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

Ruffner, John W.; Muchinsky, Paul M.

doi:10.1016/0030-5073(78)90049-1

Cited by 8 publications

(5 citation statements)

References 27 publications

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“…For simplicity, there are no time subscripts in the depiction of the classic lens model. Peterson, Hammond, and Summers (1965); Ruffner and Muchinsky (1978);and Summers (1969). These studies have shown that participants are generally responsive to changes in cue validity but do not optimally adapt their utilization to these changes.…”

Section: Multiple Cue Learningmentioning

confidence: 99%

“…Moreover, some types of change may be more difficult to learn than others. This latter effect has been more extensively investigated in subsequent studies, showing that adaptation to changes in cue validity depends on the particulars of the environment before the change, such as the previous validity of the changing cue (Dudycha et al, 1973;Lindberg & Brehmer, 1976) and the distribution of cue validities over the cues (Ruffner & Muchinsky, 1978).…”

Section: Multiple Cue Learningmentioning

confidence: 99%

“…Multiple cue learning research has almost exclusively been conducted with such stationary environments. Exceptions are studies by Dudycha, Dumoff, and Dudycha (1973), Lindberg and Brehmer (1976), Peterson, Hammond, and Summers (1965), Ruffner and Muchinsky (1978), and Summers (1969). These studies have shown that participants are generally re-sponsive to changes in cue validity but do not optimally adapt their utilization to these changes.…”

Section: Multiple Cue Learningmentioning

confidence: 99%

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Learning in a changing environment.

Speekenbrink¹,

Shanks²

2010

Journal of Experimental Psychology: General

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Multiple cue probability learning studies have typically focused on stationary environments. We present three experiments investigating learning in changing environments. A fine-grained analysis of the learning dynamics shows that participants were responsive to both abrupt and gradual changes in cue-outcome relations. We found no evidence that participants adapted to these types of change in qualitatively different ways. Also, in contrast to earlier claims that these tasks are learned implicitly, participants showed good insight into what they learned. By fitting formal learning models, we investigated whether participants learned global functional relationships or made localized predictions from similar experienced exemplars. Both a local (the Associative Learning Model) and a global learning model (the novel Bayesian Linear Filter) fitted the data of the first two experiments. However, the results of Experiment 3, which was specifically designed to discriminate between local and global learning models, provided more support for global learning models. Finally, we present a novel model to account for the cue competition effects found in previous research and displayed by some of our participants. Nothing endures but change Heraclitus (540 BC -480 BC).Predicting future events from past experience is a fundamental aspect of daily life. For instance, policy makers have to predict the outcome of interventions and sports coaches must

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Section: Multiple Cue Learningmentioning

confidence: 99%

Section: Multiple Cue Learningmentioning

confidence: 99%

Section: Multiple Cue Learningmentioning

confidence: 99%

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Learning in a changing environment.

Speekenbrink¹,

Shanks²

2010

Journal of Experimental Psychology: General

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“…This literature tends to look at tasks involving continuous linear relations between cue and target dimensions, whereas the categorization literature tends to look at tasks involving isolated clusters of correlated dimensions that correspond to separate categories. Within the cue probability learning literature, there has been work on the ability of participants to adjust to shifting cue validities (e.g., Ruffner & Muchinsky, 1978). Overall, this literature indicates that participants become less accurate after cue validity shifts but that they are relatively successful in redirecting their attention toward those cues that have become more predictive of the target dimension.…”

Section: Related Literaturesmentioning

confidence: 99%

Effect of memory decay on predictions from changing categories.

Elliott¹,

Anderson²

1995

Journal of Experimental Psychology: Learning, Memory, and Cogni

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In contrast to the static categories assumed in most categorization experiments, many real-world categories undergo gradual and systematic change in their definitions over time. Four experiments were carried out to study such category change. In these studies, participants successfully adjusted as category change occurred, but also showed a lingering and cumulative effect of past observations. The participants' performance was closely modeled by incorporating memory decay for past observations into J. R. Anderson's (1990, 1991) rational categorization algorithm and into a version of R. M. Nosofsky's (1986) exemplar categorization model. The resulting models suggest that the decay function is closer to a power law than to an exponential and that decay occurs both by item and by time, with the item decay being stronger than the time decay. The finding of power law decay gives additional support to claims that exemplar memories are used in categorization.

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“…Although some studies have investigated judgmental processes with unstable cue-criterion relations (see, e.g., Ruffner & Muchinsky, 1978), there has been little recognition of the ubiquity of such phenomena and how they are often a consequence of continuous processing. As a concrete example, imagine a sensitive interview where one would not want to pose the direct question of interest before acquiring a more complete appreciation of the situation.…”

Section: Cue-criterion Relationsmentioning

confidence: 99%

Beyond discrete biases: Functional and dysfunctional aspects of judgmental heuristics.

Hogarth

1981

Psychological Bulletin

587

279

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Although recent research has identified systematic dysfunctional consequences of judgmental heuristics, the conditions under which such heuristics can be valid have not been specified. In particular, whereas judgment is part of a continuous, interactive process that people use to cope with the environment, most judgment research has focused on discrete incidents. This has led to underestimating the importance of feedback in ongoing processes and the unquestioned acceptance of several assumptions implicit in the discrete, normative models used to evaluate judgmental performance. In the present paper, these issues are elaborated in considering the distinctions between discrete and continuous approaches to the study of judgment and choice. It is argued that several biases identified in discrete incidents result from heuristics that are functional in the more natural continuous environment. Thus, a discrete–continuous perspective is necessary in evaluating judgmental heuristics. Questions are raised concerning both the meaning and applicability of discrete, normative theories of judgment and choice. (136 ref)

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

Cited by 8 publications

References 27 publications

Learning in a changing environment.

Learning in a changing environment.

Effect of memory decay on predictions from changing categories.

Beyond discrete biases: Functional and dysfunctional aspects of judgmental heuristics.

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