Underway spectrophotometry in the Fram Strait (European Arctic Ocean): a highly resolved chlorophyll a data source for complementing satellite ocean color.

Satellite remote sensing of chlorophyll a concentration (Chl-a) in the Arctic Ocean is spatially and temporally limited and needs to be supplemented and validated with substantial volumes of in situ observations. Here, we evaluated the capability of obtaining highly resolved in situ surface Chl-a using underway spectrophotometry operated during two summer cruises in 2015 and 2016 in the Fram Strait. Results showed that Chl-a measured using high pressure liquid chromatography (HPLC) was well related (R2 = 0.90) to the collocated particulate absorption line height at 676 nm obtained from the underway spectrophotometry system. This enabled continuous surface Chl-a estimation along the cruise tracks. When used to validate Chl-a operational products as well as to assess the Chl-a algorithms of the aqua moderate resolution imaging spectroradiometer (MODIS-A) and Sentinel-3 Ocean Land Color Imager (OLCI) Level 2 Chl-a operational products, and from OLCI Level 2 products processed with Polymer atmospheric correction algorithm (version 4.1), the underway spectrophotometry based Chl-a data sets proved to be a much more sufficient data source by generating over one order of magnitude more match-ups than those obtained from discrete water samples. Overall, the band ratio (OCI, OC4) Chl-a operational products from MODIS-A and OLCI as well as OLCI C2RCC products showed acceptable results. The OLCI Polymer standard output provided the most reliable Chl-a estimates, and nearly as good results were obtained from the OCI algorithm with Polymer atmospheric correction method. This work confirms the great advantage of the underway spectrophotometry in enlarging in situ Chl-a data sets for the Fram Strait and improving satellite Chl-a validation and Chl-a algorithm assessment over discrete water sample analysis in the laboratory.

Simultaneous modeling of choice, confidence, and response time in visual perception.

How can choice, confidence, and response times be modeled simultaneously? Here, we propose the new dynamical weighted evidence and visibility (dynWEV) model, an extension of the drift-diffusion model of decision-making, to account for choices, reaction times, and confidence simultaneously. The decision process in a binary perceptual task is described as a Wiener process accumulating sensory evidence about the choice options bounded by two constant thresholds. To account for confidence judgments, we assume a period of postdecisional accumulation of sensory evidence and parallel accumulation of information about the reliability of the present stimulus. We examined model fits in two experiments, a motion discrimination task with random dot kinematograms and a postmasked orientation discrimination task. A comparison between the dynWEV model, two-stage dynamical signal detection theory, and several versions of race models of decision-making showed that only dynWEV produced acceptable fits of choices, confidence, and reaction time. This finding suggests that confidence judgments depend not only on choice evidence but also on a parallel estimate of stimulus discriminability and postdecisional accumulation of evidence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Measures of metacognitive efficiency across cognitive models of decision confidence.

Meta-d'/d' has become the quasi-gold standard to quantify metacognitive efficiency because meta-d'/d' was developed to control for discrimination performance, discrimination criteria, and confidence criteria even without the assumption of a specific generative model underlying confidence judgments. Using simulations, we demonstrate that meta-d'/d' is not free from assumptions about confidence models: Only when we simulated data using a generative model of confidence according to which the evidence underlying confidence judgments is sampled independently from the evidence utilized in the choice process from a truncated Gaussian distribution, meta-d'/d' was unaffected by discrimination performance, discrimination task criteria, and confidence criteria. According to five alternative generative models of confidence, there exist at least some combination of parameters where meta-d'/d' is affected by discrimination performance, discrimination criteria, and confidence criteria. A simulation using empirically fitted parameter sets showed that the magnitude of the correlation between meta-d'/d' and discrimination performance, discrimination task criteria, and confidence criteria depends heavily on the generative model and the specific parameter set and varies between negligibly small and very large. These simulations imply that a difference in meta-d'/d' between conditions does not necessarily reflect a difference in metacognitive efficiency but might as well be caused by a difference in discrimination performance, discrimination task criterion, or confidence criteria. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Modelling visibility judgments using models of decision confidence.

How can we explain the regularities in subjective reports of human observers about their subjective visual experience of a stimulus? The present study tests whether a recent model of confidence in perceptual decisions, the weighted evidence and visibility model, can be generalized from confidence to subjective visibility. In a postmasked orientation identification task, observers reported the subjective visibility of the stimulus after each single identification response. Cognitive modelling revealed that the weighted evidence and visibility model provided a superior fit to the data compared with the standard signal detection model, the signal detection model with unsystematic noise superimposed on ratings, the postdecisional accumulation model, the two-channel model, the response-congruent evidence model, the two-dimensional Bayesian model, and the constant noise and decay model. A comparison between subjective visibility and decisional confidence revealed that visibility relied more on the strength of sensory evidence about features of the stimulus irrelevant to the identification judgment and less on evidence for the identification judgment. It is argued that at least two types of evidence are required to account for subjective visibility, one related to the identification judgment, and one related to the strength of stimulation.

Approaches to measure class importance in Knowledge Graphs.

The amount, size, complexity, and importance of Knowledge Graphs (KGs) have increased during the last decade. Many different communities have chosen to publish their datasets using Linked Data principles, which favors the integration of this information with many other sources published using the same principles and technologies. Such a scenario requires to develop techniques of Linked Data Summarization. The concept of a class is one of the core elements used to define the ontologies which sustain most of the existing KGs. Moreover, classes are an excellent tool to refer to an abstract idea which groups many individuals (or instances) in the context of a given KG, which is handy to use when producing summaries of its content. Rankings of class importance are a powerful summarization tool that can be used both to obtain a superficial view of the content of a given KG and to prioritize many different actions over the data (data quality checking, visualization, relevance for search engines…). In this paper, we analyze existing techniques to measure class importance and propose a novel approach called ClassRank. We compare the class usage in SPARQL logs of different KGs with the importance ranking produced by the approaches evaluated. Then, we discuss the strengths and weaknesses of the evaluated techniques. Our experimentation suggests that ClassRank outperforms state-of-the-art approaches measuring class importance.

Confidence in masked orientation judgments is informed by both evidence and visibility.

How do human observers determine their degree of belief that they are correct in a decision about a visual stimulus-that is, their confidence? According to prominent theories of confidence, the quality of stimulation should be positively related to confidence in correct decisions, and negatively to confidence in incorrect decisions. However, in a backward-masked orientation task with a varying stimulus onset asynchrony (SOA), we observed that confidence in incorrect decisions also increased with stimulus quality. Model fitting to our decision and confidence data revealed that the best explanation for the present data was the new weighted evidence-and-visibility model, according to which confidence is determined by evidence about the orientation as well as by the general visibility of the stimulus. Signal detection models, postdecisional accumulation models, two-channel models, and decision-time-based models were all unable to explain the pattern of confidence as a function of SOA and decision correctness. We suggest that the metacognitive system combines several cues related to the correctness of a decision about a visual stimulus in order to calculate decision confidence.