researchers. MS and task performance information were determined by analyzing the action logs. Pre- and post-tests were used to assess learning gains. The results revealed that the use of MS was a function of the state of student confusion. However, confusion resolution was not related to MS behaviour, and MS did not moderate the effect of confusion on student task performance in Betty’s Brain or on learning gains.

Confusion and its dynamics during device comprehension with breakdown scenarios

The incidence and dynamics of confusion during complex learning and problem solving were investigated in an experiment where participants first read illustrated texts on everyday devices (e.g., an electric bell) followed by breakdown scenarios reflecting device malfunctions (e.g., “When a person rang the bell there was a short ding and then no sound was heard”). The breakdown scenarios were expected to trigger impasses and put participants in a state of cognitive disequilibrium where they would experience confusion and engage in effortful confusion resolution activities in order to restore equilibrium. The results confirmed that participants reported more confusion when presented with the breakdown scenarios compared to control scenarios that involved focusing on important device components in the absence of malfunctions. A second-by-second analysis of the dynamics of confusion yielded two characteristic trajectories that distinguished participants who partially resolved their confusion from those who remained confused. Participants who were successful in partial confusion resolution while processing the breakdowns outperformed their counterparts on knowledge assessments after controlling for scholastic aptitude, engagement, and frustration. This effect was amplified for those who were highly confused by the breakdowns. There was no direct breakdown vs. control effect on learning, but being actively engaged and partially resolving confusion during breakdown processing were positive predictors of increased learning with the breakdown compared to control scenarios. Implications of our findings for theories that highlight the role of impasses, cognitive disequilibrium, and confusion to learning are discussed.

Regorafenib-induced hyperammonemic encephalopathy

Regorafenib improves progression-free survival as a late-line treatment for patients with metastatic gastrointestinal stromal tumour (GIST). As a multitargeted tyrosine kinase inhibitor (TKI), the expected adverse events of regorafenib are similar to those reported with imatinib, sunitinib or sorafenib. We report the first case of hyperammonemic encephalopathy related to regorafenib in a patient with metastatic GIST. A 61-year-old man maintained on regorafenib for metastatic GIST presented with acute confusion. Discontinuation of regorafenib led to complete resolution of confusion, which later recurred with hyperammonemia on recommencing regorafenib. Cessation of regorafenib and normalization of hyperammonemia then resulted in resolution of confusion. Regorafenib withdrawal and recommencement had influenced the confusional state and hyperammonemia in this patient. There is a probable relationship between regorafenib and metabolic encephalopathy. There are case reports of similar encephalopathy thought to be induced by other multitargeted TKI, and, as such, a class effect could be postulated.

Confusion and complex learning during interactions with computer learning environments

Folk wisdom holds that being confused is detrimental to learning. However, research on emotions and learning suggest a somewhat more complex relationship between confusion and learning outcomes. In fact, it has been proposed that impasses that trigger states of cognitive disequilibrium and confusion can create opportunities for deep learning of conceptually difficult content. This paper discusses four computer learning environments that either naturally or artificially induce confusion in learners in order to create learning opportunities. First, an Intelligent Tutoring System called AutoTutor that engenders confusion through challenging problems and vague hints is described. The remaining three environments were specifically designed to induce confusion through a number of different interventions. These interventions include device breakdowns, contradictory information, and false feedback. The success and limitations of confusion induction and the impact of confusion resolution on learning are discussed. Potential methods to help learners productively manage their confusion instead of being hopelessly confused are also discussed.

Front-back confusion resolution in three-dimensional sound localization using databases built with a dummy head

Sound localization plays an important role in everyday life. It helps us to separate sounds coming from different sources and thus to acquire acoustic information. This paper describes an algorithm for localizing the position of a sound source, as recorded by dummy head microphones. The recorded signals are considered to be basic, random signals within an imaginary round room. The goal of this research is to localize random signals produced from different positions using information about basic signals. The method used is based on the identification of similarities between basic and random signals. It includes an interaural time difference comparison at the beginning, and continues with further analysis of the differences in signal spectrums. One of the main issues arising in sound localization is the problem of front-back confusion, and this paper shows how it was resolved by the use of reference signals.

Poisson multiple-access contention with binary feedback

Communication over a random multiple-access, collision-type, time-slotted, packet-switched channel with binary feedback is considered for a Poisson infinite-user model. Three different types of binary feedback are introduced and confusion resolution algorithms (CRA) are discussed for each. The CRA proposed for the feedback that tells only whether or not there was a collision in the previous slot achieves a throughput of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.442</tex> . The CRA proposed for the feedback that tells whether or not the previous slot was empty achieves a throughput of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.279</tex> .