Perception Research Research Articles

Understanding urban wealth perception using a hybrid dataset and ranking‐scoring framework

AbstractResearch on urban perception using street view images has developed rapidly. Global urban perception datasets commonly used in local urban perception research may be inaccurate because of the differences in the characteristics across cities. Building a new local urban perception dataset requires a practical and efficient solution to construct high‐availability datasets and a corresponding urban perception framework. In this study, we developed a simple and effective ranking‐scoring framework containing two stages. First, we constructed a hybrid dataset in a 1:1 ratio, based on the global and local datasets. Next, we built a ranking‐scoring network that uses deep learning models to obtain the features of images and a learning‐to‐rank approach to determine perceptual scores. To validate the effectiveness of our method, we examined the perception of urban wealth in Wuhan, China. Our framework showed high accuracy when the hybrid dataset was used. Furthermore, we analyzed the differences between the spatial distribution of the perception results of the hybrid model and the global model in the study area. In addition, we analyzed driving factors for wealth perception, such as visual elements and road types. The results verified that the hybrid dataset is more suitable for local urban perception. Our framework could assist researchers in conducting fast and accurate urban perception of local cities, using a hybrid dataset and ranking‐scoring, thus enabling urban planners to provide more consideration to users' perceptions during the planning process without dwelling upon additional burdens.

Developmental History of the Vocal Tract Organ

The Vocal Tract Organ has had a number of iterations resulting from advances in available technology as well as requirements of perceptual experiments and performance paradigms. The objective of this paper is to relate the development history of the Vocal Tract Organ from the original vision to what it is today as a modern version of the Vox Humana pipe organ stop for application in voice production and perception research. Descriptive METHODS/DESIGN: The latest Vocal Tract Organ is a polyphonic eight-channel eight-stop one manual Vocal Tract Organ that enables tab stop selected three-D printed vocal tracts to be used to create sound. This version includes eight stops (four for female vowel oral tracts and four for male vowel oral tracts). The stops are implemented using conventionally engraved pipe organ stop tabs labeled "Vox Humana Female" or "Vox Humana Male" followed by the three-D printed vowel: "EE", "AH", "ER" or "UU." This is described alongside the development stages from which it emerged and covers all previous versions of the Vocal Tract Organ. At the heart of the latest instrument is a Bela BeagleBone Black with a Bela cape audio expander board which incorporates eight 16-bit audio outputs at 44.1 kHz sampling rate (earlier versions based on the Arduino Mega board were limited to 8-bit audio at an audio sampling rate of 16.384 kHz which limited the overall output spectrum). The latest Vocal Tract Organ is programmed using the audio graphical programming language Pure Data which is directly compatible with the Bela system. The Pure Data patch creates eight larynx outputs at the pitches set by the keys depressed on the keyboard and these are routed to Vocal Tract Organ loudspeakers with three-D printed vocal tracts attached. The Bela system has enabled real-time synthesis of eight-note polyphonic sounds to eight separate three-D printed vocal tracts, each being selectable via an organ tab stop switch. The instrument has been cased in a purpose-designed and built prototype laser-cut enclosure that incorporates the eight tab stops, a MIDI keyboard input, a pipe organ style swell (volume) pedal connection, four stereo (eight channels) audio amplifiers and terminal connections for the eight loudspeakers. The Vocal Tract Organ functions as a musical instrument for performance and as an instrument for vowel and pitch perception research. Implementing it with the Bela family of processors allows for low audio latency of 1 ms and rapid prototyping due to being able to program directly with the high-level graphical audio programming language, Pure data (Pd).

#YouAreWhatYouTweetCHHH: Identity and fricative epithesis in French-language tweets

AbstractPhrase-final fricative epithesis (PFFE), often indicated in informal writing with a final -h or -ch, e.g. beaucoup_h, oui_ch, is a sociophonetic variable of Hexagonal French in which utterance-final vowels give way to intense fricative-like whistles. Production research has found PFFE to be used at equal rates among men and women, and perception research has found that native French speakers perceive it to mark either formal speech or intense affect. This research furthers the special issue’s line of inquiry on French variation in forms of digital media by extending the analysis to a sociophonetic variable with a robust life on Twitter. The study compares the pragmatic value of tweets containing PFFE with previously described values and then examines interactions of gender, hostword phrasal location and lexical frequency on its realization. 96.8% of PFFE occurrences in the 2060-token corpus were classified into the pragmatic categories of Formality and Intense Affect. Results suggest that PFFE has become a salient enough sociophonetic variable that 21st-century French users represent it graphically in their tweets, however, its usage is structurally more permissive than in spoken language, signaling that it has taken on an iconic value in digital spaces.

Sketch2Pose

Artists frequently capture character poses via raster sketches, then use these drawings as a reference while posing a 3D character in a specialized 3D software --- a time-consuming process, requiring specialized 3D training and mental effort. We tackle this challenge by proposing the first system for automatically inferring a 3D character pose from a single bitmap sketch, producing poses consistent with viewer expectations. Algorithmically interpreting bitmap sketches is challenging, as they contain significantly distorted proportions and foreshortening. We address this by predicting three key elements of a drawing, necessary to disambiguate the drawn poses: 2D bone tangents, self-contacts, and bone foreshortening. These elements are then leveraged in an optimization inferring the 3D character pose consistent with the artist's intent. Our optimization balances cues derived from artistic literature and perception research to compensate for distorted character proportions. We demonstrate a gallery of results on sketches of numerous styles. We validate our method via numerical evaluations, user studies, and comparisons to manually posed characters and previous work. Code and data for our paper are available at http://www-labs.iro.umontreal.ca/bmpix/sketch2pose/.

Climate Autobiography Timeline: Adapting Timeline Research Methods to the Study of Climate Perceptions

Abstract Climate perception is a growing area of study in the social sciences and one that has implications on the tools and strategies we use to communicate climate change risk information. However, the range of climate perception studies remains limited, focused primarily on perceptions of day-to-day weather, sudden-onset severe events, or long-term permanent change. Phenomena situated between these extremes (e.g., annual- to decadal-scale variability) are largely missing from social science of climate research. Whether this is due to limited perception by research participants, is due to limited research attention, or is a reflection of the methods commonly applied to human dimensions of climate research, this gap precludes analysis of the full range of complex climate experiences and their influence on climate perception and understanding. In this paper, we offer a proof of concept for the climate autobiography timeline (CAT), a visual timeline tool developed to assess climate perception while prompting an ordered consideration of time, with the goal of eliciting insights into complex and long-term climate experiences such as low-frequency climate variability. Results are based off a preliminary application of the CAT across focus groups conducted in Newfoundland and Labrador, a province of Canada that is subject to low-frequency climate variability and frequent high-impact weather. Results reveal three key findings: 1) weather and climate narratives are commonly anchored to two time periods, potentially obscuring perceptions of variability; 2) narratives focus on socially important weather and climate phenomena; and 3) the social and visual coconstruction of weather and climate narratives may yield more holistic representations of local climate knowledge. Significance Statement The purpose of this work is to highlight the utility of timeline research methods to the study of climate perception research. Specifically, the climate autobiography timeline (CAT) serves as a tool that can address limitations of research tools commonly applied to the study of climate perceptions, notably the inability for current methods to elicit and organize complex climate experiences. Failure to capture these experiences may prevent a holistic and socially grounded understanding of climate perceptions. Drawing from a preliminary application of CATs in the province of Newfoundland and Labrador in Canada, we highlight how the tool can provide information complementary to, but distinct from, data collected through more commonly used methods such as interviews or surveys. This approach holds promise for analyses of long-term climate history, impacts of historical severe events, and cultural impact of weather and climate.

The Application Of Blended Learning to Student's Perception and Satisfaction Review by Gender

This research aims to find out how students' perceptions and satisfaction with the application of Blended Learning in the Social Studies Department at Madrasah Aliyah Negeri (MAN) 1 and Madrasah Aliyah Negeri (MAN) 2 Pekanbaru model based on gender. This type of research is Quantitative Research. Proportional Stratified Random Sampling technique was used to take samples in this study. The data used are 114 respondents from students of MAN 1 Pekanbaru and 50 respondents from students of MAN 2 Pekanbaru Model. This research method uses quantitative descriptive analysis. The data used are interviews and questionnaires. Validity Test and Reliability Test were used before the research instrument was distributed to the respondents. Data Analysis Techniques using t test. The results of the Perception and Satisfaction research of students are positively and significantly influenced by the application of Blended Learning to students of MAN 1 and MAN 2 Pekanbaru Model, and there is a significant difference between Perception and Satisfaction of Students and Students. Satisfaction with the implementation of BL in MAN 1 Pekanbaru and MAN 2 Pekanbaru model by utilizing the CSI calculation, it can be seen that satisfaction with the implementation of BL shows the category of being satisfied with the range of 66-80% and quite satisfied which is in the range of 51%-65%.

스마트 팜 식재료에 대한 소비자 인식과 프리미엄 지불의사에 미치는 영향

Recently, “smart farming” that combines ICT and agriculture has become an issue, and the food industry using smart farm ingredients has also emerged. Most of the smart farm studies have focused on producers’ intention to adopt technology, but consumer perception research is very insufficient. This study investigated smart farm food consumer perceptions with an experimental stimulus which was divided into food types (vegetable vs. meat) and manipulated with cultivation pictures (general vs. smart farm), pesticide-free labeling (presented vs. non-presented) and animal welfare labeling (presented vs. non-presented). As a result, for vegetables, general cultivated ingredients showed higher perceived naturalness (PN) and likability than smart farms. There was a moderating effect of pesticide-free labeling in the relationship between cultivation types (CT) and PN. Also, the same with animal welfare labeling. There was a moderating effect only for animal welfare labeling in the relationship between CT and likability. In the case of meat, there was a moderating effect of food technophobia in the relationship both between CT and PN in meat, and between CT and likability. Finally, for vegetables, both CT and likability had a positive effect on the willingness to pay a premium (WTP), and for meat, only likability had a positive effect on WTP. This study has the uniqueness of investigating consumers' perceptions of smart farm food ingredients that have been little researched, and it is meaningful to provide basic data to the restaurant industry for activating smart farms.

Face dissimilarity judgments are predicted by representational distance in morphable and image-computable models

Human vision is attuned to the subtle differences between individual faces. Yet we lack a quantitative way of predicting how similar two face images look and whether they appear to show the same person. Principal component-based three-dimensional (3D) morphable models are widely used to generate stimuli in face perception research. These models capture the distribution of real human faces in terms of dimensions of physical shape and texture. How well does a "face space" based on these dimensions capture the similarity relationships humans perceive among faces? To answer this, we designed a behavioral task to collect dissimilarity and same/different identity judgments for 232 pairs of realistic faces. Stimuli sampled geometric relationships in a face space derived from principal components of 3D shape and texture (Basel face model [BFM]). We then compared a wide range of models in their ability to predict the data, including the BFM from which faces were generated, an active appearance model derived from face photographs, and image-computable models of visual perception. Euclidean distance in the BFM explained both dissimilarity and identity judgments surprisingly well. In a comparison against 16 diverse models, BFM distance was competitive with representational distances in state-of-the-art deep neural networks (DNNs), including novel DNNs trained on BFM synthetic identities or BFM latents. Models capturing the distribution of face shape and texture across individuals are not only useful tools for stimulus generation. They also capture important information about how faces are perceived, suggesting that human face representations are tuned to the statistical distribution of faces.

On the Role of Neural Oscillations Across Timescales in Speech and Music Processing.

This mini review is aimed at a clinician-scientist seeking to understand the role of oscillations in neural processing and their functional relevance in speech and music perception. We present an overview of neural oscillations, methods used to study them, and their functional relevance with respect to music processing, aging, hearing loss, and disorders affecting speech and language. We first review the oscillatory frequency bands and their associations with speech and music processing. Next we describe commonly used metrics for quantifying neural oscillations, briefly touching upon the still-debated mechanisms underpinning oscillatory alignment. Following this, we highlight key findings from research on neural oscillations in speech and music perception, as well as contributions of this work to our understanding of disordered perception in clinical populations. Finally, we conclude with a look toward the future of oscillatory research in speech and music perception, including promising methods and potential avenues for future work. We note that the intention of this mini review is not to systematically review all literature on cortical tracking of speech and music. Rather, we seek to provide the clinician-scientist with foundational information that can be used to evaluate and design research studies targeting the functional role of oscillations in speech and music processing in typical and clinical populations.

Perceiving Body Height From Connected Speech: Higher Fundamental Frequency Is Associated With the Speaker's Height.

To a certain degree, human listeners can perceive a speaker's body size from their voice. The speaker's voice pitch or fundamental frequency (Fo) and the vocal formant frequencies are the voice parameters that have been most intensively studied in past body size perception research (particularly for body height). Artificially lowering the Fo of isolated vowels from male speakers improved listeners' accuracy of binary (i.e., tall vs not tall) body height perceptions. This has been explained by the theory that a denser harmonic spectrum provided by a low pitch improved the perceptual resolution of formants that aid formant-based size assessments. In the present study, we extended this research using connected speech (i.e., words and sentences) pronounced by speakers of both sexes. Unexpectedly, we found that raising Fo, not lowering it, increased the participants' perceptual performance in two binary discrimination tasks of body size. We explain our new finding in the temporal domain by the dynamic and time-varying acoustic properties of connected speech. Increased Fo might increase the sampling density of sound wave acoustic cycles and provide more detailed information, such as higher resolution, on the envelope shape.

Face identity coding in the deep neural network and primate brain

A central challenge in face perception research is to understand how neurons encode face identities. This challenge has not been met largely due to the lack of simultaneous access to the entire face processing neural network and the lack of a comprehensive multifaceted model capable of characterizing a large number of facial features. Here, we addressed this challenge by conducting in silico experiments using a pre-trained face recognition deep neural network (DNN) with a diverse array of stimuli. We identified a subset of DNN units selective to face identities, and these identity-selective units demonstrated generalized discriminability to novel faces. Visualization and manipulation of the network revealed the importance of identity-selective units in face recognition. Importantly, using our monkey and human single-neuron recordings, we directly compared the response of artificial units with real primate neurons to the same stimuli and found that artificial units shared a similar representation of facial features as primate neurons. We also observed a region-based feature coding mechanism in DNN units as in human neurons. Together, by directly linking between artificial and primate neural systems, our results shed light on how the primate brain performs face recognition tasks.

Sketching the Landscape of Speech Perception Research (2000-2020): A Bibliometric Study.

Based on 6,407 speech perception research articles published between 2000 and 2020, a bibliometric analysis was conducted to identify leading countries, research institutes, researchers, research collaboration networks, high impact research articles, central research themes and trends in speech perception research. Analysis of highly cited articles and researchers indicated three foundational theoretical approaches to speech perception, that is the motor theory, the direct realism and the computational approach as well as four non-native speech perception models, that is the Speech Learning Model, the Perceptual Assimilation Model, the Native Language Magnet model, and the Second Language Linguistic Perception model. Citation networks, term frequency analysis and co-word networks revealed several central research topics: audio-visual speech perception, spoken word recognition, bilingual and infant/child speech perception and learning. Two directions for future research were also identified: (1) speech perception by clinical populations, such as hearing loss children with cochlear implants and speech perception across lifespan, including infants and aged population; (2) application of neurocognitive techniques in investigating activation of different brain regions during speech perception. Our bibliometric analysis can facilitate research advancements and future collaborations among linguists, psychologists and brain scientists by offering a bird view of this interdisciplinary field.

A database for aesthetic classification of Chinese traditional music

Artificial intelligence has been a research highlight in recent years. Therefore, quantitative analysis about human perception has been of great concern, for instance, affective computing based on music or picture materials. As music is one of the main art forms of auditory aesthetics, its quantitative studies related to aesthetic perception show great potential. This study proposed the evaluation method of aesthetic categories for Chinese traditional music. According to this method, a dataset with aesthetic-emotional multi-annotation was established. The dataset was composed of 500 clips of Chinese traditional music, and it consisted of five aesthetic categories. The distribution characteristics of different aesthetic categories in the emotional dimension space were analysed. Furthermore, by extracting corresponding acoustical features, we tested the accuracy of different classifiers for aesthetic classification. The results showed that the highest accuracy was 65.37% by logistic regression. This work provided a data foundation for the quantitative research and aesthetics computation of Chinese traditional music. The database also can be used for the research of cross-cultural music perception.

18‐1: Invited Paper: Modeling and Optimizing Human‐in‐the‐Loop Visual Perception Using Immersive Displays: A Review

New and rapidly‐evolving classes of display devices bridge the gap between us and the immersive experiences of the future. The most intimate of these displays are the Virtual‐ and Augmented‐Reality (VR and AR) ones, because they are capable of presenting synthetic environments that rival those in the real world. This ecosystem of personal and highly‐immersive displays offers new challenges for research in computer graphics, display technologies, and human visual perception. While the extensive advancements in the areas of display and computer graphics technologies traditionally end at the on‐screen “image,” there are several untapped opportunities for advances that exploit the interplay between the display characteristics and how our visual system perceives them.In this article, we review recent progress in understanding and modeling the perception of immersive displays, as well as perceptually optimizing display technologies for immersive experiences. We present this review in the form of a taxonomy that maps the various properties of modern displays with the perceptual phenomenon that most closely interacts with them. From this taxonomy, we deduce several unsolved challenges in understanding human perception of displays, as well as perceptually‐optimal characteristics of future displays.

Perceptual geography and its significance in the conditions of decentralization for polyethnic regions of Ukraine

This article is devoted to highlighting the importance of perceptual geography as a socio-geographical science in the research of public perception of various geographical places, territories and spaces. Today, the process of decentralization continues in Ukraine, as a result of which a new administrative and territorial system has been formed in Ukraine since January 1, 2021. It is perceptual and geographical research that can reveal the mental peculiarities of the population’s perception of the unification of settlements into territorial communities, which form a system of consolidated districts. Therefore, the study of such features of the development of perceptual geography in Ukraine is quite relevant and especially significant for polyethnic regions, among which ethno-contact Chernivtsi oblast is a vivid example.

Automated Vehicles: Use, Share, Own? Young Adults’ Perceptions of Automated Vehicles

Automated Vehicles (AVs) will change the transportation landscape in still-uncertain ways. The timeline for this change depends not just on the pace of technological advancement, but also on public attitudes about AVs. These attitudes include perceptions of safety and tolerance of new vehicle ownership structures that will likely emerge when and if fully self-driving models are available. This study aims to determine the extent to which young people are open to using AVs and forgoing ownership of conventional personal vehicles in favor of AV-based shared mobility. With the potential for widespread AV deployment in the near term, the perspectives of this age group are important for understanding the impacts of AVs on the mobility landscape. An online survey was distributed through professional networks across the country in early 2020 to gauge comfort levels in riding in AVs, relying on shared mobility, and owning an AV. Univariate and bivariate chi-square tests were performed to test the correlation between explanatory variables and perceptions of AVs. The responses revealed ambivalence toward AVs. Significant relationships indicate that gender identity and urbanicity matter when it comes to willingness to use AVs and shared mobility. Results also show that young adults may not be as ready for AVs as some have hypothesized. The results of this study help address gaps in AV perception research and gauge current attitudes of young adults toward a future of transportation that includes connected and automated vehicles.

Unsupervised depth estimation for ship target based on single view UAV image

ABSTRACT The goal of depth estimation is to obtain the depth image which reflects the distance between the object and the camera point. Depth image can provide depth information for tasks, such as three-dimensional (3D) reconstruction and distance perception. With the development of the environment perception research of the unmanned ship and unmanned aerial vehicle (UAV), depth estimation has been widely used in the field of water transportation. However, the research of ship depth estimation based on monocular images has just started, and there is no unsupervised deep learning depth estimation method for ship target based on single view UAV image. This paper proposes an unsupervised deep learning ship depth estimation method based on single view UAV images. Firstly, the realistic rendering software is used to construct a new ship training dataset with simple backgrounds and regular lighting conditions. Secondly, based on the differentiable rendering framework, a knowledge distillation depth estimation network is designed to train a student network with much smaller number of model parameters. Finally, the ship depth estimation network is obtained through unsupervised training. The experiment results show that the designed knowledge distillation depth estimation network can generate better depth estimation results compared with the current state-of-the-art (SOTA) method, and the weight file size of our model is smaller.

Fairness perceptions of artificial intelligence decision-making

<p id="p00005">Inequality is the biggest challenge for global social and economic development, which has the potential to impede the goal of global sustainable development. One way to reduce such inequality is to use artificial intelligence (AI) for decision-making. However, recent research has found that while AI is more accurate and is not influenced by personal bias, people are generally averse to AI decision-making and perceive it as being less fair. Given the theoretical and practical importance of fairness perceptions of AI decision-making, a growing number of researchers have recently begun investigating how individuals form fairness perceptions in regard to AI decision-making. However, existing research is generally quite scattered and disorganized, which has limited researchers’ and practitioners’ understanding of fairness perceptions of AI decision-making from a conceptual and systematic perspective. Thus, this review first divided the relevant research into two categories based on the type of decision makers. The first category is fairness perception research in which AI is the decision-maker. Drawn upon moral foundations theory, fairness heuristic theory, and fairness theory, these studies explain how AI characteristics (i.e., transparency, controllability, rule, and appropriateness) and individual characteristics (demographics, personalities, and values) affect individuals’ fairness perceptions. Existing research revealed that there were three main underlying cognitive mechanisms underlying the relationship between AI or individual characteristics and their fairness perceptions of AI decision-making: (a) individual characteristics and AI appropriateness affect individuals’ fairness perceptions via their moral intuition; (b) AI transparency affects individuals’ fairness perceptions via their perceived understandability; and (c) AI controllability affects individuals’ fairness perceptions via individuals’ needs fulfillment. The second category is fairness perception research that compares AI and humans as decision-makers. Based on computers are social actors (CASA) hypothesis, the algorithm reductionism perspective, and the machine heuristic model, these studies explained how individuals’ different perceptions of attributes between AI and humans (i.e., mechanistic attributes vs. societal attributes, simplified attributes vs. complex attributes, objective attributes vs. subjective attributes) affect individuals’ fairness perceptions and have revealed some inconsistent research findings. Specifically, some studies found that individuals perceive AI decision makers as being mechanical (i.e., lack of emotion and human touch) and simplified (i.e., decontextualization) than human decision makers, which leads individuals perceive that the decisions made by humans rather than AI are fairer. However, other studies found that compared to human decision makers, individuals regard AI decision makers as being more objective (i.e., consistent, neutral, and free of responsibility) than human decision makers, which leads individuals perceive that the decisions made by AI rather than human are fairer. Also, a small number of studies found that there is no significant difference in individuals’ fairness perceptions between AI decision makers and human decision makers. Such mixed findings reveal that individuals’ fairness perceptions of decision-making may be dependent on the specifical attributes of AI that individuals perceived in different contexts. Based on this systematic review, we proposed five promising directions for future research to help expand fairness perception literature in the context of AI decision-making. That is, (a) exploring the affective mechanisms underlying the relationship between AI or individual characteristics and their fairness perceptions of AI decision-making; (b) exploring the antecedents of interactional fairness perceptions of AI decision-making; (c) exploring fairness perceptions when robotic AI is the decision maker; (d) clarifying the boundary conditions when AI decision-making is considered to be fairer than human decision-making, versus when human decision-making is considered to be fairer than AI decision-making; and (e) exploring fairness perceptions when AI and humans make decisions jointly. We hope this review contribute to the understanding of individuals' fairness perceptions of AI decision-making theoretically and practically.

Intelligibility of medically related sentences in quiet, speech-shaped noise, and hospital noise.

Noise in healthcare settings, such as hospitals, often exceeds levels recommended by health organizations. Although researchers and medical professionals have raised concerns about the effect of these noise levels on spoken communication, objective measures of behavioral intelligibility in hospital noise are lacking. Further, no studies of intelligibility in hospital noise used medically relevant terminology, which may differentially impact intelligibility compared to standard terminology in speech perception research and is essential for ensuring ecological validity. Here, intelligibility was measured using online testing for 69 young adult listeners in three listening conditions (i.e., quiet, speech-shaped noise, and hospital noise: 23 listeners per condition) for four sentence types. Three sentence types included medical terminology with varied lexical frequency and familiarity characteristics. A final sentence set included non-medically related sentences. Results showed that intelligibility was negatively impacted by both noise types with no significant difference between the hospital and speech-shaped noise. Medically related sentences were not less intelligible overall, but word recognition accuracy was significantly positively correlated with both lexical frequency and familiarity. These results support the need for continued research on how noise levels in healthcare settings in concert with less familiar medical terminology impact communications and ultimately health outcomes.

Factual Divergence and Risk Perceptions

The communication of information (‘facts’) by experts to the general public becomes challenging if there is an absence of trust in experts, and in the institutions they represent. There has been a perceived sense of a decline of public trust in expertise and science, an issue of concern highlighted by political leaders such as MP Michael Gove and Former US President Donald Trump. This paper presents a synthesis of differing fields of study to reflect upon the dynamic between expertise, risk and public response in democratic practice. It incorporates studies and theories from the fields of psychology, risk analysis, communication, epistemology and political studies to answer questions such as: what evidence is there that public antipathy toward experts exists? Is a war being waged between laypeople and experts, and what does this mean for public policy? How can risk perception research effectively contribute to the debate on trust in expertise? The role of heuristics and psychology in decision-making is explored (as well as a brief sojourn into the sociology of risk) with regard to how risk perceptions are formed, and the role that experts play in this dynamic. In this article, I argue that the reports of a decline in trust in expertise are not based in evidence; in fact, experts and scientists are generally trusted by the public at large. However, this trust cannot be taken for granted as it continues to represent an increasingly critical element in communicating risk and upholding democratic principles in governance.