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.

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.

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.

When people understand language about their own actions they activate premotor regions they use to perform these actions.Do people understand language about other peoples actions by imagining how they perform these actions themselves, orhow they perceive others performing them? Here, we recorded BOLD fMRI while left- and right-handers read about andthen imagined their own unimanual actions (e.g. you write) or others actions (e.g. she writes). When imagining theirown manual actions, participants preferentially activated PMC circuits controlling their dominant hand. By contrast, whenimagining others actions, participants PMC activity reflected both how they perform actions themselves and how theytypically see actions performed by right-handers (about 90% of people they see). Language-induced motor imagery forour own actions reflects how we use our own bodies, whereas imagery for others actions also reflects how others use theirbodies, even if their bodies differ from our own.

Does silently talking to yourself in the third-person constitute a relatively effortless form of self control? We hypothesized that it does under the premise that third-person self-talk leads people to think about the self similar to how they think about others, which provides them with the psychological distance needed to facilitate self control. We tested this prediction by asking participants to reflect on feelings elicited by viewing aversive images (Study 1) and recalling negative autobiographical memories (Study 2) using either "I" or their name while measuring neural activity via ERPs (Study 1) and fMRI (Study 2). Study 1 demonstrated that third-person self-talk reduced an ERP marker of self-referential emotional reactivity (i.e., late positive potential) within the first second of viewing aversive images without enhancing an ERP marker of cognitive control (i.e., stimulus preceding negativity). Conceptually replicating these results, Study 2 demonstrated that third-person self-talk was linked with reduced levels of activation in an a priori defined fMRI marker of self-referential processing (i.e., medial prefrontal cortex) when participants reflected on negative memories without eliciting increased levels of activity in a priori defined fMRI markers of cognitive control. Together, these results suggest that third-person self-talk may constitute a relatively effortless form of self-control.

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.

The functional interaction between the brain's two hemispheres includes a unique set of connections between corresponding regions in opposite hemispheres (i.e., homotopic regions) that are consistently reported to be exceptionally strong compared with other interhemispheric (i.e., heterotopic) connections. The strength of homotopic functional connectivity (FC) is thought to be mediated by the regions' shared functional roles and their structural connectivity. Recently, homotopic FC was reported to be stable over time despite the presence of dynamic FC across both intrahemispheric and heterotopic connections. Here we build on this work by considering whether homotopic FC is also stable across conditions. We additionally test the hypothesis that strong and stable homotopic FC is supported by the underlying structural connectivity. Consistent with previous findings, interhemispheric FC between homotopic regions were significantly stronger in both humans and macaques. Across conditions, homotopic FC was most resistant to change and therefore was more stable than heterotopic or intrahemispheric connections. Across time, homotopic FC had significantly greater temporal stability than other types of connections. Temporal stability of homotopic FC was facilitated by direct anatomical projections. Importantly, temporal stability varied with the change in conductive properties of callosal axons along the anterior-posterior axis. Taken together, these findings suggest a notable role for the corpus callosum in maintaining stable functional communication between hemispheres.

The ability to delay gratification in childhood has been linked to positive outcomes in adolescence and adulthood. Here we examine a subsample of participants from a seminal longitudinal study of self-control throughout a subject's life span. Self-control, first studied in children at age 4 years, is now re-examined 40 years later, on a task that required control over the contents of working memory. We examine whether patterns of brain activation on this task can reliably distinguish participants with consistently low and high self-control abilities (low versus high delayers). We find that low delayers recruit significantly higher-dimensional neural networks when performing the task compared with high delayers. High delayers are also more homogeneous as a group in their neural patterns compared with low delayers. From these brain patterns, we can predict with 71% accuracy, whether a participant is a high or low delayer. The present results suggest that dimensionality of neural networks is a biological predictor of self-control abilities.