Camouflaging moving objects: crypsis and masquerade

Motion is generally assumed to “break” camouflage. However, although camouflage cannot conceal a group of moving animals, it may impair a predator’s ability to single one out for attack, even if that discrimination is not based on a color difference. Here, we use a computer-based task in which humans had to detect the odd one out among moving objects, with “oddity” based on shape. All objects were either patterned or plain, and either matched the background or not. We show that there are advantages of matching both group-mates and the background. However, when patterned objects are on a plain background (i.e., no background matching), the advantage of being among similarly patterned distractors is only realized when the group size is larger (10 compared to 5). In a second experiment, we present a paradigm for testing how coloration interferes with target-distractor discrimination, based on an adaptive staircase procedure for establishing the threshold. We show that when the predator only has a short time for decision-making, displaying a similar pattern to the distractors and the background affords protection even when the difference in shape between target and distractors is large. We conclude that, even though motion breaks camouflage, being camouflaged could help group-living animals reduce the risk of being singled out for attack by predators.

Dynamic Dazzle Distorts Speed Perception

Static high contrast (‘dazzle’) patterns, such as zigzags, have been shown to reduce the perceived speed of an object. It has not escaped our notice that this effect has possible military applications and here we report a series of experiments on humans, designed to establish whether dynamic dazzle patterns can cause distortions of perceived speed sufficient to provide effective defence in the field, and the extent to which these effects are robust to a battery of manipulations. Dynamic stripe patterns moving in the same direction as the target are found to increase the perceived speed of that target, whilst dynamic stripes moving in the opposite direction to the target reduce the perceived speed. We establish the optimum position for such dazzle patches; confirm that reduced contrast and the addition of colour do not affect the performance of the dynamic dazzle, and finally, using the CO₂ challenge, show that the effect is robust to stressful conditions.

Fever in the returning traveller: the importance of sensitivity

We report the case of a 28-year-old man, presenting with episodes of fever and rigours, having recently returned from Cameroon and Uganda. Initial investigations for malaria were negative, and the patient was sent home without a clear diagnosis. Subsequent review of the blood film revealed the presence of Plasmodium ovale. This case highlights the importance of repeated and careful inspection of blood films, given the relatively low sensitivity of rapid diagnostic tests in P. ovale infection. It also illustrates the importance of the travel history in the diagnosis of malaria.

Moving in groups: how density and unpredictable motion affect predation risk

One of the most widely applicable benefits of aggregation is a per capita reduction in predation risk. Many factors can contribute to this but, for moving groups, an increased difficulty in tracking and targeting one individual amongst many has received particular attention. This "confusion effect" has been proposed to result from a bottleneck in information processing, a hypothesis supported by both modelling and experiment. If the competition for limited attention is localised to the particular part of the visual field where the target is located, prey density is likely to be the key factor rather than group numbers per se. Furthermore, unpredictability of prey movement may enhance confusion, but both factors have received insufficient attention from empiricists: undoubtedly because of the difficulty of experimental manipulation in natural systems. We used a computer-based target tracking task with human subjects to manipulate effects of number and density independently, in factorial combination with motion path predictability. Density, rather than number, drove the confusion effect in our experiment and acted synergistically with the unpredictability of the direction of motion. The experimental paradigm we present offers the potential for isolating other factors affecting predation success on group-living prey, and forging links with the psychological literature on object tracking and visual search.

Camouflage, detection and identification of moving targets

Nearly all research on camouflage has investigated its effectiveness for concealing stationary objects. However, animals have to move, and patterns that only work when the subject is static will heavily constrain behaviour. We investigated the effects of different camouflages on the three stages of predation-detection, identification and capture-in a computer-based task with humans. An initial experiment tested seven camouflage strategies on static stimuli. In line with previous literature, background-matching and disruptive patterns were found to be most successful. Experiment 2 showed that if stimuli move, an isolated moving object on a stationary background cannot avoid detection or capture regardless of the type of camouflage. Experiment 3 used an identification task and showed that while camouflage is unable to slow detection or capture, camouflaged targets are harder to identify than uncamouflaged targets when similar background objects are present. The specific details of the camouflage patterns have little impact on this effect. If one has to move, camouflage cannot impede detection; but if one is surrounded by similar targets (e.g. other animals in a herd, or moving background distractors), then camouflage can slow identification. Despite previous assumptions, motion does not entirely 'break' camouflage.

Eye movements to natural images as a function of sex and personality

Women and men are different. As humans are highly visual animals, these differences should be reflected in the pattern of eye movements they make when interacting with the world. We examined fixation distributions of 52 women and men while viewing 80 natural images and found systematic differences in their spatial and temporal characteristics. The most striking of these was that women looked away and usually below many objects of interest, particularly when rating images in terms of their potency. We also found reliable differences correlated with the images' semantic content, the observers' personality, and how the images were semantically evaluated. Information theoretic techniques showed that many of these differences increased with viewing time. These effects were not small: the fixations to a single action or romance film image allow the classification of the sex of an observer with 64% accuracy. While men and women may live in the same environment, what they see in this environment is reliably different. Our findings have important implications for both past and future eye movement research while confirming the significant role individual differences play in visual attention.

Uncertainty plus prior equals rational bias: an intuitive Bayesian probability weighting function

Empirical research has shown that when making choices based on probabilistic options, people behave as if they overestimate small probabilities, underestimate large probabilities, and treat positive and negative outcomes differently. These distortions have been modeled using a nonlinear probability weighting function, which is found in several nonexpected utility theories, including rank-dependent models and prospect theory; here, we propose a Bayesian approach to the probability weighting function and, with it, a psychological rationale. In the real world, uncertainty is ubiquitous and, accordingly, the optimal strategy is to combine probability statements with prior information using Bayes' rule. First, we show that any reasonable prior on probabilities leads to 2 of the observed effects; overweighting of low probabilities and underweighting of high probabilities. We then investigate 2 plausible kinds of priors: informative priors based on previous experience and uninformative priors of ignorance. Individually, these priors potentially lead to large problems of bias and inefficiency, respectively; however, when combined using Bayesian model comparison methods, both forms of prior can be applied adaptively, gaining the efficiency of empirical priors and the robustness of ignorance priors. We illustrate this for the simple case of generic good and bad options, using Internet blogs to estimate the relevant priors of inference. Given this combined ignorant/informative prior, the Bayesian probability weighting function is not only robust and efficient but also matches all of the major characteristics of the distortions found in empirical research.

Why the leopard got its spots: relating pattern development to ecology in felids

A complete explanation of the diversity of animal colour patterns requires an understanding of both the developmental mechanisms generating them and their adaptive value. However, only two previous studies, which involved computer-generated evolving prey, have attempted to make this link. This study examines variation in the camouflage patterns displayed on the flanks of many felids. After controlling for the effects of shared ancestry using a fully resolved molecular phylogeny, this study shows how phenotypes from plausible felid coat pattern generation mechanisms relate to ecology. We found that likelihood of patterning and pattern attributes, such as complexity and irregularity, were related to felids' habitats, arboreality and nocturnality. Our analysis also indicates that disruptive selection is a likely explanation for the prevalence of melanistic forms in Felidae. Furthermore, we show that there is little phylogenetic signal in the visual appearance of felid patterning, indicating that camouflage adapts to ecology over relatively short time scales. Our method could be applied to any taxon with colour patterns that can reasonably be matched to reaction-diffusion and similar models, where the kinetics of the reaction between two or more initially randomly dispersed morphogens determines the outcome of pattern development.

The relationship between language and the environment: information theory shows why we have only three lightness terms

The surface reflectance of objects is highly variable, ranging between 4% for, say, charcoal and 90% for fresh snow. When stimuli are presented simultaneously, people can discriminate hundreds of levels of visual intensity. Despite this, human languages possess a maximum of just three basic terms for describing lightness. In English, these are white (or light), black (or dark), and gray. Why should this be? Using information theory, combined with estimates of the distribution of reflectances in the natural world and the reliability of lightness recall over time, we show that three lightness terms is the optimal number for describing surface reflectance properties in a modern urban or indoor environment. We also show that only two lightness terms would be required in a forest or rural environment.

Stability of the color-opponent signals under changes of illuminant in natural scenes

Illumination varies greatly both across parts of a natural scene and as a function of time, whereas the spectral reflectance function of surfaces remains more stable and is of much greater relevance when searching for specific targets. This study investigates the functional properties of postreceptoral opponent-channel responses, in particular regarding their stability against spatial and temporal variation in illumination. We studied images of natural scenes obtained in UK and Uganda with digital cameras calibrated to produce estimated L-, M-, and S-cone responses of trichromatic primates (human) and birds (starling). For both primates and birds we calculated luminance and red-green opponent (RG) responses. We also calculated a primate blue-yellow-opponent (BY) response. The BY response varies with changes in illumination, both across time and across the image, rendering this factor less invariant. The RG response is much more stable than the BY response across such changes in illumination for primates, less so for birds. These differences between species are due to the greater separation of bird L and M cones in wavelength and the narrower bandwidth of the cone action spectra. This greater separation also produces a larger chromatic signal for a given change in spectral reflectance. Thus bird vision seems to suffer a greater degree of spatiotemporal "clutter" than primate vision, but also enhances differences between targets and background. Therefore, there may be a trade-off between the degree of chromatic clutter in a visual system versus the degree of chromatic difference between a target and its background. Primate and bird visual systems have found different solutions to this trade-off.

Multisensory temporal order judgments: when two locations are better than one

In Experiment 1, participants were presented with pairs of stimuli (one visual and the other tactile) from the left and/or right of fixation at varying stimulus onset asynchronies and were required to make unspeeded temporal order judgments (TOJs) regarding which modality was presented first. When the participants adopted an uncrossed-hands posture, just noticeable differences (JNDs) were lower (i.e., multisensory TOJs were more precise) when stimuli were presented from different positions, rather than from the same position. This spatial redundancy benefit was reduced when the participants adopted a crossed-hands posture, suggesting a failure to remap visuotactile space appropriately. In Experiment 2, JNDs were also lower when pairs of auditory and visual stimuli were presented from different positions, rather than from the same position. Taken together, these results demonstrate that people can use redundant spatial cues to facilitate their performance on multisensory TOJ tasks and suggest that previous studies may have systematically overestimated the precision with which people can make such judgments. These results highlight the intimate link between spatial and temporal factors in determining our perception of the multimodal objects and events in the world around us.

Identifying the structure in cuttlefish visual signals

The common cuttlefish (Sepia officinalis) communicates and camouflages itself by changing its skin colour and texture. Hanlon and Messenger (1988 Phil. Trans. R. Soc. Lond. B 320, 437-487) classified these visual displays, recognizing 13 distinct body patterns. Although this conclusion is based on extensive observations, a quantitative method for analysing complex patterning has obvious advantages. We formally define a body pattern in terms of the probabilities that various skin features are expressed, and use Bayesian statistical methods to estimate the number of distinct body patterns and their visual characteristics. For the dataset of cuttlefish coloration patterns recorded in our laboratory, this statistical method identifies 12-14 different patterns, a number consistent with the 13 found by Hanlon and Messenger. If used for signalling these would give a channel capacity of 3.4 bits per pattern. Bayesian generative models might be useful for objectively describing the structure in other complex biological signalling systems.

On variability in the density of corticocortical and thalamocortical connections

Variability is an important but neglected aspect of connectional neuroanatomy. The quantitative density of the 'same' corticocortical or thalamocortical connection may vary by over two orders of magnitude between different injections of the same tracer. At present, however, the frequency distribution of connection densities is unknown. Therefore, it is unclear what kind of sampling strategies or statistical methods are appropriate for quantitative studies of connectivity. Nor is it clear if the measured variability represents differences between subjects, or if it is simply a consequence of intra-individual differences resulting from experimental technique and the exact placement of tracers relative to local spatial and laminar variation in connectivity. We used quantitative measurements of the density of a large number of corticocortical and thalamocortical connections from our own laboratories and from the literature. Variability in the density of given corticocortical and thalamocortical connections is high, with the standard deviation of density proportional to the mean. The frequency distribution is close to exponential. Therefore, analysis methods relying on the normal distribution are not appropriate. We provide an appendix that gives simple statistical guidance for samples drawn from exponentially distributed data. For a given corticocortical or thalamocortical connection density, between-individual standard deviation is 0.85 to 1.25 times the within-individual standard deviation. Therefore, much of the variability reported in conventional neuroanatomical studies (with one tracer deposited per animal) is due to within-individual factors. We also find that strong, but not weak, corticocortical connections are substantially more variable than thalamocortical connections. We propose that the near exponential distribution of connection densities is a simple consequence of 'patchy' connectivity. We anticipate that connection data will be well described by the negative binomial, a class of distribution that applies to events occurring in clumped or patchy substrates. Local patchiness may be a feature of all corticocortical connections and could explain why strong corticocortical connections are more variable than strong thalamocortical connections. This idea is supported by the columnar patterns of many corticocortical but few thalamocortical connections in the literature.

Insights into motion perception by observer modeling

The statistical efficiency of human observers performing a simplified version of the motion detection task of Salzman and Newsome [Science 264, 231 (1994)] is high but not perfect. This reduced efficiency may be caused by noise internal to the observers or by the observers' using strategies that are different from that used by an ideal machine. We therefore investigated which of three simple models best accounts for the observers' performance. The models compared were a motion detector that uses the proportion of dots in the first frame that move coherently (as would an ideal machine), a model that bases its decision on the number of dots that move, and a model that differentially weights motions that occur at different locations in the visual field (for instance, differentially weights the point of fixation and the periphery). We compared these models by explicitly modeling the human observers' performance. We recorded the exact stimulus configuration on each trial together with the observer's response, and, for the different models, we found the parameters that best predicted the observer's performance in a least-squares sense. We then used N-fold cross validation to compare the models and hence the associated hypotheses. Our results show that the performance of observers is based on the proportion, not the absolute number, of dots that are moving and that there was no evidence of any differential spatial weighting. Whereas this method of modeling the observers' response is demonstrated only for one simple psychophysical paradigm, it is general and can be applied to any psychophysical framework in which the entire stimulus can be recorded.

Responses of neurons in primary and inferior temporal visual cortices to natural scenes

The primary visual cortex (V1) is the first cortical area to receive visual input, and inferior temporal (IT) areas are among the last along the ventral visual pathway. We recorded, in area V1 of anaesthetized cats and area IT of awake macaque monkeys, responses of neurons to videos of natural scenes. Responses were analysed to test various hypotheses concerning the nature of neural coding in these two regions. A variety of spike-train statistics were measured including spike-count distributions, interspike interval distributions, coefficients of variation, power spectra, Fano factors and different sparseness measures. All statistics showed non-Poisson characteristics and several revealed self-similarity of the spike trains. Spike-count distributions were approximately exponential in both visual areas for eight different videos and for counting windows ranging from 50 ms to 5 seconds. The results suggest that the neurons maximize their information carrying capacity while maintaining a fixed long-term-average firing rate, or equivalently, minimize their average firing rate for a fixed information carrying capacity.

Nonlinear principal components analysis of neuronal spike train data

Many recent approaches to decoding neural spike trains depend critically on the assumption that for low-pass filtered spike trains, the temporal structure is optimally represented by a small number of linear projections onto the data. We therefore tested this assumption of linearity by comparing a linear factor analysis technique (principal components analysis) with a nonlinear neural network based method. It is first shown that the nonlinear technique can reliably identify a neuronally plausible nonlinearity in synthetic spike trains. However, when applied to the outputs from primary visual cortical neurons, this method shows no evidence for significant temporal nonlinearities. The implications of this are discussed.

Optimal, unsupervised learning in invariant object recognition

A means for establishing transformation-invariant representations of objects is proposed and analyzed, in which different views are associated on the basis of the temporal order of the presentation of these views, as well as their spatial similarity. Assuming knowledge of the distribution of presentation times, an optimal linear learning rule is derived. Simulations of a competitive network trained on a character recognition task are then used t highlight the success of this learning rule in relation to simple Hebbian learning and to show that the theory can give accurate quantitative predictions for the optimal parameters for such networks.

Searching for filters with 'interesting' output distributions: an uninteresting direction to explore?

It has been independently proposed, by Barlow, Field, Intrator and co-workers, that the receptive fields of neurons in V1 are optimized to generate 'sparse', Kurtotic, or 'interesting' output probability distributions. We investigate the empirical evidence for this further and argue that filters can produce 'interesting' output distributions simply because natural images have variable local intensity variance. If the proposed filters have zero DC, then the probability distribution of filter outputs (and hence the output Kurtosis) is well predicted simply from these effects of variable local variance. This suggests that finding filters with high output Kurtosis does not necessarily signal interesting image structure. It is then argued that finding filters that maximize output Kurtosis generates filters that are incompatible with observed physiology. In particular the optimal difference-of-Gaussian (DOG) filter should have the smallest possible scale, an on-centre off-surround cell should have a negative DC, and that the ratio of centre width to surround width should approach unity. This is incompatible with the physiology. Further, it is also predicted that oriented filters should always be oriented in the vertical direction, and of all the filters tested, the filter with the highest output Kurtosis has the lowest signal-to-noise ratio (the filter is simply the difference of two neighbouring pixels). Whilst these observations are not incompatible with the brain using a sparse representation, it does argue that little significance should be placed on finding filters with highly Kurtotic output distributions. It is therefore argued that other constraints are required in order to understand the development of visual receptive fields.