Complex working memory span tasks and higher-order cognition: A latent-variable analysis of the relationship between processing and storage. Memory, 17(6), 635-654.

There are few behavioral effects as ubiquitous as the speed-accuracy tradeoff (SAT). From insects to rodents to primates, the tendency for decision speed to covary with decision accuracy seems an inescapable property of choice behavior. Recently, the SAT has received renewed interest, as neuroscience approaches begin to uncover its neural underpinnings and computational models are compelled to incorporate it as a necessary benchmark. The present work provides a comprehensive overview of SAT. First, I trace its history as a tractable behavioral phenomenon and the role it has played in shaping mathematical descriptions of the decision process. Second, I present a “users guide” of SAT methodology, including a critical review of common experimental manipulations and analysis techniques and a treatment of the typical behavioral patterns that emerge when SAT is manipulated directly. Finally, I review applications of this methodology in several domains.

SUMMARY Intelligent agents balance speed of responding with accuracy of deciding. Stochastic accumulator models commonly explain this speed-accuracy tradeoff by strategic adjustment of response threshold. Several laboratories identify specific neurons in prefrontal and parietal cortex with this accumulation process, yet no neurophysiological correlates of speed-accuracy tradeoff have been described. We trained macaque monkeys to trade speed for accuracy on cue during visual search and recorded the activity of neurons in the frontal eye field. Unpredicted by any model, we discovered that speed-accuracy tradeoff is accomplished through several distinct adjustments. Visually responsive neurons modulated baseline firing rate, sensory gain, and the duration of perceptual processing. Movement neurons triggered responses with activity modulated in a direction opposite of model predictions. Thus, current stochastic accumulator models provide an incomplete description of the neural processes accomplishing speed-accuracy tradeoffs. The diversity of neural mechanisms was reconciled with the accumulator framework through an integrated accumulator model constrained by requirements of the motor system.

Stochastic accumulator models account for response time in perceptual decision-making tasks by assuming that perceptual evidence accumulates to a threshold. The present investigation mapped the firing rate of frontal eye field (FEF) visual neurons onto perceptual evidence and the firing rate of FEF movement neurons onto evidence accumulation to test alternative models of how evidence is combined in the accumulation process. The models were evaluated on their ability to predict both response time distributions and movement neuron activity observed in monkeys performing a visual search task. Models that assume gating of perceptual evidence to the accumulating units provide the best account of both behavioral and neural data. These results identify discrete stages of processing with anatomically distinct neural populations and rule out several alternative architectures. The results also illustrate the use of neurophysiological data as a model selection tool and establish a novel framework to bridge computational and neural levels of explanation. Keywordsperceptual decision making; stochastic accumulator models; mental chronometry; frontal eye field Mathematical psychology has converged on a general framework to explain the time course of perceptual decisions. Models that assume perceptual information accumulates to a response threshold provide excellent accounts of decision-making behavior (Bogacz, Brown, Moehlis, Holmes, & Cohen, 2006;Nosofsky & Palmeri, 1997;Palmeri, 1997;Ratcliff & Rouder, 1998;Smith & Van Zandt, 2000;Usher & McClelland, 2001). These accumulator models entail at least two distinct processes: (a) A stimulus must be encoded with respect to the current task to represent perceptual evidence, and (b) some mechanism must accumulate that evidence to reach a decision. Models that assume very different decisionmaking architectures can account for many of the same behavioral phenomena (S. Brown & Heathcote, 2005;S. D. Brown & Heathcote, 2008;. Recently, the observation that the pattern of activity of certain neurons resembles an accumulation to threshold sparked a synthesis of mathematical psychology and neurophysiology (Beck et al., 2008;Boucher, Palmeri, Logan, & Schall, 2007;Bundesen, Habekost, & Kyllingsbaek, 2005;Carpenter, Reddi, & Anderson, 2009;Ditterich, 2006b;Mazurek, Roitman, Ditterich, & Shadlen, 2003;Niwa & Ditterich, 2008;Ratcliff, Cherian, & Segraves, 2003;Ratcliff, Hasegawa, Hasegawa, Smith, & Segraves, 2007;Schall, 2004;Wang, 2002;Wong, Huk, Shadlen, & Wang, 2007;Wong & Wang, 2006). This synthesis is powerful because neurophysiology can constrain key assumptions about the representation of perceptual evidence, the mechanisms that accumulate evidence to threshold, and how the two interact.Correspondence concerning this article should be addressed to Thomas J. Palmeri, Department of Psychology, Vanderbilt University, PMB 407817, 2301 Vanderbilt Place, Nashville, TN 37240-7817. thomas.j.palmeri@vanderbilt.edu. NIH Public Access Author ManuscriptPsychol Rev. Author manuscript; availa...

Complex span tasks, assumed by many to measure an individual's working memory capacity, are predictive of several aspects of higher-order cognition. However, the underlying cause of the relationships between "processing-and-storage" tasks and cognitive abilities is still hotly debated nearly 30 years after the tasks were first introduced. The current study utilised latent constructs across verbal, numerical, and spatial content domains to examine a number of questions regarding the predictive power of complex span tasks. In particular, the relations among processing time, processing accuracy, and storage accuracy from the complex span tasks were examined, in combination with their respective relationships with fluid intelligence. The results point to a complicated pattern of unique and shared variance among the constructs. Implications for various theories of working memory are discussed.