Encoding, repetition, and the mirror effect in recognition memory: Symmetry in motion

Attention/likelihood theory has been used to explain the mirror effect in recognition memory. The theory also predicts that any manipulation that affects the recognition of old items will also affect recognition of the new items—more specifically, that all the underlying distributions will move and that they will move symmetrically on the decision axis. In five experiments, we tested this prediction. The first two experiments used encoding tasks during study to change recognition performance for high- and low-frequency words. The results show symmetrical dispersion of the underlying distributions. The final three experiments used repetition to increase recognition performance. Repetition produced a symmetrical pattern of movement that was different from that produced by encoding task. This pattern is, however, also covered by attention/likelihood theory. A further extension of the theory was used to predict response times.     

The effect of “irrelevant” variables on decision making: Criterion shifts in preferential choice?

In decision research, it is widely assumed that decision makers have subjective experiences which indicate definitively, albeit with random fluctuations, whether or not one alternative is preferable to another. Further, it is assumed that subjects' choices reflect this underlying preference order. It follows from this position, as a logical consequence rather than an empirical observation, that if a manipulation affects people's choices, it does so by changing the way people subjectively experience the alternatives (presumably via people's encoding or combination of attribute information). In psychophysics, an analogous theoretical position has been relinquished. Specifically, it is no longer believed that observers in a detection experiment have subjective experiences which indicate definitively, albeit with error, whether a signal has been perceived. Rather, according to signal detection theory, the observer's sensory experience can be portrayed as a quantifiable sensation that must be converted into an overt report of “signal present” or “signal absent” by comparing the sensation with a criterion. In this paper, we describe two models of preferential choice, the lexicographic criterion model and a probabilistic criterion model, that replace the notion of definitive preference with an analogue of signal detection theory. In the models, the decision maker is represented as having quantifiable inclinations, rather than definitive preferences, that must be converted into an overt choice by comparing the inclinations with a criterion. By attributing the effects of experimental manipulations to shifts in the criterion, rather than to changes in people's inclinations, the model offers a testable alternative to the proposition that altered choices imply altered preferences.     

Signal recognition and the probability-matching decision rule

In a visual binary recognition task in which three levels of signal strength were presented at each of three levels of signal probability, subjects’ behavior was consistent with the signal detection theory notion that the sensory and the decision processes are independent, but did not support the contention that the decision processes obey a likelihood ratio rule. Instead, the evidence suggests that subjects were attempting to probability-match, adjusting the relative frequencies of their responses to correspond with those of the stimuli. The results are discussed in relation to previous findings, against the background of signal detection theory.     

Source monitoring and multivariate signal detection theory, with a model for selection

Participants in source monitoring studies, in addition to determining whether an item is old or new, also discriminate the source of the item, such as whether the item was presented in a male or female voice. This article shows how to apply multivariate signal detection theory (SDT) to source monitoring. An interesting aspect of one version of the source monitoring procedure, from the perspective of multivariate SDT, is that it involves a type of selection, in that a discrimination response is observed only if the detection decision is that an item is old. If the selection is ignored, then the estimate of the discrimination parameter can be biased; the nature and magnitude of the bias are illustrated. A bivariate signal detection model that recognizes selection is presented and its application is illustrated. The approach to source monitoring via multivariate SDT provides new results that are informative about underlying psychological processes.     

Adaptive criterion setting in perceptual decision making

Pigeons responded in a perceptual categorization task with six different stimuli (shades of gray), three of which were to be classified as "light" or "dark", respectively. Reinforcement probability for correct responses was varied from 0.2 to 0.6 across blocks of sessions and was unequal for correct light and dark responses. Introduction of a new reinforcement contingency resulted in a biphasic process of adjustment: First, choices were strongly biased towards the favored alternative, which was followed by a shift of preference back towards unbiased choice allocation. The data are well described by a signal detection model in which adjustment to a change in reinforcement contingency is modeled as the change of a criterion along a decision axis with fixed stimulus distributions. Moreover, the model shows that pigeons, after an initial overadjustment, distribute their responses almost optimally, although the overall benefit from doing so is extremely small. The strong and swift effect of minute changes in overall reinforcement probability precludes a choice strategy directly maximizing expected value, contrary to the assumption of signal detection theory. Instead, the rapid adjustments observed can be explained by a model in which reinforcement probabilities for each action, contingent on perceived stimulus intensity, determine choice allocation.     

An application of signal detection theory with finite mixture distributions to source discrimination

A mixture extension of signal detection theory is applied to source discrimination. The basic idea of the approach is that only a portion of the sources (say A or B) of items to be discriminated is encoded or attended to during the study period. As a result, in addition to 2 underlying probability distributions associated with the 2 sources, there is a 3rd distribution that represents items for which sources were not attended to. Thus, over trials, the observed response results from a mixture of an attended (A or B) distribution and a nonattended distribution. The situation differs in an interesting way from detection in that, for detection, there is mixing only on signal trials and not on noise trials, whereas for discrimination, there is mixing on both A and B trials. Predictions of the mixture model are examined for data from several recent studies and in a new experiment.     

Signal detection theory with finite mixture distributions: theoretical developments with applications to recognition memory

An extension of signal detection theory (SDT) that incorporates mixtures of the underlying distributions is presented. The mixtures can be motivated by the idea that a presentation of a signal shifts the location of an underlying distribution only if the observer is attending to the signal; otherwise, the distribution is not shifted or is only partially shifted. Thus, trials with a signal presentation consist of a mixture of 2 (or more) latent classes of trials. Mixture SDT provides a general theoretical framework that offers a new perspective on a number of findings. For example, mixture SDT offers an alternative to the unequal variance signal detection model; it can also account for nonlinear normal receiver operating characteristic curves, as found in recent research.     

Decision behavior with changing signal strength

The Theory of Signal Detectability (TSD) has nearly replaced classical notions of the threshold because of its ability to separate sensory and decision processes in weak-signal detection and recognition paradigms. The primary emphasis of recent work has concentrated on the sensory rather than the decision aspects and almost all work has been exclusively at one signal strength. We propose a model to describe behavior at different signal strengths based on subjective rather than objective distributions. The model predicts performance at a constant likelihood ratio (LR) criterion (even though subjective distributions are the basis for determining cutoff criteria) unless the observer adopts a Subjective Neyman-Pearson (SNP) objective. Results from an experiment in visual discrimination suggest that some observers in fact operate at constant objective LR's as signal strength is varied randomly over a wide range. The objective LR's of the other subjects changed dramatically with signal strength, but this behavior is consistent with the use of a Subjective Neyman-Pearson (SNP) decision rule and the linear relation between subjective and objective log LR's found in the studies of subjective probability.     

In defense of the signal detection interpretation of remember/know judgments

Donaldson (1996) argued that remember/know judgments can be conceptualized within a signal detection framework by assuming that they are based on two criteria situated along a strength-of-memory decision axis. According to this model, items that exceed a high criterion receive a remember response, whereas items that only exceed a lower criterion receive a know response. Although a variety of findings have been presented in evidence against this idea, Dunn (2004) recently showed that detection theory is fully compatible with those findings. We present a variety of new results and new analyses that weigh strongly in favor of the detection interpretation. We further show that a dual-process account of recognition memory is compatible with a unidimensional detection model despite the common notion that such a model necessarily assumes a single process. The key assumption of this model is that individual recognition decisions are based on both recollection and familiarity (not on one process or the other).     

Decision noise may mask criterion shifts: Reply to Balakrishnan and MacDonald (2008)

J. D. Balakrishnan and J. A. MacDonald (2008) argue that RTbased measures of signal detection processes provide evidence against signal detection theory’s notion of a flexible decision criterion. They argue that this evidence is immune to the alternative explanation proposed by S. T. Mueller and C. T. Weidemann (2008), that decision noise may mask criterion shifts. We show that noise in response times can produce the same effects as are produced by noise in confidence ratings. Given these results, the evidence is not sufficient to categorically reject the notion of a flexible response policy implemented through shifts in a decision criterion. nt]mis|This research was supported in part by a postdoctoral fellowship to C.T.W. from the German Academic Exchange Service (DAAD).     

The mirror effect and mixture signal detection theory

The mirror effect for word frequency refers to the finding that low-frequency words have higher hit rates and lower false alarm rates than high-frequency words. This result is typically interpreted in terms of conventional signal detection theory (SDT), in which case it indicates that the order of the underlying old item distributions mirrors the order of the new item distributions. However, when viewed in terms of a mixture version of SDT, the order of hits and false alarms does not necessarily imply the same order in the underlying distributions because of possible effects of mixing. A reversal in underlying distributions did not appear for fits of mixture SDT models to data from 4 experiments.     

Comparing single‐ and dual‐process models of memory development

This experiment examined single‐process and dual‐process accounts of the development of visual recognition memory. The participants, 6–7‐year‐olds, 9–10‐year‐olds and adults, were presented with a list of pictures which they encoded under shallow or deep conditions. They then made recognition and confidence judgments about a list containing old and new items. We replicated the main trends reported by Ghetti and Angelini ( 2008 ) in that recognition hit rates increased from 6 to 9 years of age, with larger age changes following deep than shallow encoding. Formal versions of the dual‐process high threshold signal detection model and several single‐process models (equal variance signal detection, unequal variance signal detection, mixture signal detection) were fit to the developmental data. The unequal variance and mixture signal detection models gave a better account of the data than either of the other models. A state‐trace analysis found evidence for only one underlying memory process across the age range tested. These results suggest that single‐process memory models based on memory strength are a viable alternative to dual‐process models for explaining memory development.     

Detection in a four-observation two-channel task

Two models are proposed for a four-observation two-channel forced-choice paradigm of signal detection. The first model, an extension of signal detection theory, fails, while the second model, an extension of the classical decision threshold model, succeeds.     

Using PPT to account for randomness in perception

According to many theories of decision making, of which signal detection theory is the most prominent, randomness is the main factor responsible for imperfect performance. These theories imply that correcting for attenuation due to randomness should result in perfect scores as long as the participants use nonextreme decision criteria. On the basis of a recent advance termed potential performance theory (Trafimow & Rice, Psychological Review 115:447-462, 2008), we performed auditory and visual detection experiments and corrected the scores for attenuation. Most participants in both experiments tended to perform at a less-than-perfect level, even after their scores were corrected. The findings demonstrate that at least one systematic factor influences detection that is not included in signal detection theory.     

Should I go with my gut? Investigating the benefits of emotion-focused decision making

Deliberative decision strategies have historically been considered the surest path to sound decisions; however, recent evidence and theory suggest that affective strategies may be equally as effective. In four experiments we examined conditions under which affective versus deliberative decision strategies might result in higher decision quality. While consciously focusing on feelings versus details, participants made choices that varied in complexity, in extent of subsequent conscious deliberation allowed, and in domain. Results indicate that focusing on feelings versus details led to superior objective and subjective decision quality for complex decisions. However, when using a feeling-focused approach, subsequent deliberation after encoding resulted in reduced choice quality. These results suggest that affective decision strategies may be more effective relative to deliberative strategies for certain complex decisions.     

Decision noise: An explanation for observed violations of signal detection theory

In signal detection theory (SDT), responses are governed by perceptual noise and a flexible decision criterion. Recent criticisms of SDT (see, e.g., Balakrishnan, 1999) have identified violations of its assumptions, and researchers have suggested that SDT fundamentally misrepresents perceptual and decision processes. We hypothesize that, instead, these violations of SDT stem from decision noise: the inability to use deterministic response criteria. In order to investigate this hypothesis, we present a simple extension of SDT—the decision noise model—with which we demonstrate that shifts in a decision criterion can be masked by decision noise. In addition, we propose a new statistic that can help identify whether the violations of SDT stem from perceptual or from decision processes. The results of a stimulus classification experiment—together with model fits to past experiments—show that decision noise substantially affects performance. These findings suggest that decision noise is important across a wide range of tasks and needs to be better understood in order to accurately measure perceptual processes.     

决策信心校准水平对元认知监控作用的影响

决策信心是个体对自身决策正确性的主观评价,是对决策过程的元认知体验。决策信心校准指决策信心水平与实际的决策正确率之间的匹配程度,其指标有信心水平和决策正确率的相关系数及Type II信号检测论中的ROC曲线下面积（Aroc）等。已有研究发现进行决策信心评估能够增强对当前或后续决策的元认知监控作用,但目前尚不清楚这种效应是否依赖于个体的决策信心校准水平。本研究通过设置知觉决策后是否进行决策信心评估（有信心评估与无信心评估）两种条件,考察个体决策信心的校准水平（Aroc）对元认知监控作用的影响。结果显示：1）与无信心评估条件相比,有信心评估的决策反应时显著增长,决策正确率显著提高（p<0.005）;2）Aroc与有、无信心评估条件下决策正确率的增加值显著正相关（r=0.25,p=0.034）,且高Aroc组的决策正确率增加值显著高于低Aroc组（p<0.05）。结果表明,在知觉决策过程中加入决策信心评估具有增强元认知监控作用的效应,体现为决策时间的增长和决策正确率的提高。并且,这种效应的大小依赖于个体的决策信心校准水平,校准水平越高元认知监控作用越好。