Long-lasting generalization triggered by a single trial event in the invasive crayfish Procambarus clarkii

ABSTRACTBehavioural flexibility allows adaptation to environmental changes, a situation that invasive species have often to face when colonizing new territories. Such flexibility arises from a set of cognitive mechanisms among which generalization plays a key role, as it allows the transfer of past solutions to solve similar new problems. By means of a habituation paradigm, we studied generalization in the invasive crayfish Procambarus clarkii. Once crayfish had habituated their defensive response to a specific water jet, we tested whether habituation transferred to a new type of water jet. Although habituation did not generalize when the new stimulus was initially presented, it surprisingly emerged 15 and 45 days later. Hence, remarkably, in P. clarkii, a single presentation of a new event was sufficient to trigger a long-lasting form of learning generalization from previous similar stimuli, a cognitive ability that may concur in providing adaptive advantages to this invasive species.

Managing Bias When Library Collections Become Data

Developments in AI research have dramatically changed what we can do with data and how we can learn from data. At the same time, implementations of AI amplify the prejudices in data often framed as ‘data bias’ and ‘algorithmic bias.’ Libraries, tasked with deciding what is worth keeping, are inherently discriminatory and yet remain trusted sources of information. As libraries begin to systematically approach their collections as data, will they be able to adopt and adapt the AI-driven tools to traditional practices? Drawing on the work of the AI initiative within Stanford Libraries, the Fantastic Futures conference on AI for libraries, archives, and museums, and recent scholarship on data bias and algorithmic bias, this article encourages libraries to engage critically with AI and help shape applications of the technology to reflect the ethos of libraries for the benefit of libraries themselves and the patrons they serve. A brief examination of two core concepts in machine learning, generalization and unstructured data, provides points of comparison to library practices in order to uncover the theoretical assumptions driving the different domains. The comparison also offers a point of entry for libraries to adopt machine learning methods on their own terms.

Perceptual learning, talker specificity, and sound change

Perceptual learning is when listeners hear novel speech input and shift their subsequent perceptual behavior. In this paper we consider the relationship between sound change and perceptual learning. We spell out the connections we see between perceptual learning and different approaches to sound change and explain how a deeper empirical understanding of the properties of perceptual learning might benefit sound change models. We propose that questions about when listeners generalize their perceptual learning to new talkers might be of of particular interest to theories of sound change. We review the relevant literature, noting that studies of perceptual learning generalization across talkers of the same gender are lacking. Finally, we present new experimental data aimed at filling that gap by comparing cross-talker generalization of fricative boundary perceptual learning in same-gender and different-gender pairs. We find that listeners are much more likely to generalize what they have learned across same-gender pairs, even when the different-gender pairs have more similar fricatives. We discuss implications for sound change.