Recasting a biologically motivated computational model within a Fechnerian and random utility framework [0.03%]
基于生物动机的计算模型在费希纳和随机利用框架下的重构
Clintin P Davis-Stober,Nicholas Brown,Sanghyuk Park et al.
Clintin P Davis-Stober et al.
The selective integration model of Tsetsos et al. (2016a) is a biologically motivated computational framework that aims to model intransitive preference and choice. Tsetsos et al. (2016a) concluded that a noisy system can lead to violations...
A martingale analysis of first passage times of time-dependent Wiener diffusion models [0.03%]
时间依赖维纳扩散模型的首次通过时间的鞅分析
Vaibhav Srivastava,Samuel F Feng,Jonathan D Cohen et al.
Vaibhav Srivastava et al.
Research in psychology and neuroscience has successfully modeled decision making as a process of noisy evidence accumulation to a decision bound. While there are several variants and implementations of this idea, the majority of these model...
Chelsea Voskuilen,Roger Ratcliff,Philip L Smith
Chelsea Voskuilen
Optimality studies and studies of decision-making in monkeys have been used to support a model in which the decision boundaries used to evaluate evidence collapse over time. This article investigates whether a diffusion model with collapsin...
How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters [0.03%]
注意力对知觉决策的影响：单次试验EEG与漂移扩散模型参数的关系
Michael D Nunez,Joachim Vandekerckhove,Ramesh Srinivasan
Michael D Nunez
Perceptual decision making can be accounted for by drift-diffusion models, a class of decision-making models that assume a stochastic accumulation of evidence on each trial. Fitting response time and accuracy to a drift-diffusion model prod...
Braden A Purcell,Thomas J Palmeri
Braden A Purcell
Accumulator models explain decision-making as an accumulation of evidence to a response threshold. Specific model parameters are associated with specific model mechanisms, such as the time when accumulation begins, the average rate of evide...
A tutorial on the free-energy framework for modelling perception and learning [0.03%]
基于自由能量框架的感知和学习模型教程
Rafal Bogacz
Rafal Bogacz
This paper provides an easy to follow tutorial on the free-energy framework for modelling perception developed by Friston, which extends the predictive coding model of Rao and Ballard. These models assume that the sensory cortex infers the ...
Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models [0.03%]
基于浅层和深层计算模型的固定特征集和混合特征集解释视觉表征的RSA分析
Seyed-Mahdi Khaligh-Razavi,Linda Henriksson,Kendrick Kay et al.
Seyed-Mahdi Khaligh-Razavi et al.
Studies of the primate visual system have begun to test a wide range of complex computational object-vision models. Realistic models have many parameters, which in practice cannot be fitted using the limited amounts of brain-activity data t...
Identification of probabilities [0.03%]
概率的识别
Paul M B Vitányi,Nick Chater
Paul M B Vitányi
Within psychology, neuroscience and artificial intelligence, there has been increasing interest in the proposal that the brain builds probabilistic models of sensory and linguistic input: that is, to infer a probabilistic model from a sampl...
Michael S Pratte,Frank Tong
Michael S Pratte
The development of mathematical models to characterize perceptual and cognitive processes dates back almost to the inception of the field of psychology. Since the 1990s, human functional neuroimaging has provided for rapid empirical and the...
A Comparison Model of Reinforcement-Learning and Win-Stay-Lose-Shift Decision-Making Processes: A Tribute to W.K. Estes [0.03%]
对强化学习和胜利维持失败转移决策过程的比较模型研究——献给W.K.Estes
Darrell A Worthy,W Todd Maddox
Darrell A Worthy
W.K. Estes often championed an approach to model development whereby an existing model was augmented by the addition of one or more free parameters, and a comparison between the simple and more complex, augmented model determined whether th...