scholarly journals Counterfactual Thinking and Recency Effects in Causal Judgment

2020 ◽  
Author(s):  
Paul Henne ◽  
Aleksandra Kulesza ◽  
Karla Perez ◽  
Augustana Houcek
Keyword(s):  
Causal Structure ◽  
Counterfactual Thinking ◽  
Recency Effect ◽  
Causal Modeling ◽  
Primacy Effect ◽  
Causal Judgment ◽  
Extended Model ◽  
Recent Event ◽  
Basketball Game ◽  
Counterfactual Model

People tend to judge more recent events, relative to earlier ones, as the cause of some particular outcome. For instance, people are more inclined to judge that the last basket, rather than the first, caused the team to win the basketball game. This recency effect, however, reverses in cases of overdetermination: people judge that earlier events, rather than more recent ones, caused the outcome when the event is individually sufficient but not individually necessary for the outcome. In five experiments (N = 5507), we find evidence for the recency effect and the primacy effect for causal judgment. Traditionally, these effects have been a problem for counterfactual views of causal judgment. However, an extension of a recent counterfactual model of causal judgment explains both the recency and the primacy effect. In line with the predictions of the extended counterfactual model, we also find that, regardless of causal structure, people tend to imagine the counterfactual alternative to the more recent event rather than to the earlier one (Experiment 2). Moreover, manipulating this tendency affects causal judgments in the ways predicted by this extended model: asking participants to imagine the counterfactual alternative to the earlier event weakens (and sometimes eliminates) the interaction between recency and causal structure, and asking participants to imagine the counterfactual alternative to the more recent event strengthens the interaction between recency and causal structure (Experiments 3 & 5). We discuss these results in relation to work on counterfactual thinking and causal modeling.

Serial Position and Growth Curves in Demand Concept Recall

1970 ◽  
Vol 30 (1) ◽  
pp. 139-142
Author(s):  
Charles J. Gadway
Keyword(s):  
Problem Solving ◽  
Serial Position ◽  
Serial Position Effect ◽  
Position Effect ◽  
Growth Curves ◽  
Complex Problem ◽  
Recency Effect ◽  
Primacy Effect ◽  
Complex Problem Solving

Since the serial-position effect has been demonstrated to diminish as learning progresses, it was hypothesized that only a weak recency serial-position effect would result from demand recall of concepts in a complex problem-solving situation. Six groups of 14 Ss solved 15 problems, 5 soluble by each of 3 concepts (rules), recalled from prior instruction. Each group received a different permutation of the 3 concepts. The serial-position effect appeared to be minimal for demand recall of concepts with the predicted weak recency effect (.01 < p < .05) but no primacy effect.

Primacy and Recency Effects in Immediate Free Recall of Sequences of Spatial Positions

2007 ◽  
Vol 105 (2) ◽  
pp. 483-500 ◽  
Author(s):  
Rita Bonanni ◽  
Patrizio Pasqualetti ◽  
Carlo Caltagirone ◽  
Giovanni Augusto Carlesimo
Keyword(s):  
Free Recall ◽  
Serial Position ◽  
Serial Position Curve ◽  
Presentation Rate ◽  
Recency Effect ◽  
Primacy Effect ◽  
Spatial Position ◽  
Recency Effects ◽  
Task Requirement ◽  
Recall Paradigm

This study evaluated the serial position curve based on free recall of spatial position sequences. To evaluate the memory processes underlying spatial recall, some manipulations were introduced by varying the length of spatial sequences (Exp. 1) and modifying the presentation rate of individual positions (Exp. 2). A primacy effect emerged for all sequence lengths, while a recency effect was evident only in the longer sequences. Moreover, slowing the presentation rate increased the magnitude of the primacy effect and abolished the recency effect. The main novelty of the present results is represented by the finding that better recall of early items in a sequence of spatial positions does not depend on the task requirement of an ordered recall but it can also be observed in a free recall paradigm.

scholarly journals Causal Analysis for Performance Modeling of Computer Programs

2007 ◽  
Vol 15 (3) ◽  
pp. 121-136 ◽  
Author(s):  
Jan Lemeire ◽  
Erik Dirkx ◽  
Frederik Verbist
Keyword(s):  
Experimental Data ◽  
Performance Modeling ◽  
Structure Learning ◽  
Causal Structure ◽  
Learning Algorithms ◽  
Statistical Investigation ◽  
Causal Modeling ◽  
Jpeg 2000 ◽  
Information Theoretic ◽  
Qualitative Models

Causal modeling and the accompanying learning algorithms provide useful extensions for in-depth statistical investigation and automation of performance modeling. We enlarged the scope of existing causal structure learning algorithms by using the form-free information-theoretic concept of mutual information and by introducing the complexity criterion for selecting direct relations among equivalent relations. The underlying probability distribution of experimental data is estimated by kernel density estimation. We then reported on the benefits of a dependency analysis and the decompositional capacities of causal models. Useful qualitative models, providing insight into the role of every performance factor, were inferred from experimental data. This paper reports on the results for a LU decomposition algorithm and on the study of the parameter sensitivity of the Kakadu implementation of the JPEG-2000 standard. Next, the analysis was used to search for generic performance characteristics of the applications.

scholarly journals Counterfactual thinking and recency effects in causal judgment

Cognition ◽  
2021 ◽  
Vol 212 ◽  
pp. 104708
Author(s):  
Paul Henne ◽  
Aleksandra Kulesza ◽  
Karla Perez ◽  
Augustana Houcek
Keyword(s):  
Counterfactual Thinking ◽  
Causal Judgment ◽  
Recency Effects

Subject Awareness and Order Effects in Persuasive Communications

1963 ◽  
Vol 12 (2) ◽  
pp. 523-529 ◽  
Author(s):  
Robert E. Lana ◽  
Ralph L. Rosnow
Keyword(s):  
College Students ◽  
Recency Effect ◽  
Primacy Effect ◽  
Order Effects ◽  
Opinion Change ◽  
Persuasive Communications ◽  
Controversial Topic ◽  
Secondary Hypothesis ◽  
Primary Hypothesis

This study was performed with 128 college students acting as Ss. The primary hypothesis that Ss confronted with a hidden pretest in an opinion change study will yield a significant recency effect, and Ss confronted with an exposed pretest will yield a significant primacy effect, was rejected. A primacy effect is in evidence when the pretest is hidden, and no directional effects are present when the pretest is exposed. The secondary hypothesis that a group exposed to a highly controversial topic will yield a significant primacy effect, and a group exposed to a topic of medium controversy will yield a recency effect, or no effect at all, was also rejected.

scholarly journals Inference from explanation

2020 ◽  
Author(s):  
Lara Kirfel ◽  
Thomas Icard ◽  
Tobias Gerstenberg
Keyword(s):  
Causal Relationship ◽  
Causal Explanation ◽  
Causal Structure ◽  
Causal Judgment ◽  
Basic Information ◽  
Causal Explanations ◽  
Communicative Function ◽  
Series Of Experiments

What do we communicate with causal explanations? Upon being told, "E because C", one might learn that C and E both occurred, and perhaps that there is a causal relationship between C and E. In fact, causal explanations systematically disclose much more than this basic information. Here, we offer a communication-theoretic account of explanation that makes specific predictions about the kinds of inferences people draw from others' explanations. We test these predictions in a case study involving the role of norms and causal structure. In Experiment 1, we demonstrate that people infer the normality of a cause from an explanation when they know the underlying causal structure. In Experiment 2, we show that people infer the causal structure from an explanation if they know the normality of the cited cause. We find these patterns both for scenarios that manipulate the statistical and prescriptive normality of events. Finally, we consider how the communicative function of explanations, as highlighted in this series of experiments, may help to elucidate the distinctive roles that normality and causal structure play in causal judgment, paving the way toward a more comprehensive account of causal explanation.

Computer Presentation of Pattern-of-Success: A Primacy Effect Verified

1987 ◽  
Vol 65 (2) ◽  
pp. 379-387 ◽  
Author(s):  
Owen Pratz
Keyword(s):  
Motor Coordination ◽  
Recency Effect ◽  
Original Study ◽  
Primacy Effect ◽  
Pattern Perception ◽  
Visual Motor Coordination ◽  
Visual Motor

Several studies have examined the effect of pattern of success on perceived level of success using a serial-trial task. In the original study of this issue, subjects were influenced more by their level of success at the end of a series of trials, a recency effect. Subsequent studies have found, instead, a primacy effect. This study replicates the original study in which a recency effect was found and assesses the generalizability of the results with other tasks. Three forms of task were used: (a) analogies items, (b) tachistoscopic pattern perception, and (c) a visual-motor coordination task. The experimental procedures were programmed in BASIC for the Commodore 64 to facilitate further extension and replication. Computer presentation was both efficient and effective. A primacy effect was found for all three tasks.

scholarly journals The Discovery of Long-Run Causal Order: A Preliminary Investigation

Econometrics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 31
Author(s):  
Kevin D. Hoover
Keyword(s):  
Time Series ◽  
Causal Structure ◽  
Preliminary Investigation ◽  
Causal Modeling ◽  
Causal Order ◽  
Long Run ◽  
Weak Exogeneity

The relation between causal structure and cointegration and long-run weak exogeneity is explored using some ideas drawn from the literature on graphical causal modeling. It is assumed that the fundamental source of trending behavior is transmitted from exogenous (and typically latent) trending variables to a set of causally ordered variables that would not themselves display nonstationary behavior if the nonstationary exogenous causes were absent. The possibility of inferring the long-run causal structure among a set of time-series variables from an exhaustive examination of weak exogeneity in irreducibly cointegrated subsets of variables is explored and illustrated.

scholarly journals Explaining and predicting human behavior and social dynamics in simulated virtual worlds: reproducibility, generalizability, and robustness of causal discovery methods

2021 ◽  
Author(s):  
Svitlana Volkova ◽  
Dustin Arendt ◽  
Emily Saldanha ◽  
Maria Glenski ◽  
Ellyn Ayton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Behavior ◽  
Virtual Worlds ◽  
State Of The Art ◽  
Causal Structure ◽  
Ground Truth ◽  
The State ◽  
Causal Modeling ◽  
Causal Discovery ◽  
Learning Data

AbstractGround Truth program was designed to evaluate social science modeling approaches using simulation test beds with ground truth intentionally and systematically embedded to understand and model complex Human Domain systems and their dynamics Lazer et al. (Science 369:1060–1062, 2020). Our multidisciplinary team of data scientists, statisticians, experts in Artificial Intelligence (AI) and visual analytics had a unique role on the program to investigate accuracy, reproducibility, generalizability, and robustness of the state-of-the-art (SOTA) causal structure learning approaches applied to fully observed and sampled simulated data across virtual worlds. In addition, we analyzed the feasibility of using machine learning models to predict future social behavior with and without causal knowledge explicitly embedded. In this paper, we first present our causal modeling approach to discover the causal structure of four virtual worlds produced by the simulation teams—Urban Life, Financial Governance, Disaster and Geopolitical Conflict. Our approach adapts the state-of-the-art causal discovery (including ensemble models), machine learning, data analytics, and visualization techniques to allow a human-machine team to reverse-engineer the true causal relations from sampled and fully observed data. We next present our reproducibility analysis of two research methods team’s performance using a range of causal discovery models applied to both sampled and fully observed data, and analyze their effectiveness and limitations. We further investigate the generalizability and robustness to sampling of the SOTA causal discovery approaches on additional simulated datasets with known ground truth. Our results reveal the limitations of existing causal modeling approaches when applied to large-scale, noisy, high-dimensional data with unobserved variables and unknown relationships between them. We show that the SOTA causal models explored in our experiments are not designed to take advantage from vasts amounts of data and have difficulty recovering ground truth when latent confounders are present; they do not generalize well across simulation scenarios and are not robust to sampling; they are vulnerable to data and modeling assumptions, and therefore, the results are hard to reproduce. Finally, when we outline lessons learned and provide recommendations to improve models for causal discovery and prediction of human social behavior from observational data, we highlight the importance of learning data to knowledge representations or transformations to improve causal discovery and describe the benefit of causal feature selection for predictive and prescriptive modeling.

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