调查注意力排斥效应的机制:一个扩散模型分析分析
Jayce D Rushton1, Rebecca K Lawrence2, David K Sewell3
1The University of Queensland, QLD, Brisbane, 4072, Australia. j.rushton@uq.net.au.
Psychonomic bulletin & review
|January 3, 2024
概括
本研究使用扩散决策模型 (DDM) 探索注意力排斥效应 (ARE). 研究结果澄清了空间注意力或决策过程是否驱动这种感知现象.
科学领域:
- 认知神经科学 认知神经科学
- 视觉感知 视觉感知 视觉感知
- 决策 决策 决策 决策
背景情况:
- 注意力排斥效应 (ARE) 涉及在焦点注意力之外的主观空间扩张,可能与视觉受感场变化有关.
- 扩散决策模型 (DDM) 通过将响应时间分解为证据积累,谨慎,偏见和编码/执行来量化决策.
- 区分ARE的注意力与决策驱动因素对于理解空间注意力机制至关重要.
研究的目的:
- 调查导致注意力排斥效应 (ARE) 的注意力和决策因素.
- 利用扩散决策模型 (DDM) 来独立评估对ARE的感知和决策层次贡献.
- 澄清ARE的基本机制及其对空间注意力的影响.
主要方法:
- 使用扩散决策模型 (DDM) 来分析选择和响应时间.
- 解释证据积累率作为感知变化和响应偏差作为决策层面的变化.
- 使用提示操纵来独立评估对ARE的注意力和决策影响.
主要成果:
- 证据积累率的变化与感知刺激的表现有关.
- 响应偏差的变化与决策层次的过程有关.
- 线索操纵对这些DDM参数产生了差异的影响,为ARE驱动器提供了洞察力.
结论:
- 注意力排斥效应 (ARE) 似乎受到感知和决策过程的影响.
- 对DDM参数的独立分析有助于解开对ARE的注意力和决策贡献.
- 这项研究增强了对注意力,感知和空间意识中的决策之间的复杂相互作用的理解.
相关概念视频
Mechanistic Models: Compartment Models in Individual and Population Analysis
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Pharmacodynamic Models: Emax Drug–Concentration Effect Model
The Emax drug-concentration effect model is central to pharmacodynamics in drug discovery and development. This model is predicated on the receptor occupancy theory, which posits that the effect of a drug is directly related to the number of receptors occupied by the drug and the resultant complex formation.The model describes the reversible interaction between a drug (C) and a receptor (R) to form a drug-receptor complex (RC). The kinetics of this interaction are quantified by an equation that...
Pharmacodynamic Models: Linear Concentration–Effect Model
The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing drug...
Modeling with Differential Equations
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
Methods of Medium Optimization
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...


