Typology of topological relations using machine translation

Languages describe spatial relations in different manners. It is however hypothesized that highly frequent ways of categorizing spatial relations across languages correspond to the natural ways humans conceptualize them. In this study, we explore the use of machine translation to gather data in semantic typology to address whether different languages show similarities in how they carve up space. We collected spatial descriptions in English, translated them using machine translation, and subsequently extracted spatial terms automatically. Our results suggest that most spatial descriptions are accurately translated. Despite limitations in our extraction of spatial terms, we obtain meaningful patterns of spatial relation categorization across languages. We discuss translation limits for semantic typology and possible future directions

Typology of topological relations using machine translation

Languages describe spatial relations in different manners. It is however hypothesized that highly frequent ways of categorizing spatial relations across languages correspond to the natural ways humans conceptualize them. In this study, we explore the use of machine translation to gather data in semantic typology to address whether different languages show similarities in how they carve up space. We collected spatial descriptions in English, translated them using machine translation, and subsequently extracted spatial terms automatically. Our results suggest that most spatial descriptions are accurately translated. Despite limitations in our extraction of spatial terms, we obtain meaningful patterns of spatial relation categorization across languages. We discuss translation limits for semantic typology and possible future directions.</p

Typology of topological relations using machine translation

Languages describe spatial relations in different manners. It is however hypothesized that highly frequent ways of categorizing spatial relations across languages correspond to the natural ways humans conceptualize them. In this study, we explore the use of machine translation to gather data in semantic typology to address whether different languages show similarities in how they carve up space. We collected spatial descriptions in English, translated them using machine translation, and subsequently extracted spatial terms automatically. Our results suggest that most spatial descriptions are accurately translated. Despite limitations in our extraction of spatial terms, we obtain meaningful patterns of spatial relation categorization across languages. We discuss translation limits for semantic typology and possible future directions

Guinea baboons (Papio papio) show an agent preference in chasing interactions

Languages tend to describe who is doing what to whom by placing subjects before objects. This bias for agents is reflected in event cognition: agents capture more attention than patients in human adults and infants. We investigated whether this agent preference is unique to humans. We presented Guinea baboons (Papio papio, N = 13) with a change detection paradigm with chasing animations. The baboons had to respond to a colour change which was applied to either the chaser/agent or the chasee/patient. They were faster to detect a change to the chaser than to the chasee, which cannot be explained by low-level features in our stimuli. Our study suggests that baboons show an agent preference similar to human infants and adults. This may be an evolutionarily old mechanism that is shared between humans and other primates, which could have become externalised in language as a tendency to place the subject first

Causal perception in Guinea Baboons (Papio papio)

In humans, simple 2D visual displays of launching events (“Michottean launches”) can evoke the impression of causality. Direct launching events are regarded as causal, but similar events where a spatial and/or temporal gap is added between the movements of the two objects, as non-causal. In the present study, we investigated the evolutionary origins of this phenomenon and tested whether Guinea baboons (Papio papio) perceive causality in launching events. We used a discrimination and categorisation task of Michottean launches. Our results indicate that Guinea baboons discriminate between different events, but we did not find a learning advantage for a categorisation based on causality. This implies that they focused on the spatial and temporal gap to achieve accurate categorisation, but not on causality per se. Currently we cannot rule out that Guinea baboons have causal representations of Michottean events, but our findings point to a feature-based discrimination strategy in a sorting task

Causal perception in Papio papio

In this study, we ask whether causal perception is present in a nonhuman primate: the Guinea baboon (Papio papio). We will attempt to train Guinea baboons to discriminate ‘Michottean’ causal from non-causal events. Furthermore, we will test whether it is easier to base such a discrimination on the abstract concept of causality compared to using low-level spatiotemporal properties. Also, we will test if the baboons are sensitive to event roles, known as agent and patient, which are present in causal, but not non-causal, events

Preprint: A comparative study of causal perception in Guinea baboons (Papio papio) and human adults

In humans, simple 2D visual displays of launching events (“Michottean launches”) can evoke the impression of causality. Direct 8 launching events are regarded as causal, but similar events with a temporal and/or spatial gap between the movements of the 9 two objects, as non-causal. This ability to distinguish between causal and non-causal events is perceptual in nature and develops early and preverbally in infancy. In the present study we investigated the evolutionary origins of this phenomenon and tested whether Guinea baboons (Papio papio) perceive causality in launching events. We used a novel paradigm which was designed to distinguish between the use of causality and the use of spatiotemporal properties. Our results indicate that Guinea baboons successfully discriminate between different Michottean events, but we did not find a learning advantage for a categorisation based on causality as was the case for human adults. Our results imply that, contrary to humans, baboons focused on the spatial and temporal gaps to achieve accurate categorisation, but not on causality per se. Understanding how animals perceive causality is important to figure out whether non-human animals comprehend events similarly to humans. Our study hints at a different manner of processing physical causality for Guinea baboons and human adults

Introducing a Central African Primate Vocalisation Dataset for Automated Species Classification

Automated classification of animal vocalisations is a potentially powerful wildlife monitoring tool. Training robust classifiers requires sizable annotated datasets, which are not easily recorded in the wild. To circumvent this problem, we recorded four primate species under semi-natural conditions in a wildlife sanctuary in Cameroon with the objective to train a classifier capable of detecting species in the wild. Here, we introduce the collected dataset, describe our approach and initial results of classifier development. To increase the efficiency of the annotation process, we condensed the recordings with an energy/change based automatic vocalisation detection. Segmenting the annotated chunks into training, validation and test sets, initial results reveal up to 82% unweighted average recall test set performance in four-class primate species classification

Introducing a Central African Primate Vocalisation Dataset for Automated Species Classification

Automated classification of animal vocalisations is a potentially powerful wildlife monitoring tool. Training robust classifiers requires sizable annotated datasets, which are not easily recorded in the wild. To circumvent this problem, we recorded four primate species under semi-natural conditions in a wildlife sanctuary in Cameroon with the objective to train a classifier capable of detecting species in the wild. Here, we introduce the collected dataset, describe our approach and initial results of classifier development. To increase the efficiency of the annotation process, we condensed the recordings with an energy/change based automatic vocalisation detection. Segmenting the annotated chunks into training, validation and test sets, initial results reveal up to 82% unweighted average recall test set performance in four-class primate species classification