Psychology and neurobiology of simple decisions.

By Philip L. Smith, Roger Ratcliff. 2004

1,126 citations recorded by the source corpus.

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In Cognitive Neuroscience

Citations in the corpus

Probabilistic population codes for Bayesian decision making (2008, background): “…ﬁrst glance, it might appear that our model does not differ much from previous neural models of decision making (Machens et al., 2005; Mazurek et al., 2003; Ratcliff and Rouder, 1998; Reddi and Carpenter, 2000; Smith and Ratcliff, 2004; Usher and McClelland, 2001; Wang, 2002; Wong and Wang, 2006).”
Annual Research Review: On the relations among self-regulation, self-control, executive functioning, effortful control, cognitive control, impulsivity, risk-taking, and inhibition for developmental psychopathology (2016, neutral): “Tam, & Moore, 2012; Smith & Ratcliff, 2004), their computational extensions (Botvinick & Cohen, 2014), and neural accounts (Petersen & Posner, 2012).”
The Neuromodulatory System: A Framework for Survival and Adaptive Behavior in a Challenging World (2008, background): “Indeed, the major targets of the neuromodulators shown in Figure 1 are areas noted for driving behavior (K. C. Berridge, 2004), conditioning responses (McGaugh, 2004), attention (Sarter, Gehring, & Kozak, 2006), and making decisions (Schall, 2001; Smith & Ratcliff, 2004).”
HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python (2013, methodology): “Sequential sampling models (SSMs) (Townsend and Ashby, 1983) have established themselves as the de-facto standard for modeling response-time data from simple two-alternative forced choice decision making tasks (Smith and Ratcliff, 2004).”
How the brain integrates costs and benefits during decision making (2010, background): “Specifically, monkeys’ behavior shows a speed–accuracy tradeoff, and neurons in the lateral intraparietal cortex (area LIP) accumulate evidence in favor of a particular decision alternative until a decision boundary is reached (9, 10).”
Changing your mind: a computational mechanism of vacillation (2009, neutral): “2, black curves) consistent with previous studies in humans and monkeys1,9,14.”
The simplest complete model of choice response time: linear ballistic accumulation. (2008, methodology): “One of the reasons for the success of (Ratcliff's, 1978, 2002; Ratcliff & Smith, 2004) diffusion model was its welldeveloped analytic solutions for binary choices.”
Spectral fingerprints of large-scale neuronal interactions (2012, background): “Owing to the difficulty of this task, subjects had to integrate the weak 'evidence' for motion over tim”
Neuronal Reward and Decision Signals: From Theories to Data. (2015, background): “Evidences are continuously compared between options through mutual inhibition at the input level (254, 302, 447, 454, 549, 565).”
Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions (2011, positive): “The standard drift-diffusion model (DDM), as well as closely related versions, such as the leaky competitive accumulator (LCA) model (3, 4, 9), have been highly successful in providing quantitative explanations of the psychometrics, chronometrics, and neurometrics of binary perceptual choice (2, 10–”
The Drift Diffusion Model can account for the accuracy and reaction time of value-based choices under high and low time pressure (2010, methodology): “…we consider a more complicated version of the DDM with additional parameters, which we refer to as the full DDM (fDDM), which has been shown to provide a more accurate description of reaction time distributions than the sDDM (Ratcliff & McKoon, 2008; Ratcliff & Smith, 2004; Ratcliff, et al., 1999).”