Trial-to-trial variability in decision making can be caused by variability in information processing as well as by variability in response caution. In this paper, we study which neural components code for trial-to-trial adjustments in response caution using a new computational approach that quantifies response caution on a single-trial level. We found that the frontostriatal network updates the amount of response caution. In particular, when human participants were required to respond quickly, we found a positive correlation between trial-to-trial fluctuations in response caution and the hemodynamic response in the presupplementary motor area and dorsal anterior cingulate. In contrast, on trials that required a change from a speeded response mode to a more accurate response mode or vice versa, we found a positive correlation between response caution and hemodynamic response in the anterior cingulate proper. These results indicate that for each decision, response caution is set through corticobasal ganglia functioning, but that individual choices differ according to the mechanisms that trigger changes in response caution.

Our understanding of cognition has been advanced by two traditionally nonoverlapping and non-interacting groups. Mathematical psychologists rely on behavioral data to evaluate formal models of cognition, whereas cognitive neuroscientists rely on statistical models to understand patterns of neural activity, often without any attempt to make a connection to the mechanism supporting the computation. Both approaches suffer from critical limitations as a direct result of their focus on data at one level of analysis (cf. Marr, 1982), and these limitations have inspired researchers to attempt to combine both neural and behavioral measures in a cross-level integrative fashion. The importance of solving this problem has spawned several entirely new theoretical and statistical frameworks developed by both mathematical psychologists and cognitive neuroscientists. However, with each new approach comes a particular set of limitations and benefits. In this article, we survey and characterize several approaches for linking brain and behavioral data. We organize these approaches on the basis of particular cognitive modeling goals: (1) using the neural data to constrain a behavioral model, (2) using the behavioral model to predict neural data, and (3) fitting both neural and behavioral data simultaneously. Within each goal, we highlight a few particularly successful approaches for accomplishing that goal, and discuss some applications. Finally, we provide a conceptual guide to choosing among various analytic approaches in performing model-based cognitive neuroscience.

Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) non-decision time. Inferences about these psychological factors hinge upon the validity of the models' parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two-condition data sets, we manipulated properties of participants' behavior in a two-alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these "modeler's degrees of freedom" did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models.

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Trial-to-trial fluctuations in an observer's state of mind have a direct influence on their behavior. However, characterizing an observer's state of mind is difficult to do with behavioral data alone, particularly on a single-trial basis. In this article, we extend a recently developed hierarchical Bayesian framework for integrating neurophysiological information into cognitive models. In so doing, we develop a novel extension of the well-studied drift diffusion model (DDM) that uses single-trial brain activity patterns to inform the behavioral model parameters. We first show through simulation how the model outperforms the traditional DDM in a prediction task with sparse data. We then fit the model to experimental data consisting of a speed-accuracy manipulation on a random dot motion task. We use our cognitive modeling approach to show how prestimulus brain activity can be used to simultaneously predict response accuracy and response time. We use our model to provide an explanation for how activity in a brain region affects the dynamics of the underlying decision process through mechanisms assumed by the model. Finally, we show that our model performs better than the traditional DDM through a cross-validation test. By combining accuracy, response time, and the blood oxygen level-dependent response into a unified model, the link between cognitive abstraction and neuroimaging can be better understood.

We propose and demonstrate the shifted Wald (SW) distribution as both a useful measurement tool and intraindividual process model for psychological response time (RT) data. Furthermore, we develop a methodology and fitting approach that readers can easily access. As a measurement tool, the SW provides a detailed quantification of the RT data that is more sophisticated than mean and SD comparisons. As an intraindividual process model, the SW provides a cognitive model for the response process in terms of signal accumulation and the threshold needed to respond. The details and importance of both of these features are developed, and we show how the approach can be easily generalized to a variety of experimental domains. The versatility and usefulness of the approach is demonstrated on 3 published data sets, each with a different canonical mode of responding: manual, vocal, and oculomotor modes. In addition, model-fitting code is included with the article. (PsycINFO Database Record

When people make decisions quickly, accuracy suffers. Traditionally, speed-accuracy tradeoffs (SAT) have been almost exclusively ascribed to changes in the amount of sensory evidence required to support a response (response caution) and the neural correlates associated with the later stages of decision making (e.g., motor response generation and execution). Here, we investigated whether performance decrements under speed pressure also reflect suboptimal information processing in early sensory areas such as primary visual cortex (V1). Human subjects performed an orientation discrimination task while emphasizing either response speed or accuracy. A model of choice behavior revealed that the rate of sensory evidence accumulation was selectively modulated when subjects emphasized accuracy, but not speed, suggesting that changes in sensory processing also influence the SAT. We then used functional MRI (fMRI) and a forward encoding model to derive orientation-selective tuning functions based on activation patterns in V1. When accuracy was emphasized, the extent to which orientation-selective tuning profiles exhibited a theoretically optimal gain pattern predicted both response accuracy and the rate of sensory evidence accumulation. However, these relationships were not observed when subjects emphasized speed. Collectively, our findings suggest that, in addition to lowered response thresholds, the performance decrements observed during speeded decision making may result from a failure to optimally process sensory signals.

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