Order effects in contingency learning: The role of task complexity

Marsh, Jessecae K.; Ahn, Woo-Kyoting

doi:10.3758/bf03193580

Cited by 32 publications

(52 citation statements)

References 19 publications

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“…The fact that RTs were not significantly modulated by the statistical properties of the sequence (i.e., the effect found in Exps. 1 and 2) is consistent with the idea that learning about multiple medications simultaneously increased the complexity of the learning task (Marsh & Ahn, 2006) and reduced the magnitude of the RT effects observed in Experiments 1 and 2. Moreover, accuracies to the tones on majority (M = .91, SD = .09) and minority (M = .92, SD = .10) trials were not different.…”

Section: Resultssupporting

confidence: 77%

“…Though this technique clearly represents a strong initial test of learning models, it has failed to generate direct evidence regarding the underlying processes that may be unfolding as learners encounter individual pieces of information. Researchers have claimed that specific variants of the input-output paradigm are able to demonstrate the existence of trial-by-trial processing (e.g., Chapman & Chapman, 1969;Dennis & Ahn, 2001;Katagiri, Kao, Simon, Castro, & Wasserman, 2007;Lopez, Shanks, Almaraz, & Fernandez, 1998;Luhmann & Ahn, 2011;Yates & Curley, 1986); however, subsequent work has demonstrated this not to be the case (e.g., Danks & Schwartz, 2006;Marsh & Ahn, 2006;Stout, Amundson, & Miller, 2005). Regardless, the very nature of the standard input-output paradigm clearly places an overwhelming emphasis on accurately predicting the transformation from covariation to judgment, with far less regard for the psychological mechanisms by which such a transformation comes about.…”

Section: The Need For Direct Evidencementioning

confidence: 99%

“…To remedy this, in Experiment 3 we required learners to simultaneously learn about three different causes (i.e., within the same trial sequence). Prior research has shown that simultaneously learning about more than one cause tends to increase cognitive load (Marsh & Ahn, 2006), which suggests that this modification should slow the rate at which participants learn the contingencies, and thus reduce the likelihood that causal judgments would be near ceiling.…”

Section: Methodsmentioning

confidence: 99%

“…For example, the Rescorla-Wagner model exhibits recency effects, in which recent covariation information exerts a disproportionate influence on associative strengths. However, research showed that order effects could be driven by memory-related processes (Stout et al, 2005), by changes in learning rates (Danks & Schwartz, 2005), or by the availability of working memory resources (Marsh & Ahn, 2006). Another approach asked learners to make trial-by-trial contingency judgments throughout the learning sequence (for a review, see Hogarth & Einhorn, 1992).…”

Section: Converging Measures Of Learningmentioning

confidence: 99%

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Evidence for online processing during causal learning

Luhmann

2014

Learn Behav

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Many models of learning describe both the end product of learning (e.g., causal judgments) and the cognitive mechanisms that unfold on a trial-by-trial basis. However, the methods employed in the literature typically provide only indirect evidence about the unfolding cognitive processes. Here, we utilized a simultaneous secondary task to measure cognitive processing during a straightforward causal-learning task. The results from three experiments demonstrated that covariation information is not subject to uniform cognitive processing. Instead, we observed systematic variation in the processing dedicated to individual pieces of covariation information. In particular, observations that are inconsistent with previously presented covariation information appear to elicit greater cognitive processing than do observations that are consistent with previously presented covariation information. In addition, the degree of cognitive processing appears to be driven by learning per se, rather than by nonlearning processes such as memory and attention. Overall, these findings suggest that monitoring learning processes at a finer level may provide useful psychological insights into the nature of learning. Keywords Causal learning . Human learningWhen forming beliefs about causal relationships, people frequently rely on information about whether the potential cause tends to occur with its potential effect. For example, imagine a person becoming sick each time she eats a certain kind of nut, but not after eating other sorts of food. Eventually, she will likely believe that the nuts cause her sickness and avoid eating them in the future. Prevailing theories of causal learning (Busemeyer, 1991;Cheng, 1997;Einhorn & Hogarth, 1986;Jenkins & Ward, 1965;Rescorla & Wagner, 1972;Schustack & Sternberg, 1981;White, 2002) have formalized such evidence using a covariation matrix such as that shown in Fig. 1. Assuming that events can be either present or absent, there are four types of observations, each represented by a different cell in the covariation matrix. For example, cell A of the matrix represents the frequency with which the cause and the effect are both present. Evidence from cells A and D (positive covariation) tends to suggest that the cause generates the outcome, whereas evidence from cells B and C (negative covariation) tends to suggest that the cause prevents the outcome (see Luhmann & Ahn, 2011, for systematic exceptions).A variety of learning theories, both associative and nonassociative, describe the processes that operate to transform sequences of covariation information into causal judgments (Danks, Griffiths, & Tenenbaum, 2003;Hogarth & Einhorn, 1992;Lu, Rojas, Beckers, & Yuille, 2008;Luhmann & Ahn, 2007;Mackintosh, 1975;Pearce, 1994;Pearce & Hall, 1980;Rescorla & Wagner, 1972;Wagner, 1981). These models specify what causal judgments should be made on the basis of any given set of covariation information, and provide details of the constituent processes that transform individual pieces of covariation information...

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Section: Resultssupporting

confidence: 77%

Section: The Need For Direct Evidencementioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Converging Measures Of Learningmentioning

confidence: 99%

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Evidence for online processing during causal learning

Luhmann

2014

Learn Behav

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“…The order effects found in previous studies (Dennis & Ahn, 2001; López et al, 1998; Marsh & Ahn, 2006) could have been generated by noninterpretational mechanisms (Danks & Schwartz, 2005), such as increased or decreased attention (e.g., fatigue, boredom, or context change; Anderson & Hubert, 1963; Hendrick & Costantini, 1970; Stewart, 1965), or by discounting of later information as being less reliable or valid than earlier information (Anderson & Jacobson, 1965). …”

Section: Order Effectsmentioning

confidence: 84%

Expectations and interpretations during causal learning.

Luhmann¹,

Ahn²

2011

Journal of Experimental Psychology: Learning, Memory, and Cogni

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In existing models of causal induction, 4 types of covariation information (i.e., presence/absence of an event followed by presence/absence of another event) always exert identical influences on causal strength judgments (e.g., joint presence of events always suggests a generative causal relationship). In contrast, we suggest that, due to expectations developed during causal learning, learners give varied interpretations to covariation information as it is encountered and that these interpretations influence the resulting causal beliefs. In Experiments 1A–1C, participants’ interpretations of observations during a causal learning task were dynamic, expectation based, and, furthermore, strongly tied to subsequent causal judgments. Experiment 2 demonstrated that adding trials of joint absence or joint presence of events, whose roles have been traditionally interpreted as increasing causal strengths, could result in decreased overall causal judgments and that adding trials where one event occurs in the absence of another, whose roles have been traditionally interpreted as decreasing causal strengths, could result in increased overall causal judgments. We discuss implications for traditional models of causal learning and how a more top-down approach (e.g., Bayesian) would be more compatible with the current findings.

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Memory for Patient Information as a Function of Experience in Mental Health

Marsh

Ahn

2012

Applied Cognitive Psychology

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Summary Mental health clinicians are tasked to diagnose and treat the millions of people worldwide seeking help for mental health issues. This paper investigates the memory clinicians have for patient information. We hypothesize that clinicians encapsulate mental health knowledge through experience into more abstract concepts, as in other domains changing what clinicians remember about patients compared with non-professionals. We tested memory for realistic patient–therapist interactions in experienced clinicians, intermediately trained graduate students, and laypeople. Clinicians recalled fewer facts than intermediate trainees and as many as laypeople. Furthermore, clinicians reported more abstracted information than all other participants, providing the first empirical demonstration of knowledge encapsulation in the memory of mental health clinicians. We discuss how our results fit into the existing literature on clinical expertise in mental health and the implications of our findings for future research relevant to mental health care.

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Order effects in contingency learning: The role of task complexity

Cited by 32 publications

References 19 publications

Evidence for online processing during causal learning

Evidence for online processing during causal learning

Expectations and interpretations during causal learning.

Memory for Patient Information as a Function of Experience in Mental Health

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