Environmental influences on affect and cognition: A study of natural and commercial semi-public spaces

Research has consistently shown differences in affect and cognition after exposure to different physical environments. The time course of these differences emerging or fading during exploration of environments is less explored, as most studies measure dependent variables only before and after environmental exposure. In this within-subject study, we used repeated surveys to measure differences in thought content and affect throughout a 1-h environmental exploration of a nature conservatory and a large indoor mall. At each survey, participants reported on aspects of their most recent thoughts (e.g., thinking of the present moment vs. the future; thinking positively vs. negatively) and state affect. Using Bayesian multi-level models, we found that while visiting the conservatory, participants were more likely to report thoughts about the past, more positive and exciting thoughts, and higher feelings of positive affect and creativity. In the mall, participants were more likely to report thoughts about the future and higher feelings of impulsivity. Many of these differences in environments were present throughout the 1-h walk, however some differences were only evident at intermediary time points, indicating the importance of collecting data during exploration, as opposed to only before and after environmental exposures. We also measured cognitive performance with a dual n-back task. Results on 2-back trials replicated results from prior work that interacting with nature leads to improvements in working-memory performance. This study furthers our understanding of how thoughts and feelings are influenced by the surrounding physical environment and has implications for the design and use of public spaces

The world is random: a cognitive perspective on perceived disorder

Research on the consequences of perceiving disorder is largely sociological and concerns broken windows theory, which states that signs of social disorder cause further social disorder. The predominant psychological explanations for this phenomena are primarily social. In contrast, I propose a parsimonious cognitive model (world-is-random model; WIR), which basically proposes that disorder primes randomness-related concepts, which results in a reduction in and threat to the sense of personal control, which has diverse affective, judgmental, and behavioral consequences. I review recent developments on the psychological consequences of perceiving disorder and argue that WIR can explain all of these findings. I also cover select correlational studies from the sociological literature and explain how WIR can at least partly explain for their diverse findings. In a general discussion, I consider possible alternative psychological models and argue that they do not adequately explain the most recent psychological research on disorder. I then propose future directions which include determining whether perceiving disorder causes a unique psychology and delimiting boundary conditions

How Anticipated Emotions Guide Self-Control Judgments

When considering whether to enact or not to enact a tempting option, people often anticipate how their choices will make them feel, typically resulting in a “mixed bag” of conflicting emotions. Building on earlier work, we propose an integrative theoretical model of this judgment process and empirically test its main propositions using a novel procedure to capture and integrate both the intensity and duration of anticipated emotions. We identify and theoretically integrate four highly relevant key emotions, pleasure, frustration, guilt, and pride. Whereas the former two (basic hedonic) emotions are anticipated to dissipate relatively quickly, the latter two (self-conscious) emotions are anticipated to be more long-lived. Regarding the relative weighting of emotions, we obtained evidence for a relative guilt bias and pride neglect under default conditions. Furthermore, we identify situational influences on this judgment process and find that rendering self-conscious emotions more situationally salient positively impacts self-control decision-making. We discuss how these findings build on an integrative theory of self-control and how they are useful for the design of choice environments and interventions

Can the High-Level Semantics of a Scene be Preserved in the Low-Level Visual Features of that Scene? A Study of Disorder and Naturalness

Real-world scenes contain low-level visual features (e.g., edges, colors) and high-level semantic features (e.g., objects and places). Traditional visual perception models assume that integration of low-level visual features and segmentation of the scene must occur before high-level semantics are perceived. This view implies that low-level visual features of a scene alone do not carry semantic information related to that scene. Here we present evidence that suggests otherwise. We show that high-level semantics can be preserved in low-level visual features, and that different high-level semantics can be preserved in different types of low-level visual features. Specifically, the ‘disorder’ of a scene is preserved in edge features better than color features, whereas the converse is true for ‘naturalness.’ These findings suggest that semantic processing may start earlier than thought before, and integration of low-level visual features and segmentation of the scene may occur after semantic processing has begun, or in parallel

Overt attentional correlates of memorability of scene images and their relationships to scene semantics

Computer vision-based research has shown that scene semantics (e.g., presence of meaningful objects in a scene) can predict memorability of scene images. Here, we investigated whether and to what extent overt attentional correlates, such as fixation map consistency (also called inter-observer congruency of fixation maps) and fixation counts, mediate the relationship between scene semantics and scene memorability. First, we confirmed that the higher the fixation map consistency of a scene, the higher its memorability. Moreover, both fixation map consistency and its correlation to scene memorability were the highest in the first 2 seconds of viewing, suggesting that meaningful scene features that contribute to producing more consistent fixation maps early in viewing, such as faces and humans, may also be important for scene encoding. Second, we found that the relationship between scene semantics and scene memorability was partially (but not fully) mediated by fixation map consistency and fixation counts, separately as well as together. Third, we found that fixation map consistency, fixation counts, and scene semantics significantly and additively contributed to scene memorability. Together, these results suggest that eye-tracking measurements can complement computer vision-based algorithms and improve overall scene memorability prediction

Image Feature Types and Their Predictions of Aesthetic Preference and Naturalness

Previous research has investigated ways to quantify visual information of a scene in terms of a visual processing hierarchy, i.e., making sense of visual environment by segmentation and integration of elementary sensory input. Guided by this research, studies have developed categories for low-level visual features (e.g., edges, colors), high-level visual features (scene-level entities that convey semantic information such as objects), and how models of those features predict aesthetic preference and naturalness. For example, in Kardan et al. (2015a), 52 participants provided aesthetic preference and naturalness ratings, which are used in the current study, for 307 images of mixed natural and urban content. Kardan et al. (2015a) then developed a model using low-level features to predict aesthetic preference and naturalness and could do so with high accuracy. What has yet to be explored is the ability of higher-level visual features (e.g., horizon line position relative to viewer, geometry of building distribution relative to visual access) to predict aesthetic preference and naturalness of scenes, and whether higher-level features mediate some of the association between the low-level features and aesthetic preference or naturalness. In this study we investigated these relationships and found that low- and high- level features explain 68.4% of the variance in aesthetic preference ratings and 88.7% of the variance in naturalness ratings. Additionally, several high-level features mediated the relationship between the low-level visual features and aaesthetic preference. In a multiple mediation analysis, the high-level feature mediators accounted for over 50% of the variance in predicting aesthetic preference. These results show that high-level visual features play a prominent role predicting aesthetic preference, but do not completely eliminate the predictive power of the low-level visual features. These strong predictors provide powerful insights for future research relating to landscape and urban design with the aim of maximizing subjective well-being, which could lead to improved health outcomes on a larger scale