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The Hurst exponent (H) isolated in fractal analyses of neuroimaging time-series is implicated broadly in cognition. Within this literature, H is associated with multiple mental disorders, suggesting that H is transdimensionally associated with psychopathology. Here, we unify these results and demonstrate a pattern of decreased H with increased general psychopathology and attention-deficit/hyperactivity factor scores during a working memory task in 1,839 children. This pattern predicts current and future cognitive performance in children and some psychopathology in 703 adults. This pattern also defines psychological and functional axes associating psychopathology with an imbalance in resource allocation between fronto-parietal and sensory-motor regions, driven by reduced resource allocation to fronto-parietal regions. This suggests the hypothesis that impaired working memory function in psychopathology follows from a reduced cognitive resource pool and a reduction in resources allocated to the task at hand.

ObjectivesBrain segmentation of infant magnetic resonance (MR) images is vitally important in studying developmental mental health and disease. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here, we introduce a deep neural network BIBSNet (Baby andInfantBrainSegmentation NeuralNetwork), an open-source, community-driven model that relies on data augmentation and a large sample size of manually annotated images to facilitate the production of robust and generalizable brain segmentations.Experimental DesignIncluded in model training and testing were MR brain images on 84 participants with an age range of 0-8 months (median postmenstrual ages of 13.57 months). Using manually annotated real and synthetic segmentation images, the model was trained using a 10-fold cross-validation procedure. Testing occurred on MRI data processed with the DCAN labs infant-ABCD-BIDS processing pipeline using segmentations produced from gold standard manual annotation, joint-label fusion (JLF), and BIBSNet to assess model performance.Principal ObservationsUsing group analyses, results suggest that cortical metrics produced using BIBSNet segmentations outperforms JLF segmentations. Additionally, when analyzing individual differences, BIBSNet segmentations perform even better.ConclusionsBIBSNet segmentation shows marked improvement over JLF segmentations across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF and can be easily included in other processing pipelines.

Scale invariant neural dynamics are a relatively new but effective means of measuring changes in brain states as a result of varied cognitive load and task difficulty. This study is the first to test whether scale invariance (as measured by the Hurst exponent, H) can be used with functional near-infrared spectroscopy (fNIRS) to quantify cognitive load. We analyzed H extracted from the fNIRS time series while participants completed an N-back working memory task. Consistent with what has been demonstrated in fMRI, the current results showed that scale-invariance analysis significantly differentiated between task and rest periods as calculated from both oxy- (HbO) and deoxy-hemoglobin (HbR) concentration changes. Results from both channel-averaged H and a multivariate partial least squares approach (Task PLS) demonstrated higher H during the 1-back task than the 2-back task. These results were stronger for H derived from HbR than from HbO. As fNIRS is relatively portable and robust to motion-related artifacts, these preliminary results shed light on the promising future of measuring cognitive load in real life settings.Author SummaryScale invariance reflects a pattern of self-similarity (or fractalness) across a time series of brain data. In human neuroscience studies using EEG and fMRI, higher scale invariance has been associated with individuals being in a state of minimal cognitive effort or while performing a relatively easy task compared to doing something more challenging. Functional near-infrared spectroscopy (fNIRS) is a flexible neuroimaging technique that can be used in naturalistic settings and measures the same underlying biological signal as fMRI. We expected that, if scale invariant brain states are indeed robust indicators of cognitive load or task difficulty, we should be able to replicate previous findings in fNIRS. Consistent with this hypothesis, we find that more scale invariant brain states are indeed associated with less cognitively demanding and more restful brain states in fNIRS data. This finding opens up a wide array of potential applications for monitoring cognitive load and fatigue in real-life settings, such as during driving, learning in schools, or during interpersonal interactions.

The Hurst exponent (H) isolated in fractal analyses of neuroimaging time-series is implicated broadly in cognition. The connection between H and the mathematics of criticality makes it a candidate measure of individual differences in cognitive resource allocation. Relationships between H and multiple mental disorders have been detected, suggesting that H is transdiagnostically associated with psychopathology. Here, we demonstrate a gradient of decreased H with increased general psychopathology and attention-deficit/hyperactivity extracted factor scores during a working memory task which predicts concurrent and future working memory performance in 1,839 children. This gradient defines psychological and functional axes which indicate that psychopathology is associated with an imbalance in resource allocation between fronto-parietal and sensory-motor regions, driven by reduced resource allocation to fonto-parietal regions. This suggests the hypothesis that impaired cognitive function associated with psychopathology follows from a reduced cognitive resource pool and a reduction in resources allocated to the task at hand.

Sustained attention and working memory are central cognitive processes that vary between individuals, fluctuate over time, and have consequences for life and health outcomes. Here we characterize the functional brain architecture of these abilities in 9-11-year-old children using models based on functional magnetic resonance imaging functional connectivity. Using data from the Adolescent Brain Cognitive Development (ABCD) Study, we asked whether connectome-based models built to predict sustained attention and working memory in adults generalize to capture inter- and intra-individual differences in sustained attention and working memory performance in youth. Results revealed that a predefined connectome-based model of sustained attention predicted children9s performance on the 0-back task, an attentionally taxing low-working-memory-load task. A predefined connectome-based model of working memory, on the other hand, also predicted performance on the 2-back task, an attentionally taxing high-working-memory-load task. The sustained attention model9s predictive power was comparable to that achieved when predicting adults9 0-back performance and by a connectome-based model of cognition defined in the ABCD sample itself. Finally, the working memory model predicted children9s recognition memory for n-back task stimuli. Together these results demonstrate that connectome-based models of sustained attention and working memory generalize to youth, reflecting the functional architecture of these processes in the developing brain.

The importance of motion correction when processing resting state functional magnetic resonance imaging (rs-fMRI) data is well-established in adult cohorts. This includes adjustments based on self-limited, large amplitude subject head motion, as well as factitious rhythmic motion induced by respiration. In adults, such respiration artifact can be effectively removed by applying a notch filter to the motion trace, resulting in higher amounts of data retained after frame censoring (e.g., “scrubbing”) and more reliable correlation values. Due to the unique physiological and behavioral characteristics of infants and toddlers, rs-fMRI processing pipelines, including methods to identify and remove colored noise due to subject motion, must be appropriately modified to accurately reflect true neuronal signal. These younger cohorts are characterized by higher respiration rates and lower-amplitude head movements than adults; thus, the presence and significance of comparable respiratory artifact and the subsequent necessity of applying similar techniques remain unknown. Herein, we identify and characterize the consistent presence of respiratory artifact in rs-fMRI data collected during natural sleep in infants and toddlers across two independent cohorts (aged 8–24 months) analyzed using different pipelines. We further demonstrate how removing this artifact using an age-specific notch filter allows for both improved data quality and data retention in measured results. Importantly, this work reveals the critical need to identify and address respiratory-driven head motion in fMRI data acquired in young populations through the use of age-specific motion filters as a mechanism to optimize the accuracy of measured results in this population.

In the following experiments, we examined whether perceptions of naturalness in architecture are linked to objective visual patterns, and we investigated how natural patterns influence aesthetic evaluations of architectural scenes. Experiment 1 revealed that visual patterns of architecture explained over half of the variance in scene naturalness ratings. In Experiment 2, aesthetic preference ratings were found to relate closely to natural patterns in architecture. In Experiment 3, participants completed an image arrangement task, and multidimensional scaling (MDS) analysis was performed on the data to determine the underlying dimensions that drove scene similarity judgements. Naturalness and preference ratings both correlated strongly with MDS Dimension 1. We interpreted this dimension as representing latent perceptions of naturalistic aesthetics and found that it mediated the effects of natural patterns on scene preference. Together, these results suggest that naturalistic visual patterns may play an important role in aesthetic evaluations of architectural scenes.

The physical and social environment that surrounds us has a profound impact on our brains and behaviour. This impact is so fundamental that a complete understanding of neural mechanisms cannot be developed without taking into account the extensive interactions between neurobiology, psychology, behaviour and the environment.

Reports an error in "Criterion validity and relationships between alternative hierarchical dimensional models of general and specific psychopathology" by Tyler M. Moore, Antonia N. Kaczkurkin, E. Leighton Durham, Hee Jung Jeong, Malerie G. McDowell, Randolph M. Dupont, Brooks Applegate, Jennifer L. Tackett, Carlos Cardenas-Iniguez, Omid Kardan, Gaby N. Akcelik, Andrew J. Stier, Monica D. Rosenberg, Donald Hedeker, Marc G. Berman and Benjamin B. Lahey (Journal of Abnormal Psychology, Advanced Online Publication, Jul 16, 2020, np). In the article (http://dx.doi.org/10.1037/abn0000601), an acknowledgment is missing from the author note. The missing acknowledgement is included in the erratum. (The following abstract of the original article appeared in record 2020-50590-001.) Psychopathology can be viewed as a hierarchy of correlated dimensions. Many studies have supported this conceptualization, but they have used alternative statistical models with differing interpretations. In bifactor models, every symptom loads on both the general factor and 1 specific factor (e.g., internalizing), which partitions the total explained variance in each symptom between these orthogonal factors. In second-order models, symptoms load on one of several correlated lower-order factors. These lower-order factors load on a second-order general factor, which is defined by the variance shared by the lower-order factors. Thus, the factors in second-order models are not orthogonal. Choosing between these valid statistical models depends on the hypothesis being tested. Because bifactor models define orthogonal phenotypes with distinct sources of variance, they are optimal for studies of shared and unique associations of the dimensions of psychopathology with external variables putatively relevant to etiology and mechanisms. Concerns have been raised, however, about the reliability of the orthogonal specific factors in bifactor models. We evaluated this concern using parent symptom ratings of 9-10 year olds in the ABCD Study. Psychometric indices indicated that all factors in both bifactor and second-order models exhibited at least adequate construct reliability and estimated replicability. The factors defined in bifactor and second-order models were highly to moderately correlated across models, but have different interpretations. All factors in both models demonstrated significant associations with external criterion variables of theoretical and clinical importance, but the interpretation of such associations in second-order models was ambiguous due to shared variance among factors. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Psychopathology can be viewed as a hierarchy of correlated dimensions. Many studies have supported this conceptualization, but they have used alternative statistical models with differing interpretations. In bifactor models, every symptom loads on both the general factor and one specific factor (e.g., internalizing), which partitions the total explained variance in each symptom between these orthogonal factors. In second-order models, symptoms load on one of several correlated lower-order factors. These lower-order factors load on a second-order general factor, which is defined by the variance shared by the lower-order factors. Thus, the factors in second-order models are not orthogonal. Choosing between these valid statistical models depends on the hypothesis being tested. Because bifactor models define orthogonal phenotypes with distinct sources of variance, they are optimal for studies of shared and unique associations of the dimensions of psychopathology with external variables putatively relevant to etiology and mechanisms. Concerns have been raised, however, about the reliability of the orthogonal specific factors in bifactor models. We evaluated this concern using parent symptom ratings of 9-10 year olds in the ABCD Study. Psychometric indices indicated that all factors in both bifactor and second-order models exhibited at least adequate construct reliability and estimated replicability. The factors defined in bifactor and second-order models were highly to moderately correlated across models, but have different interpretations. All factors in both models demonstrated significant associations with external criterion variables of theoretical and clinical importance, but the interpretation of such associations in second-order models was ambiguous due to shared variance among factors.General Scientific SummarySome investigators have proposed that viewing the correlated symptoms of psychopathology as a hierarchy in which all symptoms are related to both a general (p) factor of psychopathology and a more specific factor will make it easier to distinguish potential risk factors and mechanisms that are nonspecifically related to all forms of psychopathology versus those that are associated with specific dimensions of psychopathology. Parent ratings of child psychopathology items from the Adolescent Brain Cognitive Development (ABCD) Study were analyzed using two alternative statistical models of the proposed hierarchy. All factors of psychopathology defined in both bifactor and second-order models demonstrated adequate psychometric properties and criterion validity, but associations of psychopathology factors with external variables were more easily interpreted in bifactor than in second-order models.

Although practicing a task generally benefits later performance on that same task (practice effect), there are large, and mostly unexplained, individual differences in reaping the benefits from practice. One promising avenue to model and predict such differences comes from recent research showing that brain networks can extract functional advantages from operating in the vicinity of criticality, a state in which brain network activity is more scale-free. As such, we hypothesized that individuals with more scale-free fMRI activity, indicated by BOLD time series with a higher Hurst exponent (H), gain more benefits from practice. In this study, participants practiced a test of working memory and attention, the dual n-back task (DNB), watched a video clip as a break, and then performed the DNB again, during MRI. To isolate the practice effect, we divided the participants into two groups based on improvement in performance from the first to second DNB task run. We identified regions and connections in which H and functional connectivity related to practice effects in the last run. More scale-free brain activity in these regions during the preceding runs (either first DNB or video) distinguished individuals who showed greater DNB performance improvements over time. In comparison, functional connectivity (r2) in the identified connections did not reliably classify the two groups in the preceding runs. Finally, we replicated both H and r2 results from study 1 in an independent fMRI dataset of participants performing multiple runs of another working memory and attention task (word completion). We conclude that the brain networks can accommodate further practice effects in individuals with higher scale-free BOLD activity.

Natural environments have powerful aesthetic appeal linked to their capacity for psychological restoration. In contrast, disorderly environments are aesthetically aversive, and have various detrimental psychological effects. But in our research, we have repeatedly found that natural environments are perceptually disorderly. What could explain this paradox? We present 3 competing hypotheses: the aesthetic preference for naturalness is more powerful than the aesthetic aversion to disorder (the nature-trumps-disorder hypothesis); disorder is trivial to aesthetic preference in natural contexts (the harmless-disorder hypothesis); and disorder is aesthetically preferred in natural contexts (the beneficial-disorder hypothesis). Utilizing novel methods of perceptual study and diverse stimuli, we rule in the nature-trumps-disorder hypothesis and rule out the harmless-disorder and beneficial-disorder hypotheses. In examining perceptual mechanisms, we find evidence that high-level scene semantics are both necessary and sufficient for the nature-trumps-disorder effect. Necessity is evidenced by the effect disappearing in experiments utilizing only low-level visual stimuli (i.e., where scene semantics have been removed) and experiments utilizing a rapid-scene-presentation procedure that obscures scene semantics. Sufficiency is evidenced by the effect reappearing in experiments utilizing noun stimuli which remove low-level visual features. Furthermore, we present evidence that the interaction of scene semantics with low-level visual features amplifies the nature-trumps-disorder effect-the effect is weaker both when statistically adjusting for quantified low-level visual features and when using noun stimuli which remove low-level visual features. These results have implications for psychological theories bearing on the joint influence of low- and high-level perceptual inputs on affect and cognition, as well as for aesthetic design. (PsycINFO Database Record

Disorderly environments are linked to disorderly behaviors. Broken windows theory (Wilson & Kelling, 1982), an influential theory of crime and rule-breaking, assumes that scene-level social disorder cues (e.g., litter, graffiti) cause people to reason that they can get away with breaking rules. But what if part of the story is not about such complex social reasoning? Recent research suggests that basic visual disorder cues may be sufficient to encourage complex rule-breaking behavior. To test this hypothesis, we first conducted a set of experiments (Experiments 1-3) in which we identified basic visual disorder cues that generalize across visual stimuli with a variety of semantic content. Our results revealed that spatial features (e.g., nonstraight edges, asymmetry) are more important than color features (e.g., hue, saturation, value) for visual disorder. Exploiting this knowledge, we then reconstructed stimuli contrasted in terms of visual disorder, but absent of scene-level social disorder cues, to test whether visual disorder alone encourages cheating in a second set of experiments (Experiments 4 and 5). In these experiments, manipulating visual disorder increased the likelihood of cheating by up to 35% and the average magnitude of cheating by up to 87%. This work suggests that theories of rule-breaking that assume that complex social reasoning (e.g., about norms, policing, poverty) is necessary, should be reconsidered (e.g., Kelling & Coles, 1997; Sampson & Raudenbush, 2004). Furthermore, these experiments show that simple perceptual properties of the environment can affect complex behavior and sheds light on the extent to which our actions are within our control. (PsycINFO Database Record

Despite being intuitive, cognitive effort has proven difficult to define quantitatively. Here, we proposed to study cognitive effort by investigating the degree to which the brain deviates from its default state, where brain activity is scale-invariant. Specifically, we measured such deviations by examining changes in scale-invariance of brain activity as a function of task difficulty and posited suppression of scale-invariance as a proxy for exertion of cognitive effort. While there is some fMRI evidence supporting this proposition, EEG investigations on the matter are scant, despite the EEG signal being more suitable for analysis of scale invariance (i.e., having a much broader frequency range). In the current study we validated the correspondence between scale-invariance (H) of cortical activity recorded by EEG and task load during two working memory (WM) experiments with varying set sizes. Then, we used this neural signature to disentangle cognitive effort from the number of items stored in WM within participants. Our results showed monotonic decreases in H with increased set size, even after set size exceeded WM capacity. This behavior of H contrasted with behavioral performance and an oscillatory indicator of WM load (i.e., alpha-band desynchronization), both of which showed a plateau at difficulty levels surpassing WM capacity. This is the first reported evidence for the suppression of scale-invariance in EEG due to task difficulty, and our work suggests that H suppression may be used to quantify changes in cognitive effort even when working memory load is at maximum capacity.

It has recently been shown that the perception of visual features of the environment can influence thought content. Both low-level (e.g., fractalness) and high-level (e.g., presence of water) visual features of the environment can influence thought content, in real-world and experimental settings where these features can make people more reflective and contemplative in their thoughts. It remains to be seen, however, if these visual features retain their influence on thoughts in the absence of overt semantic content, which could indicate a more fundamental mechanism for this effect. In this study, we removed this limitation, by creating scrambled edge versions of images, which maintain edge content from the original images but remove scene identification. Non-straight edge density is one visual feature which has been shown to influence many judgements about objects and landscapes, and has also been associated with thoughts of spirituality. We extend previous findings by showing that non-straight edges retain their influence on the selection of a “Spiritual & Life Journey” topic after scene identification removal. These results strengthen the implication of a causal role for the perception of low-level visual features on the influence of higher-order cognitive function, by demonstrating that in the absence of overt semantic content, low-level features, such as edges, influence cognitive processes.

Studying factors that contribute to scene memorability is important for understanding human vision and memory. Here we demonstrated in two different eye-tracking datasets that the higher the fixation map consistency (also called inter-observer congruency of fixation maps) of a scene, the higher its memorability is. Fixation map consistency and, more importantly, its correlation to scene memorability were the highest in the first 2 s of viewing, suggesting that scene features (other than center bias) that contribute to producing more consistent fixation maps early in viewing may also be important for scene encoding. We also found that although fixation count was positively correlated with scene memorability, it was not significantly correlated with fixation map consistency, suggesting that these eye-tracking measures reflect different attentional mechanisms. Using the proxies of scene semantics and mediation analyses, we found that the relationship between scene semantics and scene memorability was partially (but not fully) mediated by attentional mechanisms. Finally, we found that fixation map consistency, fixation count, and scene semantics all significantly and differently contributed to scene memorability. Together, these results suggest 2 s of eye-tracking can complement computer vision-based algorithms in better predicting scene memorability.

Interactions with natural environments and nature-related stimuli have been found to be beneficial to cognitive performance, in particular on executive cognitive tasks with high demands on directed attention processes. However, results vary across different studies. The aim of the present paper was to evaluate the effects of nature vs. urban environments on cognitive performance across all of our published and new/unpublished studies testing the effects of different interactions with nature vs. urban/built control environments, on an executive-functioning test with high demands on directed attention—the backwards digit span (BDS) task. Specific aims in this study were to: (1) evaluate the effect of nature vs. urban environment interactions on BDS across different exposure types (e.g., real-world vs. artificial environments/stimuli); (2) disentangle the effects of testing order (i.e., effects caused by the order in which experimental conditions are administered) from the effects of the environment interactions, and (3) test the (mediating) role of affective changes on BDS performance. To this end, data from 13 experiments are presented, and pooled data-analyses are performed. Results from the pooled data-analyses (N = 528 participants) showed significant time-by-environment interactions with beneficial effects of nature compared to urban environments on BDS performance. There were also clear interactions with the order in which environment conditions were tested. Specifically, there were practice effects across environment conditions in first sessions. Importantly, after parceling out initial practice effects, the positive effects of nature compared to urban interactions on BDS performance were magnified. Changes in positive or negative affect did not mediate the beneficial effects of nature on BDS performance. These results are discussed in relation to the findings of other studies identified in the literature. Uncontrolled and confounding order effects (i.e., effects due to the order of experimental conditions, rather than the treatment conditions) may explain some of the inconsistent findings across studies in the literature on nature effects on cognitive performance. In all, these results highlight the robustness of the effects of natural environments on cognition, particularly when confounding order effects have been considered, and provide a more nuanced account of when a nature intervention will be most effective.

Past work demonstrates that humans behave differently towards women across their menstrual cycles, even after exclusively visual exposure to women's faces. People may look at women's faces differently as a function of women's menstrual cycles. Analyses of participants' scanpaths (eye movement patterns) while they looked at women at different phases of their menstrual cycles revealed that observers exhibit more consistent scanpaths when examining women's faces when women are in a menstrual cycle phase that typically corresponds with peak fertility, whereas they exhibit more variable patterns when looking at women's faces when they are in phases that do not correspond with fertility. A multivariate classifier on participants' scanpaths predicted whether they were looking at the face of a woman in a more typically fertile- versus non-fertile-phase of her menstrual cycle with above-chance accuracy. These findings demonstrate that people look at women's faces differently as a function of women's menstrual cycles, and suggest that people are sensitive to fluctuating visual cues associated with women's menstrual cycle phase.

Several studies in cancer research have suggested that cognitive dysfunction following chemotherapy, referred to in lay terms as “chemobrain”, is a serious problem. At present, the changes in integrative brain function that underlie such dysfunction remain poorly understood. Recent developments in neuroimaging suggest that patterns of functional connectivity can provide a broadly applicable neuromarker of cognitive performance and other psychometric measures. The current study used multivariate analysis methods to identify patterns of disruption in resting state functional connectivity of the brain due to chemotherapy and the degree to which the disruptions can be linked to behavioral measures of distress and cognitive performance. Sixty two women (22 healthy control, 18 patients treated with adjuvant chemotherapy, and 22 treated without chemotherapy) were evaluated with neurocognitive measures followed by self-report questionnaires and open eyes resting-state fMRI scanning at three time points: diagnosis (M0, pre-adjuvant treatment), 1 month (M1), and 7 months (M7) after treatment. The results indicated deficits in cognitive health of breast cancer patients immediately after chemotherapy that improved over time. This psychological trajectory was paralleled by a disruption and later recovery of resting-state functional connectivity, mostly in the parietal and frontal brain regions. Mediation analysis showed that the functional connectivity alteration pattern is a separable treatment symptom from the decreased cognitive health. Current study indicates that more targeted support for patients should be developed to ameliorate these multi-faceted side effects of chemotherapy treatment on neural functioning and cognitive health., Highlights • Chemotherapy increases fatigue, depression, cognitive & physical burdens • Patients also show decreased parieto-frontal resting-state functional connectivity • These cognitive and physical burdens are reduced 7 months after treatment • The functional connectivity pattern also returns to baseline levels by 7 months • The functional connectivity fluctuations do not explain the cognitive burdens

Adults demonstrate aesthetic preferences for natural environments over urban ones. This preference has influenced theories like Biophilia to explain why nature is beneficial. While both adults and children show cognitive and affective benefits after nature exposure, it is unknown whether children demonstrate nature preferences. In the current study, 4-to-11-year-old children and their parents rated their preferences for images of nature and urban scenes. Parents’ preferences matched those of a normative adult sample. However, children demonstrated robust preferences for urban over natural environments, and those urban preferences significantly decreased with age. Nature exposure around the home and nature-related activities, as reported by parents, did not predict children’s preferences. Children with more nearby nature, however, had lower reported inattentiveness, but interestingly, this was unrelated to children’s preferences for nature. These results provide an important step into future research on the role of preference in how children and adults benefit from nature.

Previous research has shown that eye-movements change depending on both the visual features of our environment, and the viewer's top-down knowledge. One important question that is unclear is the degree to which the visual goals of the viewer modulate how visual features of scenes guide eye-movements. Here, we propose a systematic framework to investigate this question. In our study, participants performed 3 different visual tasks on 135 scenes: search, memorization, and aesthetic judgment, while their eye-movements were tracked. Canonical correlation analyses showed that eye-movements were reliably more related to low-level visual features at fixations during the visual search task compared to the aesthetic judgment and scene memorization tasks. Different visual features also had different relevance to eye-movements between tasks. This modulation of the relationship between visual features and eye-movements by task was also demonstrated with classification analyses, where classifiers were trained to predict the viewing task based on eye movements and visual features at fixations. Feature loadings showed that the visual features at fixations could signal task differences independent of temporal and spatial properties of eye-movements. When classifying across participants, edge density and saliency at fixations were as important as eye-movements in the successful prediction of task, with entropy and hue also being significant, but with smaller effect sizes. When classifying within participants, brightness and saturation were also significant contributors. Canonical correlation and classification results, together with a test of moderation versus mediation, suggest that the cognitive state of the observer moderates the relationship between stimulus-driven visual features and eye-movements. (PsycINFO Database Record

How eye movements reflect underlying cognitive processes during scene viewing has been a topic of considerable theoretical interest. In this study, we used eye-movement features and their distributions over time to successfully classify mental states as indexed by the behavioral task performed by participants. We recorded eye movements from 72 participants performing 3 scene-viewing tasks: visual search, scene memorization, and aesthetic preference. To classify these tasks, we used statistical features (mean, standard deviation, and skewness) of fixation durations and saccade amplitudes, as well as the total number of fixations. The same set of visual stimuli was used in all tasks to exclude the possibility that different salient scene features influenced eye movements across tasks. All of the tested classification algorithms were successful in predicting the task within a single participant. The linear discriminant algorithm was also successful in predicting the task for each participant when the training data came from other participants, suggesting some generalizability across participants. The number of fixations contributed most to task classification; however, the remaining features and, in particular, their covariance provided important task-specific information. These results provide evidence on how participants perform different visual tasks. In the visual search task, for example, participants exhibited more variance and skewness in fixation durations and saccade amplitudes, but also showed heightened correlation between fixation durations and the variance in fixation durations. In summary, these results point to the possibility that eye-movement features and their distributional properties can be used to classify mental states both within and across individuals.

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.

We employed eye-tracking to investigate how performing different tasks on scenes (e.g., intentionally memorizing them, searching for an object, evaluating aesthetic preference) can affect eye movements during encoding and subsequent scene memory. We found that scene memorability decreased after visual search (one incidental encoding task) compared to intentional memorization, and that preference evaluation (another incidental encoding task) produced better memory, similar to the incidental memory boost previously observed for words and faces. By analyzing fixation maps, we found that although fixation map similarity could explain how eye movements during visual search impairs incidental scene memory, it could not explain the incidental memory boost from aesthetic preference evaluation, implying that implicit mechanisms were at play. We conclude that not all incidental encoding tasks should be taken to be similar, as different mechanisms (e.g., explicit or implicit) lead to memory enhancements or decrements for different incidental encoding tasks.

Top-down influences on observers’ overt attention and how they interact with the features of the visual environment have been extensively investigated, but the cultural and developmental aspects of these modulations have been understudied. In this study we investigated these effects for US and Yucatec Mayan infants, children, and adults. Mayan and US participants viewed videos of two actors performing daily Mayan and US tasks in the foreground and the background while their eyes were tracked. Our region of interest analysis showed that viewers from the US looked significantly less at the foreground activity and spent more time attending to the ‘contextual’ information (static background) compared to Mayans. To investigate how and what visual features of videos were attended to in a comprehensive manner, we used multivariate methods which showed that visual features are attended to differentially by each culture. Additionally, we found that Mayan and US infants utilize the same eye-movement patterns in which fixation duration and saccade amplitude are altered in response to the visual stimuli independently. However, a bifurcation happens by age 6, at which US participants diverge and engage in eye-movement patterns where fixation durations and saccade amplitudes are altered simultaneously.

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.

Can viewing others experiencing stress create a “contagious” physiological stress response in the observer? To investigate second-hand stress, we first created a stimulus set of videos, which featured participants speaking under either minimal stress, high stress, or while recovering from stress. We then recruited a second set of participants to watch these videos. All participants (speakers and observers) were monitored via electrocardiogram. Cardiac activity of the observers while watching the videos was then analyzed and compared to that of the speakers. Furthermore, we assessed dispositional levels of empathy in observers to determine how empathy might be related to the degree of stress contagion. Results revealed that depending on the video being viewed, observers experienced differential changes in cardiac activity that were based on the speaker’s stress level. Additionally, this is the first demonstration that individuals high in dispositional empathy experience these physiological changes more quickly.

Adults demonstrate aesthetic preferences for natural environments over urban ones. This preference has influenced theories like Biophilia to explain why nature is beneficial. While both adults and children show cognitive and affective benefits after nature exposure, it is unknown whether children demonstrate nature preferences. In the current study, 4-to-11-year-old children and their parents rated their preferences for images of nature and urban scenes. Parents' preferences matched those of a normative adult sample. However, children demonstrated robust preferences for urban over natural environments, and those urban preferences significantly decreased with age. Nature exposure around the home and nature-related activities, as reported by parents, did not predict children's preferences. Children with more nearby nature, however, had lower reported inattentiveness, but interestingly, this was unrelated to children's preferences for nature. These results provide an important step into future research on the role of preference in how children and adults benefit from nature.

Previous research has shown that viewing images of nature scenes can have a beneficial effect on memory, attention and mood. In this study we aimed to determine whether the preference of natural versus man-made scenes is driven by bottom-up processing of the low-level visual features of nature. We used participants’ ratings of perceived naturalness as well as aesthetic preference for 307 images with varied natural and urban content. We then quantified ten low-level image features for each image (a combination of spatial and color properties). These features were used to predict aesthetic preference in the images, as well as to decompose perceived naturalness to its predictable (modelled by the low-level visual features) and non-modelled aspects. Interactions of these separate aspects of naturalness with the time it took to make a preference judgment showed that naturalness based on low-level features related more to preference when the judgment was faster (bottom-up). On the other hand perceived naturalness that was not modelled by low-level features was related more to preference when the judgment was slower. A quadratic discriminant classification analysis showed how relevant each aspect of naturalness (modelled and non-modelled) was to predicting preference ratings, as well as the image features on their own. Finally, we compared the effect of color-related and structure-related modelled naturalness, and the remaining unmodelled naturalness in predicting aesthetic preference. In summary bottom-up (color and spatial) properties of natural images captured by our features and the non-modelled naturalness are important to aesthetic judgments of natural and man-made scenes, with each predicting unique variance.

Studies have shown that natural environments can enhance health and here we build upon that work by examining the associations between comprehensive greenspace metrics and health. We focused on a large urban population center (Toronto, Canada) and related the two domains by combining high-resolution satellite imagery and individual tree data from Toronto with questionnaire-based self-reports of general health perception, cardio-metabolic conditions and mental illnesses from the Ontario Health Study. Results from multiple regressions and multivariate canonical correlation analyses suggest that people who live in neighborhoods with a higher density of trees on their streets report significantly higher health perception and significantly less cardio-metabolic conditions (controlling for socio-economic and demographic factors). We find that having 10 more trees in a city block, on average, improves health perception in ways comparable to an increase in annual personal income of $10,000 and moving to a neighborhood with $10,000 higher median income or being 7 years younger. We also find that having 11 more trees in a city block, on average, decreases cardio-metabolic conditions in ways comparable to an increase in annual personal income of $20,000 and moving to a neighborhood with $20,000 higher median income or being 1.4 years younger.

Previous research has shown that interacting with natural environments vs. more urban or built environments can have salubrious psychological effects, such as improvements in attention and memory. Even viewing pictures of nature vs. pictures of built environments can produce similar effects. A major question is: What is it about natural environments that produces these benefits? Problematically, there are many differing qualities between natural and urban environments, making it difficult to narrow down the dimensions of nature that may lead to these benefits. In this study, we set out to uncover visual features that related to individuals' perceptions of naturalness in images. We quantified naturalness in two ways: first, implicitly using a multidimensional scaling analysis and second, explicitly with direct naturalness ratings. Features that seemed most related to perceptions of naturalness were related to the density of contrast changes in the scene, the density of straight lines in the scene, the average color saturation in the scene and the average hue diversity in the scene. We then trained a machine-learning algorithm to predict whether a scene was perceived as being natural or not based on these low-level visual features and we could do so with 81% accuracy. As such we were able to reliably predict subjective perceptions of naturalness with objective low-level visual features. Our results can be used in future studies to determine if these features, which are related to naturalness, may also lead to the benefits attained from interacting with nature.