Evidence for cumulative cultural evolution in bird song

In studies of cumulative cultural evolution in non-human animals, the focus is most often on incremental changes that increase the efficacy of an existing form of socially learned behaviour, such as the refinement of migratory pathways. In this paper, we compare the songs of different species to describe patterns of evolution in the acoustic structure of bird songs, and explore the question of what building blocks might underlie cumulative cultural evolution of bird song using a comparative approach. We suggest that three steps occurred: first, imitation of independent sounds, or notes, via social learning; second, the formation of categories of note types; and third, assembling note types into sequences with defined structures. Simple sequences can then be repeated to form simple songs or concatenated with other sequences to form segmented songs, increasing complexity. Variant forms of both the notes and the sequencing rules may then arise due to copy errors and innovation. Some variants may become established in the population because of learning biases or selection, increasing signal efficiency, or because of cultural drift. Cumulative cultural evolution of bird songs thus arises from cognitive processes such as vocal imitation, categorization during memorization and learning biases applied to basic acoustic building blocks. This article is part of a discussion meeting issue ‘The emergence of collective knowledge and cumulative culture in animals, humans and machines’.

Biased credit assignment in motivational learning biases arises through prefrontal influences on striatal learning

Actions are biased by the outcomes they can produce: Humans are more likely to show action under reward prospect, but hold back under punishment prospect. Such motivational biases derive not only from biased response selection, but also from biased learning: humans tend to attribute rewards to their own actions, but are reluctant to attribute punishments to having held back. The neural origin of these biases is unclear; in particular, it remains open whether motivational biases arise solely from an evolutionarily old, subcortical architecture or also due to younger, cortical influences. Simultaneous EEG-fMRI allowed us to track which regions encoded biased prediction errors in which order. Biased prediction errors occurred in cortical regions (ACC, vmPFC, PCC) before subcortical regions (striatum). These results highlight that biased learning is not a mere feature of the basal ganglia, but arises through prefrontal cortical contributions, revealing motivational biases to be a potentially flexible, sophisticated mechanism.

Vocabulary Development and the Shape Bias in Children With Hearing Loss

Purpose Although children with hearing loss (HL) can benefit from cochlear implants (CIs) and hearing aids (HAs), they often show language delays. Moreover, little is known about the mechanisms by which children with HL learn words. One mechanism by which typically hearing (TH) children learn words is by acquiring word learning biases such as the “shape bias,” that is, generalizing the names of novel solid objects by similarity in shape. In TH children, the shape bias emerges out of regularities in the early vocabulary and, once acquired, has consequences for subsequent vocabulary development. Method Here, we ask whether children with HL exhibit similar word learning biases as TH children. In the current study, nineteen 2- to 3.5-year-old children with HL generalized the names of novel objects by similarity in shape or material. We compared their performance to that of 20 TH children matched on age and 20 TH children matched on vocabulary size. Results Children with HL were significantly less likely than age-matched TH children and vocabulary-matched TH children to generalize novel names to objects of the same shape. However, there was also an interaction such that vocabulary has a stronger effect on novel noun generalization for those with HL than for those who are TH. Exploratory analyses of children with HL reveal similar novel noun generalization and vocabulary sizes in children who use CIs and those who use HAs, regardless of hearing age or degree of HL. Conclusion Together, the results suggest that, although vocabulary knowledge drives development of the shape bias in general for all children, it may be especially important for children with HL, who are at risk for language delays.

Motivational learning biases are differentially modulated by genetic determinants of striatal and prefrontal dopamine function

Dopaminergic neurotransmission plays a pivotal role in appetitively motivated behavior in mammals, including humans. Notably, action and valence are not independent in motivated tasks, and it is particularly difficult for humans to learn the inhibition of an action to obtain a reward. We have previously observed that the carriers of the DRD2/ANKK1 TaqIA A1 allele, that has been associated with reduced striatal dopamine D2 receptor expression, showed a diminished learning performance when required to learn response inhibition to obtain rewards, a finding that was replicated in two independent cohorts. With our present study, we followed two aims: first, we aimed to replicate our finding on the DRD2/ANKK1 TaqIA polymorphism in a third independent cohort (N = 99) and to investigate the nature of the genetic effects more closely using trial-by-trial behavioral analysis and computational modeling in the combined dataset (N = 281). Second, we aimed to assess a potentially modulatory role of prefrontal dopamine availability, using the widely studied COMT Val108/158Met polymorphism as a proxy. We first report a replication of the above mentioned finding. Interestingly, after combining all three cohorts, exploratory analyses regarding the COMT Val108/158Met polymorphism suggest that homozygotes for the Met allele, which has been linked to higher prefrontal dopaminergic tone, show a lower learning bias. Our results corroborate the importance of genetic variability of the dopaminergic system in individual learning differences of action–valence interaction and, furthermore, suggest that motivational learning biases are differentially modulated by genetic determinants of striatal and prefrontal dopamine function.

A logical framework to study concept-learning biases in the presence of multiple explanations

When people seek to understand concepts from an incomplete set of examples and counterexamples, there is usually an exponentially large number of classification rules that can correctly classify the observed data, depending on which features of the examples are used to construct these rules. A mechanistic approximation of human concept-learning should help to explain how humans prefer some rules over others when there are many that can be used to correctly classify the observed data. Here, we exploit the tools of propositional logic to develop an experimental framework that controls the minimal rules that are simultaneously consistent with the presented examples. For example, our framework allows us to present participants with concepts consistent with a disjunction and also with a conjunction, depending on which features are used to build the rule. Similarly, it allows us to present concepts that are simultaneously consistent with two or more rules of different complexity and using different features. Importantly, our framework fully controls which minimal rules compete to explain the examples and is able to recover the features used by the participant to build the classification rule, without relying on supplementary attention-tracking mechanisms (e.g. eye-tracking). We exploit our framework in an experiment with a sequence of such competitive trials, illustrating the emergence of various transfer effects that bias participants’ prior attention to specific sets of features during learning.

The cultural evolution of cultural evolution

What makes fast, cumulative cultural evolution work? Where did it come from? Why is it the sole preserve of humans? We set out a self-assembly hypothesis: cultural evolution evolved culturally. We present an evolutionary account that shows this hypothesis to be coherent, plausible, and worthy of further investigation. It has the following steps: (0) in common with other animals, early hominins had significant capacity for social learning; (1) knowledge and skills learned by offspring from their parents began to spread because bearers had more offspring, a process we call CS1 (or Cultural Selection 1); (2) CS1 shaped attentional learning biases; (3) these attentional biases were augmented by explicit learning biases (judgements about what should be copied from whom). Explicit learning biases enabled (4) the high-fidelity, exclusive copying required for fast cultural accumulation of knowledge and skills by a process we call CS2 (or Cultural Selection 2) and (5) the emergence of cognitive processes such as imitation, mindreading and metacognition—‘cognitive gadgets' specialized for cultural learning. This self-assembly hypothesis is consistent with archaeological evidence that the stone tools used by early hominins were not dependent on fast, cumulative cultural evolution, and suggests new priorities for research on ‘animal culture'. This article is part of the theme issue ‘Foundations of cultural evolution’.

Infinite use of finite means? Evaluating the generalization of center embedding learned from an artificial grammar

Human language is often assumed to make "infinite use of finite means" - that is, to generate an infinite number of possible utterances from a finite number of building blocks. From an acquisition perspective, this assumed property of language is interesting because learners must acquire their languages from a finite number of examples. To acquire an infinite language, learners must therefore generalize beyond the finite bounds of the linguistic data they have observed. In this work, we use an artificial language learning experiment to investigate whether people generalize in this way. We train participants on sequences from a simple grammar featuring center embedding, where the training sequences have at most two levels of embedding, and then evaluate whether participants accept sequences of a greater depth of embedding. We find that, when participants learn the pattern for sequences of the sizes they have observed, they also extrapolate it to sequences with a greater depth of embedding. These results support the hypothesis that the learning biases of humans favor languages with an infinite generative capacity.

Motivational learning biases are differentially modulated by genetic determinants of striatal and prefrontal dopamine function

Dopaminergic neurotransmission plays a pivotal role in appetitively motivated behavior in mammals, including humans. Notably, action and valence are not independent in motivated tasks, and it is particularly difficult for humans to learn the inhibition of an action to obtain a reward. We have previously observed that the carriers of the DRD2/ANKK1 TaqIA A1 allele, that has been associated with reduced striatal dopamine D2 receptor expression, showed a diminished learning performance when required to learn response inhibition to obtain rewards, a finding that was replicated in two independent cohorts. In the present study, we first report a replication of this finding in a third independent cohort of 99 participants. Interestingly, after combining all three cohorts (total N = 281), exploratory analyses regarding the COMT Val108/158Met polymorphism suggest that homozygotes for the Met allele, which has been linked to higher prefrontal dopaminergic tone, show a lower learning bias. Our results corroborate the importance of genetic variability of the dopaminergic system in individual learning differences of action-valence interaction and, furthermore, suggest that motivational learning biases are differentially modulated by genetic determinants of striatal and prefrontal dopamine function.