# Cognitive Psychology

• Modeling 2-alternative forced-choice tasks: Accounting for both magnitude and difference effects
Publication date: June 2018
Source:Cognitive Psychology, Volume 103

Author(s): Roger Ratcliff, Chelsea Voskuilen, Andrei Teodorescu

We present a model-based analysis of two-alternative forced-choice tasks in which two stimuli are presented side by side and subjects must make a comparative judgment (e.g., which stimulus is brighter). Stimuli can vary on two dimensions, the difference in strength of the two stimuli and the magnitude of each stimulus. Differences between the two stimuli produce typical RT and accuracy effects (i.e., subjects respond more quickly and more accurately when there is a larger difference between the two). However, the overall magnitude of the pair of stimuli also affects RT and accuracy. In the more common two-choice task, a single stimulus is presented and the stimulus varies on only one dimension. In this two-stimulus task, if the standard diffusion decision model is fit to the data with only drift rate (evidence accumulation rate) differing among conditions, the model cannot fit the data. However, if either of one of two variability parameters is allowed to change with stimulus magnitude, the model can fit the data. This results in two models that are extremely constrained with about one tenth of the number of parameters than there are data points while at the same time the models account for accuracy and correct and error RT distributions. While both of these versions of the diffusion model can account for the observed data, the model that allows across-trial variability in drift to vary might be preferred for theoretical reasons. The diffusion model fits are compared to the leaky competing accumulator model which did not perform as well.

• The role of sensorimotor processes in social group contagion
Publication date: June 2018
Source:Cognitive Psychology, Volume 103

Author(s): Emiel Cracco, Marcel Brass

Although it is well known that action observation triggers an imitative response, not much is known about how these responses develop as a function of group size. Research on social contagion suggests that imitative tendencies initially increase but then stabilize as groups become larger. However, these findings have mainly been explained in terms of interpretative processes. Across seven experiments (N = 322), the current study investigated the contribution of sensorimotor processes to social group contagion by looking at the relation between group size and automatic imitation in a task that involved minimal interpretation. The results of Experiments 1–2 revealed that automatic imitation increased with group size according to an asymptotic curve on congruent trials but a linear curve on incongruent trials. The results of Experiments 3–7 showed that the asymptote on congruent trials disappeared when no control was needed, namely in the absence of incongruent trials. This suggests that the asymptote in the relation between group size and automatic imitation can be explained in terms of strategic control mechanisms that aim to prevent unintended imitative responses. The findings of the current study are in close correspondence with previous research in the social domain and as such support the hypothesis that sensorimotor processes contribute to the relation between group size and social contagion.

• Beyond Markov: Accounting for independence violations in causal reasoning
Publication date: June 2018
Source:Cognitive Psychology, Volume 103

Author(s): Bob Rehder

Although many theories of causal cognition are based on causal graphical models, a key property of such models—the independence relations stipulated by the Markov condition—is routinely violated by human reasoners. This article presents three new accounts of those independence violations, accounts that share the assumption that people’s understanding of the correlational structure of data generated from a causal graph differs from that stipulated by causal graphical model framework. To distinguish these models, experiments assessed how people reason with causal graphs that are larger than those tested in previous studies. A traditional common cause network ($Y 1 ← X → Y 2$) was extended so that the effects themselves had effects ($Z 1 ← Y 1 ← X → Y 2 → Z 2$). A traditional common effect network ($Y 1 → X ← Y 2$) was extended so that the causes themselves had causes ($Z 1 → Y 1 → X ← Y 2 ← Z 2$). Subjects’ inferences were most consistent with the beta-Q model in which consistent states of the world—those in which variables are either mostly all present or mostly all absent—are viewed as more probable than stipulated by the causal graphical model framework. Substantial variability in subjects’ inferences was also observed, with the result that substantial minorities of subjects were best fit by one of the other models (the dual prototype or a leaky gate models). The discrepancy between normative and human causal cognition stipulated by these models is foundational in the sense that they locate the error not in people’s causal reasoning but rather in their causal representations. As a result, they are applicable to any cognitive theory grounded in causal graphical models, including theories of analogy, learning, explanation, categorization, decision-making, and counterfactual reasoning. Preliminary evidence that independence violations indeed generalize to other judgment types is presented.

• Subjective randomness as statistical inference
Publication date: June 2018
Source:Cognitive Psychology, Volume 103

Author(s): Thomas L. Griffiths, Dylan Daniels, Joseph L. Austerweil, Joshua B. Tenenbaum

Some events seem more random than others. For example, when tossing a coin, a sequence of eight heads in a row does not seem very random. Where do these intuitions about randomness come from? We argue that subjective randomness can be understood as the result of a statistical inference assessing the evidence that an event provides for having been produced by a random generating process. We show how this account provides a link to previous work relating randomness to algorithmic complexity, in which random events are those that cannot be described by short computer programs. Algorithmic complexity is both incomputable and too general to capture the regularities that people can recognize, but viewing randomness as statistical inference provides two paths to addressing these problems: considering regularities generated by simpler computing machines, and restricting the set of probability distributions that characterize regularity. Building on previous work exploring these different routes to a more restricted notion of randomness, we define strong quantitative models of human randomness judgments that apply not just to binary sequences – which have been the focus of much of the previous work on subjective randomness – but also to binary matrices and spatial clustering.

• Editorial Board
Publication date: May 2018
Source:Cognitive Psychology, Volume 102

• Young infants expect an unfamiliar adult to comfort a crying baby: Evidence from a standard violation-of-expectation task and a novel infant-triggered-video task
Publication date: May 2018
Source:Cognitive Psychology, Volume 102

Author(s): Kyong-sun Jin, Jessica L. Houston, Renée Baillargeon, Ashley M. Groh, Glenn I. Roisman

Do infants expect individuals to act prosocially toward others in need, at least in some contexts? Very few such expectations have been uncovered to date. In three experiments, we examined whether infants would expect an adult alone in a scene with a crying baby to attempt to comfort the baby. In the first two experiments, 12- and 4-month-olds were tested using the standard violation-of-expectation method. Infants saw videotaped events in which a woman was performing a household chore when a baby nearby began to cry; the woman either comforted (comfort event) or ignored (ignore event) the baby. Infants looked significantly longer at the ignore than at the comfort event, and this effect was eliminated if the baby laughed instead of cried. In the third experiment, 8-month-olds were tested using a novel forced-choice violation-of-expectation method, the infant-triggered-video method. Infants faced two computer monitors and were first shown that touching the monitors triggered events: One monitor presented the comfort event and the other monitor presented the ignore event. Infants then chose which event they wanted to watch again by touching the corresponding monitor. Infants significantly chose the ignore over the comfort event, and this effect was eliminated if the baby laughed. Thus, across ages and methods, infants provided converging evidence that they expected the adult to comfort the crying baby. These results indicate that expectations about individuals’ actions toward others in need are already present in the first year of life, and, as such, they constrain theoretical accounts of early prosociality and morality.

• The speed of memory errors shows the influence of misleading information: Testing the diffusion model and discrete-state models
Publication date: May 2018
Source:Cognitive Psychology, Volume 102

Author(s): Jeffrey J. Starns, Chad Dubé, Matthew E. Frelinger

In this report, we evaluate single-item and forced-choice recognition memory for the same items and use the resulting accuracy and reaction time data to test the predictions of discrete-state and continuous models. For the single-item trials, participants saw a word and indicated whether or not it was studied on a previous list. The forced-choice trials had one studied and one non-studied word that both appeared in the earlier single-item trials and both received the same response. Thus, forced-choice trials always had one word with a previous correct response and one with a previous error. Participants were asked to select the studied word regardless of whether they previously called both words “studied” or “not studied.” The diffusion model predicts that forced-choice accuracy should be lower when the word with a previous error had a fast versus a slow single-item RT, because fast errors are associated with more compelling misleading memory retrieval. The two-high-threshold (2HT) model does not share this prediction because all errors are guesses, so error RT is not related to memory strength. A low-threshold version of the discrete state approach predicts an effect similar to the diffusion model, because errors are a mixture of responses based on misleading retrieval and guesses, and the guesses should tend to be slower. Results showed that faster single-trial errors were associated with lower forced-choice accuracy, as predicted by the diffusion and low-threshold models.

• How people learn about causal influence when there are many possible causes: A model based on informative transitions
Publication date: May 2018
Source:Cognitive Psychology, Volume 102

Author(s): Cory Derringer, Benjamin Margolin Rottman

Four experiments tested how people learn cause-effect relations when there are many possible causes of an effect. When there are many cues, even if all the cues together strongly predict the effect, the bivariate relation between each individual cue and the effect can be weak, which can make it difficult to detect the influence of each cue. We hypothesized that when detecting the influence of a cue, in addition to learning from the states of the cues and effect (e.g., a cue is present and the effect is present), which is hypothesized by multiple existing theories of learning, participants would also learn from transitions – how the cues and effect change over time (e.g., a cue turns on and the effect turns on). We found that participants were better able to identify positive and negative cues in an environment in which only one cue changed from one trial to the next, compared to multiple cues changing (Experiments 1A, 1B). Within a single learning sequence, participants were also more likely to update their beliefs about causal strength when one cue changed at a time (‘one-change transitions’) than when multiple cues changed simultaneously (Experiment 2). Furthermore, learning was impaired when the trials were grouped by the state of the effect (Experiment 3) or when the trials were grouped by the state of a cue (Experiment 4), both of which reduce the number of one-change transitions. We developed a modification of the Rescorla-Wagner algorithm to model this ‘Informative Transitions’ learning processes.