Perceptual Discrimination In Static and Dynamic Noise: Probing the Limits of the Diffusion Model of Decision Making
چکیده
Twelve experiments investigated factors that affect the time course of perceptual processing and its effects on subsequent decision making. Stimuli in letter discrimination and brightness discrimination tasks were degraded with static and dynamic noise. The onset and the time course of decision making were quantified by fitting the data with the diffusion model. Dynamic noise and, to a lesser extent, static noise, produced large shifts in the leading edge of the RT distribution in letter discrimination but had little effect in brightness discrimination. These shifts are inconsistent with the idea that noise reduces the rate of evidence accumulation. Instead, they imply that noise delays the time at which evidence accumulation begins. The shifts in the RT distributions are shown not to be the result of strategic processes or the result of using different stimuli in different tasks. These results imply that decision making is time locked to the perceptual processing of the stimulus features needed to do the task. Two mechanisms that could produce this time locking are described. Web Page for Examples of Stimuli (dynamic presentation and static pictures). http://star.psy.ohio-state.edu/percept_stimuli.html JEP:G 2009-0244 Ratcliff & Smith Page 3 When a new stimulus first appears in the visual field, how do we make a decision about its identity? A partial answer to this fundamental question was given in the literature on stage models of information processing (Sternberg, 1969).The answer, which has its origins in the work of Donders (1868) a century earlier, holds that information processing consists of a sequence of mental operations, or processing stages, which transform stimulus energy arriving at sensory receptors into overt responses. To make a decision about a stimulus we must first encode it perceptually, then match the perceptual representation against knowledge stored in memory, and then select and execute an appropriate behavioral response. The process of matching stimulus information against stored knowledge is well described by diffusion models (Ratcliff, 1978; Ratcliff & Smith, 2004). These models hold that decisions are made by accumulating noisy information over time until a criterion quantity of evidence for a response is obtained. The evidence can be thought of as the goodness-of-match between the encoded stimulus and the memory representation of the response alternatives for the task. This view of decision making implies that perceptual processes and decision processes must be closely coupled, both in time, and in their underlying neural mechanisms. Before the process of evidence accumulation can begin, an encoded representation of the stimulus must be formed. Some mechanism must then tell the decision mechanism to begin accumulating information; that is, something must tell the decision mechanism when to "turn on." In this article, we investigate the temporal relationship between perception and decision making. The experiments we report identify factors that affect the time course of perceptual processing and its effects on subsequent decision making. Our aim in carrying out these experiments was to try to identify how and when the process of evidence accumulation is initiated. We find that there are situations in which the diffusion model, in its current simplest form, fails to provide a satisfactory account of experimental data. This failure is highly illuminating as it points to a need to develop system models in which perceptual processes, decision processes, and the relationship between them are all specified. To date, diffusion models of speeded, two-choice decision making have been very successful in accounting for performance in a wide variety of experimental tasks. These include low-level perceptual tasks, such as simple reaction time, brightness discrimination, and signal JEP:G 2009-0244 Ratcliff & Smith Page 4 detection (Ratcliff, 2002; Ratcliff & Rouder, 1998; Ratcliff, Van Zandt & McKoon, 1999; Smith, 1995; Smith, Ratcliff, & Wolfgang, 2004), and higher-level cognitive tasks such as lexical decisions and recognition memory (Ratcliff, 1978; Ratcliff, Thapar & McKoon, 2004; Ratcliff, Gomez, & McKoon, 2004; Ratcliff & Smith, 2004). The models account for response time (RT) distributions for correct responses and errors, and the way in which the distributions change as a function of stimulus discriminability, response bias, speed-accuracy instructions, and other experimental variables. Indeed, there appears to be no experimental paradigm that produces systematic discrepancies between theory and data. This has led some researchers to question whether diffusion models can account for any pattern of experimental data whatsoever. To show that is not the case, Ratcliff (2002) described several patterns of empirical data that would falsify his diffusion model (Ratcliff, 1978). Among them was one in which the mean and the leading edge of the distribution changed by a similar amount with changes in stimulus discriminability. If stimulus discriminability affects only the rate of evidence accumulation -represented in the diffusion model as a change in drift rate -the model predicts that the leading edge of the RT distribution will change very little relative to the rest of the distribution (i.e., its median or its tail). In this article, we present data from a letter discrimination task and a brightness discrimination task, in which stimuli are presented in static or dynamic noise. In the letter discrimination task, a white letter is presented on a black background. The letter is degraded by randomly reversing the contrast polarity of some proportion of the pixels in both the letter and the background. That is, some proportion of the letter pixels are changed to black while the same proportion of the background pixels are changed to white. When this proportion equals 50%, the display becomes a homogeneous, random array of black and white pixels, and the letter is no longer visible. In the dynamic version of the task, a different random sample of pixels is changed every 10 or 16.67 ms in the experiments reported below. Phenomenologically, when the proportion of inverted pixels is high (e.g., 45%), the stimulus letter seems to gradually emerge from the noise, whereas when the proportion is low (e.g., 15%), the letter seems to become perceptually available almost immediately. The critical finding is that the leading edge of the RT distribution in this task increases by 50-100 ms more than is JEP:G 2009-0244 Ratcliff & Smith Page 5 predicted by the diffusion model as discriminability is changed from its highest to its lowest level (assuming that only drift rate varies as a function of discriminability). Although we use the Ratcliff (1978) diffusion model to fit our data in this article, other models that use racing diffusion processes (Ratcliff, 2006; Ratcliff & Smith, 2004; Smith, 2000; Usher & McClelland, 2001) face a similar problem. These models cannot produce a large change in the leading edge of the RT distribution with only a change in the drift rate. This presents a significant theoretical challenge for models of perceptual decision making. In the brightness discrimination task, the stimulus consists of a homogeneous array of black and white pixels. Subjects are required to judge whether the array contains more black or white pixels and to make a "dark" or "light" response. In the experiments described here, we consider a dynamic version of this task, in which a different random sample of black and white pixels is presented every 10 ms, just as in the letter discrimination task. Unlike letter discrimination, this paradigm produces RT distributions in which the leading edge of the RT distribution shows little change with changes in discriminability and the diffusion model fits the data well. Before describing the experiments, we review the diffusion model and its main empirical predictions. We then present the results from 12 experiments that help us identify the conditions under which a delay in the leading edge is obtained. For each experiment, we fit the diffusion model under the assumption that the only parameter of the model that changes with stimulus discriminability is the drift rate. This allows us to identify conditions under which the model fits the experimental data and when it misses, and to characterize the size of the resulting discrepancy.