Confidence ratings do not distinguish imagination from reality.

Perceptual reality monitoring refers to the ability to distinguish internally triggered imagination from externally triggered reality. Such monitoring can take place at perceptual or cognitive levels-for example, in lucid dreaming, perceptual experience feels real but is accompanied by a cognitive insight that it is not real. We recently developed a paradigm to reveal perceptual reality monitoring errors during wakefulness in the general population, showing that imagined signals can be erroneously attributed to perception during a perceptual detection task. In the current study, we set out to investigate whether people have insight into perceptual reality monitoring errors by additionally measuring perceptual confidence. We used hierarchical Bayesian modeling of confidence criteria to characterize metacognitive insight into the effects of imagery on detection. Over two experiments, we found that confidence criteria moved in tandem with the decision criterion shift, indicating a failure of reality monitoring not only at a perceptual but also at a metacognitive level. These results further show that such failures have a perceptual rather than a decisional origin. Interestingly, offline queries at the end of the experiment revealed global, task-level insight, which was uncorrelated with local, trial-level insight as measured with confidence ratings. Taken together, our results demonstrate that confidence ratings do not distinguish imagination from reality during perceptual detection. Future research should further explore the different cognitive dimensions of insight into reality judgments and how they are related.

Folk psychological attributions of consciousness to large language models.

Technological advances raise new puzzles and challenges for cognitive science and the study of how humans think about and interact with artificial intelligence (AI). For example, the advent of large language models and their human-like linguistic abilities has raised substantial debate regarding whether or not AI could be conscious. Here, we consider the question of whether AI could have subjective experiences such as feelings and sensations ('phenomenal consciousness'). While experts from many fields have weighed in on this issue in academic and public discourse, it remains unknown whether and how the general population attributes phenomenal consciousness to AI. We surveyed a sample of US residents (n = 300) and found that a majority of participants were willing to attribute some possibility of phenomenal consciousness to large language models. These attributions were robust, as they predicted attributions of mental states typically associated with phenomenality-but also flexible, as they were sensitive to individual differences such as usage frequency. Overall, these results show how folk intuitions about AI consciousness can diverge from expert intuitions-with potential implications for the legal and ethical status of AI.

Robust self-supervised denoising of voltage imaging data using CellMincer.

Voltage imaging enables high-throughput investigation of neuronal activity, yet its utility is often constrained by a low signal-to-noise ratio (SNR). Conventional denoising algorithms, such as those based on matrix factorization, impose limiting assumptions about the noise process and the spatiotemporal structure of the signal. While deep learning based denoising techniques offer greater adaptability, existing approaches fail to fully exploit the fast temporal dynamics and unique short- and long-range dependencies within voltage imaging datasets. Here, we introduce CellMincer, a novel self-supervised deep learning method designed specifically for denoising voltage imaging datasets. CellMincer operates on the principle of masking and predicting sparse sets of pixels across short temporal windows and conditions the denoiser on precomputed spatiotemporal auto-correlations to effectively model long-range dependencies without the need for large temporal denoising contexts. We develop and utilize a physics-based simulation framework to generate realistic datasets for rigorous hyperparameter optimization and ablation studies, highlighting the key role of conditioning the denoiser on precomputed spatiotemporal auto-correlations to achieve 3-fold further reduction in noise. Comprehensive benchmarking on both simulated and real voltage imaging datasets, including those with paired patch-clamp electrophysiology (EP) as ground truth, demonstrates CellMincer's state-of-the-art performance. It achieves substantial noise reduction across the entire frequency spectrum, enhanced detection of subthreshold events, and superior cross-correlation with ground-truth EP recordings. Finally, we demonstrate how CellMincer's addition to a typical voltage imaging data analysis workflow improves neuronal segmentation, peak detection, and ultimately leads to significantly enhanced separation of functional phenotypes.

Modeling interpretable correspondence between cell state and perturbation response with CellCap.

Single-cell transcriptomics, in conjunction with genetic and compound perturbations, offers a robust approach for exploring cellular behaviors in diverse contexts. Such experiments allow uncovering cell-state-specific responses to perturbations, a crucial aspect in unraveling the intricate molecular mechanisms governing cellular behavior and potentially discovering novel regulatory pathways and therapeutic targets. However, prevailing computational methods predominantly focus on predicting average cellular responses, disregarding the inherent response heterogeneity associated with cell state diversity. In this study, we present CellCap, a deep generative model designed for the end-to-end analysis of single-cell perturbation experiments. CellCap employs sparse dictionary learning in a latent space to deconstruct cell-state-specific perturbation responses into a set of transcriptional response programs. These programs are then utilized by each perturbation condition and each cell at varying degrees. The incorporation of specific model design choices, such as dot-product cross-attention between cell states and response programs, along with a linearly-decoded latent space, underlay the interpretation power of CellCap. We evaluate CellCap's model interpretability through multiple simulated scenarios and apply it to two real single-cell perturbation datasets. These datasets feature either heterogeneous cellular populations or a complex experimental setup. Our results demonstrate that CellCap successfully uncovers the relationship between cell state and perturbation response, unveiling novel insights overlooked in previous analyses. The model's interpretability, coupled with its effectiveness in capturing heterogeneous responses, positions CellCap as a valuable tool for advancing our understanding of cellular behaviors in the context of perturbation experiments.

Different components of cognitive-behavioral therapy affect specific cognitive mechanisms.

Psychological therapies are among the most effective treatments for common mental health problems-however, we still know relatively little about how exactly they improve symptoms. Here, we demonstrate the power of combining theory with computational methods to parse effects of different components of cognitive-behavioral therapies onto underlying mechanisms. Specifically, we present data from a series of randomized-controlled experiments testing the effects of brief components of behavioral and cognitive therapies on different cognitive processes, using well-validated behavioral measures and associated computational models. A goal setting intervention, based on behavioral activation therapy activities, reliably and selectively reduced sensitivity to effort when deciding how to act to gain reward. By contrast, a cognitive restructuring intervention, based on cognitive therapy materials, reliably and selectively reduced the tendency to attribute negative everyday events to self-related causes. The effects of each intervention were specific to these respective measures. Our approach provides a basis for beginning to understand how different elements of common psychotherapy programs may work.

Tests for consciousness in humans and beyond.

Which systems/organisms are conscious? New tests for consciousness ('C-tests') are urgently needed. There is persisting uncertainty about when consciousness arises in human development, when it is lost due to neurological disorders and brain injury, and how it is distributed in nonhuman species. This need is amplified by recent and rapid developments in artificial intelligence (AI), neural organoids, and xenobot technology. Although a number of C-tests have been proposed in recent years, most are of limited use, and currently we have no C-tests for many of the populations for which they are most critical. Here, we identify challenges facing any attempt to develop C-tests, propose a multidimensional classification of such tests, and identify strategies that might be used to validate them.

Perspective: Potatoes, Quality Carbohydrates, and Dietary Patterns.

Potatoes have long been a staple food in many cultures and cuisines, but they have gained a reputation as a low-quality carbohydrate source that should be avoided in the diet. Historically, this view has been justified by citing the glycemic index of potatoes as the main indicator of their quality. However, their nutrient composition should also be considered. The association of potatoes with energy-dense Western dietary patterns has also contributed to a perception that potatoes are inherently unhealthy. Although some studies have suggested an association between potato consumption and increased risk of health problems, such as type 2 diabetes, these associations may be confounded by fried potato intake and are strongest at intake levels higher than average consumption rates. Epidemiologic data suggest total potato intake is not a health risk in Eastern populations and can be consumed as part of a healthy diet. Furthermore, clinical trial data demonstrate that potatoes' health impact, irrespective of preparation, is similar to legumes and comparable with refined grains, with few deleterious effects found. These findings highlight the importance of moving beyond the glycemic index and adopting a more nuanced evaluation of the epidemiologic data to better understand the health impact of potato intake. Ultimately, the negative reputation of potatoes stems from an overinterpretation of their glycemic index and association with unhealthy Western dietary patterns, as well as oversimplification of the epidemiologic data. By considering carbohydrate quality, it becomes clear that potatoes can be part of a healthy diet given the proper consideration.

Distinguishing between intrinsic and instrumental sources of the value of choice.

Considerable evidence suggests that people value the freedom to choose. However, it is unclear whether this preference for choice stems purely from choice's intrinsic value, or whether people prefer to choose because it tends to provide instrumental information about desirable outcomes. To address this question, participants completed a novel choice task in which they could freely choose to exert choice or not, manipulating the level of instrumental contingency between participants' choices and eventual outcomes, which we operationalized using the information-theoretic concept of mutual information. Across two experiments (N = 100 each), we demonstrate a marked preference for choice, but importantly found that participants' preference for free choice is weakened when actions are decoupled from outcomes. Taken together, our results demonstrate that a significant factor in people's preference for choice is an assumption about the instrumental value of choice, suggesting against a purely intrinsic value of choice.

Identifying content-invariant neural signatures of perceptual vividness.

Some conscious experiences are more vivid than others. Although perceptual vividness is a key component of human consciousness, how variation in this magnitude property is registered by the human brain is unknown. A striking feature of neural codes for magnitude in other psychological domains, such as number or reward, is that the magnitude property is represented independently of its sensory features. To test whether perceptual vividness also covaries with neural codes that are invariant to sensory content, we reanalyzed existing magnetoencephalography and functional MRI data from two distinct studies which quantified perceptual vividness via subjective ratings of awareness and visibility. Using representational similarity and decoding analyses, we find evidence for content-invariant neural signatures of perceptual vividness distributed across visual, parietal, and frontal cortices. Our findings indicate that the neural correlates of subjective vividness may share similar properties to magnitude codes in other cognitive domains.

Metacognition and Confidence: A Review and Synthesis.

Determining the psychological, computational, and neural bases of confidence and uncertainty holds promise for understanding foundational aspects of human metacognition. While a neuroscience of confidence has focused on the mechanisms underpinning subpersonal phenomena such as representations of uncertainty in the visual or motor system, metacognition research has been concerned with personal-level beliefs and knowledge about self-performance. I provide a road map for bridging this divide by focusing on a particular class of confidence computation: propositional confidence in one's own (hypothetical) decisions or actions. Propositional confidence is informed by the observer's models of the world and their cognitive system, which may be more or less accurate-thus explaining why metacognitive judgments are inferential and sometimes diverge from task performance. Disparate findings on the neural basis of uncertainty and performance monitoring are integrated into a common framework, and a new understanding of the locus of action of metacognitive interventions is developed.

The future of metacognition research: Balancing construct breadth with measurement rigor.

Foundational work in the psychology of metacognition identified a distinction between metacognitive knowledge (stable beliefs about one's capacities) and metacognitive experiences (local evaluations of performance). More recently, the field has focused on developing tasks and metrics that seek to identify metacognitive capacities from momentary estimates of confidence in performance, and providing precise computational accounts of metacognitive failure. However, this notable progress in formalising models of metacognitive judgments may come at a cost of ignoring broader elements of the psychology of metacognition - such as how stable meta-knowledge is formed, how social cognition and metacognition interact, and how we evaluate affective states that do not have an obvious ground truth. We propose that construct breadth in metacognition research can be restored while maintaining rigour in measurement, and highlight promising avenues for expanding the scope of metacognition research. Such a research programme is well placed to recapture qualitative features of metacognitive knowledge and experience while maintaining the psychophysical rigor that characterises modern research on confidence and performance monitoring.

Charting the Chronology of Research on Added Sugars: A Scoping Review and Evidence Map.

The objective of this study was to conduct a scoping review and produce a publicly available database characterizing the design and reporting elements of the literature on dietary added sugars and select health outcomes. Relevant studies published from 1990 to 2021 were identified to create a database containing information on study and population characteristics, reported added sugars source and concentrations, dietary energy balance, total energy intake, and outcome measures related to body composition, obesity, cardiovascular disease, and diabetes mellitus. There were 245 publications identified, 22% of which describe interventions, and 78% describe observational studies. Publications pertaining to added sugars have risen dramatically since 2010, led by studies primarily assessing body composition (36%) or cardiovascular health (32%), including adults (65%), measuring liquid-only sources of added sugars (56%). Over 65% of studies reported total energy intake, 61% controlled for total energy intake in the design and analysis, and fewer than 5% of studies reported the energy balance of subjects. There has been a significant increase in research on added sugars since 2010, with substantial heterogeneity across all facets of methodology-study designs, exposures and outcomes of interest, terminology, and reporting of dietary intake data-thus limiting the ability to synthesize evidence in this scope of the literature. This evidence map highlights gaps and important areas for improvement to strengthen the state of research and better inform future policies and dietary recommendations on added sugars.

Reproducible image-based profiling with Pycytominer.

Technological advances in high-throughput microscopy have facilitated the acquisition of cell images at a rapid pace, and data pipelines can now extract and process thousands of image-based features from microscopy images. These features represent valuable single-cell phenotypes that contain information about cell state and biological processes. The use of these features for biological discovery is known as image-based or morphological profiling. However, these raw features need processing before use and image-based profiling lacks scalable and reproducible open-source software. Inconsistent processing across studies makes it difficult to compare datasets and processing steps, further delaying the development of optimal pipelines, methods, and analyses. To address these issues, we present Pycytominer, an open-source software package with a vibrant community that establishes an image-based profiling standard. Pycytominer has a simple, user-friendly Application Programming Interface (API) that implements image-based profiling functions for processing high-dimensional morphological features extracted from microscopy images of cells. Establishing Pycytominer as a standard image-based profiling toolkit ensures consistent data processing pipelines with data provenance, therefore minimizing potential inconsistencies and enabling researchers to confidently derive accurate conclusions and discover novel insights from their data, thus driving progress in our field.

Transcriptional profile of the rat cardiovascular system at single cell resolution.

Background: Despite the critical role of the cardiovascular system, our understanding of its cellular and transcriptional diversity remains limited. We therefore sought to characterize the cellular composition, phenotypes, molecular pathways, and communication networks between cell types at the tissue and sub-tissue level across the cardiovascular system of the healthy Wistar rat, an important model in preclinical cardiovascular research. We obtained high quality tissue samples under controlled conditions that reveal a level of cellular detail so far inaccessible in human studies. Methods and Results: We performed single nucleus RNA-sequencing in 78 samples in 10 distinct regions including the four chambers of the heart, ventricular septum, sinoatrial node, atrioventricular node, aorta, pulmonary artery, and pulmonary veins (PV), which produced an aggregate map of 505,835 nuclei. We identified 26 distinct cell types and additional subtypes, including a number of rare cell types such as PV cardiomyocytes and non-myelinating Schwann cells (NMSCs), and unique groups of vascular smooth muscle cells (VSMCs), endothelial cells (ECs) and fibroblasts (FBs), which gave rise to a detailed cell type distribution across tissues. We demonstrated differences in the cellular composition across different cardiac regions and tissue-specific differences in transcription for each cell type, highlighting the molecular diversity and complex tissue architecture of the cardiovascular system. Specifically, we observed great transcriptional heterogeneities among ECs and FBs. Importantly, several cell subtypes had a unique regional localization such as a subtype of VSMCs enriched in the large vasculature. We found the cellular makeup of PV tissue is closer to heart tissue than to the large arteries. We further explored the ligand-receptor repertoire across cell clusters and tissues, and observed tissue-enriched cellular communication networks, including heightened Nppa - Npr1/2/3 signaling in the sinoatrial node. Conclusions: Through a large single nucleus sequencing effort encompassing over 500,000 nuclei, we broadened our understanding of cellular transcription in the healthy cardiovascular system. The existence of tissue-restricted cellular phenotypes suggests regional regulation of cardiovascular physiology. The overall conservation in gene expression and molecular pathways across rat and human cell types, together with our detailed transcriptional characterization of each cell type, offers the potential to identify novel therapeutic targets and improve preclinical models of cardiovascular disease.

Dietary consumption of beef and red meat: a scoping review and evidence map on cognitive outcomes across the lifespan.

OBJECTIVE: Mixed evidence exists on the impact of beef consumption on cognition. The goal was to create an evidence map capturing studies assessing beef consumption and cognition to reveal gaps and opportunities in the body of literature. DESIGN: A scoping review was conducted to locate studies up to March 2022 using PubMed and backwards citation screening. Data were extracted by two independent reviewers with conflict resolution, and a database was created and made publicly available. SETTING: Intervention and observational studies. PARTICIPANTS: Humans of any age, sex and/or health status, without moderate to severe cognitive impairment and/or abnormalities. RESULTS: Twenty-two studies were identified that quantified beef or red meat intake and assessed cognition. Six studies assessed beef intake, with the remaining studies describing intake of red meat that may or may not include beef. Nine articles described randomised controlled trials (RCT), mostly conducted in children. Thirteen described observational studies, primarily conducted on adults and seniors. The most common cognitive domains measured included intelligence and general cognition, and memory. The majority of controlled studies were rated with high risk of bias, with the majority of observational trials rated with serious or greater risk of bias. CONCLUSIONS: Red meat and beef intake and cognition is largely understudied. There is a significant lack of replication across study designs, populations, exposures and outcomes measured. The quality of the research would be considerably enhanced by focused assessments of beef intake (and not red meat in general) and specific cognitive domains, along with improved adherence to reporting standards.

The parapoxvirus Orf virus inhibits dsDNA-mediated type I IFN expression via STING-dependent and STING-independent signalling pathways.

Type I interferons (IFNs) are critical in the host defence against viruses. They induce hundreds of interferon-stimulated genes (ISGs) many of which have an antiviral role. Poxviruses induce IFNs via their pathogen-associated molecular patterns, in particular, their genomic DNA. In a majority of cell types, dsDNA is detected by a range of cytoplasmic DNA sensors that mediate type I IFN expression via stimulator of interferon genes (STING). Orf virus (ORFV) induces cutaneous pustular skin lesions and is the type species of the Parapoxvirus genus within the Poxviridae family. The aim of this study was to investigate whether ORFV modulates dsDNA-induced type I IFN expression via STING-dependent signalling pathways in human dermal fibroblasts (hNDF) and THP-1 cells. We showed that ORFV infection of these cell types treated with poly(dA:dT) resulted in strong inhibition of expression of IFN-ß. In hNDFs, we showed using siRNA knock-down that STING was essential for type I IFN induction. IFN-ß expression was further reduced when both STING and RIG-I were knocked down. In addition, HEK293 cells that do not express STING or Toll-like receptors also produce IFN-ß following stimulation with poly(dA:dT). The 5' triphosphate dsRNA produced by RNA polymerase III specifically results in the induction of type I IFNs through the RIG-I receptor. We showed that ORFV infection resulted in strong inhibition of IFN-ß expression in HEK293 cells stimulated with poly(dA:dT). Overall, this study shows that ORFV potently counteracts the STING-dependent and STING-independent IFN response by antagonizing dsDNA-activated IFN signalling pathways.

Predictions and rewards affect decision-making but not subjective experience.

To survive, organisms constantly make decisions to avoid danger and maximize rewards in information-rich environments. As a result, decisions about sensory input are not only driven by sensory information but also by other factors, such as the expected rewards of a decision (known as the payoff matrix) or by information about temporal regularities in the environment (known as cognitive priors or predictions). However, it is unknown to what extent these different types of information affect subjective experience or whether they merely result in nonperceptual response criterion shifts. To investigate this question, we used three carefully matched manipulations that typically result in behavioral shifts in decision criteria: a visual illusion (Müller-Lyer condition), a punishment scheme (payoff condition), and a change in the ratio of relevant stimuli (base rate condition). To gauge shifts in subjective experience, we introduce a task in which participants not only make decisions about what they have just seen but are also asked to reproduce their experience of a target stimulus. Using Bayesian ordinal modeling, we show that each of these three manipulations affects the decision criterion as intended but that the visual illusion uniquely affects sensory experience as measured by reproduction. In a series of follow-up experiments, we use computational modeling to show that although the visual illusion results in a distinct drift-diffusion (DDM) parameter profile relative to nonsensory manipulations, reliance on DDM parameter estimates alone is not sufficient to ascertain whether a given manipulation is perceptual or nonperceptual.

Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender.

Droplet-based single-cell assays, including single-cell RNA sequencing (scRNA-seq), single-nucleus RNA sequencing (snRNA-seq) and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), generate considerable background noise counts, the hallmark of which is nonzero counts in cell-free droplets and off-target gene expression in unexpected cell types. Such systematic background noise can lead to batch effects and spurious differential gene expression results. Here we develop a deep generative model based on the phenomenology of noise generation in droplet-based assays. The proposed model accurately distinguishes cell-containing droplets from cell-free droplets, learns the background noise profile and provides noise-free quantification in an end-to-end fashion. We implement this approach in the scalable and robust open-source software package CellBender. Analysis of simulated data demonstrates that CellBender operates near the theoretically optimal denoising limit. Extensive evaluations using real datasets and experimental benchmarks highlight enhanced concordance between droplet-based single-cell data and established gene expression patterns, while the learned background noise profile provides evidence of degraded or uncaptured cell types.

Paradoxical evidence weighting in confidence judgments for detection and discrimination.

When making discrimination decisions between two stimulus categories, subjective confidence judgments are more positively affected by evidence in support of a decision than negatively affected by evidence against it. Recent theoretical proposals suggest that this "positive evidence bias" may be due to observers adopting a detection-like strategy when rating their confidence-one that has functional benefits for metacognition in real-world settings where detectability and discriminability often go hand in hand. However, it is unknown whether, or how, this evidence-weighting asymmetry affects detection decisions about the presence or absence of a stimulus. In four experiments, we first successfully replicate a positive evidence bias in discrimination confidence. We then show that detection decisions and confidence ratings paradoxically suffer from an opposite "negative evidence bias" to negatively weigh evidence even when it is optimal to assign it a positive weight. We show that the two effects are uncorrelated and discuss our findings in relation to models that account for a positive evidence bias as emerging from a confidence-specific heuristic, and alternative models where decision and confidence are generated by the same, Bayes-rational process.

Interoceptive and metacognitive facets of fatigue in multiple sclerosis.

Numerous disorders are characterised by fatigue as a highly disabling symptom. Fatigue plays a particularly important clinical role in multiple sclerosis (MS) where it exerts a profound impact on quality of life. Recent concepts of fatigue grounded in computational theories of brain-body interactions emphasise the role of interoception and metacognition in the pathogenesis of fatigue. So far, however, for MS, empirical data on interoception and metacognition are scarce. This study examined interoception and (exteroceptive) metacognition in a sample of 71 persons with a diagnosis of MS. Interoception was assessed by prespecified subscales of a standard questionnaire (Multidimensional Assessment of Interoceptive Awareness [MAIA]), while metacognition was investigated with computational models of choice and confidence data from a visual discrimination paradigm. Additionally, autonomic function was examined by several physiological measurements. Several hypotheses were tested based on a preregistered analysis plan. In brief, we found the predicted association of interoceptive awareness with fatigue (but not with exteroceptive metacognition) and an association of autonomic function with exteroceptive metacognition (but not with fatigue). Furthermore, machine learning (elastic net regression) showed that individual fatigue scores could be predicted out-of-sample from our measurements, with questionnaire-based measures of interoceptive awareness and sleep quality as key predictors. Our results support theoretical concepts of interoception as an important factor for fatigue and demonstrate the general feasibility of predicting individual levels of fatigue from simple questionnaire-based measures of interoception and sleep.