Two-stage dynamic signal detection: A theory of choice, decision time, and confidence.

Abstract

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.

Harm to others outweighs harm to self in moral decision making

“…Because uncertainty in decision making is generally reflected in longer response times (28,29), this explanation for hyperaltruistic valuation of pain is also consistent with our finding that hyperaltruism was greater in deciders who were slower to decide the fate of receivers than of themselves. However, the uncertainty account makes an additional prediction concerning behavioral noise.…”

The Ising Decision Maker: A binary stochastic network for choice response time.

“…Second, there is the issue of decision confidence. To address this, one could consider using postdecision activity, as proposed by Pleskac and Busemeyer (2010).…”

Computational mate choice: Theory and empirical evidence

“…As in most of signal detection models, in Phelps et al's (2006) model, uncertainty may be viewed as the random sampling error that arises from the integration of independent pieces of evidence over a fixed evaluation time (Castellano, 2009a;Pleskac and Busemeyer, 2010). In fact, suppose that (i) a female, during a fixed time interval T, uses n pieces of evidence to assess the attractiveness of a mate, (ii) each piece of evidence can be either a positive (+1 = the male is an appropriate mate) or a null evaluation (0 = no evidence that the male is an appropriate mate), and (iii) p is the probability of a positive evaluation.…”

“…Sequential-sampling models of decision making are a dynamic variant of signal detection models (Pleskac and Busemeyer, 2010), in that they drop the assumption that decision makers use a fixed sample size of evidence and assume that decision is made as soon as the accumulating noisy evidence reaches a fixed threshold (Castellano, 2009a;Castellano and Cermelli, 2011). Unlike signal detection models, sequential-sampling models incorporate time in the decision process and, thus, they could be used for investigating speed-accuracy tradeoffs in decision making (Chittka et al, 2009;Sullivan, 1994) and for making testable predictions on the relationship between choice probability and time response (Kacelnik et al, 2011;Shapiro et al, 2008).…”