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大脑体积大小并不能预测在序列反转学习测试中的学习策略。

Brain size does not predict learning strategies in a serial reversal learning test.

机构信息

Department of Zoology/Ethology, Stockholm University, Svante Arrhenius väg 18B, 10691 Stockholm, Sweden

Department of Zoology/Ethology, Stockholm University, Svante Arrhenius väg 18B, 10691 Stockholm, Sweden.

出版信息

J Exp Biol. 2020 Aug 4;223(Pt 15):jeb224741. doi: 10.1242/jeb.224741.

Abstract

Reversal learning assays are commonly used across a wide range of taxa to investigate associative learning and behavioural flexibility. In serial reversal learning, the reward contingency in a binary discrimination is reversed multiple times. Performance during serial reversal learning varies greatly at the interspecific level, as some animals adopt a rule-based strategy that enables them to switch quickly between reward contingencies. A larger relative brain size, generating enhanced learning ability and increased behavioural flexibility, has been proposed to be an important factor underlying this variation. Here, we experimentally tested this hypothesis at the intraspecific level. We used guppies () artificially selected for small and large relative brain size, with matching differences in neuron number, in a serial reversal learning assay. We tested 96 individuals over 10 serial reversals and found that learning performance and memory were predicted by brain size, whereas differences in efficient learning strategies were not. We conclude that variation in brain size and neuron number is important for variation in learning performance and memory, but these differences are not great enough to cause the larger differences in efficient learning strategies observed at higher taxonomic levels.

摘要

反转学习任务在广泛的分类群中被普遍用于研究联想学习和行为灵活性。在连续反转学习中,二元辨别任务的奖励关联会被多次反转。在连续反转学习中,不同物种之间的表现差异很大,因为一些动物采用了基于规则的策略,使它们能够在奖励关联之间快速切换。有人提出,相对较大的脑容量是造成这种差异的一个重要因素,它可以提高学习能力和行为灵活性。在这里,我们在种内水平上对这一假设进行了实验测试。我们使用了经过人为选择的小和大相对脑容量的孔雀鱼(),其神经元数量也有相应的差异,在连续反转学习任务中进行了测试。我们对 96 只个体进行了 10 次连续反转测试,发现学习表现和记忆可以由脑容量来预测,而有效的学习策略的差异则不能。我们得出的结论是,脑容量和神经元数量的变化对学习表现和记忆的变化很重要,但这些差异还不足以导致在更高的分类学水平上观察到的有效学习策略的更大差异。

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