Monitoring in Preschool Children Fades with the Calibration Feedback Higher fluid intelligence leads to better accuracy in metacognitive monitoring, but in school age this influence is moderated by the child’s development and education. The goal of the study is to examine the interaction between fluid intelligence and performance feedback or calibration feedback on monitoring accuracy in 88 preschool children. The children in the group that received performance (PF) or calibration feedback (CF) were significantly more accurate at monitoring than the children without feedback (NF). Fluid intelligence correlated with monitoring accuracy for the whole dataset and explained 49% of variance in monitoring accuracy in the NF group; 26% in the PF group (feedback alone explained 20%) and only 12% in the CF group, not reaching significance (however, feedback alone explained 26%). Results indicate that calibration feedback could potentially fulfil the role of later education and development in improving monitoring accuracy and moderate the effect of fluid intelligence already in pre-schoolers.
Calibration is an important issue in research investigating the appropriateness of probability judgements. Traditionally, calibration has been studied using normative standards, which suggested a general bias (overconfidence). Instead of the normative approach, which considers the rightness of probability judgements, the support theory focuses on how judgements are made. The authors' aim was to study the effect of knowledge and feedback on calibration. They selected groups differing in the amount of their knowledge and in the frequency of the feedback relating to this knowledge. They found that only the amount, and not the feedback, influenced calibration. The results are interpreted in accordance of the support theory: intensity (the stereotypical image about the group's knowledge) is used as a basis for calibration, while the weight of the information (feedback) is neglected.
Three studies focused on estimation of present and futurę achievements after success and failure in relation to differences in status. The results of Study 1 and 3 showed that high power participants rated actual and future achievements in a more optimistic way, and expected higher quality of life in the future than low power participants. The results of Study 2 and 3 showed that evaluation of own achievements by low power participants, compared to high power participants, was more dependent on the obtained feedback (positive or negative). The studies demonstrated that possessing power leads to a more positive evaluation of own achievements and sensitizes people to the feedback information but only in those domains which are consistent with content of the information. In the high power group neither positive nor negative feedback had any influence on estimation of own achievements in other domains, such as social status or quality of life.
We focused on the effect of various types of feedback in a game-based fluid reasoning test called Triton and the Hungry Ocean on elementary school students (ages 8-12; total N = 321). The feedback types were four: no feedback (A), simple (correct/wrong feedback; B), elaborated (correct solution shown; C), and learner-controlled feedback (student chooses between feedback types; D). We did not observe an effect of any feedback type on performance (i.e., there were no between-group differences). However, within group D, students overall tended to choose elaborated feedback more often as task difficulty increased (r = .92), and those in group D who generally tended to choose elaborated feedback also tended to perform better even after controlling for intellect.
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In this paper we present a theoretical framework for novelty based feedback regulation in artificial neural networks. Novelty is assessed on the basis of monitoring the coherence of network dynamics. The result of novelty detection is dynamically coupled to parameters that control the dynamics of the recognition process. The paper presents a new measure of novelty detection - the strength of the local field - and presents new simulation results concerning novelty detection. It also integrates previously published models and simulation results into a general dynamical model of feedback regulation.
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