Extraordinary claims, extraordinary evidence? A discussion

Roberts (2020, Learning & Behavior, 48[2], 191-192) discussed research claiming honeybees can do arithmetic. Some readers of this research might regard such claims as unlikely. The present authors used this example as a basis for a debate on the criterion that ought to be used for publication of results or conclusions that could be viewed as unlikely by a significant number of readers, editors, or reviewers.Peer reviewe

Statistics in the service of science : don’t let the tail wag the dog

Statistical modeling is generally meant to describe patterns in data in service of the broader scientific goal of developing theories to explain those patterns. Statistical models support meaningful inferences when models are built so as to align parameters of the model with potential causal mechanisms and how they manifest in data. When statistical models are instead based on assumptions chosen by default, attempts to draw inferences can be uninformative or even paradoxical—in essence, the tail is trying to wag the dog. These issues are illustrated by van Doorn et al. (this issue) in the context of using Bayes Factors to identify effects and interactions in linear mixed models. We show that the problems identified in their applications (along with other problems identified here) can be circumvented by using priors over inherently meaningful units instead of default priors on standardized scales. This case study illustrates how researchers must directly engage with a number of substantive issues in order to support meaningful inferences, of which we highlight two: The first is the problem of coordination, which requires a researcher to specify how the theoretical constructs postulated by a model are functionally related to observable variables. The second is the problem of generalization, which requires a researcher to consider how a model may represent theoretical constructs shared across similar but non-identical situations, along with the fact that model comparison metrics like Bayes Factors do not directly address this form of generalization. For statistical modeling to serve the goals of science, models cannot be based on default assumptions, but should instead be based on an understanding of their coordination function and on how they represent causal mechanisms that may be expected to generalize to other related scenarios

Learning and Memory Processes Following Cochlear Implantation:The Missing Piece of the Puzzle

At the present time, there is no question that cochlear implants work and often work very well in quiet listening conditions for many profoundly deaf children and adults. The speech and language outcomes data published over the last two decades document quite extensively the clinically significant benefits of cochlear implants. Although there now is a large body of evidence supporting the efficacy of cochlear implants as a medical intervention for profound hearing loss in both children and adults, there still remain a number of challenging unresolved clinical and theoretical issues that deal with the effectiveness of cochlear implants in individual patients that have not yet been successfully resolved. In this paper, we review recent findings on learning and memory, two central topics in the field of cognition that have been seriously neglected in research on cochlear implants. Our research findings on sequence learning, memory and organization processes, and retrieval strategies used in verbal learning and memory of categorized word lists suggests that basic domain-general learning abilities may be the missing piece of the puzzle in terms of understanding the cognitive factors that underlie the enormous individual differences and variability routinely observed in speech and language outcomes following cochlear implantation