Form and objective of the decision rule in absolute identification

In several conditions of a line length identification experiment, the subjects’ decision making strategies were systematically biased against the responses on the edges of the stimulus range. When the range and number of the stimuli were small, the bias caused the percentage of correct responses to be highest in the center and lowest on the extremes of the range. Two general classes of decision rules that would explain these results are considered. The first class assumes that subjects intend to adopt an optimal decision rule, but systematically misrepresent one or more parameters of the decision making context. The second class assumes that subjects use a different measure of performance than the one assumed by the experimenter: instead of maximizing the chances of a correct response, the subject attempts to minimize the expected size of the response error (a “fidelity criterion”). In a second experiment, extended experience and feedback did not diminish the bias effect, but explicitly penalizing all response errors equally, regardless of their size, did reduce or eliminate it in some subjects. Both results favor the fidelity criterion over the optimal rule.     

Understanding variability in binary and continuous choice

Excessive variability in binary choice (categorical judgment) can take the form of probability matching rather than the normatively correct behavior of deterministically choosing the more likely alternative. Excessive variability in continuous choice (judgment rating) can take the form of underconfidence, understating the probability of highly likely events and overstating the probability of very unlikely events. We investigated the origins of choice variability in terms of noise prior to decision (at the evidence stage) and at the decision stage. A version of the well-known medical diagnosis task was conducted with binary and continuous choice on each trial. Noise at evidence stage was reduced by allowing the subjects to view historical summaries of prior relevant trials, and noise at the decision stage was reduced by giving the subjects a numerical score on the basis of their continuous choice and the actual outcome. Both treatments greatly reduced variability. Cash payments based on the numerical score had a less reliable incremental effect in our experiment. The overall results are more consistent with a Logit model of decision than with a simple criterion (or maximization) rule or a simple probabilitymatching rule.     

The Effects of Clarity of Decision Rule and Favorability of Verdict on Satisfaction with Resolution of Conflicts1

Favorability of outcome and clarity of decision rule were manipulated in a simulated conflict resolved by an impartial third party. Overall, subjects who received favorable outcomes were more satisfied than were those who received unfavorable outcomes. In addition, favorability of verdict and clarity of decision rule interacted in affecting overall satisfaction. Among subjects receiving an unfavorable verdict, those whose outcomes had been determined by a defined decision rule experienced greater satisfaction than those whose outcomes were based on an unclear, undefined rule. However, given a favorable outcome, the effects of clarity of decision rule were reversed-subjects reported greater satisfaction with the undefined than the defined rule. These results are discussed with reference to the particularism/universalism of defined and undefined decision rules.     

Learning From Peers’ Eye Movements in the Absence of Expert Guidance: A Proof of Concept Using Laboratory Stock Trading,Eye Tracking,and Machine Learning

Existing research shows that people can improve their decision skills by learning what experts paid attention to when faced with the same problem. However, in domains like financial education, effective instruction requires frequent, personalized feedback given at the point of decision, which makes it time‐consuming for experts to provide and thus, prohibitively costly. We address this by demonstrating an automated feedback mechanism that allows amateur decision‐makers to learn what information to attend to from one another, rather than from an expert. In the first experiment, eye movements of N = 100 subjects were recorded while they repeatedly performed a standard behavioral finance investment task. Consistent with previous studies, we found that a significant proportion of subjects were affected by decision bias. In the second experiment, a different group of N = 100 subjects faced the same task but, after each choice, they received individual, machine learning‐generated feedback on whether their pre‐decision eye movements resembled those made by Experiment 1 subjects prior to good decisions. As a result, Experiment 2 subjects learned to analyze information similarly to their successful peers, which in turn reduced their decision bias. Furthermore, subjects with low Cognitive Reflection Test scores gained more from the proposed form of process feedback than from standard behavioral feedback based on decision outcomes.     

Methodological pitfalls in predicting counseling success

Prediction models employing multiple linear regression of raw scores, multiple linear regression of factor scores, the single best predictor, and a nine-point decision rule index were compared. The subjects were 296 clients undergoing vocational counseling and evaluation. Predictor variables included performance ratings, demographic variables, and WAIS subtest scores; the criterion was employment status upon program completion. The least statistically sophisticated model, employing the single best predictor, was the most successful approach. Considerable shrinkage in power of prediction was demonstrated upon cross-validation particularly for multiple linear regression of raw scores model, indicating the necessity of cross-validating prediction schemes. Additional suggestions are made to those designing prediction studies.     

The effects of perceptual condition on proofreading for misspellings

In two experiments, subjects proofread text in which misspelled words were created by replacing a single letter with another one. The following perceptual conditions were included: clear-cut photocopies of typewritten text, clear-cut text presented on a CRT screen, ditto copies that varied in legibility, and text that included extraneous noise characters superimposed on some letters. In all conditions, subjects adopted a hierarchical feature test that gave first priority to resolving letter envelope and second priority to discriminating other letter features. When clear-cut text with no extraneous noise was proofread, subjects used a sophisticated-guessing decision rule that tolerated misspellings involving missing letter features (as when c replaced e) but not added ones (as when e replaced c). This asymmetrical rule was modified, however, when subjects were exposed to text that included extraneous noise that was confusable with the letter features. In those circumstances, subjects adopted a decision rule that tolerated misspellings involving missing features or any added features that resembled the noise.     

Decision rules in the perception and categorization of multidimensional stimuli

This article examines decision processes in the perception and categorization of stimuli constructed from one or more components. First, a general perceptual theory is used to formally characterize large classes of existing decision models according to the type of decision boundary they predict in a multidimensional perceptual space. A new experimental paradigm is developed that makes it possible to accurately estimate a subject's decision boundary in a categorization task. Three experiments using this paradigm are reported. Three conclusions stand out: (a) Subjects adopted deterministic decision rules, that is, for a given location in the perceptual space, most subjects always gave the same response; (b) subjects used decision rules that were nearly optimal; and (c) the only constraint on the type of decision bound that subjects used was the amount of cognitive capacity it required to implement. Subjects were not constrained to make independent decisions on each component or to attend to the distance to each prototype.     

A diffusion model explanation of the worst performance rule for reaction time and IQ

The worst performance rule for cognitive tasks [Coyle, T.R. (2003). IQ, the worst performance rule, and Spearman's law: A reanalysis and extension. Intelligence, 31, 567-587] in which reaction time is measured is the result that IQ scores correlate better with longer (i.e., 0.7 and 0.9 quantile) reaction times than shorter (i.e., 0.1 and 0.3 quantile) reaction times. We show that this pattern of correlations can be predicted by the diffusion model [Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85, 59-108], in two ways: either assuming that the rate of accumulation of information toward a decision is higher for higher IQ subjects or assuming that the criterial amounts of information they require before a decision are lower. Importantly, the model explains both reaction times and accuracy, so the two possibilities can be distinguished.     

Optimal correction for guessing in multiple-choice tests

Subjects’ decisions in multiple-choice tests are an interesting domain for the analysis of decision making under uncertainty. When the test is graded using a rule that penalizes wrong answers, each item can be viewed as a lottery where a rational examinee would choose whether to omit (sure reward) or answer (take the lottery) depending on risk aversion and level of knowledge. We formalize students as heterogeneous decision makers with different risk attitudes and levels of knowledge. Building on IRT, we compute the optimal penalty given students’ optimal behavior and the trade-off between bias and measurement error. Although MCQ examinations are frequently used, there is no consensus as to whether a penalty for wrong answers should be used or not. For example, examinations for medical licensing in some countries include MCQ sections with penalty while in others there is no penalty for wrong answers. We contribute to this discussion with a formal analysis of the effects of penalties; our simulations indicate that the optimal penalty is relatively high for perfectly rational students but also when they are not fully rational: even though penalty discriminates against risk averse students, this effect is small compared with the measurement error that it prevents.     

The effects of control on perceived fairness of procedures and outcomes

On the basis of Thibaut and Walker's theory of procedural justice, it was predicted that subjects who experienced control through choosing a trial decision rule would be more satisfied with the outcome of a dispute and the conflict resolution procedure than would yoked subjects who were not given control. Two additional conditions were added to the design in order to investigate the extent to which control had an influence on trial evaluations independent of being allowed to discuss the rules and obtaining a preferred rule in the absence of actual choice. Regardless of role in the dispute (accuser, accused, or no knowledge) and the nature of the rule adopted, those subjects who exercised control through rule choice evaluated all aspects of the trial experience more positively than subjects who did not exercise control through rule choice. The results also revealed that discussion of the rules and obtaining the preferred rule without choice contributed positively to evaluations of the trial procedure and verdict.     

Searching for certainty in an uncertain world: the difficulty of giving up the experiential for the rational mode of thinking

Our research explores predictions that people make in a simple environment consisting of sequences of a binary signal followed by two possible outcomes. In order to optimize their prediction success, respondents should use a very simple decision rule, called maximization, whereby they consistently predict according to the signal. In line with past research, our findings show that even respondents who realized after the experiment that maximization is optimal failed to use it during the experiment itself. We discuss conditions that weaken or reinforce behaving according to the optimal rule in a repeated choice situation. Experiment 1 shows that individuals who are forced to plan their strategy and justify their actions are more likely to discover and use the optimal rule than those not forced to do so. Thinking about the appropriateness of one's performance can be done in two different orientations: focusing on the past (justifying past actions) or on the future (planning future action). Experiment 2 shows that planning induces rule‐base thinking, while justifying fails to do so. These findings are discussed within a theoretical framework which suggest an interplay between the experiential and the rational modes of processing. Copyright © 2003 John Wiley & Sons, Ltd.     

Orienting attention within visual fields: how efficient is interhemispheric transfer?

Five experiments are reported examining the effect of attentional orienting on lexical decisions within visual half-fields. In Experiment 1, following baseline performance, subjects were instructed to improve performance to the right or left of the fixation point. In Experiment 2, trials were run in blocks with all items to one side of the fixation point. In Experiment 3, completely valid position indicators as to the location of the next item to be shown were presented prior to the stimulus item. In Experiment 4, to examine practice effects, no instructions or cuing were given to subjects. In Experiment 5, subjects were urged to improve performance, but with no instructions as to location. As a summary of our results, it can be stated that (a) consistent visual field differences in lexical decision performance are present, even when subjects were informed, prior to viewing, of the spatial location of the next stimulus item. (b) Lexical decision information initially input to one cerebral hemisphere is primarily processed in that hemisphere. Interhemispheric transfer of this type of language information seems to be done primarily as the end product of a cognitive process.     

Suboptimal tradeoffs in information seeking

Explicit information-seeking actions are needed to evaluate alternative actions in problem-solving tasks. Information-seeking costs are often traded off against the utility of information. We present three experiments that show how subjects adapt to the cost and information structures of environments in a map-navigation task. We found that subjects often stabilize at suboptimal levels of performance. A Bayesian satisficing model (BSM) is proposed and implemented in the ACT-R architecture to predict information-seeking behavior. The BSM uses a local decision rule and a global Bayesian learning mechanism to decide when to stop seeking information. The model matched the human data well, suggesting that adaptation to cost and information structures can be achieved by a simple local decision rule. The local decision rule, however, often limits exploration of the environment and leads to suboptimal performance. We propose that suboptimal performance is an emergent property of the dynamic interactions between cognition and the environment.     

Misperceptions of feedback in dynamic decision making

In recent years laboratory experiments have shed significant light on the behavior of economic agents in a variety of microeconomic and decision-theoretic contexts such as auction markets, portfolio choice, and preference elicitation. Despite the success of experimental techniques in the micro domain, there has been relatively little work linking the behavior of decision makers to the dynamics of larger organizations such as corporations, industries, or the macroeconomy. This paper presents a laboratory experiment in which subjects manage a simulated economy. Subjects must invest sufficient capital plant and equipment to satisfy demand. Subjects were given complete and perfect information regarding the structure of the simulated economy, the values of all variables, and the past history of the system. Nevertheless, the overwhelming majority of the subjects generate significant and costly oscillations. A simple decision rule based on the anchoring and adjustment heuristic is shown to simulate the subjects' decisions quite well. Several distinct sources of the subjects' poor performance are identified and termed “misperceptions of feedback.” The decision rule is related to various models of economic fluctuations; implications for experimental investigation of dynamic decision making in aggregate systems are explored.     

Group Decision as a Technique for Obtaining Compliance: Some Added Considerations Concerning How Altruism Leads to Callousness

Bennett (1955) reported that a critical condition for producing Lewin's (1958) classic group discussion-decision effect (is., high compliance with a request from an authority figure) was a group consensus strongly favoring compliance. This finding prompted investigation of variables potentially affecting such a consensus. A first study found that group consensus is significantly less likely to favor compliance if subjects are allowed to make a majority rather than an individual decision. Group size had no significant effects on decision-making. A second study replicated this effect and investigated several explanations for it. These results suggest that, at least when compliance is not in the subjects' best interest, the Lewinian group discussion-decision effect will be less likely to be found if a majority decision role is followed as opposed to an individual decision rule. The implication of these data for social engineering is discussed.     

Decision factors affecting line orientation judgments in the method of single stimuli

Just noticeable differences in orientation are smaller at principal standard orientations than at oblique standard orientations when they are measured with the method of single stimuli. We determined whether this oblique effect is due to an anisotropy in decision factors. A first series of experiments showed that the subjects compare the stimulus with an internal criterion, and that this decision rule is used at all standard orientations. A second series of experiments determined the influence on the oblique effect of nonsensorial variables related to criterion setting. The results strongly suggest that the effect is not due to a criterial noise anisotropy and that criterion-setting processes are similar at principal and oblique standard orientations. The latter conclusion was also supported by an analysis of the sequential stimulus and response dependencies in this task. Hence, it appears that the oblique effect in line orientation discrimination, when it is measured with the method of single stimuli, is due not to decision factors but to a sensorially based anisotropy.     

Analysis of proposed three-class classification decision rules in terms of the ideal observer decision rule

We analyze recently proposed decision rules for three-class classification from the point of view of ideal observer decision theory. We consider three-class decision rules proposed by Scurfield, by Chan et al., and by Mossman. Scurfield's decision rule is shown to be a special case of the three-class ideal observer decision rule in three different situations. Chan et al. start with an ideal observer model and specify its decision-consequence utility structure in a way that causes two of the decision lines used by the ideal observer to overlap and the third line to become undefined. Finally, we show that, for a particular and obvious choice of ideal-observer-related decision variables, the Mossman decision rule cannot be a special case of the ideal observer decision rule. Despite the considerable difficulties presented by the three-class classification task, the three-class ideal observer provides a useful framework for analyzing a variety of three-class decision strategies.     

The dynamics of multimodal integration: The averaging diffusion model

We combine extant theories of evidence accumulation and multi-modal integration to develop an integrated framework for modeling multimodal integration as a process that unfolds in real time. Many studies have formulated sensory processing as a dynamic process where noisy samples of evidence are accumulated until a decision is made. However, these studies are often limited to a single sensory modality. Studies of multimodal stimulus integration have focused on how best to combine different sources of information to elicit a judgment. These studies are often limited to a single time point, typically after the integration process has occurred. We address these limitations by combining the two approaches. Experimentally, we present data that allow us to study the time course of evidence accumulation within each of the visual and auditory domains as well as in a bimodal condition. Theoretically, we develop a new Averaging Diffusion Model in which the decision variable is the mean rather than the sum of evidence samples and use it as a base for comparing three alternative models of multimodal integration, allowing us to assess the optimality of this integration. The outcome reveals rich individual differences in multimodal integration: while some subjects’ data are consistent with adaptive optimal integration, reweighting sources of evidence as their relative reliability changes during evidence integration, others exhibit patterns inconsistent with optimality.