Citation counts and journal impact factors do not capture some indicators of research quality in the behavioural and brain sciences

Citation data and journal impact factors are important components of faculty dossiers and figure prominently in both promotion decisions and assessments of a researcher’s broader societal impact. Although these metrics play a large role in high-stakes decisions, the evidence is mixed about whether they are strongly correlated with indicators of research quality. We use data from a large-scale dataset comprising 45 144 journal articles with 667 208 statistical tests and data from 190 replication attempts to assess whether citation counts and impact factors predict three indicators of research quality: (i) the accuracy of statistical reporting, (ii) the evidential value of the reported data and (iii) the replicability of a given experimental result. Both citation counts and impact factors were weak and inconsistent predictors of research quality, so defined, and sometimes negatively related to quality. Our findings raise the possibility that citation data and impact factors may be of limited utility in evaluating scientists and their research. We discuss the implications of these findings in light of current incentive structures and discuss alternative approaches to evaluating research

Memory Constraints on Hypothesis Generation and Decision Making

Hypothesis generation is the process people use to generate explanations for patterns of data, which is an act vital to everyday problem solving. It is the basis for decision making in many professions, such as medicine, intelligence and reconnaissance analysis, auditing, and fault detection in nuclear power plants. Even laypeople’s impressions of acquaintances’ personalities based on behavioral patterns can be considered a case of hypothesis generation. This article provides an overview of research elucidating the cognitive processes that underlie hypothesis generation and decision making.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Causality, Randomness, Intelligibility, and the Epistemology of the Cell

Because the basic unit of biology is the cell, biological knowledge is rooted in the epistemology of the cell, and because life is the salient characteristic of the cell, its epistemology must be centered on its livingness, not its constituent components. The organization and regulation of these components in the pursuit of life constitute the fundamental nature of the cell. Thus, regulation sits at the heart of biological knowledge of the cell and the extraordinary complexity of this regulation conditions the kind of knowledge that can be obtained, in particular, the representation and intelligibility of that knowledge. This paper is essentially split into two parts. The first part discusses the inadequacy of everyday intelligibility and intuition in science and the consequent need for scientific theories to be expressed mathematically without appeal to commonsense categories of understanding, such as causality. Having set the backdrop, the second part addresses biological knowledge. It briefly reviews modern scientific epistemology from a general perspective and then turns to the epistemology of the cell. In analogy with a multi-faceted factory, the cell utilizes a highly parallel distributed control system to maintain its organization and regulate its dynamical operation in the face of both internal and external changes. Hence, scientific knowledge is constituted by the mathematics of stochastic dynamical systems, which model the overall relational structure of the cell and how these structures evolve over time, stochasticity being a consequence of the need to ignore a large number of factors while modeling relatively few in an extremely complex environment

Probing for and Quantifying Agonist Hydrogen Bonds in α6β2 Nicotinic Acetylcholine Receptors

Designing subtype-selective agonists for neuronal nicotinic acetylcholine receptors (nACh¬R) is a challenging and significant goal aided by intricate knowledge of each subtype’s binding patterns. We previously reported that in α6β2 receptors, acetylcholine makes a functional cation-π interaction with Trp149, but nicotine and TC299423 do not, suggesting a distinctive binding site. This work explores hydrogen binding at the backbone carbonyl associated with α6β2 Trp149. Substituting the i+1 residue, Thr150, with its α-hydroxy analogue (Tah) attenuates the carbonyl’s hydrogen bond accepting ability. At α6(T150Tah)β2, nicotine shows a 24-fold loss of function, TC299423 shows a modest loss, and acetylcholine shows no effect. Nicotine was further analyzed via a double-mutant cycle analysis utilizing N’-methylnicotinium, which indicated a hydrogen bond in α6β2 with a ΔΔG of 2.6 kcal/mol. Thus, even though nicotine does not make the conserved cation-π interaction with Trp149, it still makes a functional hydrogen bond to its associated backbone carbonyl

Integration of the ecological and error models of overconfidence using a multiple-trace memory model.

This research examined a memory processes account of the calibration of probability judgments. A multiple-trace memory model, MINERVA-DM (MDM; Dougherty, Ogden, & Gettys, 1999), was used to integrate the ecological (Brunswikian) and the error (Thurstonian) models of overconfidence. The model predicts that overconfidence should decrease both as a function of experience and as a function of encoding quality. Both increased experience and improved encoding quality result in lower error variance in the output of the model, which in turn leads to better calibration. Three experiments confirmed these predictions. Implications of MDM's account of overconfidence are discussed

Heterologous expression and nonsense suppression provide insights into agonist behavior at α6β2 nicotinic acetylcholine receptors

The α6-containing subtypes of the nicotinic acetylcholine receptor (nAChR) are localized to presynaptic terminals of the dopaminergic pathways of the central nervous system. Selective ligands for these nAChRs are potentially useful in both Parkinson's disease and addiction. For these and other goals, it is important to distinguish the binding behavior of agonists at the α6-β2 binding site versus other subtypes. To study this problem, we apply nonsense suppression-based non-canonical amino acid mutagenesis. We report a combination of four mutations in α6β2 that yield high-level heterologous expression in Xenopus oocytes. By varying mRNA injection ratios, two populations were observed with unique characteristics, likely due to differing stoichiometries. Responses to nine known nAChR agonists were analyzed at the receptor, and their corresponding EC50 values and efficacies are reported. The system is compatible with nonsense suppression, allowing structure–function studies between Trp149 – a conserved residue on loop B found to make a cation-π interaction at several nAChR subtypes – and several agonists. These studies reveal that acetylcholine forms a strong cation-π interaction with the conserved tryptophan, while nicotine and TC299423 do not, suggesting a unique pharmacology for the α6β2 nAChR

Successful Cessation Programs that Reduce Comorbidity May Explain Surprisingly Low Smoking Rates Among Hospitalized COVID-19 Patients

A recent, non-peer-reviewed meta-analysis suggests that smoking may reduce the risk of hospitalization with COVID-19 because the prevalence of smoking among hospitalized COVID-19 is less than that of the general population. However, there are alternative explanations for this phenomena based on (1) the failure to report, or accurately record, smoking history during emergency hospital admissions and (2) a pre-disposition to avoid smoking among COVID-19 patients with tobacco-related comorbidities (a type of “reverse” causation). For example, urine testing of hospitalized patients in Australia for cotinine showed that smokers were under-counted by 37% because incoming patients failed to inform staff about their smoking behavior. Face-to-face interviews can introduce bias into the responses to attitudinal and behavioral questions not present in the self-completion interviews typically used to measure smoking prevalence in the general population. Subjects in face-to-face interviews may be unwilling to admit socially undesirable behavior and attitudes under direct questioning. Reverse causation may also contribute to the difference between smoking prevalence in the COVID-19 and general population. Patients hospitalized with COVID-19 may be simply less prone to use tobacco than the general population. A potentially robust “reverse causation” hypothesis for reduced prevalence of smokers in the COVID-19 population is the enrichment of patients in that population with serious comorbidities that motivates them to quit smoking. We judge that this “smoking cessation” mechanism may account for a significant fraction of the reduced prevalence of smokers in the COVID-19 population. Testing this hypothesis will require a focused research program

Characterization of the Effectiveness of Reporting Lists of Small Feature Sets Relative to the Accuracy of the Prior Biological Knowledge

When confronted with a small sample, feature-selection algorithms often fail to find good feature sets, a problem exacerbated for high-dimensional data and large feature sets. The problem is compounded by the fact that, if one obtains a feature set with a low error estimate, the estimate is unreliable because training-data-based error estimators typically perform poorly on small samples, exhibiting optimistic bias or high variance. One way around the problem is limit the number of features being considered, restrict features sets to sizes such that all feature sets can be examined by exhaustive search, and report a list of the best performing feature sets. If the list is short, then it greatly restricts the possible feature sets to be considered as candidates; however, one can expect the lowest error estimates obtained to be optimistically biased so that there may not be a close-to-optimal feature set on the list. This paper provides a power analysis of this methodology; in particular, it examines the kind of results one should expect to obtain relative to the length of the list and the number of discriminating features among those considered. Two measures are employed. The first is the probability that there is at least one feature set on the list whose true classification error is within some given tolerance of the best feature set and the second is the expected number of feature sets on the list whose true errors are within the given tolerance of the best feature set. These values are plotted as functions of the list length to generate power curves. The results show that, if the number of discriminating features is not too small—that is, the prior biological knowledge is not too poor—then one should expect, with high probability, to find good feature sets

RELIABILTY OF ELECTROMAGNETIC TRACKING IN DESCRIBING PITCHING MECHANICS

The purpose of this study was to establish the reliability of an electromagnetic tracking device (ETD) in analyzing young baseball pitchers. Two data collection sessions in which throwing kinematics were recorded were conducted across a five day span. Joint kinematics were calculated using the International Shoulder Group recommendations. Correlation analyses examining inter-day reliability of the ETD showed that the system was within acceptable limits (r > 0.73). Throughout the selected instances of the pitch cycle, the ETD used in the current study was shown to be reliable across multiple data collection session with ICCs ranging from r = 0.73 to 0.86. It appears so long as the setup, sensor attachment, and digitization protocols remain consistent across data collection sessions, ETD’s are a reliable tool in analyzing throwing movements in younger subjects