Systematic Distortions Research Articles

Iterative Removal of G-PCC Attribute Compression Artifacts Based on a Graph Neural Network

As a compression standard, Geometry-based Point Cloud Compression (G-PCC) can effectively reduce data by compressing both geometric and attribute information. Even so, due to coding errors and data loss, point clouds (PCs) still face distortion challenges, such as the encoding of attribute information may lead to spatial detail loss and visible artifacts, which negatively impact visual quality. To address these challenges, this paper proposes an iterative removal method for attribute compression artifacts based on a graph neural network. First, the geometric coordinates of the PCs are used to construct a graph that accurately reflects the spatial structure, with the PC attributes treated as signals on the graph’s vertices. Adaptive graph convolution is then employed to dynamically focus on the areas most affected by compression, while a bi-branch attention block is used to restore high-frequency details. To maintain overall visual quality, a spatial consistency mechanism is applied to the recovered PCs. Additionally, an iterative strategy is introduced to correct systematic distortions, such as additive bias, introduced during compression. The experimental results demonstrate that the proposed method produces finer and more realistic visual details, compared to state-of-the-art techniques for PC attribute compression artifact removal. Furthermore, the proposed method significantly reduces the network runtime, enhancing processing efficiency.

Carbon market design and market sentiment

Concerns about systematic price distortions in the EU Emission Trading System (ETS) have risen in recent years. This paper shows how carbon-market design affects the impact of market sentiment, i.e. systematic deviations of price expectations from fundamentals by at least some market participants, on equilibrium prices. The Market Stability Reserve (MSR) of the EU ETS that adjusts supply based on past allowance banking undermines self-stabilization of the carbon market by discouraging rational intertemporal arbitrage. The MSR increases vulnerability of the EU ETS to market sentiments. Initially, the allowance price responds more to distorted expectations while the MSR (partially) prevents the distortion to spread across periods. Making the MSR more responsive to banking decisions increases the likelihood that distorted expectations turn out to be correct ex post. In contrast, a mechanism that adjust the cap based on the current allowance price does not face a trade off between stabilizing prices in the present and the future.

Method for designing a grid-slit spectrometer with low spectral-line bending

The classic single-slit spectrometer exhibits low field utilisation in the direction of the slit width and requires a push-broom observation method to visualise moving point targets. Therefore, we designed a spectrometer with a grid slit to overcome these limitations. First, a grid slit composed of two sets of vertically arranged slit arrays, each containing five slits, was established. Then, the spectral-line bending of the off-axis slits was analysed, and the parameters of the dispersive elements that minimised the spectral-line bending of the slit arrays were selected as the initial values. Next, a merit function was established to consider the systematic distortion, and finally, the system was optimised. The proposed spectrometer design enabled a grid slit with 10 slits to be implemented, which significantly enhanced the field utilisation of the spectrometer and enabled starting-observation-mode operation. The spectral-line bending of each slit was less than 0.21 μm (less than one-third of a pixel size) and the modulation transfer function was higher than 0.7 (@45.45 lp/mm). The design and analysis results confirmed the feasibility of the proposed grid-slit design method, which can potentially be applied to other spectrometers to improve their field utilisation or to enable starting-observation-mode operations.

Voting behaviour in AMNOG benefit assessments

The study examines the voting behaviour in the G-BA in AMNOG benefit assessments since 2011, showing on the one hand that most decisions (93%) are passed unanimously. On the other hand, it is particularly striking that the National Association of Statutory Health Insurance Funds (GKV-SV) voted in favour of a lower classification of added benefit in 100% of the dissenting resolutions compared to the other benches. The result empirically illustrates a procedural conflict of interest at the GKV-SV and underpins the structural criticism regarding the governance deficits of the G-BA. Even in the unanimously adopted procedures, these deficits can lead to a systematic negative distortion of the benefit assessment. This cannot be verified due to a lack of documentation at the G-BA. Overall, the unity of the GKV-SV as regulator, referee and player in the AMNOG procedure should be scrutinised more closely. This also applies in particular to the evaluation of the more recent statutory interventions (guard rails and combination discounts), which are highly susceptible to conflicts of interest.

Complexity Matters: Normalization to Prototypical Viewpoint Induces Memory Distortion along the Vertical Axis of Scenes.

Scene memory is prone to systematic distortions potentially arising from experience with the external world. Boundary transformation, a well-known memory distortion effect along the near-far axis of the three-dimensional space, represents the observer's erroneous recall of scenes' viewing distance. Researchers argued that normalization to the prototypical viewpoint with the high-probability viewing distance influenced this phenomenon. Herein, we hypothesized that the prototypical viewpoint also exists in the vertical angle of view (AOV) dimension and could cause memory distortion along scenes' vertical axis. Human subjects of both sexes were recruited to test this hypothesis, and two behavioral experiments were conducted, revealing a systematic memory distortion in the vertical AOV in both the forced choice (n = 79) and free adjustment (n = 30) tasks. Furthermore, the regression analysis implied that the complexity information asymmetry in scenes' vertical axis and the independent subjective AOV ratings from a large set of online participants (n = 1,208) could jointly predict AOV biases. Furthermore, in a functional magnetic resonance imaging experiment (n = 24), we demonstrated the involvement of areas in the ventral visual pathway (V3/V4, PPA, and OPA) in AOV bias judgment. Additionally, in a magnetoencephalography experiment (n = 20), we could significantly decode the subjects' AOV bias judgments ∼140 ms after scene onset and the low-level visual complexity information around the similar temporal interval. These findings suggest that AOV bias is driven by the normalization process and associated with the neural activities in the early stage of scene processing.

Tactile Landmarks: the Relative Landmark Location Alters Spatial Distortions.

The influence of landmarks, that is, nearby non-target stimuli, on spatial perception has been shown in multiple ways. These include altered target localization variability near landmarks and systematic spatial distortions of target localizations. Previous studies have mostly been conducted in the visual modality using temporary, artificial landmarks or the tactile modality with persistent landmarks on the body. Thus, it is unclear whether both landmark types produce the same spatial distortions as they were never investigated in the same modality. Addressing this, we used a novel tactile setup to present temporary, artificial landmarks on the forearm and systematically manipulated their location to either be close to a persistent landmark (wrist or elbow) or in between both persistent landmarks at the middle of the forearm. Initial data (Exp. 1 and Exp. 2) suggested systematic differences of temporary landmarks based on their distance from the persistent landmark, possibly indicating different distortions of temporary and persistent landmarks. Subsequent control studies (Exp. 3 and Exp. 4) showed this effect was driven by the relative landmark location within the target distribution. Specifically, landmarks in the middle of the target distribution led to systematic distortions of target localizations toward the landmark, whereas landmarks at the side led to distortions away from the landmark for nearby targets, and toward the landmark with wider distances. Our results indicate that experimental results with temporary landmarks can be generalized to more natural settings with persistent landmarks, and further reveal that the relative landmark location leads to different effects of the pattern of spatial distortions.

Comprehensive Distortion Analysis of a Laser Direct Metal Deposition (DMD)-Manufactured Large Prototype Made of Soft Martensitic Steel 1.4313

Additive manufacturing (AM) by using direct metal deposition (DMD) often causes erratic distortion patterns, especially on large parts. This study presents a systematic distortion analysis by employing numerical approaches using transient–thermal and structural simulations, experimental approaches using tomography, X-ray diffraction (XRD), and an analytical approach calculating the buckling distortion of a piston. The most essential geometrical features are thin walls situated between massive rings. An eigenvalue buckling analysis, a DMD process, and heat treatment simulation are presented. The eigenvalue buckling simulation shows that it is highly dependent on the mesh size. The computational effort of the DMD and heat treatment simulation was reduced through simplifications. Moreover, artificial imperfections were imposed in the heat treatment simulation, which moved the part into the buckling state inspired by the experiment. Although the numerical results of both simulations are successful, the eigenvalue and DMD simulation cannot be validated through tomography and XRD. This is because tomography is unable to measure small elastic strain fields, the simulated residual stresses were overestimated, and the part removal disturbed the residual stress equilibrium. Nevertheless, the heat treatment simulation can predict the distortion pattern caused by an inhomogeneous temperature field during ambient cooling in an oven. The massive piston skirt cools down and shrinks faster than the massive core. The reduced yield strength at elevated temperatures and critical buckling load leads to plastic deformation of the thin walls.

Blind deconvolution decreases requirements on temporal resolution of DCE-MRI: Application to 2nd generation pharmacokinetic modeling

PurposeDynamic Contrast-Enhanced (DCE) MRI with 2nd generation pharmacokinetic models provides estimates of plasma flow and permeability surface-area product in contrast to the broadly used 1st generation models (e.g. the Tofts models). However, the use of 2nd generation models requires higher frequency with which the dynamic images are acquired (around 1.5 s per image). Blind deconvolution can decrease the demands on temporal resolution as shown previously for one of the 1st generation models. Here, the temporal-resolution requirements achievable for blind deconvolution with a 2nd generation model are studied. MethodsThe 2nd generation model is formulated as the distributed-capillary adiabatic-tissue-homogeneity (DCATH) model. Blind deconvolution is based on Parker's model of the arterial input function. The accuracy and precision of the estimated arterial input functions and the perfusion parameters is evaluated on synthetic and real clinical datasets with different levels of the temporal resolution. ResultsThe estimated arterial input functions remained unchanged from their reference high-temporal-resolution estimates (obtained with the sampling interval around 1 s) when increasing the sampling interval up to about 5 s for synthetic data and up to 3.6–4.8 s for real data. Further increasing of the sampling intervals led to systematic distortions, such as lowering and broadening of the 1st pass peak. The resulting perfusion-parameter estimation error was below 10% for the sampling intervals up to 3 s (synthetic data), in line with the real data perfusion-parameter boxplots which remained unchanged up to the sampling interval 3.6 s. ConclusionWe show that use of blind deconvolution decreases the demands on temporal resolution in DCE-MRI from about 1.5 s (in case of measured arterial input functions) to 3–4 s. This can be exploited in increased spatial resolution or larger organ coverage.

Perception, action, and the body model

In 1992, Goodale and Milner proposed to study the visual system based on function, thus dissociating vision for perception (ventral stream) and vision for action (dorsal stream). This became known as the Perception and Action model (PAM). Following the PAM in the visual system, a somatosensory PAM was proposed including a body representation for perception and a separate for action. This review explores the body model of the hand and how it relates to the PAM. The body model refers to the internal representation of the body that is responsible for position sense. Previous research has shown that the representation of the hand features systematic distortions: an overestimation of hand width and an underestimation of finger length. These distortions have been reported using different paradigms, different body parts, and in various settings. Thus, body model distortions appear to be a characteristic of human body representation. If the body model of the hand is distorted, how can actions like reaching and grasping be accurate? We review evidence that body model distortions may in fact provide a functional benefit to our actions, that cortical maps in the somatosensory and motor cortices reflect these distortions, and that actions rely on a distorted body model. We argue that the body model is a product of both the ventral and dorsal somatosensory streams. Further, we suggest that the body model is an example of the inextricable link between the two streams.

Mechanism and Hints Behind the Anchoring Effect in Sales, Auction, and Willingness to Pay

The anchoring effect, a cognitive bias, has substantial implications in various fields, including behavioural economics. It influences individuals' assessments and choices by anchoring them to specific initial values and causing insufficient adjustments. This article explores the anchoring effect's mechanisms and its impact on B2B sales, auctions, and willingness to pay. It also offers guidance for business owners, price-setters, and auctioneers to leverage the anchoring effect to their advantage. In B2B sales, results indicate that when salespeople are informed of higher reference discounts, they tend to offer significantly higher discounts to new customers, which leads to conservatism and cause a systematic distortion in various economic contexts. This demonstrates the positive impact of reference discounts on pricing decisions, driven by a tendency to avoid risk. Auction item sequences influencing bidding outcomes. Auctions featuring more valuable items first yield higher revenues, as buyers anchor their expectations to the initial prices. Sellers can strategically position items to maximize profits, particularly when the initial auction prices are set lower than market expectations. Studies also reveal that Lower anchoring prices may decrease willingness to pay (WTP) while higher initial reference points can lead to higher WTP. Additionally, the use of "up to" in advertising discounts can positively affect WTP due to an asymmetry effect.

Automatic Scoring of Verbal Divergent Thinking Tests: From Lexical Databases to Large Language Models

The article explores the evolution of methods for automatically assessing verbal divergent thinking tests. Resear - chers have increasingly focused on the ability to evaluate the originality of respondents' answers by calculating their semantic distance from the stimulus task. From 2009 to 2019, latent semantic analysis became the primary method for assessing semantic distances. Overall, in terms of internal consistency and correlation with expert ratings, its application yielded satisfactory results, maintaining an acceptable balance of quality and effort expended. However, issues emerged (dependence on the corpus used, result instability, systematic distortions related to the length of analyzed responses), prompting researchers to transition to more advanced models of distributional semantics (GloVe, Word2Vec etc.), large language models, and supervised learning. Large language models, especially those fine-tuned on creativity test materials, demonstrated higher effectiveness compared to models assessing semantic distances and approached expert evaluations. In addition to evaluating originality, the article considers works proposing methods for automatic assessment of elaboration, flexibility, associative flow, and divergent semantic integration. References to online platforms that allow for automatic assessments of originality in responses to divergent tests are provided. The issue of interpreting results obtained through large language models is discussed. A drawback of using these models is the lack of understanding of the basis on which judgments of the originality of creative products are made. The perspectives of applying explainable artificial intelligence for evaluating results of verbal and non-verbal tests of creative thinking are being discussed.

Behavioral representational similarity analysis reveals how episodic learning is influenced by and reshapes semantic memory

While semantic and episodic memory have been shown to influence each other, uncertainty remains as to how this interplay occurs. We introduce a behavioral representational similarity analysis approach to assess whether semantic space can be subtly re-sculpted by episodic learning. Eighty participants learned word pairs that varied in semantic relatedness, and learning was bolstered via either testing or restudying. Next-day recall is superior for semantically related pairs, but there is a larger benefit of testing for unrelated pairs. Analyses of representational change reveal that successful recall is accompanied by a pulling together of paired associates, with cue words in semantically related (but not unrelated) pairs changing more across learning than target words. Our findings show that episodic learning is associated with systematic and asymmetrical distortions of semantic space which improve later recall by making cues more predictive of targets, reducing interference from potential lures, and establishing novel connections within pairs.

Multisensory integration reduces landmark distortions for tactile but not visual targets.

Target localization is influenced by the presence of additionally presented nontargets, termed landmarks. In both the visual and tactile modality, these landmarks led to systematic distortions of target localizations often resulting in a shift toward the landmark. This shift has been attributed to averaging the spatial memory of both stimuli. Crucially, everyday experiences often rely on multiple modalities, and multisensory research suggests that inputs from different senses are optimally integrated, not averaged, for accurate perception, resulting in more reliable perception of cross-modal compared with uni-modal stimuli. As this could also lead to a reduced influence of the landmark, we wanted to test whether landmark distortions would be reduced when presented in a different modality or whether landmark distortions were unaffected by the modalities presented. In two experiments (each n = 30) tactile or visual targets were paired with tactile or visual landmarks. Experiment 1 showed that targets were less shifted toward landmarks from the different than the same modality, which was more pronounced for tactile than for visual targets. Experiment 2 aimed to replicate this pattern with increased visual uncertainty to rule out that smaller localization shifts of visual targets due to low uncertainty had led to the results. Still, landmark modality influenced localization shifts for tactile but not visual targets. The data pattern for tactile targets is not in line with memory averaging but seems to reflect the effects of multisensory integration, whereas visual targets were less prone to landmark distortions and do not appear to benefit from multisensory integration.NEW & NOTEWORTHY In the present study, we directly tested the predictions of two different accounts, namely, spatial memory averaging and multisensory integration, concerning the degree of landmark distortions of targets across modalities. We showed that landmark distortions were reduced across modalities compared to distortions within modalities, which is in line with multisensory integration. Crucially, this pattern was more pronounced for tactile than for visual targets.

Aberrant distortion of variance components in multilevel models under conflation of level-specific effects.

Methodologists have often acknowledged that, in multilevel contexts, level-1 variables may have distinct within-cluster and between-cluster effects. However, a prevailing notion in the literature is that separately estimating these effects is primarily important when there is specific interest in doing so. Consequently, in practice, researchers uninterested in disaggregating these effects (or unaware of their difference) routinely fit models that conflate them. Furthermore, even researchers who properly disaggregate the fixed components in a model (avoid fixed conflation) may still inadvertently and unknowingly conflate the random effects (fail to avoid random conflation). The purpose of this article is to elucidate an unappreciated consequence of such fixed or random conflation, namely, that it can cause systematic distortion in all variance components, yielding uninterpretable variances that adversely affect the entire model. In this article, I provide novel mathematical derivations, simulations, and pedagogical illustrations of such variance distortion, showing how it leads to several aberrant consequences: (1) error variances at level-1 and level-2 can systematically increase (in the population) with the addition of predictors; (2) there can be a large apparent degree of between-cluster random-effect variability in cases in which there is actually no between-cluster outcome variability; (3) R-squared measures of explained variance can be severely biased, uninterpretable, and well below the logical bound of 0; and (4) inference for all fixed components of the model-not just the conflated slopes themselves-can be compromised. I conclude with recommendations for practice, including cautionary notes on interpreting results from prior research that had specified conflated slopes. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The MillenniumTNG Project: semi-analytic galaxy formation models on the past lightcone

ABSTRACT Upcoming large galaxy surveys will subject the standard cosmological model, Lambda Cold Dark Matter, to new precision tests. These can be tightened considerably if theoretical models of galaxy formation are available that can predict galaxy clustering and galaxy–galaxy lensing on the full range of measurable scales, throughout volumes as large as those of the surveys, and with sufficient flexibility that uncertain aspects of the underlying astrophysics can be marginalized over. This, in particular, requires mock galaxy catalogues in large cosmological volumes that can be directly compared to observation, and can be optimized empirically by Monte Carlo Markov Chains or other similar schemes, thus eliminating or estimating parameters related to galaxy formation when constraining cosmology. Semi-analytic galaxy formation methods implemented on top of cosmological dark matter simulations offer a computationally efficient approach to construct physically based and flexibly parametrized galaxy formation models, and as such they are more potent than still faster, but purely empirical models. Here, we introduce an updated methodology for the semi-analytic L-Galaxies code, allowing it to be applied to simulations of the new MillenniumTNG project, producing galaxies directly on fully continuous past lightcones, potentially over the full sky, out to high redshift, and for all galaxies more massive than $\sim 10^8\, {\rm M}_\odot$. We investigate the numerical convergence of the resulting predictions, and study the projected galaxy clustering signals of different samples. The new methodology can be viewed as an important step towards more faithful forward-modelling of observational data, helping to reduce systematic distortions in the comparison of theory to observations.

Information acquisition and rating agencies

For decades credit rating agencies were viewed as trusted arbiters of creditworthiness and their ratings as important tools for managing risk. The common narrative is that the value of ratings was compromised by the evolution of the industry to a form where issuers pay for ratings. In this paper we consider both an investor-pays and an issuer-pays set-ups and show that if the investor-pays version can overcome the free-rider problem it is efficient, and otherwise leads to under-provision of information; while if the issuer-pays can force disclosure, it is efficient, but otherwise it leads to less revealing information because of the systematic distortion in revealed information along with over-investment in information. We show that in both these arrangements credit ratings have value in equilibrium and how reputation insures that, in equilibrium, ratings will reflect sound assessments of credit worthiness. We argue that regulatory reliance on ratings and the increasing importance of risk-weighted capital in prudential regulation have more likely contributed to distorted ratings than the matter of who pays for them.

Comparing visual memories to similar visual inputs risks lasting memory distortion.

Maintaining perceptual experiences in visual working memory (VWM) allows us to flexibly accomplish various tasks, but some tasks come at a price. For example, comparing VWM representations to novel perceptual inputs can induce inadvertent memory distortions. If these distortions can persist, they may explain why everyday memories often become unreliable after people perform perceptual comparisons (e.g., eyewitness testimony). Here, we conducted two experiments to assess the consequences of perceptual comparisons using real-world objects that were temporarily maintained in VWM (n = 32) or recalled from visual long-term memory back into VWM (n = 30). In each experiment, young adults reported systematic memory distortions following perceptual comparisons. These distortions increased in magnitude with the delay between encoding and comparisons and were preserved when memories were retrieved again a day later. These findings suggest that perceptual comparisons play a mechanistic role in everyday memory distortions, including situations where memory accuracy is vital. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Big hype about ChapGPT in medicine : Is it something for rhythmologists? What must be taken into consideration?

ChatGPT, achatbot based on alarge language model, is currently attracting much attention. Modern machine learning (ML) architectures enable the program to answer almost any question, to summarize, translate, and even generate its own texts, all in atext-based dialogue with the user. Underlying technologies, summarized under the acronym NLP (natural language processing), go back to the 1960s. In almost all areas including medicine, ChatGPT is raising enormous hopes. It can easily pass medical exams and may be useful in patient care, diagnostic and therapeutic assistance, and medical research. The enthusiasm for this new technology shown even by medical professionals is surprising. Although the system knows much, it does not know everything; not everything it outputs is accurate either. Every output has to be carefully checked by the user for correctness, which is often not easily done since references to sources are lacking. Issues regarding data protection and ethics also arise. Today's language models are not free of bias and systematic distortion. These shortcomings have led to calls for stronger regulation of the use of ChatGPT and an increasing number of similar language models. However, this new technology represents an enormous progress in knowledge processing and dissemination. Numerous scenarios in which ChatGPT can provide assistance are conceivable, including in rhythmology. In the future, it will be crucial to render the models error-free and transparent and to clearly define the rules for their use. Responsible use requires systematic training to improve the digital competence of users, including physicians who use such programs.

The internal fragility of representative democracy: was Schumpeter right?

In recent decades, the rapid emergence of new political leaders capturing growing social discontent with populist promises has highlighted elements of the internal fragility of democracy. Schumpeter predicts such fragility in Capitalism, Socialism and Democracy, where, in analyzing thse “competitive struggle for the popular vote,” he makes two assumptions that reverse the classical model of rational decision-making: the limited use of “conscious rationality” by voters and the potential unfairness of political leaders. These two elements introduce systematic distortions into the process of collective decision-making in democracy, suggesting a possible explanation for the rise of populism. Based on delegation, representative democracy must function despite the small amount of intellectual effort that most citizens put into forming their political opinions. Ideologies have historically functioned as implicit heuristics, allowing citizens to evaluate political facts, shape their expectations, and simplify political reasoning; as such, they have been the tools used by parties to give credibility to their programs while at the same time affiliating and polarizing the electorate. While they continue to serve as anchors for defining political identity, today's ideologies are fragmented and therefore less effective in supporting long-term programs and retaining voters; thus, keeping voters close to their political affiliation requires massive use of the media, and for emerging parties lacking strong identitarian values, a short-term political offer becomes less risky than formulating long-term political strategies. At the same time, the decline of classical ideologies makes any political commitment to long-term perspectives hardly credible to the electorate. This leads to an adverse selection process in which populist programs have a better chance of success than long-term policy programs. As a result, the role of the political leader as an entrepreneur is severely weakened. The competition for votes becomes unfair (since it is not based on the quality or plausibility of policy results), while at the same time the process of polarization disrupts the elements of mediation. The result is a process of democratic backsliding that can ultimately lead to a loss of trust in democracy.

Cognitive bias and how to improve sustainable decision making.

The rapid advances of science and technology have provided a large part of the world with all conceivable needs and comfort. However, this welfare comes with serious threats to the planet and many of its inhabitants. An enormous amount of scientific evidence points at global warming, mass destruction of bio-diversity, scarce resources, health risks, and pollution all over the world. These facts are generally acknowledged nowadays, not only by scientists, but also by the majority of politicians and citizens. Nevertheless, this understanding has caused insufficient changes in our decision making and behavior to preserve our natural resources and to prevent upcoming (natural) disasters. In the present study, we try to explain how systematic tendencies or distortions in human judgment and decision-making, known as "cognitive biases," contribute to this situation. A large body of literature shows how cognitive biases affect the outcome of our deliberations. In natural and primordial situations, they may lead to quick, practical, and satisfying decisions, but these decisions may be poor and risky in a broad range of modern, complex, and long-term challenges, like climate change or pandemic prevention. We first briefly present the social-psychological characteristics that are inherent to (or typical for) most sustainability issues. These are: experiential vagueness, long-term effects, complexity and uncertainty, threat of the status quo, threat of social status, personal vs. community interest, and group pressure. For each of these characteristics, we describe how this relates to cognitive biases, from a neuro-evolutionary point of view, and how these evolved biases may affect sustainable choices or behaviors of people. Finally, based on this knowledge, we describe influence techniques (interventions, nudges, incentives) to mitigate or capitalize on these biases in order to foster more sustainable choices and behaviors.