The Camouflage Machine: Optimizing protective coloration using deep learning with genetic algorithms.

Evolutionary biologists frequently wish to measure the fitness of alternative phenotypes using behavioral experiments. However, many phenotypes are complex. One example is coloration: camouflage aims to make detection harder, while conspicuous signals (e.g., for warning or mate attraction) require the opposite. Identifying the hardest and easiest to find patterns is essential for understanding the evolutionary forces that shape protective coloration, but the parameter space of potential patterns (colored visual textures) is vast, limiting previous empirical studies to a narrow range of phenotypes. Here, we demonstrate how deep learning combined with genetic algorithms can be used to augment behavioral experiments, identifying both the best camouflage and the most conspicuous signal(s) from an arbitrarily vast array of patterns. To show the generality of our approach, we do so for both trichromatic (e.g., human) and dichromatic (e.g., typical mammalian) visual systems, in two different habitats. The patterns identified were validated using human participants; those identified as the best for camouflage were significantly harder to find than a tried-and-tested military design, while those identified as most conspicuous were significantly easier to find than other patterns. More generally, our method, dubbed the "Camouflage Machine," will be a useful tool for identifying the optimal phenotype in high dimensional state spaces.

A method for detecting characteristic patterns in social interactions with an application to handover interactions.

Social interactions are a defining behavioural trait of social animals. Discovering characteristic patterns in the display of such behaviour is one of the fundamental endeavours in behavioural biology and psychology, as this promises to facilitate the general understanding, classification, prediction and even automation of social interactions. We present a novel approach to study characteristic patterns, including both sequential and synchronous actions in social interactions. The key concept in our analysis is to represent social interactions as sequences of behavioural states and to focus on changes in behavioural states shown by individuals rather than on the duration for which they are displayed. We extend techniques from data mining and bioinformatics to detect frequent patterns in these sequences and to assess how these patterns vary across individuals or changes in interaction tasks. To illustrate our approach and to demonstrate its potential, we apply it to novel data on a simple physical interaction, where one person hands a cup to another person. Our findings advance the understanding of handover interactions, a benchmark scenario for social interactions. More generally, we suggest that our approach permits a general perspective for studying social interactions.

Treacherous Pavements: Paving Slab Patterns Modify Intended Walking Directions.

Current understanding in locomotion research is that, for humans, navigating natural environments relies heavily on visual input; in contrast, walking on even ground in man-made obstacle and hazard-free environments is so highly automated that visual information derived from floor patterns should not affect locomotion and in particular have no impact on the direction of travel. The vision literature on motion perception would suggest otherwise; specifically that oblique floor patterns may induce substantial veering away from the intended direction of travel due to the so-called aperture problem. Here, we tested these contrasting predictions by letting participants walk over commonly encountered floor patterns (paving slabs) and investigating participants' ability to walk "straight ahead" for different pattern orientations. We show that, depending on pattern orientation, participants veered considerably over the measured travel distance (up to 8% across trials), in line with predictions derived from the literature on motion perception. We argue that these findings are important to the study of locomotion, and, if also observed in real world environments, might have implications for architectural design.

Reward is assessed in three dimensions that correspond to the semantic differential.

If choices are to be made between alternatives like should I go for a walk or grab a coffee, a 'common currency' is needed to compare them. This quantity, often known as reward in psychology and utility in economics, is usually conceptualised as a single dimension. Here we propose that to make a comparison between different options it is important to know not only the average reward, but also both the risk and level of certainty (or control) associated with an option. Almost all objects can be the subject of choice, so if these dimensions are required in order to make a decision, they should be part of the meaning of those objects. We propose that this ubiquity is unique, so if we take an average over many concepts and domains these three dimensions (reward, risk, and uncertainty) should emerge as the three most important dimensions in the "meaning" of objects. We investigated this possibility by relating the three dimensions of reward to an old, robust and extensively studied factor analytic instrument known as the semantic differential. Across a very wide range of situations, concepts and cultures, factor analysis shows that 50% of the variance in rating scales is accounted for by just three dimensions, with these dimensions being Evaluation, Potency, and Activity [1]. Using a statistical analysis of internet blog entries and a betting experiment, we show that these three factors of the semantic differential are strongly correlated with the reward history associated with a given concept: Evaluation measures relative reward; Potency measures absolute risk; and Activity measures the uncertainty or lack of control associated with a concept. We argue that the 50% of meaning captured by the semantic differential is simply a summary of the reward history that allows decisions to be made between widely different options.

Social anxiety modulates subliminal affective priming.

BACKGROUND: It is well established that there is anxiety-related variation between observers in the very earliest, pre-attentive stage of visual processing of images such as emotionally expressive faces, often leading to enhanced attention to threat in a variety of disorders and traits. Whether there is also variation in early-stage affective (i.e. valenced) responses resulting from such images, however, is not yet known. The present study used the subliminal affective priming paradigm to investigate whether people varying in trait social anxiety also differ in their affective responses to very briefly presented, emotionally expressive face images. METHODOLOGY/PRINCIPAL FINDINGS: Participants (n = 67) completed a subliminal affective priming task, in which briefly presented and smiling, neutral and angry faces were shown for 10 ms durations (below objective and subjective thresholds for visual discrimination), and immediately followed by a randomly selected Chinese character mask (2000 ms). Ratings of participants' liking for each Chinese character indicated the degree of valenced affective response made to the unseen emotive images. Participants' ratings of their liking for the Chinese characters were significantly influenced by the type of face image preceding them, with smiling faces generating more positive ratings than neutral and angry ones (F(2,128) = 3.107, p<0.05). Self-reported social anxiety was positively correlated with ratings of smiling relative to neutral-face primed characters (Pearson's r = .323, p<0.01). Individual variation in self-reported mood awareness was not associated with ratings. CONCLUSIONS: Trait social anxiety is associated with individual variation in affective responding, even in response to the earliest, pre-attentive stage of visual image processing. However, the fact that these priming effects are limited to smiling and not angry (i.e. threatening) images leads us to propose that the pre-attentive processes involved in generating the subliminal affective priming effect may be different from those that generate attentional biases in anxious individuals.