Can vocal conditioning trigger a semiotic ratchet in marmosets?

The complexity of human communication has often been taken as evidence that our language reflects a true evolutionary leap, bearing little resemblance to any other animal communication system. The putative uniqueness of the human language poses serious evolutionary and ethological challenges to a rational explanation of human communication. Here we review ethological, anatomical, molecular and computational results across several species to set boundaries for these challenges. Results from animal behavior, cognitive psychology, neurobiology, and semiotics indicate that human language shares multiple features with other primate communication systems, such as specialized brain circuits for sensorimotor processing, the capability for indexical (pointing) and symbolic (referential) signaling, the importance of shared intentionality for associative learning, affective conditioning and parental scaffolding of vocal production. The most substantial differences lie in the higher human capacity for symbolic compositionality, fast vertical transmission of new symbols across generations, and irreversible accumulation of novel adaptive behaviors (cultural ratchet). We hypothesize that increasingly-complex vocal conditioning of an appropriate animal model may be sufficient to trigger a semiotic ratchet, evidenced by progressive sign complexification, as spontaneous contact calls become indexes, then symbols and finally arguments (strings of symbols). To test this hypothesis, we outline a series of conditioning experiments in the common marmoset (Callithrix jacchus). The experiments are designed to probe the limits of vocal communication in a prosocial, highly vocal primate 35 million years far from the human lineage, so as to shed light on the mechanisms of semiotic complexification and cultural transmission, and serve as a naturalistic behavioral setting for the investigation of language disorders

Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations.

Automatic classification of vocalization type could potentially become a useful tool for acoustic the monitoring of captive colonies of highly vocal primates. However, for classification to be useful in practice, a reliable algorithm that can be successfully trained on small datasets is necessary. In this work, we consider seven different classification algorithms with the goal of finding a robust classifier that can be successfully trained on small datasets. We found good classification performance (accuracy > 0.83 and F1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available

Classification of all eight classes of vocalizations using different algorithms.

<p>90% of the samples were used for the training set. Time refers to the time required to classify one sample in milliseconds.</p

The effect of training set size on classification performance.

<p>For the sake of visual clarity, the results of OPF using the distance metrics Bray-Curtis and Chi-Square, and SVM using linear and polynomial kernels are excluded.</p

Confusion matrix for the classification of the principal Tsik class into sub-classes using OPF with Manhattan distance metric and 90% of the samples for training set.

<p>Confusion matrix for the classification of the principal Tsik class into sub-classes using OPF with Manhattan distance metric and 90% of the samples for training set.</p

The number of calls considering each class.

<p>The number of calls considering each class.</p

Vocalization exemplars.

<p>Amplitude and time-frequency spectrograms are shown for representative exemplars of the marmoset call types considered in this study. A: Alarm, B: Chirp, C: Loud shrill, D: Phee-2, E: Phee-3, F: Phee-4, G: Seep, H: Trill, I: Tsik, J: Tsik-Ek, K: Twitter.</p

Confusion matrix considering the classification of all eight classes of vocalizations using OPF with Manhattan distance and 90% of the samples for training set.

<p>Confusion matrix considering the classification of all eight classes of vocalizations using OPF with Manhattan distance and 90% of the samples for training set.</p