Mechanisms underlying speech sound discrimination and categorization in humans and zebra finches

Category Learning as a Cognitive Foundation of Language Evolution

Category learning gives rise to category formation, which is a crucial ability in human cognition. Category learning is also one of the required learning abilities in language development. Understanding the evolution of category learning thus can shed light on the evolution of human cognition and language. The current paper emphasizes its foundational role in language evolution by reviewing behavioral and neurological studies on category learning across species. In doing so, we first review studies on the critical role of category learning in learning sounds, words, and grammatical patterns of language. Next, from a comparative perspective, we review studies on category learning conducted on different species of nonhuman animals, including invertebrates and vertebrates, suggesting that category learning displays evolutionary continuity. Then, from a neurological perspective, we focus on the prefrontal cortex and the basal ganglia. Reviewing the involvement of these structures in vertebrates and the proposed homologous brain structure to the basal ganglia in invertebrates in category learning, as well as in language processing in humans, suggests that the neural basis of category learning likely has an ancient origin dating back to invertebrates. With evidence from both behavioral and neurological levels in both nonhuman animals and humans, we conclude that category learning lays a crucial cognitive foundation for language evolution.

Bird song comparison using deep learning trained from avian perceptual judgments

Our understanding of bird song, a model system for animal communication and the neurobiology of learning, depends critically on making reliable, validated comparisons between the complex multidimensional syllables that are used in songs. However, most assessments of song similarity are based on human inspection of spectrograms, or computational methods developed from human intuitions. Using a novel automated operant conditioning system, we collected a large corpus of zebra finches' (Taeniopygia guttata) decisions about song syllable similarity. We use this dataset to compare and externally validate similarity algorithms in widely-used publicly available software (Raven, Sound Analysis Pro, Luscinia). Although these methods all perform better than chance, they do not closely emulate the avian assessments. We then introduce a novel deep learning method that can produce perceptual similarity judgements trained on such avian decisions. We find that this new method outperforms the established methods in accuracy and more closely approaches the avian assessments. Inconsistent (hence ambiguous) decisions are a common occurrence in animal behavioural data; we show that a modification of the deep learning training that accommodates these leads to the strongest performance. We argue this approach is the best way to validate methods to compare song similarity, that our dataset can be used to validate novel methods, and that the general approach can easily be extended to other species.

Zebra finches (Taeniopygia guttata) demonstrate cognitive flexibility in using phonology and sequence of syllables in auditory discrimination

Zebra finches rely mainly on syllable phonology rather than on syllable sequence when they discriminate between two songs. However, they can also learn to discriminate two strings containing the same set of syllables by their sequence. How learning about the phonological characteristics of syllables and their sequence relate to each other and to the composition of the stimuli is still an open question. We compared whether and how the zebra finches' relative sensitivity for syllable phonology and syllable sequence depends on the differences between syllable strings. Two groups of zebra finches were trained in a Go-Left/Go-Right task to discriminate either between two strings in which each string contained a unique set of song syllables ('Different-syllables group') or two strings in which both strings contained the same set of syllables, but in a different sequential order ('Same-syllables group'). We assessed to what extent the birds in the two experimental groups attend to the spectral characteristics and the sequence of the syllables by measuring the responses to test strings consisting of spectral modifications or sequence changes. Our results showed no difference in the number of trials needed to discriminate strings consisting of either different or identical sets of syllables. Both experimental groups attended to changes in spectral features in a similar way, but the group for which both training strings consisted of the same set of syllables responded more strongly to changes in sequence than the group for which the training strings consisted of different sets of syllables. This outcome suggests the presence of an additional learning process to learn about syllable sequence when learning about syllable phonology is not sufficient to discriminate two strings. Our study thus demonstrates that the relative importance of syllable phonology and sequence depends on how these features vary among stimuli. This indicates cognitive flexibility in the acoustic features that songbirds might use in their song recognition.

Smart sharks: a review of chondrichthyan cognition

450 million years of evolution have given chondrichthyans (sharks, rays and allies) ample time to adapt perfectly to their respective everyday life challenges and cognitive abilities have played an important part in that process. The diversity of niches that sharks and rays occupy corresponds to matching diversity in brains and behaviour, but we have only scratched the surface in terms of investigating cognition in this important group of animals. The handful of species that have been cognitively assessed in some detail over the last decade have provided enough data to safely conclude that sharks and rays are cognitively on par with most other vertebrates, including mammals and birds. Experiments in the lab as well as in the wild pose their own unique challenges, mainly due to the handling and maintenance of these animals as well as controlling environmental conditions and elimination of confounding factors. Nonetheless, significant advancements have been obtained in the fields of spatial and social cognition, discrimination learning, memory retention as well as several others. Most studies have focused on behaviour and the underlying neural substrates involved in cognitive information processing are still largely unknown. Our understanding of shark cognition has multiple practical benefits for welfare and conservation management but there are obvious gaps in our knowledge. Like most marine animals, sharks and rays face multiple threats. The effects of climate change, pollution and resulting ecosystem changes on the cognitive abilities of sharks and stingrays remain poorly investigated and we can only speculate what the likely impacts might be based on research on bony fishes. Lastly, sharks still suffer from their bad reputation as mindless killers and are heavily targeted by commercial fishing operations for their fins. This public relations issue clouds people's expectations of shark intelligence and is a serious impediment to their conservation. In the light of the fascinating results presented here, it seems obvious that the general perception of sharks and rays as well as their status as sentient, cognitive animals, needs to be urgently revisited.

Testing analogical rule transfer in pigeons (Columba livia)

Categorization is an essential cognitive process useful for transferring knowledge from previous experience to novel situations. The mechanisms by which trained categorization behavior extends to novel stimuli, especially in animals, are insufficiently understood. To understand how pigeons learn and transfer category membership, seven pigeons were trained to classify controlled, bi-dimensional stimuli in a two-alternative forced-choice task. Following either dimensional, rule-based (RB) or information integration (II) training, tests were conducted focusing on the "analogical" extension of the learned discrimination to novel regions of the stimulus space (Casale, Roeder, & Ashby, 2012). The pigeons' results mirrored those from human and non-human primates evaluated using the same analogical task structure, training and testing: the pigeons transferred their discriminative behavior to the new extended values following RB training, but not after II training. Further experiments evaluating rule-based models and association-based models suggested the pigeons use dimensions and associations to learn the task and mediate transfer to stimuli within the novel region of the parametric stimulus space.