Multiple Temporal and Object-Based Strategies Across Learning for a Selective Detection Task in Mice.
Multiple Temporal and Object-Based Strategies Across Learning for a Selective Detection Task in Mice.
复制标题
小鼠选择性检测任务的多种时间和基于对象的学习策略。
DOI：
10.1101/2023.02.13.528412
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Zagha,Edward
中科院分区：
文献类型：
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作者：
Marrero,Krista;Aruljothi,Krithiga;Zareian,Behzad;Zhang,Zhaoran;Zagha,Edward
目标导向的行为范式不可避免地涉及时间过程，如预期、期望、时机、等待和保留。然而，在基于对象的任务范式的广泛使用，时间特征的表征往往被忽视。在这里，我们纵向分析了小鼠从幼稚到专家的表现在体感选择性检测任务。除了跟踪信号检测理论中的标准测量外，我们还描述了时间特征的学习。我们发现，小鼠从一般的采样策略过渡到刺激检测和刺激歧视。在这些转换过程中，小鼠学会等待，因为它们预期预期的刺激呈现，并在刺激呈现后确定它们的反应时间。通过建立和实施标准化的措施，我们表明，在任务中的等待和时间的发展与刺激检测和歧视的学习重叠。我们还调查了时间和基于对象的学习轨迹的性别差异，发现男性学习策略具有独特性，而女性学习策略则更加顺序和刻板。总体而言，我们的研究结果强调了基于对象的任务学习中的多种时间策略，并强调了在表征学习的行为和神经元方面时考虑不同的时间和基于对象的特征的重要性。
Goal-directed behavior paradigms inevitably involve temporal processes, such as anticipation, expectation, timing, waiting, and withholding. And yet, amongst the vast use of object-based task paradigms, characterizations of temporal features are often neglected. Here, we longitudinally analyzed mice from naïve to expert performance in a somatosensory selective detection task. In addition to tracking standard measures from signal detection theory, we also characterized learning of temporal features. We find that mice transition from general sampling strategies to stimulus detection and stimulus discrimination. During these transitions, mice learn to wait as they anticipate an expected stimulus presentation and to time their response after a stimulus presentation. By establishing and implementing standardized measures, we show that the development of waiting and timing in the task overlaps with learning of stimulus detection and discrimination. We also investigated sex differences in temporal and object-based trajectories of learning, finding that males learn strategies idiosyncratically and that females learn strategies more sequentially and stereotypically. Overall, our findings emphasize multiple temporal strategies in learning for an object-based task and highlight the importance of considering diverse temporal and object-based features when characterizing behavioral and neuronal aspects of learning.