Dual Diffusion Model for Single-Cell Recording Data From the Superior Colliculus in a Brightness-Discrimination Task

Abstract

Ratcliff R, Hasegawa YT, Hasegawa RP, Smith PL, Segraves MA. Dual diffusion model for single-cell recording data from the superior colliculus in a brightness-discrimination task. J Neurophysiol 97: 1756Neurophysiol 97: -1774Neurophysiol 97: , 2007. First published November 22, 2006; doi:10.1152/jn.00393.2006. Monkeys made saccades to one of two peripheral targets based on the brightness of a central stimulus. Task difficulty was manipulated by varying the ratio of stimulus black-and-white pixels. Correct response probability for two monkeys varied directly with difficulty. Deep layer SC neurons exhibited robust presaccadic activity the magnitude of which was unaffected by task difficulty when the stimulus specified a saccade toward a target within the neuron's response field. Activity after stimuli specifying saccades to targets outside the response field was affected by task difficulty, increasing as the task became more difficult. A quantitative model derived from studies of human decision-making was fit to the behavioral data. The model assumes that information from the stimulus drives two independent diffusion processes. Simulated paths from the model were compared with neuron activity, assuming that firing rate is linearly related to position in the accumulation process. The firing rate data show delayed availability of discriminative information for fast, intermediate, and slow decisions when activity is aligned on the stimulus and very small differences in discriminative information when aligned on the saccade. The model produces exactly these patterns of results. The accumulation process is highly variable, allowing the process both to make errors, as is the case for the behavioral performance, and also to account for the firing rate results. Thus the dual diffusion model provides a quantitative account for both the behavior in a simple decision-making task as well as the patterns of activity in competing populations of neurons. I N T R O D U C T I O NResearch in neural decision making is at the point of identifying the mechanisms that implement simple decisions (Glimcher and Sparks 1992;Gold and Shadlen 2000;Hanes and Schall 1996; Newsome 1999, 2001;Kim and Shadlen 1999;Krauzlis and Dill 2002;McPeek and Keller 2002;Ratcliff et al. 2003a;Roitman and Shadlen 2002;Romo et al. 2002;Sparks 1999). In parallel to this work, psychology has produced a set of models that describe simple rapid two-choice decision making Townsend 1992, 1993;Diederich 1997;LaBerge 1994;Laming 1968;Link 1975;Link and Heath 1975; Pike 1966Pike , 1973Ratcliff 1978Ratcliff , 1981Ratcliff , 1988 Rouder 1998, 2000;Ratcliff and Smith 2004;Ratcliff et al. 1999;Roe et al. 2001;Smith 1995;Smith and Ratcliff 2004;Smith and Van Zandt 2000;Stone 1960;Townsend and Ashby 1983). We present a diffusion model that explicitly relates these two domains in the context of a single experiment. The model is applied to simple twochoice decisions and accounts for both behavioral data, namely, accuracy and correct and error response time (RT) distributi...

The Functional Link between Area MT Neural Fluctuations and Detection of a Brief Motion Stimulus

“…Similar models have also linked neural activity with RT in motion detection and attentionswitching tasks (Cook and Maunsell, 2002;). Although the model did a good job capturing the behavioral performance and mean RTs in all stimulus conditions, it did not fully capture RT distributions as well as other threshold models (Carpenter and Williams, 1995;Hanes and Schall, 1996;Carpenter, 2004;Smith and Ratcliff, 2004;Ratcliff et al, 2007). However, we do not think that adding free parameters to better account for non-decision or motor delays would have affected the functional link between the model neurons and the motion detection performance of the model.…”

Decisions in Changing Conditions: The Urgency-Gating Model

“…It might appear that a diffusion model (Stone, 1960;Laming, 1968;Ratcliff, 1978;Smith and Ratcliff, 2004;Ratcliff et al, 2007) will predict the same timing in bias-for and bias-against trials because it accumulates the difference in sensory evidence for the two options. However, note that, in bias-for trials, there is no moment in time at which evidence favors the wrong target (i.e., the success probability function never falls below 0.5), and so the difference in evidence is always positive (favoring the correct choice).…”

“…In particular, assuming that the rate of integration is subject to variability, these models can explain error rates and distributions of reaction times (RTs) in a wide variety of tasks (Ratcliff, 1978;Carpenter and Williams, 1995;Reddi and Carpenter, 2000;Reddi et al, 2003;Smith and Ratcliff, 2004). Furthermore, recent neurophysiological data on decision tasks have provided evidence for accumulation processes in the superior colliculus (Munoz and Wurtz, 1995;Munoz et al, 2000;Ratcliff et al, 2003Ratcliff et al, , 2007Shen and Paré, 2007), the lateral intraparietal area (LIP) (Roitman and Shadlen, 2002;Leon and Shadlen, 2003), the frontal eye fields Shadlen, 2000, 2003), and the prefrontal cortex (Kim and Shadlen, 1999). Finally, integration of samples toward a bound is reminiscent of the "sequential probability ratio test" (SPRT) (Wald, 1945;Bogacz et al, 2006), an optimal procedure for making decisions on the basis of information that arrives over time (Wald and Wolfowitz, 1948).…”

The neural processes underlying perceptual decision making in humans: Recent progress and future directions

“…With this theoretical framework as a strong guide, signals exhibiting buildto-threshold dynamics have been found in several areas of the monkey brain, including parietal (e.g. Roitman and Shadlen, 2002;Hanks et al, 2006), frontal (Hanes and Schall, 1996;Kim and Shadlen, 1999) and subcortical (Ratcliff et al, 2007;Ding and Gold, 2010) oculomotor areas. This work has paved the way for a broad program of mechanistically principled research into how neural decision signals are constructed and are adapted to account for changing environmental contingencies (e.g.…”