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.     

Missing information in multiple-cue probability learning

In a multiple-cue probability learning task, participants learned to use six discrete symptoms (i.e., cues) to diagnose which of three possible flu strains a hypothetical patient suffered from. For some patients, information regarding the status of certain symptoms was not available. Various possible ways in which the missing cue information might be processed were distinguished and tested in a series of three experiments (Ns = 80, 109, and 61). The results suggest that the judged probability of the outcome variable (i.e., flu strain) was assessed by "filling in" the missing cue information with a mean value based on previous observations. The predictions of other methods of processing missing cue information are inconsistent with the data.     

Choice strategies in multiple-cue probability learning

Choice strategies for selecting among outcomes in multiple-cue probability learning were investigated using a simulated medical diagnosis task. Expected choice probabilities (the proportion of times each outcome was selected given each cue pattern) under alternative choice strategies were constructed from corresponding observed judged probabilities (of each outcome given each cue pattern) and compared with observed choice probabilities. Most of the participants were inferred to have responded by using a deterministic strategy, in which the outcome with the higher judged probability is consistently chosen, rather than a probabilistic strategy, in which an outcome is chosen with a probability equal to its judged probability. Extended practice in the learning environment did not affect choice strategy selection, contrary to reports from previous studies, results of which may instead be attributable to changes with practice in the variability and extremity of the perceived probabilities on which the choices were based.     

The effectiveness of feedback in multiple-cue probability learning

How effective are different types of feedback in helping us to learn multiple contingencies? This article attempts to resolve a paradox whereby, in comparison to simple outcome feedback, additional feedback either fails to enhance or is actually detrimental to performance in nonmetric multiple-cue probability learning (MCPL), while in contrast the majority of studies of metric MCPL reveal improvements at least with some forms of feedback. In three experiments we demonstrate that if feedback assists participants to infer cue polarity then it can in fact be effective in nonmetric MCPL. Participants appeared to use cue polarity information to adopt a linear judgement strategy, even though the environment was nonlinear. The results reconcile the paradoxical contrast between metric and nonmetric MCPL and support previous findings of people's tendency to assume linearity and additivity in probabilistic cue learning.     

The effectiveness of feedback in multiple-cue probability learning

How effective are different types of feedback in helping us to learn multiple contingencies? This article attempts to resolve a paradox whereby, in comparison to simple outcome feedback, additional feedback either fails to enhance or is actually detrimental to performance in nonmetric multiple-cue probability learning (MCPL), while in contrast the majority of studies of metric MCPL reveal improvements at least with some forms of feedback. In three experiments we demonstrate that if feedback assists participants to infer cue polarity then it can in fact be effective in nonmetric MCPL. Participants appeared to use cue polarity information to adopt a linear judgement strategy, even though the environment was nonlinear. The results reconcile the paradoxical contrast between metric and nonmetric MCPL and support previous findings of people's tendency to assume linearity and additivity in probabilistic cue learning.     

The role of working memory capacity in multiple-cue probability learning

Multiple-cue probability learning (MCPL) involves learning to predict a criterion when outcome feedback is provided for multiple cues. A great deal of research suggests that working memory capacity (WMC) is involved in a wide range of tasks that draw on higher level cognitive processes. In three experiments, we examined the role of WMC in MCPL by introducing measures of working memory capacity, as well as other task manipulations. While individual differences in WMC positively predicted performance in some kinds of multiple-cue tasks, performance on other tasks was entirely unrelated to these differences. Performance on tasks that contained negative cues was correlated with working memory capacity, as well as measures of explicit knowledge obtained in the learning process. When the relevant cues predicted positively, however, WMC became irrelevant. The results are discussed in terms of controlled and automatic processes in learning and judgement.     

The role of working memory capacity in multiple-cue probability learning

Multiple-cue probability learning (MCPL) involves learning to predict a criterion when outcome feedback is provided for multiple cues. A great deal of research suggests that working memory capacity (WMC) is involved in a wide range of tasks that draw on higher level cognitive processes. In three experiments, we examined the role of WMC in MCPL by introducing measures of working memory capacity, as well as other task manipulations. While individual differences in WMC positively predicted performance in some kinds of multiple-cue tasks, performance on other tasks was entirely unrelated to these differences. Performance on tasks that contained negative cues was correlated with working memory capacity, as well as measures of explicit knowledge obtained in the learning process. When the relevant cues predicted positively, however, WMC became irrelevant. The results are discussed in terms of controlled and automatic processes in learning and judgement.     

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.     

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.     

Insight and strategy in multiple-cue learning

In multiple-cue learning (also known as probabilistic category learning) people acquire information about cue-outcome relations and combine these into predictions or judgments. Previous researchers claimed that people can achieve high levels of performance without explicit knowledge of the task structure or insight into their own judgment policies. It has also been argued that people use a variety of suboptimal strategies to solve such tasks. In three experiments the authors reexamined these conclusions by introducing novel measures of task knowledge and self-insight and using "rolling regression" methods to analyze individual learning. Participants successfully learned a four-cue probabilistic environment and showed accurate knowledge of both the task structure and their own judgment processes. Learning analyses suggested that the apparent use of suboptimal strategies emerges from the incremental tracking of statistical contingencies in the environment.     

The influence of formal,substantive, and contextual task properties on the relative effectiveness of different forms of feedback in multiple-cue probability learning tasks

Previous research shows that when participants are given information that allows them to compare task properties with properties of their cognitive system (cognitive feedback) they reach higher levels of achievement in multiple-cue probability learning (MCPL) tasks than when they are shown the correct answer after each trial (outcome feedback). It was hypothesized that this finding was in part due to the use of neutral task content in nearly all previous research, for neutral content fails to provide information about task properties in the outcome feedback condition. A study was conducted to compare the level of achievement reached with cognitive and outcome feedback under three conditions which varied the congruence between task properties implied by task content and actual task properties. The study supported the above hypothesis. When task content provided no task information, the level of achievement with cognitive feedback was higher than that reached with outcome feedback, even with perfect task predictability. When the task information provided by task content was congruent with actual task properties, however, the level of achievement with outcome feedback was as high as that with cognitive feedback. And when the task information provided by task content was incongruent with actual task properties, the level of achievement with cognitive feedback was as low as that with outcome feedback. Two additional experiments suggest that the effectiveness of cognitive feedback depends on contextual task properties, such as the number of blocks of trials and the credibility of the feedback. The results indicate that the relative effectiveness of outcome and cognitive feedback depends on formal, substantive, and contextual task properties.     

On the role of causal intervention in multiple-cue judgment: positive and negative effects on learning

Previous studies have suggested better learning when people actively intervene rather than when they passively observe the stimuli in a judgment task. In 4 experiments, the authors investigated the hypothesis that this improvement is associated with a shift from exemplar memory to cue abstraction. In a multiple-cue judgment task with continuous cues, the data replicated the improvement with intervention and participants who experimented more actively produced more accurate judgments. In a multiple-cue judgment task with binary cues, intervention produced poorer accuracy and participants who experimented more actively produced poorer judgments. These results provide no support for a representational shift but suggest that the improvement with active intervention may be limited to certain tasks and environments.     

Retroactive interference in single-cue probability learning

Abstract.— When two single-cue probability learning tasks with different amounts of unpredictable variance were learned in succession. a low variance task interfered with the retention of a high variance task, but the opposite was not true. The effects were positive in that they enabled the subjects to reach a higher response consistency and, thus, a higher level of achievement. It was suggested that the subjects made use of the low variance task to reject incorrect hypotheses about the cue-criterion relation and that interference from an interpolated task occurs only when this task provides information which has functional value for the subject.     

Simulating second-guessing in on-line probability learning

Formal processes (especially metacognitive or control processes) involved in complex human problem solving are examined in a game-playing situation with one person pitted against a microcomputer algorithm. In particular, the strategy of second-guessing is simulated in real-time in an attempt to outwit the person in a binary prediction paradigm. Aspects of success and failure are suggested. A working implementation in BASIC for a TRS-80 Level II is provided.