Little Support for Discrete Item Limits in Visual Working Memory

Some theorists argue that working memory is limited to a discrete number of items, and additional items are not encoded at all. Others assume that all items are represented with variable quality. Adam, Vogel, and Awh (2017) presented evidence supporting the item-limit hypothesis: Participants reproduced visual features of up to six items in a self-chosen order. After the third or fourth response, error distributions were indistinguishable from guessing. I present four experiments with young adults (each N=24) testing the assumption that the brief, simultaneous display of visual arrays has led to failures of encoding in the experiments of Adam et al. (2017). Experiment 1 presented items slowly and sequentially. Experiment 2 presented them simultaneously but longer than in the experiments of Adam et al. (2017). Experiments 3 and 4 exactly replicated one original experiment. There was no evidence for an encoding limit. However, all four experiments failed to replicate the evidence for guessing-like error distributions. Modelling data from individuals revealed a mixture of some who do and others who don’t produce guessing-like distributions. This heterogeneity increases the credibility of an alternative to the item-limit hypothesis: Some individuals decide to guess on hard trials even when they have weak information in memory.

The Experimental Modeling of Quad-Rotor Actuators with Undefined Hardware Errors for Safety-Flight

The speed error of actuators during the flight of a quad-rotor is included in the attitude error, and this error is immediately corrected by the pilot’s observation. As the control authority of the quad-rotor changes to a computer system, the correction of the error is calculated and performed by the attitude sensor and the mathematical model of the quad-rotor. However, there is a response error to the control signal despite driving the same motor, which causes different results from the model prediction and affects the stability of the flight. Therefore, the response characteristics of hardware represented by the same mathematical model but having errors should be reflected in the modeling of the quad-rotor. In this paper, the response error of the actuators assembled with the same propellers and motors is verified through experiments. The actuators model that reflects this error is presented, and the thrust coefficient range by the propellers is also presented. Additionally, the speed error of actuators due to the voltage drop of the battery was verified through experiments, and a method for applying this error to the actuator model is presented.

Research Synthesis

Herbert Simon’s (1956) concept of satisficing provides an intuitive explanation for the reasons why respondents to surveys sometimes adopt response strategies that can lead to a reduction in data quality. As such, the concept rapidly gained popularity among researchers after it was first introduced to the field of survey methodology by Krosnick and Alwin (1987), and it has become a widely cited buzzword linked to different forms of response error. In this article, we present the findings of a systematic review involving a content analysis of journal articles published in English-language journals between 1987 and 2015 that have drawn on the satisficing concept to evaluate survey data quality. Based on extensive searches of online databases, and an initial screening exercise to apply the study’s inclusion criteria, 141 relevant articles were identified. Guided by the theory of survey satisficing described by Krosnick (1991), the methodological features of the shortlisted articles were coded, including the indicators of satisficing analyzed, the main predictors of satisficing, and the presence of main or interaction effects on the prevalence of satisficing involving indicators of task difficulty, respondent ability, and respondent motivation. Our analysis sheds light on potential differences in the extent to which satisficing theory holds for different types of response error, and highlights a number of avenues for future research.

A New Look at Lake Wobegon: Who’s in Your Canoe?

We re-examine the Lake Wobegon effect for reported GPA using data from students enrolled in principles of economics courses. Students are well known to over-report their GPA on surveys. In addition to standard measures of the mean difference, we extend the analysis to understand the distribution of response error using quantile regression analysis. We also examine how survey conditions impact response error. Our findings suggest that response error is more complicated than simple over-confidence. Students respond differently throughout the distribution of GPA and respond differently when seated near friends. Modeling response error should take these findings into account.