Timothy J. Pleskac

Two-Stage Dynamic Signal Detection: A Theory of Choice, Decision Time, and Confidence.

The 3 most often-used performance measures in the cognitive and decision sciences are choice, response or decision time, and confidence. We develop a random walk/diffusion theory-2-stage dynamic signal detection (2DSD) theory-that accounts for all 3 measures using a common underlying process. The model uses a drift diffusion process to account for choice and decision time. To estimate confidence, we assume that evidence continues to accumulate after the choice. Judges then interrupt the process to categorize the accumulated evidence into a confidence rating. The model explains all known interrelationships between the 3 indices of performance. Furthermore, the model also accounts for the distributions of each variable in both a perceptual and general knowledge task. The dynamic nature of the model also reveals the moderating effects of time pressure on the accuracy of choice and confidence. Finally, the model specifies the optimal solution for giving the fastest choice and confidence rating for a given level of choice and confidence accuracy. Judges are found to act in a manner consistent with the optimal solution when making confidence judgments.

Decisions from experience : why small samples?

In many decisions we cannot consult explicit statistics telling us about the risks involved in our actions. In lieu of such data, we can arrive at an understanding of our dicey options by sampling from them. The size of the samples that we take determines, ceteris paribus, how good our choices will be. Studies of decisions from experience have observed that people tend to rely on relatively small samples from payoff distributions, and small samples are at times rendered even smaller because of recency. We suggest one contributing and previously unnoticed reason for reliance on frugal search: Small samples amplify the difference between the expected earnings associated with the payoff distributions, thus making the options more distinct and choice easier. We describe the magnitude of this amplification effect, and the potential costs that it exacts, and we empirically test four of its implications.

Development of an automatic response mode to improve the clinical utility of sequential risk-taking tasks.

Sequential risk-taking tasks, especially the Balloon Analogue Risk Task (BART), have proven powerful and useful methods in studying and identifying real-world risk takers. A natural index in these tasks is the average number of risks the participant takes in a trial (e.g., pumps on the balloons), but this is difficult to estimate because some trials terminate early because of the consequences of those risks (e.g., when the desired number of balloon pumps exceeds the explosion point). The standard corrective strategy is to use an adjusted score that ignores such event-terminated trials. Although previous data supports the utility of this adjusted score, the authors show formally that it is biased. Therefore, the authors developed an automatic response procedure, in which respondents state at the beginning of each trial how many risks they wish to take and then observe the sequence of events unfold. A study comparing this new automatic and the original manual BART shows that the automatic procedure yields unbiased statistics whereas maintaining the BARTs predictive validity of substance use. The authors also found that providing respondents with the expected-value-maximizing strategy and complete trial-by-trial feedback increased the number of risks they were willing to take during the BART. The authors interpret these results in terms of the potential utility of the automatic version including shorter administration time, unbiased behavioral measures, and minimizing motor involvement, which is important in neuroscientific investigations or with clinical populations with motor limitations.

Neural correlates of evidence accumulation in a perceptual decision task

Sequential sampling models provide a useful framework for understanding human decision making. A key component of these models is an evidence accumulation process in which information is accrued over time to a threshold, at which point a choice is made. Previous neurophysiological studies on perceptual decision making have suggested accumulation occurs only in sensorimotor areas involved in making the action for the choice. Here we investigated the neural correlates of evidence accumulation in the human brain using functional magnetic resonance imaging (fMRI) while manipulating the quality of sensory evidence, the response modality, and the foreknowledge of the response modality. We trained subjects to perform a random dot motion direction discrimination task by either moving their eyes or pressing buttons to make their responses. In addition, they were cued about the response modality either in advance of the stimulus or after a delay. We isolated fMRI responses for perceptual decisions in both independently defined sensorimotor areas and task-defined nonsensorimotor areas. We found neural signatures of evidence accumulation, a higher fMRI response on low coherence trials than high coherence trials, primarily in saccade-related sensorimotor areas (frontal eye field and intraparietal sulcus) and nonsensorimotor areas in anterior insula and inferior frontal sulcus. Critically, such neural signatures did not depend on response modality or foreknowledge. These results help establish human brain areas involved in evidence accumulation and suggest that the neural mechanism for evidence accumulation is not specific to effectors. Instead, the neural system might accumulate evidence for particular stimulus features relevant to a perceptual task.

Decision Making and Learning While Taking Sequential Risks

A sequential risk-taking paradigm used to identify real-world risk takers invokes both learning and decision processes. This article expands the paradigm to a larger class of tasks with different stochastic environments and different learning requirements. Generalizing a Bayesian sequential risk-taking model to the larger set of tasks clarifies the roles of learning and decision making during sequential risky choice. Results show that respondents adapt their learning processes and associated mental representations of the task to the stochastic environment. Furthermore, their Bayesian learning processes are shown to interfere with the paradigms identification of risky drug use, whereas the decision-making process facilitates its diagnosticity. Theoretical implications of the results in terms of both understanding risk-taking behavior and improving risk-taking assessment methods are discussed.

A signal detection analysis of the recognition heuristic

The recognition heuristic uses a recognition decision to make an inference about an unknown variable in the world. Theories of recognition memory typically use a signal detection framework to predict this binary recognition decision. In this article, I integrate the recognition heuristic with signal detection theory to formally investigate how judges use their recognition memory to make inferences. The analysis reveals that false alarms and misses systematically influence the performance of the recognition heuristic. Furthermore, judges should adjust their recognition response criterion according to their experience with the environment to exploit the structure of information in it. Finally, the less-is-more effect is found to depend on the distribution of cue knowledge and judges’ sensitivity to the difference between experienced and novel items. Theoretical implications of this bridge between the recognition heuristic and models of recognition memory are discussed.

Ecologically Rational Choice and the Structure of the Environment

In life, risk is reward and vice versa. Unfortunately, the big rewards people desire are relatively unlikely to occur. This relationship between risk and reward or probabilities and payoffs seems obvious to the financial community and to laypeople alike. Yet theories of decision making have largely ignored it. We conducted an ecological analysis of lifes gambles, ranging from the domains of roulette and life insurance to scientific publications and artificial insemination. Across all domains, payoffs and probabilities proved intimately tied, with payoff magnitudes signaling their probabilities. In some cases, the constraints of the market result in these two core elements of choice being related via a power function; in other cases, other factors such as social norms appear to produce the inverse relationship between risks and rewards. We offer evidence that decision makers exploit this relationship in the form of a heuristic--the risk-reward heuristic--to infer the probability of a payoff during decisions under uncertainty. We demonstrate how the heuristic can help explain observed ambiguity aversion. We further show how this ecological relationship can inform other aspects of decision making, particularly the approach of using monetary lotteries to study choice under risk and uncertainty. Taken together, these findings suggest that theories of decision making need to model not only the decision process but also the environment to which the process is adapted.

Dynamics of Postdecisional Processing of Confidence

Most cognitive theories assume that confidence and choice happen simultaneously and are based on the same information. The 3 studies presented in this article instead show that confidence judgments can arise, at least in part, from a postdecisional evidence accumulation process. As a result of this process, increasing the time between making a choice and confidence judgment improves confidence resolution. This finding contradicts the notion that confidence judgments are biased by decision makers seeking confirmatory evidence. Further analysis reveals that the improved resolution is due to a reduction in confidence in incorrect responses, while confidence in correct responses remains relatively constant. These results are modeled with a sequential sampling process that allows evidence accumulation to continue after a choice is made and maps the amount of accumulated evidence onto a confidence rating. The cognitive modeling analysis reveals that the rate of evidence accumulation following a choice does slow relative to the rate preceding choice. The analysis also shows that the asymmetry between confidence in correct and incorrect choices is compatible with state-dependent decay in the accumulated evidence: Evidence consistent with the current state results in a deceleration of accumulated evidence and consequently evidence appears to have a decreasing impact on observed confidence. In contrast, evidence inconsistent with the current state results in an acceleration of accumulated evidence toward the opposite direction and consequently evidence appears to have an increasing impact on confidence. Taken together, this process-level understanding of confidence suggests a simple strategy for improving confidence accuracy: take a bit more time to make confidence judgments.

Interference effects of choice on confidence: Quantum characteristics of evidence accumulation

Significance Most cognitive and neural decision-making models—owing to their roots in classical probability theory—assume that decisions are read out of a definite state of accumulated evidence. This assumption contradicts the view held by many behavioral scientists that decisions construct rather than reveal beliefs and preferences. We present a quantum random walk model of decision-making that treats judgments and decisions as a constructive measurement process, and we report the results of an experiment showing that making a decision changes subsequent distributions of confidence relative to when no decision is made. This finding provides strong empirical support for a parameter-free prediction of the quantum model. Decision-making relies on a process of evidence accumulation which generates support for possible hypotheses. Models of this process derived from classical stochastic theories assume that information accumulates by moving across definite levels of evidence, carving out a single trajectory across these levels over time. In contrast, quantum decision models assume that evidence develops over time in a superposition state analogous to a wavelike pattern and that judgments and decisions are constructed by a measurement process by which a definite state of evidence is created from this indefinite state. This constructive process implies that interference effects should arise when multiple responses (measurements) are elicited over time. We report such an interference effect during a motion direction discrimination task. Decisions during the task interfered with subsequent confidence judgments, resulting in less extreme and more accurate judgments than when no decision was elicited. These results provide qualitative and quantitative support for a quantum random walk model of evidence accumulation over the popular Markov random walk model. We discuss the cognitive and neural implications of modeling evidence accumulation as a quantum dynamic system.

Making Assessments While Taking Repeated Risks: A Pattern of Multiple Response Pathways

Beyond simply a decision process, repeated risky decisions also require a number of cognitive processes including learning, search and exploration, and attention. In this article, we examine how multiple response pathways develop over repeated risky decisions. Using the Balloon Analogue Risk Task (BART) as a case study, we show that 2 different response pathways emerge over the course of the task. The assessment pathway is a slower, more controlled pathway where participants deliberate over taking a risk. The 2nd pathway is a faster, more automatic process where no deliberation occurs. Results imply the slower assessment pathway is taken as choice conflict increases and that the faster automatic response is a learned response. Based on these results, we modify an existing formal cognitive model of decision making during the BART to account for these dual response pathways. The slower more deliberative response process is modeled with a sequential sampling process where evidence is accumulated to a threshold, while the other response is given automatically. We show that adolescents with conduct disorder and substance use disorder symptoms not only evaluate risks differently during the BART but also differ in the rate at which they develop the more automatic response. More broadly, our results suggest cognitive models of judgment decision making need to transition from treating observed decisions as the result of a single response pathway to the result of multiple response pathways that change and develop over time.