Metacognition, public health compliance, and vaccination willingness.

Metacognition, our ability to reflect on our own beliefs, manifests itself in the confidence we have in these beliefs, and helps us guide our behavior in complex and uncertain environments. Here, we provide empirical tests of the importance of metacognition during the pandemic. Bayesian and frequentist analyses demonstrate that citizens with higher metacognitive sensitivity-where confidence differentiates correct from incorrect COVID-19 beliefs-reported higher willingness to vaccinate against COVID-19, and higher compliance with recommended public health measures. Notably, this benefit of accurate introspection held controlling for the accuracy of COVID-19 beliefs. By demonstrating how vaccination willingness and compliance may relate to insight into the varying accuracy of beliefs, rather than only the accuracy of the beliefs themselves, this research highlights the critical role of metacognitive ability in times of crisis. However, we do not find sufficient evidence to conclude that citizens with higher metacognitive sensitivity were more likely to comply with recommended public health measures when controlling for the absolute level of the confidence citizens had in their COVID-19 beliefs.

Modal and amodal cognition: an overarching principle in various domains of psychology.

Accounting for how the human mind represents the internal and external world is a crucial feature of many theories of human cognition. Central to this question is the distinction between modal as opposed to amodal representational formats. It has often been assumed that one but not both of these two types of representations underlie processing in specific domains of cognition (e.g., perception, mental imagery, and language). However, in this paper, we suggest that both formats play a major role in most cognitive domains. We believe that a comprehensive theory of cognition requires a solid understanding of these representational formats and their functional roles within and across different domains of cognition, the developmental trajectory of these representational formats, and their role in dysfunctional behavior. Here we sketch such an overarching perspective that brings together research from diverse subdisciplines of psychology on modal and amodal representational formats so as to unravel their functional principles and their interactions.

Apparent degradation forms of rhG-CSF under forced conditions: Insights for better quality-control of bioproducts.

Stability and quality control of therapeutic protein formulations is a substantial part of drug development process. The objective of this study is to obtain information about stability of a recombinant human granulocyte colony stimulating factor (rhG-CSF) against various stress factors. This will play a crucial role in the finished product formulation development. In this study, rhG-CSF was exposed to various chemical and physical stress conditions at different levels in order to identify degradation pathways and the nature of impurities generated. Experiments were performed by a combination of orthogonal analytical techniques (reversed phase chromatography (RP-HPLC), size exclusion chromatography (SEC-HPLC), polyacrylamide gel electrophoresis (SDS-PAGE) and isoelectric focusing (IEF)) to set and characterize the different degraded samples. The SEC-HPLC results suggest that the major degradation factors generating aggregated forms of the protein are basically thermal stress, freeze-thaw cycles and vortexing. Meanwhile, deamidated rhG-CSF was induced by basic pH as shown by IEF electrophoregram. As well, oxidized forms were generated increasingly with the time of exposure to hydrogen peroxide as outlined by RP-HPLC analysis. Based on these results, it was possible to define the storage and handling conditions of rhG-CSF finished product during its shelf life.

Behavioral effects of D3 receptor inhibition and 5-HT4 receptor activation on animals undergoing chronic cannabinoid exposure during adolescence.

Chronic exposure to cannabinoids during adolescence results in long-lasting behavioral deficits that match some symptomatologic aspects of schizophrenia. The aim of this study was to investigate the reversibility of the emotional and the cognitive effects of chronic exposure to cannabinoids during adolescence, via subsequent modulation of the serotoninergic 5-HT4 and dopaminergic D3 receptors. RS67333 as a 5-HT4 agonist and U-99194A as a D3 antagonist were administered separately at 1 mg/kg and 20 mg/kg, and in combination at 0.5 mg/kg and 10 mg/kg to adult animals undergoing chronic treatment with the synthetic cannabinoid receptor agonist WIN55,212-2 (1 mg/kg) during adolescence. Animals were tested for anxiety-like behavior and episodic-like memory in the open field and novel object recognition tests respectively 30 minutes after the last drug administration. Chronic WIN55,212-2 treated animals exhibited a lasting disruption of episodic memory and increased anxiety levels. The effect on episodic-like memory were partially restored by acute administration of RS67333 and U-99194A and completely by administration of both drugs in combination at lower doses. However, only RS67333 (20 mg/kg) improved the anxiogenic-like effect of WIN55,212-2. These findings give further support that chronic exposure to cannabinoids during adolescence may be used as an animal model for schizophrenia, and highlight D3 and 5-HT4 receptors as potential targets for an enhanced treatment of the cognitive aspect of this disease.

Extended access to fentanyl vapor self-administration leads to addiction-like behaviors in mice: Blood chemokine/cytokine levels as potential biomarkers.

Rodent models are useful for understanding the mechanisms that underlie opioid addiction, but most preclinical studies have focused on rewarding and consummatory aspects of opioids without components of dependence-induced escalation of drug taking or seeking. We characterized several opioid-related behaviors in mice using a model of vaporized fentanyl self-administration. Male and female C57BL/6J mice were assigned to short-access (ShA; 1 h, nondependent) or long-access (LgA; 6 h, dependent) fentanyl vapor self-administration and subsequently tested in a battery of behavioral tests, followed by blood collection during withdrawal. Compared with mice in the ShA group, mice in the LgA group escalated their fentanyl intake, were more motivated to work to obtain the drug, exhibited greater hyperalgesia, and exhibited greater signs of naloxone-precipitated withdrawal. Principal component analysis indicated the emergence of two independent behavioral constructs: "intake/motivation" and "hyperalgesia/punished seeking." In mice in the LgA condition only, "hyperalgesia/punished seeking" was associated with plasma levels of proinflammatory interleukin-17 (IL-17), chemokine (C-C motif) ligand 4 (CCL-4), and tumor necrosis factor α (TNF-α). Overall, the results suggest that extended access to opioids leads to addiction-like behavior, and some constructs that are associated with addiction-like behavior may be associated with levels of the proinflammatory cytokines/chemokines IL-17, TNF-α, and CCL-4 in blood.

Polarized climate change beliefs: No evidence for science literacy driving motivated reasoning in a U.S. national study.

A substantial literature shows that public polarization over climate change in the U.S. is most pronounced among the science literate. A dominant explanation for this phenomenon is that science literacy amplifies motivated reasoning, the tendency to interpret evidence such that it confirms prior beliefs. The present study tests the biasing account of science literacy in a study among the U.S. population that investigated both interpretation of climate change evidence and repeated belief-updating. Results replicated the typical correlational pattern of political polarization as a function of science literacy. However, results delivered little support for the core causal claim of the biasing account-that science literacy drives motivated reasoning. Hence, these results speak against a mechanism whereby science literacy driving motivated reasoning could explain polarized climate change beliefs among the science literate. This study adds to our growing understanding of the role of science literacy for public beliefs about contested science. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Contested science: Individuals with higher metacognitive insight into interpretation of evidence are less likely to polarize.

Societal polarization over contested science has increased in recent years. To explain this development, political, sociological, and psychological research has identified societal macro-phenomena as well as cognitive micro-level factors that explain how citizens reason about the science. Here we take a radically different perspective, and highlight the effects of metacognition: How citizens reason about their own reasoning. Leveraging methods from Signal Detection Theory, we investigated the importance of metacognitive insight for polarization for the heavily contested topic of climate change, and the less heavily contested topic of nanotechnology. We found that, for climate change (but not for nanotechnology), higher insight into the accuracy of own interpretations of the available scientific evidence related to a lower likelihood of polarization over the science. This finding held irrespective of the direction of the scientific evidence (endorsing or rejecting anthropogenicity of climate change). Furthermore, the polarizing effect of scientific evidence could be traced back to higher metacognitive insight fostering belief-updating in the direction of the evidence at the expense of own, prior beliefs. By demonstrating how metacognition links to polarization, the present research adds to our understanding of the drivers of societal polarization over science.

Importance of domain-specific metacognition for explaining beliefs about politicized science: The case of climate change.

One of the oldest debates in psychological research into politicized science such as nanotechnology, vaccination, or climate change centers around the role of knowledge. Does increased knowledge of the science affect beliefs about it? While research has traditionally focused on the role of object-level knowledge, here we highlight the importance of meta-knowledge: How much people believe they know about the science. Specifically, we demonstrate the importance of meta-knowledge (measured as confidence in knowledge) for explaining beliefs about science with one of the most contested examples: climate change. For a national Germany sample (N = 509), frequentist and Bayesian analyses demonstrated that climate change meta-knowledge was predictive of climate change beliefs, above and beyond object-level climate change knowledge. These results held for both the belief that climate change is risky, and the belief that climate change is anthropogenic, and when controlling for political attitude, and demographic variables. Furthermore, for a second national German sample (N = 588), confidence in climate change knowledge was a stronger predictor of climate change beliefs compared to confidence in other-domain (biological and physical) science knowledge, suggesting that outside of the respective domain, metacognitive confidence did not explain beliefs. These results highlight the relevance of domain-specific metacognition for explaining beliefs about the contested science of climate change. By demonstrating the relevance of metacognitive, rather than solely object-level thought, these results add to our understanding of the cognitive mechanisms involved in the formation of beliefs about politicized science.

Extrapolation accuracy underestimates rule learning: Evidence from the function-learning paradigm.

Understanding the development of non-linear processes such as economic or population growth is an important prerequisite for informed decisions in those areas. In the function-learning paradigm, people's understanding of the function rule that underlies the to-be predicted process is typically measured by means of extrapolation accuracy. Here we argue, however, that even though accurate extrapolation necessitates rule-learning, the reverse does not necessarily hold: Inaccurate extrapolation does not exclude rule-learning. Experiment 1 shows that more than one third of participants who would be classified as "exemplar-based learners" based on their extrapolation accuracy were able to identify the correct function shape and slope in a rule-selection paradigm, demonstrating accurate understanding of the function rule. Experiment 2 shows that higher proportions of rule learning than ruleapplication in the function-learning paradigm is not due to (i) higher a priori probabilities to guess the correct rule in the rule-selection paradigm; nor is it due to (ii) a lack of simultaneous access to all function values in the function-learning paradigm. We conclude that rule application is not tantamount to rule-learning, and that assessing rule xlearning via extrapolation accuracy underestimates the proportion of rule learners in function-learning experiments.

Drug addiction co-morbidity with alcohol: Neurobiological insights.

Addiction is a chronic disorder that consists of a three-stage cycle of binge/intoxication, withdrawal/negative affect, and preoccupation/anticipation. These stages involve, respectively, neuroadaptations in brain circuits involved in incentive salience and habit formation, stress surfeit and reward deficit, and executive function. Much research on addiction focuses on the neurobiology underlying single drug use. However, alcohol use disorder (AUD) can be co-morbid with substance use disorder (SUD), called dual dependence. The limited epidemiological data on dual dependence indicates that there is a large population of individuals suffering from addiction who are dependent on more than one drug and/or alcohol, yet dual dependence remains understudied in addiction research. Here, we review neurobiological data on neurotransmitter and neuropeptide systems that are known to contribute to addiction pathology and how the involvement of these systems is consistent or divergent across drug classes. In particular, we highlight the dopamine, opioid, corticotropin-releasing factor, norepinephrine, hypocretin/orexin, glucocorticoid, neuroimmune signaling, endocannabinoid, glutamate, and GABA systems. We also discuss the limited research on these systems in dual dependence. Collectively, these studies demonstrate that the use of multiple drugs can produce neuroadaptations that are distinct from single drug use. Further investigation into the neurobiology of dual dependence is necessary to develop effective treatments for addiction to multiple drugs.

Optimization of PEGylation reaction time and molar ratio of rhG-CSF toward increasing bioactive potency of monoPEGylated protein.

PEGylation is one of the strategies used for enhancing in vivo residence time of recombinant human Granulocyte Colony-Stimulating Factor (rhG-CSF) and therefore reducing in dose frequency to better fit with patient comfort treatment. In this study, three methoxy polyethylene glycol propionaldehydes (mPEG- ALD) of 10, 20 and 30kDa MW were utilized to produce biologically active monoPEGylated rhG-CSF with enhanced molecular weight. PEGylation reactions were carried out at room temperature and pH 5.0 in the presence of cyanoborohydride and two mPEG-ALD: protein molar ratios (3:1 and 5:1). The reactions were monitored with sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and size exclusion chromatography (SEC-HPLC). The results showed that a 2h reaction with 5:1 mPEG-ALD: protein molar ratio was sufficient to direct the reaction toward optimal yields of monoPEGylated protein (86%). Subsequently, isolation of the monoPEGylated forms was successfully achieved. The purified products were compared with respect to their purity (≥95%), identity and isoelectric focusing parameter characteristics. Biological potencies were measured by cell proliferation assay and showed 20.80-42.73% retention of bioactivities. This study highlights the possible improvement of rhG-CSF efficiency by PEGylation. Further studies will investigate in vitro and in vivo immunogenicity and toxicity of monoPEGylated conjugates.

Biomarkers for Alzheimer Disease: Classical and Novel Candidates' Review.

The biomarkers may be useful for predictive diagnosis of Alzheimer's disease (AD). The current challenge is to diagnose it in its preclinical phase. The combination of cerebrospinal fluid (CSF) biomarkers and imaging has been investigated extensively for a number of years. It can provide an increased diagnostic accuracy. This review discusses the contribution of classical biomarkers to predict AD and highlights novel candidates identified as potential markers for AD. We referred to the electronic databases PubMed/Medline and Web of Science to search for articles that were published until February 2016. Sixty-two records were included in qualitative synthesis. In the first section, the results show the contribution of biomarkers to predict and track AD considered as classical biomarkers. In the second section, the results highlight the involvement of novel candidates that should be considered for future evaluation in the characterization of the AD progression. Reported findings open prospect to define noninvasive biomarkers to predict AD before symptoms onset.

Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

Preliminary studies of acetylcholinesterase activity in the rat brain using N-phenylferrocenecarboxamide labelled by the technetium-99m.

There is currently great interest in developing radiolabeled substrates for acetylcholinesterase that would be useful in the in vivo imaging of patients with Alzheimer's disease. The reduction of acetylcholinesterase (AChE) activity in the brain has been measured in dementia disorders such as Alzheimer's disease and dementia with Lewy bodies using (11)C and (18)F-labeled acetylcholine analogues. Our aim was to develop a new 99mTc-labeled acetylcholine analogue: N-phenylferrocenecarboxamide labelled with technetium-99m (99mTc-TPCC) to study acetylcholinesterase activity. In vivo and in vitro studies demonstrated that the labelled compound was a substrate for acetylcholinesterase. The hydrolytic rate of this substrate was measured and the specificity was evaluated using the inhibitor BW 284 C51. In rat experiments, the 99mTc-TPCC showed desirable properties for studying the acetylcholinesterase in the rat brain: high hydrolytic rate and a moderate specificity of the substrate for acetylcholinesterase.

A 1-methyl-4-piperidinyl cytectrene carboxylate labeled by the technetium 99m, a radiotracer for rat brain acetylcholinesterase activity.

Alzheimer's disease (AD) is a degenerative neurological disorder that causes progressive and irreversible loss of connections between brain cells and loss of mental functions. Clinical and postmortem studies show that the biochemical changes in brains of AD patients include decrease in acetylcholinesterase (AChE) activity. Our aim was to study AChE activity using piperidinyl ester labelled with technetium-99m. In vivo and in vitro studies demonstrated that labelled piperidinyl ester was a substrate for AChE. The hydrolytic rate of this substrate was measured and the specificity was evaluated using the inhibitor BW284c51. The rhenium analogues of the technetium-labelled substrate were used to determine the affinity constant (K(m)) and the maximum reaction velocity (V(max)) because of the high specific activity of technetium. The high hydrolytic rate and high specificity of the substrate for AChE make it suitable as an in vivo radiotracer for studying AChE activity in the brain.