@article{
 title = {Modeling Melodic Perception as Relational Learning Using a Symbolic- Connectionist Architecture (DORA)},
 type = {article},
 keywords = {DORA,Melodic perception,relational learning,relative pitch,symbolic connectionist},
 id = {c842bda2-d5ea-3d21-928c-e1f1fd688328},
 created = {2017-09-01T15:53:36.936Z},
 file_attached = {true},
 profile_id = {80da7853-f7b7-36a9-8e4c-d7ddb2d9e538},
 group_id = {a2333ea3-15a4-3d40-8d36-f0d9590ca926},
 last_modified = {2017-09-01T15:53:37.110Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 abstract = {Like many other cognitive processes, the perception of music involves processes and structural considerations that are highly relational in nature. To date, no physiologically plausible model has been used to simulate and explain how infants perceive melodic content. Here we used DORA (Discovery Of Relations by Analogy; Doumas et al., 2008), a domain-general symbolic-connectionist model of relational learning, to simulate melodic perception and categorization by infants (Chang & Trehub, 1977; Trehub et al, 1984), and to provide an account of the mechanism for melodic processing in infants. Given four input semantic features for each note in the melodic stimuli sequence (two of which could be internally obtained from the other two via a comparator), DORA's performance matched the behavioral data from the infant studies. Furthermore, the ability of our model to simulate infants' behavior is evidence that structured representations of relational musical properties can be bootstrapped from unstructured feature representations.},
 bibtype = {article},
 author = {Lim, Ahnate and Doumas, Leonidas A A and Sinnett, Scott}
}

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