Abstract
Few studies have considered how signal detection parameters evolve during acquisition periods. We addressed this gap by training mice with differential prior experience in a conditional discrimination, auditory signal detection task. Naïve mice, mice given separate experience with each of the later correct choice options (Correct Choice Response Transfer, CCRT), and mice experienced in conditional discriminations (Conditional Discrimination Transfer, CDT) were trained to detect the presence or absence of a tone in white noise. We analyzed data assuming a two-period model of acquisition: a pre-solution and solution period (Heinemann EG (1983) in The Presolution period and the detection of statistical associations. In: Quantitative analyses of behavior: discrimination processes, vol. 4, pp. 21–36). Ballinger. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.536.1978andrep=rep1andtype=pdf). The pre-solution period was characterized by a selective sampling of biased response strategies until adoption of a conditional responding strategy in the solution period. Correspondingly, discriminability remained low until the solution period; criterion took excursions reflecting response–strategy sampling. Prior experience affected the length and composition of the pre-solution period. Whereas CCRT and CDT mice had shorter pre-solution periods than naïve mice, CDT and Naïve mice developed substantial criterion biases and acquired asymptotic discriminability faster than CCRT mice. To explain these data, we propose a learning model in which mice selectively sample and test different response-strategies and corresponding task structures until they exit the pre-solution period. Upon exit, mice adopt the conditional responding strategy and task structure, with action values updated via inference and generalization from the other task structures. Simulations of representative mouse data illustrate the viability of this model.
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Supporting materials can be found at https://doi.org/10.17605/OSF.IO/FJZ87.
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Notes
We chose to not estimate parameters using maximum likelihood or Bayesian approaches precisely because of the two missing components: feedback function for updating P as a function of trial and the processes that inform π. Without knowing how to specify these components, misfits could be due to the inadequacy of our approximations rather than some in-principal failure of the model. Nevertheless, we took a principled approach in which we tried to minimize RSS by slowly adjusting parameters of each module and then asking the relative import of each module.
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Acknowledgements
We would like to thank Charles Gallistel, David Freestone, Başak Akdoğan, and Jorge Mallea for helpful conversations that clarified analysis, interpretation, and presentation of data. We would also like to thank Kimberly Bowman for help with data collection. This work was conducted while Carter W. Daniels was a T32 post-doctoral fellow at Columbia University; he is now a Quantitative Scientist at Prevail Therapeutics—a wholly owned subsidiary of Eli Lilly and Co. Correspondence should be addressed to Carter W. Daniels at carter.wa.daniels@gmail.com.
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Carter W. Daniels was supported by a Fellowship in Schizophrenia Research (5T32MH018870) during this study; this study was supported by National Institutes of Mental Health Grant R01MH068073 to Peter Balsam.
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Daniels, C.W., Balsam, P.D. Prior experience modifies acquisition trajectories via response–strategy sampling. Anim Cogn 26, 1217–1239 (2023). https://doi.org/10.1007/s10071-023-01769-y
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DOI: https://doi.org/10.1007/s10071-023-01769-y