More than one way to see it: Individual heuristics in avian visual computation

Ravignani, Andrea; Westphal-Fitch, Gesche; Aust, Ulrike; Schlumpp, Martin M.; Fitch, W. Tecumseh

doi:10.1016/j.cognition.2015.05.021

Cited by 30 publications

(31 citation statements)

References 77 publications

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“…In our experiment, also, the budgerigars required fewer trials to reach the training criterion. Our findings thus confirm that different bird species trained and tested on the same stimulus sets can behave quite differently, as was observed for the keas and pigeons in a visual grammar learning task (39). In that study pigeons and keas both attended to local features of the training stimuli; the species differed in which features were used and the consistency with which a specific strategy was used among individuals.…”

Section: Discussionsupporting

confidence: 87%

“…Thus far, budgerigars have not been tested in an artificial grammar learning task, making it of interest to compare them to zebra finches. A comparison of species belonging to these two distinct clades also is of interest because the previously mentioned studies comparing another parrot species, the kea, with pigeons (also a different clade) revealed that these species used very different strategies to distinguish between two string sets consisting of different visual patterns (14,39). All keas showed the same consistent strategy, but the pigeons showed no consistent pattern at either the species or individual level (39).…”

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confidence: 99%

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Budgerigars and zebra finches differ in how they generalize in an artificial grammar learning experiment

Spierings

Cate

2016

Proc. Natl. Acad. Sci. U.S.A.

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The ability to abstract a regularity that underlies strings of sounds is a core mechanism of the language faculty but might not be specific to language learning or even to humans. It is unclear whether and to what extent nonhuman animals possess the ability to abstract regularities defining the relation among arbitrary auditory items in a string and to generalize this abstraction to strings of acoustically novel items. In this study we tested these abilities in a songbird (zebra finch) and a parrot species (budgerigar). Subjects were trained in a go/no-go design to discriminate between two sets of sound strings arranged in an XYX or an XXY structure. After this discrimination was acquired, each subject was tested with test strings that were structurally identical to the training strings but consisted of either new combinations of known elements or of novel elements belonging to other element categories. Both species learned to discriminate between the two stimulus sets. However, their responses to the test strings were strikingly different. Zebra finches categorized test stimuli with previously heard elements by the ordinal position that these elements occupied in the training strings, independent of string structure. In contrast, the budgerigars categorized both novel combinations of familiar elements as well as strings consisting of novel element types by their underlying structure. They thus abstracted the relation among items in the XYX and XXY structures, an ability similar to that shown by human infants and indicating a level of abstraction comparable to analogical reasoning.artificial grammar learning | rule learning | auditory perception | songbirds | parrots O ne of the critical features of language learning is the ability to abstract the grammatical structure from spoken language. Such abstraction allows humans to learn about regularities in their native language and to generalize these regularities to novel input. This ability is examined in a standardized way in artificial grammar learning experiments, in which humans are exposed to strings of meaningless sounds (e.g., arbitrary speech syllables) organized according to a specific grammatical structure. Several studies have shown that the ability to abstract the underlying structure from such stimuli is present in young infants (1-5) in both the acoustic and the visual domain (6-8). This domain generality and its presence at a very early age have given rise to the notion that this cognitive ability may have preceded language evolution and served as a basis for present-day linguistic complexity. If so, it raises the question to what extent this ability is confined to humans or also can be found in nonhuman animals. In this context, comparative studies on nonhuman animals are needed to reveal the level of abstraction they are able to achieve in artificial grammar learning tasks. This information might provide hypotheses about how and why the more complex human grammatical competences have arisen. The current study addresses whether two bird species, the...

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Section: Discussionsupporting

confidence: 87%

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confidence: 99%

Budgerigars and zebra finches differ in how they generalize in an artificial grammar learning experiment

Spierings

Cate

2016

Proc. Natl. Acad. Sci. U.S.A.

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“…A key issue in AGL is that participants may show above-chance performance, but nonetheless have induced a grammar different from the exact intended “target” grammar the experimenters used to generate the stimuli ( van Heijningen et al, 2009 ; Fitch and Friederici, 2012 ; Ravignani et al, 2015 ). This would be particularly problematic if an alternate grammar at a lower level of grammatical complexity could be adopted and still yield “successful” performance, as observed in previous experiments with animals ( van Heijningen et al, 2009 ; Ravignani et al, 2015 ). To evaluate this possibility we analyzed our data using a multilevel Bayesian modeling framework, implemented in R (see ESM for a detailed description of our statistical framework).…”

Section: Resultsmentioning

confidence: 99%

Artificial Grammar Learning Capabilities in an Abstract Visual Task Match Requirements for Linguistic Syntax

et al. 2018

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Whether pattern-parsing mechanisms are specific to language or apply across multiple cognitive domains remains unresolved. Formal language theory provides a mathematical framework for classifying pattern-generating rule sets (or “grammars”) according to complexity. This framework applies to patterns at any level of complexity, stretching from simple sequences, to highly complex tree-like or net-like structures, to any Turing-computable set of strings. Here, we explored human pattern-processing capabilities in the visual domain by generating abstract visual sequences made up of abstract tiles differing in form and color. We constructed different sets of sequences, using artificial “grammars” (rule sets) at three key complexity levels. Because human linguistic syntax is classed as “mildly context-sensitive,” we specifically included a visual grammar at this complexity level. Acquisition of these three grammars was tested in an artificial grammar-learning paradigm: after exposure to a set of well-formed strings, participants were asked to discriminate novel grammatical patterns from non-grammatical patterns. Participants successfully acquired all three grammars after only minutes of exposure, correctly generalizing to novel stimuli and to novel stimulus lengths. A Bayesian analysis excluded multiple alternative hypotheses and shows that the success in rule acquisition applies both at the group level and for most participants analyzed individually. These experimental results demonstrate rapid pattern learning for abstract visual patterns, extending to the mildly context-sensitive level characterizing language. We suggest that a formal equivalence of processing at the mildly context sensitive level in the visual and linguistic domains implies that cognitive mechanisms with the computational power to process linguistic syntax are not specific to the domain of language, but extend to abstract visual patterns with no meaning.

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“…Using these weights (which sum up to a total of 1), we identified the Akaike set : the set of all highest-ranked models summing up to a cumulative Akaike weight of at least 0.95 (Johnson and Omland, 2004; Ravignani et al, 2015), in order to provide a view on the robustness of the best-fitting model. By aggregating the Akaike weights in this way, we (i) gain the combined explanatory power of multiple models instead of just the best one, and (ii) counteract the volatility of the analysis: i.e., if there are relatively few models with a high Akaike weight in this Akaike set, and most of them share a particular feature, we have more confidence in the importance of this feature than by just exploring the single best model.…”

Section: Analysis and Results: Time Series Analysis For (Higher Ordermentioning

confidence: 99%

Seeking Temporal Predictability in Speech: Comparing Statistical Approaches on 18 World Languages

Jadoul

Ravignani

Thompson

et al. 2016

Front. Hum. Neurosci.

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Temporal regularities in speech, such as interdependencies in the timing of speech events, are thought to scaffold early acquisition of the building blocks in speech. By providing on-line clues to the location and duration of upcoming syllables, temporal structure may aid segmentation and clustering of continuous speech into separable units. This hypothesis tacitly assumes that learners exploit predictability in the temporal structure of speech. Existing measures of speech timing tend to focus on first-order regularities among adjacent units, and are overly sensitive to idiosyncrasies in the data they describe. Here, we compare several statistical methods on a sample of 18 languages, testing whether syllable occurrence is predictable over time. Rather than looking for differences between languages, we aim to find across languages (using clearly defined acoustic, rather than orthographic, measures), temporal predictability in the speech signal which could be exploited by a language learner. First, we analyse distributional regularities using two novel techniques: a Bayesian ideal learner analysis, and a simple distributional measure. Second, we model higher-order temporal structure—regularities arising in an ordered series of syllable timings—testing the hypothesis that non-adjacent temporal structures may explain the gap between subjectively-perceived temporal regularities, and the absence of universally-accepted lower-order objective measures. Together, our analyses provide limited evidence for predictability at different time scales, though higher-order predictability is difficult to reliably infer. We conclude that temporal predictability in speech may well arise from a combination of individually weak perceptual cues at multiple structural levels, but is challenging to pinpoint.

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More than one way to see it: Individual heuristics in avian visual computation

Abstract: Graphical abstract

Cited by 30 publications

References 77 publications

Budgerigars and zebra finches differ in how they generalize in an artificial grammar learning experiment

Budgerigars and zebra finches differ in how they generalize in an artificial grammar learning experiment

Artificial Grammar Learning Capabilities in an Abstract Visual Task Match Requirements for Linguistic Syntax

Seeking Temporal Predictability in Speech: Comparing Statistical Approaches on 18 World Languages

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