Buildup and bistability in auditory streaming as an evidence accumulation process with saturation
Buildup and bistability in auditory streaming as an evidence accumulation process with saturation
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听觉流中的积累和双稳态作为饱和证据积累过程
DOI：
10.1371/journal.pcbi.1008152
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发表时间：
2020
影响因子：
4.3
通讯作者：
Rodica Curtu
中科院分区：
文献类型：
--
作者：
Quynh;J. Rinzel;Rodica Curtu
不重叠短音A和短音B的重复三联体序列ABA_是研究听觉流形成和鸡尾酒会问题的重要范式。刺激被“听到”，要么是一种飞驰的模式（整合），要么是两条交错的溪流（隔离）；最初的感觉是典型的融合，然后是在隔离和融合之间自发的交替，每一个都占主导地位几秒钟。隔离的概率在几秒钟内从接近零增长到一个稳定的值，定义了累积函数BUF。它的平稳电平随着音调频率DF的差异而增加，BUF上升得更快。感知持续时间具有与DF相关的均值，并且呈伽玛分布。行为和计算研究通常在交替或积累过程中描述三元组流。在这里，我们的实验设计和建模包含了这两者。我们提出了一个伪神经机制模型，该模型将初级听觉皮层A1的尖峰活动作为输入，并沿着A1下游的两个网络层解决感知问题。我们的模型是直接和直观的。它描述了与电流感知相反的证据的噪声积累，当达到阈值时产生开关。积累可以在高于或低于阈值时饱和；如果在下面，开关动力学类似于吸引子状态的噪声诱导转换。我们的模型定量地解释了数据的三个关键特征：不同DF值下的buf、平均持续时间和标准化优势持续时间分布。它描述了没有竞争本身的感知变化，并强调在序列中独立处理三胞胎并在试验中平均，正如早期广泛引用的研究中实施的那样，是不够的。听觉对象分离（听觉流）在模糊刺激下得到了广泛的研究。一个重复的三连音ABA_序列，不重叠的简短纯音，A和B，频率分离，是一个有价值的刺激。研究通常集中在两个行为阶段中的一个：早期（比如，十秒钟）从默认的整合中建立起的隔离，或者后来在几秒钟的整合和隔离感知之间自发的交替（双稳定性）。我们的实验和建模包含了这两者。我们新颖的、数据驱动的、证据积累的模型解释了观察的关键特征，将初级听觉皮层记录的尖峰活动作为输入（与大多数现有的、更抽象的模型相反）。我们的研究结果强调，像一些早期的研究那样，独立评估个体三胞胎并在试验中平均是不充分的（缺乏感知持续时间统计的神经元责任，这是积累的潜在基础）。此外，我们确定了证据积累和竞争之间的新相似之处，作为大脑中选择的潜在动态过程。
A repeating triplet-sequence ABA_ of non-overlapping brief tones, A and B, is a valued paradigm for studying auditory stream formation and the cocktail party problem. The stimulus is “heard” either as a galloping pattern (integration) or as two interleaved streams (segregation); the initial percept is typically integration then followed by spontaneous alternations between segregation and integration, each being dominant for a few seconds. The probability of segregation grows over seconds, from near-zero to a steady value, defining the buildup function, BUF. Its stationary level increases with the difference in tone frequencies, DF, and the BUF rises faster. Percept durations have DF -dependent means and are gamma-like distributed. Behavioral and computational studies usually characterize triplet streaming either during alternations or during buildup. Here, our experimental design and modeling encompass both. We propose a pseudo-neuromechanistic model that incorporates spiking activity in primary auditory cortex, A1, as input and resolves perception along two network-layers downstream of A1. Our model is straightforward and intuitive. It describes the noisy accumulation of evidence against the current percept which generates switches when reaching a threshold. Accumulation can saturate either above or below threshold; if below, the switching dynamics resemble noise-induced transitions from an attractor state. Our model accounts quantitatively for three key features of data: the BUFs, mean durations, and normalized dominance duration distributions, at various DF values. It describes perceptual alternations without competition per se, and underscores that treating triplets in the sequence independently and averaging across trials, as implemented in earlier widely cited studies, is inadequate. Author summary Segregation of auditory objects (auditory streaming) is widely studied using ambiguous stimuli. A sequence of repeating triplets ABA_ of non-overlapping brief pure tones, A and B, frequency-separated, is a valued stimulus. Studies typically focus on one of two behavioral phases: the early (say, ten seconds) buildup of segregation from the default integration or later spontaneous alternations (bistability) between seconds-long integration and segregation percepts. Our experiments and modeling encompass both. Our novel, data-driven, evidence-accumulation model accounts for key features of the observations, taking as input recorded spiking activity from primary auditory cortex (as opposed to most existing, more abstract, models). Our results underscore that assessing individual triplets independently and averaging across trials, as in some earlier studies, is inadequate (lacking neuronal-accountability for percept duration statistics, the underlying basis of buildup). Further, we identify fresh parallels between evidence accumulation and competition as potential dynamic processes for choice in the brain.
影响因子：
1.2
作者：
Skinner, F K;Kopell, N;Marder, E
通讯作者：
Marder, E
影响因子：
5.3
作者：
Cao, Robin;Pastukhov, Alexander;Braun, Jochen
通讯作者：
Braun, Jochen