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A Hamiltonian Driven Quantum-Like Model for Overdistribution in Episodic Memory Recollection

机译:哈密​​顿驱动的类似量子模型的情景记忆回忆中的过度分配

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While people famously forget genuine memories over time, they also tend to mistakenly over-recall equivalent memories concerning a given event. The memory phenomenon is known by the name of emph{episodic overdistribution} and occurs both in memories of disjunctions and partitions of mutually exclusive events and has been tested, modeled and documented in the literature. The total classical probability of recalling exclusive sub-events most often exceeds the probability of recalling the composed event, i.e. a emph{subadditive} total. We present a Hamiltonian driven propagation for the Quantum Episodic Memory model developed by Brainerd (et al., 2015) for the episodic memory overdistribution in the experimental immediate emph{item false memory} paradigm (Brainerd and Reyna, 2008, 2010, 2015). Following the Hamiltonian method of Busemeyer and Bruza (2012) our model adds time-evolution of the perceived memory state through the stages of the experimental process based on psychologically interpretable parameters -- $gamma_c$ for emph{recollection capability} of cues, $kappa_p$ for bias or description-dependence by probes and $eta$ for the average gist component in the memory state at start. With seven parameters the Hamiltonian model shows good accuracy of predictions both in the EOD-disjunction and in the EOD-subadditivity paradigm. We noticed either an outspoken preponderance of the gist over verbatim trace, or the opposite, in the initial memory state when $eta$ is real. Only for complex $eta$ a mix of both traces is present in the initial state for the EOD-subadditivity paradigm.
机译:人们随着时间的流逝而忘记了真实的记忆,而他们也往往会错误地过度回忆有关给定事件的同等记忆。记忆现象以 emph {episodic overdistribution}的名称而闻名,并且在互斥事件的析取和分区记忆中都发生,并且已经在文献中进行了测试,建模和记录。召回专有子事件的总经典概率通常会超过召回组成事件的概率,即总计 emph {subadditive}。我们介绍了由Brainerd(et al。,2015)开发的哈密顿驱动的量子情景记忆模型的传播,用于实验性 emph {item false memory}范式中情景记忆的过度分布(Brainerd和Reyna,2008、2010、2015) 。遵循Busemeyer and Bruza(2012)的汉密尔顿方法,我们的模型通过基于心理可解释参数的实验过程的各个阶段,增加了感知记忆状态的时间演化-$ gamma_c $表示线索的 emph {recollection能力}, $ kappa_p $用于表示探针的偏倚或描述依赖性,而$ beta $是表示内存状态在开始时的平均要点。哈密​​顿模型具有七个参数,在EOD分离和EOD次可加性范式中均显示出良好的预测准确性。我们注意到,在$ beta $为实数的初始存储状态下,要点优于直言不讳,或者相反。仅对于EOD次可加性范式的初始状态,存在复杂的$ beta $两种迹线的混合。

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