Sensitivity to geometric shape regularity in humans and baboons: A putative signature of human singularity

What curves are parallel? The core feature of preschoolers' intuitive parallel category

Existing evidence has revealed that humans can spontaneously categorize many geometric shapes without formal education. Children around 4 years could distinguish between intersecting lines and parallel lines. Three features can be used to identify parallel lines, namely "translational congruence," "never meet," and "constant distance." This study separated them by using pairs of curves that possess only one of these features. Two experiments across 2021-2023, respectively, compared the relative priority of "translational congruence" with "constant distance," and "never meet" with "constant distance" among 3- to 5-year-old Chinese preschoolers (Ntotal = 314, 48% female). The results showed that preschoolers consistently grouped "constant distance" curves with parallel lines. This suggests that the core feature of intuitive parallel category is "constant distance" at this age.

Tactile cues compensate for unbalanced vestibular cues during progression on inclined surfaces

A previous study demonstrated that rodents on an inclined square platform traveled straight vertically or horizontally and avoided diagonal travel. Through behavior they aligned their head with the horizontal plane, acquiring similar bilateral vestibular cues - a basic requirement for spatial orientation and a salient feature of animals in motion. This behavior had previously been shown to be conspicuous in Tristram's jirds. Here, therefore jirds were challenged by testing their travel behavior on a circular arena inclined at 0°-75°. Our hypothesis was that if, as typical to rodents, the jirds would follow the curved arena wall, they would need to display a compensating mechanism to enable traveling in such a path shape, which involves a tilted frontal head axis and unbalanced bilateral vestibular cues. We found that with the increase in inclination, the jirds remained more in the lower section of the arena (geotaxis). When tested on the steep inclinations, however, their travel away from the arena wall was strictly straight up or down, in contrast to the curved paths that followed the circular arena wall. We suggest that traveling along a circular path while maintaining contact with the wall (thigmotaxis), provided tactile information that compensated for the unbalanced bilateral vestibular cues present when traveling along such curved inclined paths. In the latter case, the frontal plane of the head was in a diagonal posture in relation to gravity, a posture that was avoided when traveling away from the wall.

Prägnanz in visual perception

How do we perceptually and cognitively organize incoming stimulation? A century ago, Gestalt psychologists posited the law of Prägnanz: psychological organization will always be as 'good' as possible given the prevailing conditions. To make the Prägnanz law a useful statement, it needs to be specified further (a) what a 'good' psychological organization entails, (b) how the Prägnanz tendency can be realized, and (c) which conditions need to be taken into account. Although the Gestalt school did provide answers to these questions, modern-day mentions of Prägnanz or good Gestalt often lack these clarifications. The concept of Prägnanz has been (mis)understood in many different ways, and by looking back on the rich history of the concept, we will attempt to present a more fine-grained view and promote a renewed understanding of the central role of Prägnanz in visual perception and beyond. We review Gestalt psychology's answers to the questions listed above, and also discuss the four main uses of the Prägnanz concept in more detail: (a) a Prägnanz tendency in each organizational process, (b) Prägnanz as a property of a Gestalt, (c) Prägnanz steps as internal reference points, and (d) Prägnanz in relation to aesthetic appreciation. As a key takeaway, Prägnanz is a multifaceted Gestalt psychological concept indicating the "goodness" of an experienced organization. Both the removal of unnecessary details and the emphasis on characteristic features of the overall organization compared to a reference organization can contribute to the emergence of a 'better' Gestalt. The stimulus constellation is not the only factor in determining the goodness of an organization, also the stimulus' interaction with an individual in a specific spatial and temporal context plays a role. Taking the ideas on Prägnanz as a generative framework and keeping the original Gestalt psychological context in mind, future research on perceptual organization can improve our understanding of the principles underlying psychological organization by further specifying how different organizational principles interact in concrete situations. Public significance statement: This paper reviews what a 'good' psychological organization entails, and how the incoming stimulation is clarified in human perception to achieve the best possible psychological organization. The review debunks common misconceptions on the meaning of "goodness" and synthesizes the most important perspectives and developments on "goodness" from its conception until now.

Compositional diversity in visual concept learning

Humans leverage compositionality to efficiently learn new concepts, understanding how familiar parts can combine together to form novel objects. In contrast, popular computer vision models struggle to make the same types of inferences, requiring more data and generalizing less flexibly than people do. Here, we study these distinctively human abilities across a range of different types of visual composition, examining how people classify and generate "alien figures" with rich relational structure. We also develop a Bayesian program induction model which searches for the best programs for generating the candidate visual figures, utilizing a large program space containing different compositional mechanisms and abstractions. In few shot classification tasks, we find that people and the program induction model can make a range of meaningful compositional generalizations, with the model providing a strong account of the experimental data as well as interpretable parameters that reveal human assumptions about the factors invariant to category membership (here, to rotation and changing part attachment). In few shot generation tasks, both people and the models are able to construct compelling novel examples, with people behaving in additional structured ways beyond the model capabilities, e.g. making choices that complete a set or reconfigure existing parts in new ways. To capture these additional behavioral patterns, we develop an alternative model based on neuro-symbolic program induction: this model also composes new concepts from existing parts yet, distinctively, it utilizes neural network modules to capture residual statistical structure. Together, our behavioral and computational findings show how people and models can produce a variety of compositional behavior when classifying and generating visual objects.

Listening to trees in the forest: Attentional set influences how semantic and acoustic factors interact in auditory perception

Studies of auditory object perception claim that semantic properties dominate acoustic properties in determining identification accuracy. Yet the direction of the semantic effect is mixed, with some studies showing an advantage for detecting incongruent sounds and others reporting a congruent sound advantage. Here we examine the role of the participant's attentional set when identifying auditory objects in naturalistic soundscapes. We varied the acoustic and semantic properties of the sounds orthogonally in two experiments. In Experiment 1 participants tuned their attention broadly to detect any change between two successive soundscapes (e.g., two restaurant soundscapes, with and without a child coughing). In Experiment 2 they tuned attention more narrowly to a probe presented after a soundscape (e.g., a restaurant soundscape with a child coughing, followed by the coughing sound alone). In both experiments, semantic relations between the objects and backgrounds helped to disambiguate objects that blended acoustically with the background. When attending globally (Experiment 1), objects that were acoustically similar yet semantically incongruent tended to be missed (e.g., bouncing basketball on a construction site), as though camouflaged by the gist of the soundscape. When attending locally (Experiment 2), semantically congruent foil objects led to false positive reports under acoustically similar conditions (hammering sounds on a construction site), as though the gist of the soundscape contributed to their plausible inclusion. In summary, although attentional set had a strong influence on the specific kinds of errors made, both results pointed to participants using a semantically congruent high-level schema to report the sounds they heard.

Object geometry serves humans' intuitive physics of stability

How do humans judge physical stability? A prevalent account emphasizes the mental simulation of physical events implemented by an intuitive physics engine in the mind. Here we test the extent to which the perceptual features of object geometry are sufficient for supporting judgments of falling direction. In all experiments, adults and children judged the falling direction of a tilted object and, across experiments, objects differed in the geometric features (i.e., geometric centroid, object height, base size and/or aspect ratio) relevant to the judgment. Participants' performance was compared to computational models trained on geometric features, as well as a deep convolutional neural network (ResNet-50), none of which incorporated mental simulation. Adult and child participants' performance was well fit by models of object geometry, particularly the geometric centroid. ResNet-50 also provided a good account of human performance. Altogether, our findings suggest that object geometry may be sufficient for judging the falling direction of tilted objects, independent of mental simulation.

Neurocognitive Assessment of Mathematics-Related Capacities in Neurosurgical Patients

A precise neuropsychological assessment is of the utmost importance for neurosurgical patients undergoing the surgical excision of cerebral lesions. The assessment of mathematical abilities is usually limited to arithmetical operations while other fundamental visuo-spatial aspects closely linked to mathematics proficiency, such as the perception of numerical quantities and geometrical reasoning, are completely neglected. We evaluated these abilities with two objective and reproducible psychophysical tests, measuring numerosity perception and non-symbolic geometry, respectively. We tested sixteen neuro-oncological patients before the operation and six after the operation with classical neuropsychological tests and with two psychophysical tests. The scores of the classical neuropsychological tests were very heterogeneous, possibly due to the distinct location and histology of the tumors that might have spared (or not) brain areas subserving these abilities or allowed for plastic reorganization. Performance in the two non-symbolic tests reflected, on average, the presumed functional role of the lesioned areas, with participants with parietal and frontal lesions performing worse on these tests than patients with occipital and temporal lesions. Single-case analyses not only revealed some interesting exceptions to the group-level results (e.g., patients with parietal lesions performing well in the numerosity test), but also indicated that performance in the two tests was independent of non-verbal reasoning and visuo-spatial working memory. Our results highlight the importance of assessing non-symbolic numerical and geometrical abilities to complement typical neuropsychological batteries. However, they also suggest an avoidance of reliance on an excessively rigid localizationist approach when evaluating the neuropsychological profile of oncological patients.

Natural logic and baby LoTH

Language-of-thought hypothesis (LoTH) is having a profound impact on cognition studies. However, much remains unknown about its basic primitives and generative operations. Infant studies are fundamental, but methodologically very challenging. By distilling potential primitives from work in natural-language semantics, an approach beyond the corset of standard formal logic may be undertaken. Still, the road ahead is challenging and long.

Diverse mathematical knowledge among indigenous Amazonians

We investigate number and arithmetic learning among a Bolivian indigenous people, the Tsimane', for whom formal schooling is comparatively recent in history and variable in both extent and consistency. We first present a large-scale meta-analysis on child number development involving over 800 Tsimane' children. The results emphasize the impact of formal schooling: Children are only found to be full counters when they have attended school, suggesting the importance of cultural support for early mathematics. We then test especially remote Tsimane' communities and document the development of specialized arithmetical knowledge in the absence of direct formal education. Specifically, we describe individuals who succeed on arithmetic problems involving the number five-which has a distinct role in the local economy-even though they do not succeed on some lower numbers. Some of these participants can perform multiplication with fives at greater accuracy than addition by one. These results highlight the importance of cultural factors in early mathematics and suggest that psychological theories of number where quantities are derived from lower numbers via repeated addition (e.g., a successor function) are unlikely to explain the diversity of human mathematical ability.

Trend judgment as a perceptual building block of graphicacy and mathematics, across age, education, and culture

Data plots are widely used in science, journalism and politics, since they efficiently allow to depict a large amount of information. Graphicacy, the ability to understand graphs, has thus become a fundamental cultural skill comparable to literacy or numeracy. Here, we introduce a measure of intuitive graphicacy that assesses the perceptual ability to detect a trend in noisy scatterplots ("does this graph go up or down?"). In 3943 educated participants, responses vary as a sigmoid function of the t-value that a statistician would compute to detect a significant trend. We find a minimum level of core intuitive graphicacy even in unschooled participants living in remote Namibian villages (N = 87) and 6-year-old 1st-graders who never read a graph (N = 27). The sigmoid slope that we propose as a proxy of intuitive graphicacy increases with education and tightly correlates with statistical and mathematical knowledge, showing that experience contributes to refining graphical intuitions. Our tool, publicly available online, allows to quickly evaluate and formally quantify a perceptual building block of graphicacy.

Macaques recognize features in synthetic images derived from ventral stream neurons

Primates can recognize features in virtually all types of images, an ability that still requires a comprehensive computational explanation. One hypothesis is that visual cortex neurons learn patterns from scenes, objects, and textures, and use these patterns to interpolate incoming visual information. We have used machine learning algorithms to instantiate visual patterns stored by neurons-we call these highly activating images prototypes. Prototypes from inferotemporal (IT) neurons often resemble parts of real-world objects, such as monkey faces and body parts, a similarity established via pretrained neural networks [C. R. Ponce et al., Cell 177, 999-1009.e10 (2019)] and naïve human participants [A. Bardon, W. Xiao, C. R. Ponce, M. S. Livingstone, G. Kreiman, Proc. Natl. Acad. Sci. U.S.A. 119, e2118705119 (2022)]. However, it is not known whether monkeys themselves perceive similarities between neuronal prototypes and real-world objects. Here, we investigated whether monkeys reported similarities between prototypes and real-world objects using a two-alternative forced choice task. We trained the animals to saccade to synthetic images of monkeys, and subsequently tested how they classified prototypes synthesized from IT and primary visual cortex (V1). We found monkeys classified IT prototypes as conspecifics more often than they did random generator images and V1 prototypes, and their choices were partially predicted by convolutional neural networks. Further, we confirmed that monkeys could abstract general shape information from images of real-world objects. Finally, we verified these results with human participants. Our results provide further evidence that prototypes from cortical neurons represent interpretable abstractions from the visual world.

Graded human sensitivity to geometric and topological concepts

In a seminal study, Dehaene et al. (2006) found evidence that adults and children are sensitive to geometric and topological (GT) concepts using a novel odd-one-out task. However, performance on this task could reflect more general cognitive abilities than intuitive knowledge of GT concepts. Here, we developed a new 2-alternative forced choice (2-AFC) version of the original task where chance represents a higher bar to clear (50% vs. 16.67%) and where the role of general cognitive abilities is minimized. Replicating the original finding, American adult participants showed above-chance sensitivity to 41 of the 43 GT concepts tested. Moreover, their performance was not strongly driven by two general cognitive abilities, fluid intelligence and mental rotation, nor was it strongly associated with mathematical achievement as measured by ACT/SAT scores. The performance profile across the 43 concepts as measured by the new 2-AFC task was found to be highly correlated with the profiles as measured using the original odd-one-out task, as an analysis of data sets spanning populations and ages revealed. Most significantly, an aggregation of the 43 concepts into seven classes of GT concepts found evidence for graded sensitivity. Some classes, such as Euclidean geometry and Topology, were found to be more domain-specific: they "popped out" for participants and were judged very quickly and highly accurately. Others, notably Symmetry and Geometric transformations, were found to be more domain-general: better predicted by participants' general cognitive abilities and mathematical achievement. These results shed light on the graded nature of GT concepts in humans and challenge computational models that emphasize the role of induction.

The best game in town: The reemergence of the language-of-thought hypothesis across the cognitive sciences

Mental representations remain the central posits of psychology after many decades of scrutiny. However, there is no consensus about the representational format(s) of biological cognition. This paper provides a survey of evidence from computational cognitive psychology, perceptual psychology, developmental psychology, comparative psychology, and social psychology, and concludes that one type of format that routinely crops up is the language-of-thought (LoT). We outline six core properties of LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate-argument structure; (iv) logical operators; (v) inferential promiscuity; and (vi) abstract content. These properties cluster together throughout cognitive science. Bayesian computational modeling, compositional features of object perception, complex infant and animal reasoning, and automatic, intuitive cognition in adults all implicate LoT-like structures. Instead of regarding LoT as a relic of the previous century, researchers in cognitive science and philosophy-of-mind must take seriously the explanatory breadth of LoT-based architectures. We grant that the mind may harbor many formats and architectures, including iconic and associative structures as well as deep-neural-network-like architectures. However, as computational/representational approaches to the mind continue to advance, classical compositional symbolic structures - that is, LoTs - only prove more flexible and well-supported over time.

Problems and Mysteries of the Many Languages of Thought

"What is the structure of thought?" is as central a question as any in cognitive science. A classic answer to this question has appealed to a Language of Thought (LoT). We point to emerging research from disparate branches of the field that supports the LoT hypothesis, but also uncovers diversity in LoTs across cognitive systems, stages of development, and species. Our letter formulates open research questions for cognitive science concerning the varieties of rules and representations that underwrite various LoT-based systems and how these variations can help researchers taxonomize cognitive systems.

Symbols and mental programs: a hypothesis about human singularity

Natural language is often seen as the single factor that explains the cognitive singularity of the human species. Instead, we propose that humans possess multiple internal languages of thought, akin to computer languages, which encode and compress structures in various domains (mathematics, music, shape…). These languages rely on cortical circuits distinct from classical language areas. Each is characterized by: (i) the discretization of a domain using a small set of symbols, and (ii) their recursive composition into mental programs that encode nested repetitions with variations. In various tasks of elementary shape or sequence perception, minimum description length in the proposed languages captures human behavior and brain activity, whereas non-human primate data are captured by simpler nonsymbolic models. Our research argues in favor of discrete symbolic models of human thought.

Visual foundations of Euclidean geometry

Geometry defines entities that can be physically realized in space, and our knowledge of abstract geometry may therefore stem from our representations of the physical world. Here, we focus on Euclidean geometry, the geometry historically regarded as "natural". We examine whether humans possess representations describing visual forms in the same way as Euclidean geometry - i.e., in terms of their shape and size. One hundred and twelve participants from the U.S. (age 3-34 years), and 25 participants from the Amazon (age 5-67 years) were asked to locate geometric deviants in panels of 6 forms of variable orientation. Participants of all ages and from both cultures detected deviant forms defined in terms of shape or size, while only U.S. adults drew distinctions between mirror images (i.e. forms differing in "sense"). Moreover, irrelevant variations of sense did not disrupt the detection of a shape or size deviant, while irrelevant variations of shape or size did. At all ages and in both cultures, participants thus retained the same properties as Euclidean geometry in their analysis of visual forms, even in the absence of formal instruction in geometry. These findings show that representations of planar visual forms provide core intuitions on which humans' knowledge in Euclidean geometry could possibly be grounded.

Deep neural networks and image classification in biological vision

In this paper we consider recent advances in the use of deep convolutional neural networks to understanding biological vision. We focus on claims about the plausibility of feedforward deep convolutional neural networks (fDCNNs) as models of image classification in the biological system. Despite the putative similarity of these networks to some properties of the biological vision system, and the remarkable levels of performance accuracy of some fDCNNs, we argue that their plausibility as a framework for understanding image classification remains unclear. We highlight two key issues that we suggest are relevant to the evaluation of any form of DNN used to examine biological vision: (1) Network transparency under analysis - that is, the challenge of understanding what networks do, and how they do it. (2) Identifying appropriate benchmarks for comparing network performance and the biological system using both quantitative and qualitative performance measures. We show that there are important divergences between fDCNNs and biological vision that reflect fundamental differences in computational architectures, and representational structures, supporting image classification in these networks and the biological system.

Analyzing the misperception of exponential growth in graphs

Exponential growth is frequently underestimated, an error that can have a heavy social cost in the context of epidemics. To clarify its origins, we measured the human capacity (N = 521) to extrapolate linear and exponential trends in scatterplots. Four factors were manipulated: the function underlying the data (linear or exponential), the response modality (pointing or venturing a number), the scale on the y axis (linear or logarithmic), and the amount of noise in the data. While linear extrapolation was precise and largely unbiased, we observed a consistent underestimation of noisy exponential growth, present for both pointing and numerical responses. A biased ideal-observer model could explain these data as an occasional misperception of noisy exponential graphs as quadratic curves. Importantly, this underestimation bias was mitigated by participants' math knowledge, by using a logarithmic scale, and by presenting a noiseless exponential curve rather than a noisy data plot, thus suggesting concrete avenues for interventions.

Flexible auditory training, psychophysics, and enrichment of common marmosets with an automated, touchscreen-based system

Devising new and more efficient protocols to analyze the phenotypes of non-human primates, as well as their complex nervous systems, is rapidly becoming of paramount importance. This is because with genome-editing techniques, recently adopted to non-human primates, new animal models for fundamental and translational research have been established. One aspect in particular, namely cognitive hearing, has been difficult to assess compared to visual cognition. To address this, we devised autonomous, standardized, and unsupervised training and testing of auditory capabilities of common marmosets with a cage-based standalone, wireless system. All marmosets tested voluntarily operated the device on a daily basis and went from naïve to experienced at their own pace and with ease. Through a series of experiments, here we show, that animals autonomously learn to associate sounds with images; to flexibly discriminate sounds, and to detect sounds of varying loudness. The developed platform and training principles combine in-cage training of common marmosets for cognitive and psychoacoustic assessment with an enriched environment that does not rely on dietary restriction or social separation, in compliance with the 3Rs principle.

The authors present a cage-based stand-alone platform for autonomous, standardized, and unsupervised training and testing of visuo-auditory-cued behaviours of common marmosets. The experiments do not require dietary restriction or social separation.

Disjunctive inference in preverbal infants

Can preverbal infants utilize logical reasoning such as disjunctive inference? This logical operation requires keeping two alternatives open (A or B), until one of them is eliminated (if not A), allowing the inference: B is true. We presented to 10-month-old infants an ambiguous situation in which a female voice was paired with two faces. Subsequently, one of the two faces was presented with the voice of a male. We measured infants' preference for the correct face when both faces and the initial voice were presented again. Infant pupillary response was measured and utilized as an indicator of cognitive load at the critical moment of disjunctive inference. We controlled for other possible explanations in three additional experiments. Our results show that 10-month-olds can correctly deploy disjunction and negation to disambiguate scenes, suggesting that disjunctive inference does not rely on linguistic constructs.

Geometry intuitions without vision? A study in blind children and adults

Geometry intuitions seem to be rooted in a non-verbal system that humans possess since early age. However, the mechanisms underlying the comprehension of basic geometric concepts remain elusive. Some authors have suggested that the starting point of geometry development could be found in the visual perception of specific features in our environment, thus conferring to vision a foundational role in the acquisition of geometric skills. To examine this assumption, a test probing intuitive understanding of basic geometric concepts was presented to congenitally blind children and adults. Participants had to detect the intruder among four different shapes, from which three instantiated a specific geometrical concept and one (the intruder) violated it. Although they performed above the chance level, the blind presented poorer performance than the sighted participants who did the task in the visual modality (i.e., with the eyes open), but performed equally well than the sighted who did the task in the tactile modality (i.e., with a blindfold). We therefore provide evidence that geometric abilities are impacted by the lack of vision.