Psychological Review

The energetics of motivated cognition: A force-field analysis.
A force-field theory of motivated cognition is presented and applied to a broad variety of phenomena in social judgment and self-regulation. Purposeful cognitive activity is assumed to be propelled by a driving force and opposed by a restraining force. Potential driving force represents the maximal amount of energy an individual is prepared to invest in a cognitive activity. Effective driving force corresponds to the amount of energy he or she actually invests in attempt to match the restraining force. Magnitude of the potential driving force derives from a combination of goal importance and the pool of available mental resources, whereas magnitude of the restraining force derives from an individual's inclination to conserve resources, current task demands, and competing goals. The present analysis has implications for choice of means to achieve one's cognitive goals as well as for successful goal attainment under specific force-field constellations. Empirical evidence for these effects is considered, and the underlying theory's integrative potential is highlighted. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Modeling cross-situational word–referent learning: Prior questions.
Both adults and young children possess powerful statistical computation capabilities—they can infer the referent of a word from highly ambiguous contexts involving many words and many referents by aggregating cross-situational statistical information across contexts. This ability has been explained by models of hypothesis testing and by models of associative learning. This article describes a series of simulation studies and analyses designed to understand the different learning mechanisms posited by the 2 classes of models and their relation to each other. Variants of a hypothesis-testing model and a simple or dumb associative mechanism were examined under different specifications of information selection, computation, and decision. Critically, these 3 components of the models interact in complex ways. The models illustrate a fundamental tradeoff between amount of data input and powerful computations: With the selection of more information, dumb associative models can mimic the powerful learning that is accomplished by hypothesis-testing models with fewer data. However, because of the interactions among the component parts of the models, the associative model can mimic various hypothesis-testing models, producing the same learning patterns but through different internal components. The simulations argue for the importance of a compositional approach to human statistical learning: the experimental decomposition of the processes that contribute to statistical learning in human learners and models with the internal components that can be evaluated independently and together. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Models of recognition, repetition priming, and fluency: Exploring a new framework.
We present a new modeling framework for recognition memory and repetition priming based on signal detection theory. We use this framework to specify and test the predictions of 4 models: (a) a single-system (SS) model, in which one continuous memory signal drives recognition and priming; (b) a multiple-systems-1 (MS1) model, in which completely independent memory signals (such as explicit and implicit memory) drive recognition and priming; (c) a multiple-systems-2 (MS2) model, in which there are also 2 memory signals, but some degree of dependence is allowed between these 2 signals (and this model subsumes the SS and MS1 models as special cases); and (d) a dual-process signal detection (DPSD1) model, 1 possible extension of a dual-process theory of recognition (Yonelinas, 1994) to priming, in which a signal detection model is augmented by an independent recollection process. The predictions of the models are tested in a continuous-identification-with-recognition paradigm in both normal adults (Experiments 1–3) and amnesic individuals (using data from Conroy, Hopkins, & Squire, 2005). The SS model predicted numerous results in advance. These were not predicted by the MS1 model, though could be accommodated by the more flexible MS2 model. Importantly, measures of overall model fit favored the SS model over the others. These results illustrate a new, formal approach to testing theories of explicit and implicit memory. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The self-consistency model of subjective confidence.
How do people monitor the correctness of their answers? A self-consistency model is proposed for the process underlying confidence judgments and their accuracy. In answering a 2-alternative question, participants are assumed to retrieve a sample of representations of the question and base their confidence on the consistency with which the chosen answer is supported across representations. Confidence is modeled by analogy to the calculation of statistical level of confidence (SLC) in testing hypotheses about a population and represents the participant's assessment of the likelihood that a new sample will yield the same choice. Assuming that participants draw representations from a commonly shared item-specific population of representations, predictions were derived regarding the function relating confidence to inter-participant consensus and intra-participant consistency for the more preferred (majority) and the less preferred (minority) choices. The predicted pattern was confirmed for several different tasks. The confidence–accuracy relationship was shown to be a by-product of the consistency–correctness relationship: It is positive because the answers that are consistently chosen are generally correct, but negative when the wrong answers tend to be favored. The overconfidence bias stems from the reliability–validity discrepancy: Confidence monitors reliability (or self-consistency), but its accuracy is evaluated in calibration studies against correctness. Simulation and empirical results suggest that response speed is a frugal cue for self-consistency, and its validity depends on the validity of self-consistency in predicting performance. Another mnemonic cue—accessibility, which is the overall amount of information that comes to mind—makes an added, independent contribution. Self-consistency and accessibility may correspond to the 2 parameters that affect SLC: sample variance and sample size. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Correction to Adelman (2011).
Reports an error in "Letters in time and retinotopic space" by James S. Adelman ( Psychological Review , 2011[Oct], Vol 118[4], 570-582). In the article the link to access supplemental materials was missing. Supplemental materials are available at: http://dx.doi.org/10.1037/a0024811.supp. The online version of this article has been corrected. (The following abstract of the original article appeared in record 2011-16746-001.) Various phenomena in tachistoscopic word identification and priming (WRODS and LTRS are confused with and prime WORDS and LETTERS) suggest that position-specific channels are not used in the processing of letters in words. Previous approaches to this issue have sought alternative matching rules because they have assumed that these phenomena reveal which stimuli are good but imperfect matches to a particular word—such imperfect matches being taken by the word recognition system as partial evidence for that word. The new Letters in Time and Retinotopic Space model (LTRS) makes the alternative assumption that these phenomena reveal the rates at which different features of the stimulus are extracted, because the stimulus is ambiguous when some features are missing from the percept. LTRS is successfully applied to tachistoscopic identification and form priming data with manipulations of duration and target–foil and prime–target relationships. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The ecological rationality of state-dependent valuation.
Laboratory studies on a range of animals have identified a bias that seems to violate basic principles of rational behavior: A preference is shown for feeding options that previously provided food when reserves were low, even though another option had been found to give the same reward with less delay. The bias presents a challenge to normative models of decision making (which only take account of expected rewards and the state of the animal at the decision time). To understand the behavior, we take a broad ecological perspective and consider how valuation mechanisms evolve when the best action depends upon the environment being faced. We show that in a changing and uncertain environment, state-dependent valuation can be favored by natural selection: Individuals should allow their hunger to affect learning for future decisions. The valuation mechanism that typically evolves produces the kind of behavior seen in standard laboratory tests. By providing an insight into why learning should be affected by the state of an individual, we provide a basis for understanding psychological principles in terms of an animal's ecology. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Goal-directed decision making as probabilistic inference: A computational framework and potential neural correlates.
Recent work has given rise to the view that reward-based decision making is governed by two key controllers: a habit system, which stores stimulus–response associations shaped by past reward, and a goal-oriented system that selects actions based on their anticipated outcomes. The current literature provides a rich body of computational theory addressing habit formation, centering on temporal-difference learning mechanisms. Less progress has been made toward formalizing the processes involved in goal-directed decision making. We draw on recent work in cognitive neuroscience, animal conditioning, cognitive and developmental psychology, and machine learning to outline a new theory of goal-directed decision making. Our basic proposal is that the brain, within an identifiable network of cortical and subcortical structures, implements a probabilistic generative model of reward, and that goal-directed decision making is effected through Bayesian inversion of this model. We present a set of simulations implementing the account, which address benchmark behavioral and neuroscientific findings, and give rise to a set of testable predictions. We also discuss the relationship between the proposed framework and other models of decision making, including recent models of perceptual choice, to which our theory bears a direct connection. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Using E-Z Reader to simulate eye movements in nonreading tasks: A unified framework for understanding the eye–mind link.
Nonreading tasks that share some (but not all) of the task demands of reading have often been used to make inferences about how cognition influences when the eyes move during reading. In this article, we use variants of the E-Z Reader model of eye-movement control in reading to simulate eye-movement behavior in several of these tasks, including z-string reading, target-word search, and visual search of Landolt C s arranged in both linear and circular arrays. These simulations demonstrate that a single computational framework is sufficient to simulate eye movements in both reading and nonreading tasks but also suggest that there are task-specific differences in both saccadic targeting (i.e., decisions about where to move the eyes) and the coupling between saccadic programming and the movement of attention (i.e., decisions about when to move the eyes). These findings suggest that some aspects of the eye–mind link are flexible and can be configured in a manner that supports efficient task performance. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
A stochastic detection and retrieval model for the study of metacognition.
[Correction Notice: An erratum for this article was reported in Vol 119(1) of Psychological Review (see record 2012-00103-005). In the article incorrect equations were published. The corrected forms of Equations (1) and (2) in this article are included.] We present a signal detection-like model termed the stochastic detection and retrieval model (SDRM) for use in studying metacognition. Focusing on paradigms that relate retrieval (e.g., recall or recognition) and confidence judgments, the SDRM measures (1) variance in the retrieval process, (2) variance in the confidence process, (3) the extent to which different sources of information underlie each response, (4) simple bias (i.e., increasing or decreasing confidence criteria across conditions), and (5) metacognitive bias (i.e., contraction or expansion of the confidence criteria across conditions). In the metacognition literature, gamma correlations have been used to measure the accuracy of confidence judgments. However, gamma cannot distinguish between the first 3 attributes, and it cannot measure either form of bias. In contrast, the SDRM can distinguish among the attributes, and it can measure both forms of bias. In this way, the SDRM can be used to test competing process theories by determining the attribute that best accounts for a change across conditions. To demonstrate the SDRM's usefulness, we investigated judgments of learning (JOLs) followed by cued-recall. Through a series of nested and non-nested model comparisons applied to a new experiment, the SDRM determined that a reduction in variance during the confidence process is the most likely explanation of the delayed-JOL effect, and a stronger relation between information underlying JOLs and recall is the most likely explanation of the testing-JOL effect. Following a brief discussion of implications for JOL theories, we conclude with a broader discussion of how the SDRM can benefit metacognition research. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Optimal decision making in neural inhibition models.
In their influential Psychological Review article, Bogacz, Brown, Moehlis, Holmes, and Cohen (2006) discussed optimal decision making as accomplished by the drift diffusion model (DDM). The authors showed that neural inhibition models, such as the leaky competing accumulator model (LCA) and the feedforward inhibition model (FFI), can mimic the DDM and accomplish optimal decision making. Here we show that these conclusions depend on how the models handle negative activation values and (for the LCA) across-trial variability in response conservativeness. Negative neural activations are undesirable for both neurophysiological and mathematical reasons. However, when negative activations are truncated to 0, the equivalence to the DDM is lost. Simulations show that this concern has practical ramifications: The DDM generally outperforms truncated versions of the LCA and the FFI, and the parameter estimates from the neural models can no longer be mapped onto those of the DDM in a simple fashion. We show that for both models, truncation may be avoided by assuming a baseline activity for each accumulator. This solution allows the LCA to approximate the DDM and the FFI to be identical to the DDM. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The law of categorical judgment (corrected) extended: A note on Rosner and Kochanski (2009).
Rosner and Kochanski (2009) noticed an inconsistency in the mathematical statement of the Law of Categorical Judgment and derived “the valid equation, the Law of Categorical Judgment (Corrected)” (p. 125). The purpose of this comment is to point out that the law can be corrected in many different ways, leading to substantially different equations. The different versions have different consequences for the predicted distributions of the responses and, hence, for fitting real data. Some of these consequences are unexpected and sometimes undesirable. Researchers should be aware of the different possibilities as they may lead to pronouncedly different accounts of given data. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Correction to Jang, Wallsten, and Huber (2011).
Reports an error in "A stochastic detection and retrieval model for the study of metacognition" by Yoonhee Jang, Thomas S. Wallsten and David E. Huber ( Psychological Review , Advanced Online Publication, Nov 7, 2011, np). In the article incorrect equations were published. The corrected forms of Equations (1) and (2) in this article are included. (The following abstract of the original article appeared in record 2011-25196-001.) We present a signal detection-like model termed the stochastic detection and retrieval model (SDRM) for use in studying metacognition. Focusing on paradigms that relate retrieval (e.g., recall or recognition) and confidence judgments, the SDRM measures (1) variance in the retrieval process, (2) variance in the confidence process, (3) the extent to which different sources of information underlie each response, (4) simple bias (i.e., increasing or decreasing confidence criteria across conditions), and (5) metacognitive bias (i.e., contraction or expansion of the confidence criteria across conditions). In the metacognition literature, gamma correlations have been used to measure the accuracy of confidence judgments. However, gamma cannot distinguish between the first 3 attributes, and it cannot measure either form of bias. In contrast, the SDRM can distinguish among the attributes, and it can measure both forms of bias. In this way, the SDRM can be used to test competing process theories by determining the attribute that best accounts for a change across conditions. To demonstrate the SDRM's usefulness, we investigated judgments of learning (JOLs) followed by cued-recall. Through a series of nested and non-nested model comparisons applied to a new experiment, the SDRM determined that a reduction in variance during the confidence process is the most likely explanation of the delayed-JOL effect, and a stronger relation between information underlying JOLs and recall is the most likely explanation of the testing-JOL effect. Following a brief discussion of implications for JOL theories, we conclude with a broader discussion of how the SDRM can benefit metacognition research. (PsycINFO Database Record (c) 2012 APA, all rights reserved)

Psychological Review - Vol 119, Iss 1