A single trial analysis of EEG in recognition memory: Tracking the neural correlates of memory strength.

Recent work in perceptual decision-making has shown that although two distinct neural components differentiate experimental conditions (e.g., did you see a face or a car), only one tracked the evidence guiding the decision process. In the memory literature, there is a distinction between a fronto-central evoked potential measured with EEG beginning at 350ms that seems to track familiarity and a late parietal evoked potential that peaks at 600ms that tracks recollection. Here, we applied single-trial regressor analysis (similar to multivariate pattern analysis, MVPA) and diffusion decision modeling to EEG and behavioral data from two recognition memory experiments to test whether these two components contribute to the recognition decision process. The regressor analysis only involved whether an item was studied or not and did not involve any use of the behavioral data. Only late EEG activity distinguishes studied from not studied items that peaks at about 600ms following each test item onset predicted the diffusion model drift rate derived from the behavioral choice and reaction times (but only for studied items). When drift rate was made a linear function of the trial-level regressor values, the estimate for studied items was different than zero. This showed that the later EEG activity indexed the trial-to-trial variability in drift rate for studied items. Our results provide strong evidence that only a single EEG component reflects evidence being used in the recegnition decision process.

Pointing, looking at, and pressing keys: A diffusion model account of response modality.

Accumulation of evidence models of perceptual decision making have been able to account for data from a wide range of domains at an impressive level of precision. In particular, Ratcliff's (1978) diffusion model has been used across many different 2-choice tasks in which the response is executed via a key-press. In this article, we present 2 experiments in which we used a letter-discrimination task exploring 3 central aspects of a 2-choice task: the discriminability of the stimulus, the modality of the response execution (eye movement, key pressing, and pointing on a touchscreen), and the mapping of the response areas for the eye movement and the touchscreen conditions (consistent vs. inconsistent). We fitted the diffusion model to the data from these experiments and examined the behavior of the model's parameters. Fits of the model were consistent with the hypothesis that the same decision mechanism is used in the task with 3 different response methods. Drift rates are affected by the duration of the presentation of the stimulus while the response execution time changed as a function of the response modality.

Individual Differences and Fitting Methods for the Two-Choice Diffusion Model of Decision Making.

Methods of fitting the diffusion model were examined with a focus on what the model can tell us about individual differences. Diffusion model parameters were obtained from the fits to data from two experiments and consistency of parameter values, individual differences, and practice effects were examined using different numbers of observations from each subject. Two issues were examined, first, what sizes of differences between groups can be obtained to distinguish between groups and second, what sizes of differences would be needed to find individual subjects that had a deficit relative to a control group. The parameter values from the experiments provided ranges that were used in a simulation study to examine recovery of individual differences. This study used several diffusion model fitting programs, fitting methods, and published packages. In a second simulation study, 64 sets of simulated data from each of 48 sets of parameter values (spanning the range of typical values obtained from fits to data) were fit with the different methods and biases and standard deviations in recovered model parameters were compared across methods. Finally, in a third simulation study, a comparison between a standard chi-square method and a hierarchical Bayesian method was performed. The results from these studies can be used as a starting point for selecting fitting methods and as a basis for understanding the strengths and weaknesses of using diffusion model analyses to examine individual differences in clinical, neuropsychological, and educational testing.

Inhibition in superior colliculus neurons in a brightness discrimination task?

Simultaneous recordings were collected from between two and four buildup neurons from the left and right superior colliculi in rhesus monkeys in a simple two-choice brightness discrimination task. The monkeys were required to move their eyes to one of two response targets to indicate their decision. Neurons were identified whose receptive fields were centered on the response targets. The functional role of inhibition was examined by conditionalizing firing rate on a high versus low rate in target neurons 90 ms to 30 ms before the saccade and examining the firing rate in both contralateral and ipsilateral neurons. Two models with racing diffusion processes were fit to the behavioral data, and the same analysis was performed on simulated paths in the diffusion processes that have been found to represent firing rate. The results produce converging evidence for the lack of a functional role for inhibition between neural populations corresponding to the two decisions.

Modeling the effects of hypoglycemia on a two-choice task in adult humans.

OBJECTIVE: Previous research has demonstrated that hypoglycemia causes reaction times to be slower and more variable. Reaction time tests, however, use multiple cognitive and noncognitive processes. This study is the first to use a validated sequential sampling model (diffusion model) applied to results obtained from a simple 2-choice task in adult humans to assess the effects of hypoglycemia on the basic parameters of decision making. METHOD: Fourteen adult volunteers were tested on a numerosity discrimination task with and without reduced blood glucose concentrations. The results were analyzed with a model that dissects the components of processing that underlie decisions: the quality of the information on which a decision is based (drift rate), the critical amount of evidence that must be accumulated before a decision is made (boundary separation), and the time taken by nondecision processes. RESULTS: Hypoglycemia resulted in a reduction of mean drift rate from 0.290 to 0.211, t(13) = 4.10, p < .05. No effect of experimental state was observed on the amount of evidence required to make a decision or peripheral and motor processes. CONCLUSION: This study locates the precise processing deficit associated with hypoglycemia and provides further understanding of the precise cognitive effect of hypoglycemia. Further research into the amelioration of these effects is required.