The effects of time pressure on judgment in multiple cue probability learning

This experiment used multiple-cue probability learning to study the effects of time pressure on judgment. Undergraduate students were trained to use cues with linear or curvilinear function forms to predict a criterion. Subsequent to training, performance under time pressure was compared with selfregulated performance. Lens model analyses indicated that cognitive control deteriorated under time pressure while cognitive matching remained unchanged. This effect was limited to complex cue-criterion environments containing curvilinear function forms. The results suggest that the time pressured individual tends to be erratic even while implementing correct policy.     

The influence of shifting cue validity distributions and group discussion feedback on multiple cue probability learning

The effects of group discussion feedback and shifting cue validities on subjects' achievement, consistency, and matching in a multiple cue probability learning task were investigated in a task of constant system predictability. Students utilized distributions of meaningfully labeled cues of equal or unequal validity to predict final exam performance. Subjects' performance was examined in two conditions in which a shift in cue validities occurred and in two conditions in which the same validities were employed throughout the task. Feedback from group discussion was provided for half the subjects. Results indicated that subject performance was best when equally valid cues were utilized, especially after an anticipated shift in cue validities. Group discussion feedback facilitated performance only when a shift occurred. The results were discussed in the context of dynamic prediction environments.     

Positive and negative cue redundancy in multiple cue probability learning

In two separate experiments, 80 introductory psychology students completed a two-cue probability learning task in which cue intercorrelation was positive or negative and cue validities were positive or negative. Results indicated that Ss did not learn the negative validity tasks. In addition, there were no significant differences between groups in achievement due to cue intercorrelation within any one experiment. However, the positive redundancy groups were significantly more consistent in the employment of their decision strategy than the negative redundancy groups when cue validities were positive. The negative redundancy groups were superior to positive redundancy groups in matching; again, when cue validities were positive.     

The influence of hierarchy on probability judgment

Consider the task of predicting which soccer team will win the next World Cup. The bookmakers may judge Brazil to be the team most likely to win, but also judge it most likely that a European rather than a Latin American team will win. This is an example of a non-aligned hierarchy structure: the most probable event at the subordinate level (Brazil wins) appears to be inconsistent with the most probable event at the superordinate level (a European team wins). In this paper we exploit such structures to investigate how people make predictions based on uncertain hierarchical knowledge. We distinguish between aligned and non-aligned environments, and conjecture that people assume alignment. Participants were exposed to a non-aligned training set in which the most probable superordinate category predicted one outcome, whereas the most probable subordinate category predicted a different outcome. In the test phase participants allowed their initial probability judgments about category membership to shift their final ratings of the probability of the outcome, even though all judgments were made on the basis of the same statistical data. In effect people were primed to focus on the most likely path in an inference tree, and neglect alternative paths. These results highlight the importance of the level at which statistical data are represented, and suggest that when faced with hierarchical inference problems people adopt a simplifying heuristic that assumes alignment.     

The role of variable labels in cue probability learning tasks

Two experiments were conducted to investigate effects of variable labels on cue probability learning performance. Variable labels were manipulated to produce conditions in which the actual statistical structure was congruent or incongruent with that implied by the labels. In Experiment 1 the relevant aspect of statistical structure was the relative magnitude of validity of two cues. In Experiment 2, it was the direction of the slope relating the single cue to the criterion. In terms of task knowledge, congruent labels were better than either incongruent or abstract labels in both tasks, suggesting that congruent labels provide useful information about statistical structure. Prediction consistency with congruent labels was higher than with abstract in both experiments, and marginally better than with incongruent labels in Experiment 2. Compared to abstract labels, incongruent labels led to higher consistency in the two-cue task. In the one-cue task, both incongruent labels and neutral labels (which have content but do not imply any particular statistical structure) increased task knowledge. A proposed Rule Selection and Formation model attributes the advantages of variable labels to better retrieval, a reduced set of strategies, and modification of interpretations of labels.     

The experimental task influences cue competition in human causal learning

Participants were shown A+ and C- trials followed by AB+ and CD+ trials. These trials were embedded in a causal learning task in which participants had to learn either the relationship between different foods and allergic reactions or the relationship between different stocks and an increase in the stock market index. The authors orthogonally varied the manner in which the different cues were presented to participants during training. Cue competition was related to the causal learning scenario but not to the manner in which the different cues were presented. These results question claims of a human bias toward configural processing that were based on difficulties in finding cue competition in some previous causal learning experiments.     

Learning conditional frequencies in a probability learning task

The study was prompted by a theoretical discussion of probability learning by Estes (1976). In three separate experiments, subjects were presented with frequency information in the form of wins and losses among 3 teams, and later predicted future wins and losses. Frequencies were devised so that conditional win frequencies for a pair of teams were either inconsistent or consistent with marginal win frequencies for each team. In experiment 1, when subjects predicted future events on the basis of known past frequencies, predictions were generally based on conditional frequencies. In experiment 2 six blocks of observations were presented, with predictions following each block. What little learning did occur was in the direction of the conditional frequencies. Subjects in experiment 3 were able to learn conditional frequencies when given explicit instructions to do so. Results were discussed in terms of a two-stage hypothesis generation model that might operate within the framework of an associative theory of probability learning.     

Note on detection of cue intercorrelation in multiple-cue probability learning

Abstract.— After completion of learning tasks with two different levels of cue intercorrelation (r _u=0.00 and 0.80) the detection of cue intercorrelation was tested with either a reproduction or a recognition method in a 2×2 factorial design. The reproduction method yielded an almost perfect matching between the reproduced intercorrelations and the intercorrelations of the tasks, while the recognition method resulted in systematically lower subjective intercorrelations. For the recognition method the subjective intercorrelation was found to vary with the criterion of recognition, so that the stricter the criterion, the higher the subjective intercorrelation.     

Information integration in multiple cue judgment: a division of labor hypothesis

There is considerable evidence that judgment is constrained to additive integration of information. The authors propose an explanation of why serial and additive cognitive integration can produce accurate multiple cue judgment both in additive and non-additive environments in terms of an adaptive division of labor between multiple representations. It is hypothesized that, whereas the additive, independent linear effect of each cue can be explicitly abstracted and integrated by a serial, additive judgment process, a variety of sophisticated task properties, like non-additive cue combination, non-linear relations, and inter-cue correlation, are carried implicitly by exemplar memory. Three experiments investigating the effect of additive versus non-additive cue combination verify the predicted shift in cognitive representations as a function of the underlying combination rule.     

A note on cue probability learning: what conditioning data reveal about cue contrast

In a probabilistic learning task with two complementary cues (C₁ and C₂) and two events (E₁ and E₂) the subject has a tendency to infer that E₂ will occur when C₂ occurs after he/she has observed that E₁ frequently goes with C₁. This phenomenon was termed cue contrast because it reflects the belief that C₂ contrasts with C₁ and should indicate E₂. Two kinds of predictions following from the notion of cue contrast were tested on response proportions from the first block of trials in four previously published experiments on cue probability learning. Of a total of 15 independent observations (eight within-group comparisons; seven between-group comparisons) 13 confirmed the predictions (sign test, p<0.005). It was concluded that cue utilization is both an inductive process based on association between cues and events and an inferential process based on contrast.     

An investigation of the role of some person and situation variables in multiple cue probability learning

Making decisions using judgements of multiple non-deterministic indicators is an important task, both in everyday and professional life. Learning of such decision making has often been studied as the mapping of stimuli (cues) to an environmental variable (criterion); however, little attention has been paid to the effects of situation-by-person interactions on this learning. Accordingly, we manipulated cue and feedback presentation mode (graphic or numeric) and task difficulty, and measured individual differences in working memory capacity (WMC). We predicted that graphic presentation, fewer cues, and elevated WMC would facilitate learning, and that person and task characteristics would interact such that presentation mode compatible with the decision maker's cognitive capability (enhanced visual or verbal WMC) would assist learning, particularly for more difficult tasks. We found our predicted main effects, but no significant interactions, except that those with greater WMC benefited to a larger extent with graphic than with numeric presentation, regardless of which type of working memory was enhanced or number of cues. Our findings suggest that the conclusions of past research based predominantly on tasks using numeric presentation need to be reevaluated and cast light on how working memory helps us learn multiple cue–criterion relationships, with implications for dual-process theories of cognition.     

Adaptive changes between cue abstraction and exemplar memory in a multiple-cue judgment task with continuous cues

The majority of previous studies on multiple-cue judgment with continuous cues have involved comparisons between judgments and multiple linear regression models that integrated cues into a judgment. The authors present an experiment indicating that in a judgment task withadditive combination of multiple continuous cues, people indeed displayed abstract knowledge of the cue criterion relations that was mentally integrated into a judgment, but in a task withmultiplicative combination of continuous cues, people instead relied on retrieval of memory traces of similar judgment cases (exemplars). These results suggest that people may adopt qualitatively distinct forms of knowledge, depending on the structure of a multiple-cue judgment task. The authors discuss implications for theories of multiple-cue judgment.     

Event-related brain potentials and subjective probability in a learning task

In a paired associate learning task, subjects responded to each presentation of a nonsense syllable by typing both a three-letter associate and a rating of their confidence that this response was correct or incorrect. Average event-related potentials (ERPs) elicited by the subsequent presentation of the actual paired syllable varied with the interaction of confidence and trial outcome. A larger amplitude P300 was elicited by syllables that informed subjects that they were correct when they thought they were incorrect or that they were incorrect when they thought they were correct than by syllables that confirmed subjects expectations. That this average ERP result was indeed an effect on P300 amplitude, and not an artifact of single-trial variability in P30O latency, was confirmed with a trial-by-trial latency adjustment procedure. Consistent with findings from other tasks, P300 amplitude varied inversely with the subj ective probability of the ERP-eliciting events.     

The development of a general associative learning account of skill acquisition in a relative arrival-time judgment task

Current theory assumes that individuals only use information from the immediate environment to perform relative arrival-time judgment tasks. This article presents a theoretical analysis of the memory requirements of this task. The authors present an analysis of the inputs to the memory system and the processes that map those inputs onto outputs. The analysis generates a set of predictions regarding the specificity of transfer and the role of context during learning. In 3 experiments, participants decided whether pairs of aircraft would violate a minimum separation standard or pass each other safely. Participants were presented with pairs of aircraft in which properties of the pair varied along 3 structural and 3 surface dimensions. Contexts were defined by the co-occurrence of specific values along stimulus dimensions and the use of a neutral label. The results suggest that transfer was limited by the dimensions that were varied in training and the context in which those dimensions were varied. The discussion focuses on the problems that complex tasks like relative judgment pose for associative learning mechanisms and the development of precise models of cognition.     

The effects of cognitive feedback and task complexity in multiple-cue probability learning

Subjects learned three multiple-cue probability learning (MCPL) tasks of varying complexity under conditions of feedforward and cognitive feedback. Statistical information about the task was presented in the feedforward condition; in the cognitive feedback condition, subjects were given statistical information about their own performance in addition to information about the task. Task complexity was manipulated by varying the function forms relating the values of the cues to the values of the criterion: The least complex task contained three positive linear function forms, the intermediate task contained three inverted U-shaped function forms, and there was one negative linear function form, one inverted U-shaped function form, and one U-shaped function form in the most complex task. The hypothesis that cognitive feedback is more effective than feedforward for the more complex task used here was not confirmed: There were no differences between the two groups with regard to r_a (achievement), G (knowledge), or R_s,² (cognitive control). The level of performance on all tasks, including the complex nonlinear tasks, was very high under both conditions, however, an unexpected and important finding.     

An hypothesis generation model for judgment in nonmetric multiple-cue probability learning

The purpose of this paper is to present a two-phase hypothesis generation model to describe behavior in multiple-cue probability learning tasks with nonmetric cues. The model assumes that on each trial the subject generates two sets of hypotheses: (a) a hypothesis concerning which cue dimension (or pattern) will lead to a correct prediction on that trial and (b) a hypothesis concerning which response will be correct given the cue dimension attended to on that trial.Five-hundred-twelve subjects were assigned to 20 groups in a binary choice task involving two binary cue dimensions. Each group observed cues which differed in validity. Analysis of the data indicated that subjects attend to both cue dimensions in making judgments even when one cue has zero validity. A test of the fit of the observed data to the asymptotic response proportions predicted by the model indicated a reasonable fit.     

Explicit cue weighting in a prediction task

Two experiments were conducted to examine the strategies people use to determine the cue weights in a two-cue prediction task. In order to examine the subjects' strategies, half of the subjects in Experiment 1 were asked to state the weights explicitly for the two cues on each trial rather than directly estimating the criterion value. The subjects' estimates of the criterion were then calculated on the basis of the estimated weights. The results of Experiment 1 suggested that most subjects use a hypothesis-testing approach to determine cue weights. In addition, it appeared that most of the subjects randomly tested hypotheses about the relative importance of the cues, which did not prove to be an effective strategy. The results of Experiment 2 indicated that failure to learn the relative importance of the cues was not due to failure to test the appropriate cue weights at some point during the task, but to failure to test the hypothesized cue weights on enough trials to evaluate their adequacy sufficiently.