Symmetry perception by poultry chicks and its implications for three-dimensional object recognition

Are prime numbers special? Insights from the life sciences

Prime numbers have been attracting the interest of scientists since the first formulation of Euclid’s theorem in 300 B.C. Nowadays, physicists and mathematicians continue to formulate new theorems about prime numbers, trying to comprehensively explain their articulated properties. However, evidence from biology and experimental psychology suggest that prime numbers possess distinctive natural properties that pre-exist human grasping. The present work aims at reviewing the existing literature on prime numbers in the life sciences, including some recent experimental contributions employing newly hatched domestic chicks as animal model to test for spontaneous mechanisms allowing discrimination of primes from non-primes. Our overarching goal is that of discussing some instances of prime numbers in nature, with particular reference to their peculiar, non-mathematical, perceptual properties.

Symmetry recognition by pigeons: Generalized or not?

This note looks into the reasons why earlier reports may have arrived at differing conclusions about pigeons’ capacity to categorize bilaterally symmetric and asymmetric visual patterns. Attention is drawn to pigeons’ comparatively superior visual flicker resolution and superior visual linear acuity by reporting results of two ad-hoc experiments. This circumstance turns out to constrain conclusions drawn by earlier symmetry–asymmetry studies that used computer-generated patterns displayed on cathode ray tube monitors as these suffered from pictorial distortions. Additionally one of the studies involved patterns of inconsistent symmetry at global and local levels. A smaller-scale experiment using slide-projected unequivocal symmetric and asymmetric patterns yielded results compatible with the supposition that pigeons are capable of a symmetry–asymmetry categorization. The possibility that an artfactual cue may have inadvertently accentuated this capability in an earlier own experiment is considered.

Linear and Non-Linear Visual Feature Learning in Rat and Humans

The visual system processes visual input in a hierarchical manner in order to extract relevant features that can be used in tasks such as invariant object recognition. Although typically investigated in primates, recent work has shown that rats can be trained in a variety of visual object and shape recognition tasks. These studies did not pinpoint the complexity of the features used by these animals. Many tasks might be solved by using a combination of relatively simple features which tend to be correlated. Alternatively, rats might extract complex features or feature combinations which are nonlinear with respect to those simple features. In the present study, we address this question by starting from a small stimulus set for which one stimulus-response mapping involves a simple linear feature to solve the task while another mapping needs a well-defined nonlinear combination of simpler features related to shape symmetry. We verified computationally that the nonlinear task cannot be trivially solved by a simple V1-model. We show how rats are able to solve the linear feature task but are unable to acquire the nonlinear feature. In contrast, humans are able to use the nonlinear feature and are even faster in uncovering this solution as compared to the linear feature. The implications for the computational capabilities of the rat visual system are discussed.

A Gaze-Driven Evolutionary Algorithm to Study Aesthetic Evaluation of Visual Symmetry

Empirical work has shown that people like visual symmetry. We used a gaze-driven evolutionary algorithm technique to answer three questions about symmetry preference. First, do people automatically evaluate symmetry without explicit instruction? Second, is perfect symmetry the best stimulus, or do people prefer a degree of imperfection? Third, does initial preference for symmetry diminish after familiarity sets in? Stimuli were generated as phenotypes from an algorithmic genotype, with genes for symmetry (coded as deviation from a symmetrical template, deviation–symmetry, DS gene) and orientation (0° to 90°, orientation, ORI gene). An eye tracker identified phenotypes that were good at attracting and retaining the gaze of the observer. Resulting fitness scores determined the genotypes that passed to the next generation. We recorded changes to the distribution of DS and ORI genes over 20 generations. When participants looked for symmetry, there was an increase in high-symmetry genes. When participants looked for the patterns they preferred, there was a smaller increase in symmetry, indicating that people tolerated some imperfection. Conversely, there was no increase in symmetry during free viewing, and no effect of familiarity or orientation. This work demonstrates the viability of the evolutionary algorithm approach as a quantitative measure of aesthetic preference.

Enhanced learning of natural visual sequences in newborn chicks

To what extent are newborn brains designed to operate over natural visual input? To address this question, we used a high-throughput controlled-rearing method to examine whether newborn chicks (Gallus gallus) show enhanced learning of natural visual sequences at the onset of vision. We took the same set of images and grouped them into either natural sequences (i.e., sequences showing different viewpoints of the same real-world object) or unnatural sequences (i.e., sequences showing different images of different real-world objects). When raised in virtual worlds containing natural sequences, newborn chicks developed the ability to recognize familiar images of objects. Conversely, when raised in virtual worlds containing unnatural sequences, newborn chicks' object recognition abilities were severely impaired. In fact, the majority of the chicks raised with the unnatural sequences failed to recognize familiar images of objects despite acquiring over 100 h of visual experience with those images. Thus, newborn chicks show enhanced learning of natural visual sequences at the onset of vision. These results indicate that newborn brains are designed to operate over natural visual input.

Conditions for view invariance in the neural response to visual symmetry

Symmetry detection is slow when patterns are distorted by perspective, perhaps due to a time-consuming normalization process, or because discrimination relies on remaining weaker regularities in the retinal image. Participants viewed symmetrical or random dot patterns, either in a frontoparallel or slanted plane (±50°). One group performed a color discrimination task, while another performed a regularity discrimination task. We measured a symmetry-related event-related potential (ERP), beginning around 300 ms. During color discrimination, the ERP was reduced for slanted patterns, indexing only the remaining retinal structure. During regularity discrimination, the same ERP was view invariant, and identical for frontoparallel or slanted presentation. We conclude that normalization occurs rapidly during active symmetry discrimination, while symmetry-sensitive networks respond only to regularity in the retinal image when people are attending to other features.

Newborn chickens generate invariant object representations at the onset of visual object experience

To recognize objects quickly and accurately, mature visual systems build invariant object representations that generalize across a range of novel viewing conditions (e.g., changes in viewpoint). To date, however, the origins of this core cognitive ability have not yet been established. To examine how invariant object recognition develops in a newborn visual system, I raised chickens from birth for 2 weeks within controlled-rearing chambers. These chambers provided complete control over all visual object experiences. In the first week of life, subjects' visual object experience was limited to a single virtual object rotating through a 60° viewpoint range. In the second week of life, I examined whether subjects could recognize that virtual object from novel viewpoints. Newborn chickens were able to generate viewpoint-invariant representations that supported object recognition across large, novel, and complex changes in the object's appearance. Thus, newborn visual systems can begin building invariant object representations at the onset of visual object experience. These abstract representations can be generated from sparse data, in this case from a visual world containing a single virtual object seen from a limited range of viewpoints. This study shows that powerful, robust, and invariant object recognition machinery is an inherent feature of the newborn brain.