People are regularly bombarded with logos in an attempt to improve brand recognition, and logos are often designed with the central purpose of memorability. The ubiquitous Apple logo is a simple design and is often referred to as one of the most recognizable logos in the world. The present study examined recall and recognition for this simple and pervasive logo and to what degree metamemory (confidence judgements) match memory performance. Participants showed surprisingly poor memory for the details of the logo as measured through recall (drawings) and forced-choice recognition. Only 1 participant out of 85 correctly recalled the Apple logo, and fewer than half of all participants correctly identified the logo. Importantly, participants indicated higher levels of confidence for both recall and recognition, and this overconfidence was reduced if participants made the judgements after, rather than before, drawing the logo. The general findings did not differ between Apple and PC users. The results provide novel support for theories of attentional saturation, inattentional amnesia, and reconstructive memory; additionally they show how an availability heuristic can lead to overconfidence in memory for logos.

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People are often exposed to more information than they can actually remember. Despite this frequent form of information overload, little is known about how much information people choose to remember. Using a novel “stop” paradigm, the current research examined whether and how people choose to stop receiving new—possibly overwhelming—information with the intent to maximize memory performance. Participants were presented with a long list of items and were rewarded for the number of correctly remembered words in a following free recall test. Critically, participants in a stop condition were provided with the option to stop the presentation of the remaining words at any time during the list, whereas participants in a control condition were presented with all items. Across five experiments, we found that participants tended to stop the presentation of the items to maximize the number of recalled items, but this decision ironically led to decreased memory performance relative to the control group. This pattern was consistent even after controlling for possible confounding factors (e.g., task demands). The results indicated a general, false belief that we can remember a larger number of items if we restrict the quantity of learning materials. These findings suggest people have an incomplete understanding of how we remember excessive amounts of information.

Students learn many concepts in the introductory statistics course, but even our most successful students end up with rigid, ritualized knowledge that does not transfer easily to new situations. In this article we describe our attempt to apply theories and findings from learning science to the design of a statistics course that aims to help students build a coherent and interconnected representation of the domain. The resulting practicing connections approach provides students with repeated opportunities to practice connections between core concepts (especially the concepts of statistical model, distribution, and randomness), key representations (R programming language and computational techniques such as simulation and bootstrapping), and real-world situations statisticians face as they explore variation, model variation, and evaluate and compare statistical models. We provide a guided tour through our curriculum implemented in an interactive online textbook (CourseKata.org) and then provide some evidence that students who complete the course are able to transfer what they have learned to the learning of new statistical techniques.