Hydroponic Basil Production: Temperature Influences Volatile Organic Compound Profile, but Not Overall Consumer Preference

Altering the growing temperature during controlled-environment production not only influences crop growth and development, but can also influence volatile organic compound (VOC) production and, subsequently, sensory attributes of culinary herbs. Therefore, the objectives of this study were to (1) quantify the influence of mean daily temperature (MDT) and daily light integral (DLI) on key basil phenylpropanoid and terpenoid concentrations, (2) determine if differences in sensory characteristics due to MDT and DLI influence consumer preference, and (3) identify the sweet basil attributes consumers prefer. Thus, 2-week-old sweet basil ‘Nufar’ seedlings were transplanted into deep-flow hydroponic systems in greenhouses with target MDTs of 23, 26, 29, 32, or 35 °C and DLIs of 7, 9, or 12 mol·m−2·d−1. After three weeks, the two most recently mature leaves were harvested for gas chromatography–mass spectrometry (GC–MS) and consumer sensory analysis. Panel evaluations were conducted through a sliding door with samples served individually while panelists answered Likert scale and open-ended quality attribute and sensory questions. The DLI did not influence VOC concentrations. Increasing MDT from 23 to 36 °C during production increased 1,8 cineole, eugenol, and methyl chavicol concentrations linearly and did not affect linalool concentration. The increases in phenylpropanoid (eugenol and methyl chavicol) were greater than increases in terpenoid (1,8 cineole) concentrations. However, these increases did not impact overall consumer or flavor preference. The MDT during basil production influenced appearance, texture, and color preference of panelists. Taken together, MDT during production influenced both VOC concentrations and textural and visual attribute preference of basil but did not influence overall consumer preference. Therefore, changing the MDT during production can be used to alter plant growth and development without significantly affecting consumer preference.

Memorability: Reconceptualizing Memory as a Visual Attribute

In the world of visual memory, we often focus our study on the process of memory, but equally important are the inputs to the process—the images we are remembering. A growing body of work has shown that images have a strong power over what we will remember or forget; images have an intrinsic memorability that causes them to be remembered or forgotten across people. In this chapter, I describe our current understanding of memorability as a stimulus property, and its relationship to various aspects of vision and memory. The memorability of an image remains consistent across people, tasks, images, and timing, and shows specific stereotyped patterns in the brain that are separate from those of perception and memory. Recent evidence has proposed that memorability could represent how perceived inputs are prioritized for memory encoding. Although there is currently no comprehensive model of what makes something memorable, deep learning has shown strides in being able to predict and manipulate the memorability of an image. Armed with the memorability scores of an image, one can then create high-powered memory experiments, or develop tests that can more efficiently identify cognitive decline. There are still many open questions about memorability, but a deeper understanding will promise to give us agency over our memories and the images that create them.

ASSESSING RELATIONS AMONG LANDSCAPE PREFERENCE, INFORMATIONAL VARIABLES, AND VISUAL ATTRIBUTES

The theory of preference matrix proposes coherence and complexity as informational variables to explain landscape preferences. To understand the relationship between the perceived coherence/complexity and the visual attributes of landscape scenes, we constructed multivariate generalized linear models based on a questionnaire study. A total of 488 respondents’ ratings of the preference, the perceived coherence and complexity, and four visual attributes, namely, the openness of visual scale (openness), the richness of composing elements (richness), the orderliness of organization (orderliness), and the depth of view (depth), of a set of digitally manipulated landscape scenes were analyzed. The results showed that landscape preference needed to be explained with coherence and complexity together. Meanwhile, rather than showing the one-one connection with a single visual attribute, the degree of perceived coherence/complexity should be explained with multiple visual attributes. Ranked by explanatory power, the coherence was positively related to orderliness, negatively related to richness, and positively related to openness. The complexity was positively influenced by the level of richness, depth, and negatively influenced by orderliness and openness. Based on the results, feasible ways to build landscape environments with both preferable coherence and complexity were proposed.

Serial dependence tracks objects and scenes independently

The visual world is made up of objects and scenes. Object perception requires both discriminating an individual object from others and binding together different perceptual samples of that object across time. Such binding manifests by serial dependence, the attraction of the current perception of a visual attribute towards values of that attribute seen in the recent past. Scene perception is subserved by global mechanisms like ensemble perception, the rapid extraction of the average feature value of a group of objects. The current study examined to what extent the perception of single objects in multi-object scenes depended on previous feature values of that object, or on the average previous attribute of all objects in the scene. Results show that serial dependence occurs independently on two simultaneously present objects, that ensemble perception depends on previous ensembles, and that serial dependence of an individual object occurs only on the features of that particular object. These results suggest that the temporal integration of successive perceptual samples operates simultaneously at independent levels of visual processing.