The hot-hand fallacy in cognitive control: Repetition expectancy modulates the congruency sequence effect

In this study, the role of expectancies in cognitive control was tested. On the basis of the original interpretation of the congruency sequence effect (Gratton, Coles, & Donchin, Journal of Experimental Psychology: General 121:480–506, 1992), we sought evidence for a repetition bias steering attentional control. In a series of four Stroop experiments, we investigated how participants’ explicit predictions about the upcoming (in)congruency proactively influenced subsequent Stroop performance. Similar to the fallacious “hot-hand” belief in gambling, repeating stimulus events were overpredicted, as participants consistently expected more repetitions of the congruency level than the actual presented number of congruency-level repetitions (50 %). Moreover, behavioral adjustments (i.e., a congruency sequence effect) were only found when participants anticipated a congruency-level repetition, whereas no modulation of the Stroop effect was found following alternation predictions. We propose that proactive control processes in general, and repetition expectancy in particular, should be given more attention in current theorizing and modeling of cognitive control, which is characterized by an emphasis on reactive, conflict-induced control adjustments.     

Establishing normative data for repeated cognitive assessment: A comparison of different statistical methods

Serial cognitive assessment is conducted to monitor changes in the cognitive abilities of patients over time. At present, mainly the regression-based change and the ANCOVA approaches are used to establish normative data for serial cognitive assessment. These methods are straightforward, but they have some severe drawbacks. For example, they can only consider the data of two measurement occasions. In this article, we propose three alternative normative methods that are not hampered by these problems—that is, multivariate regression, the standard linear mixed model (LMM), and the linear mixed model combined with multiple imputation (LMM with MI) approaches. The multivariate regression method is primarily useful when a small number of repeated measurements are taken at fixed time points. When the data are more unbalanced, the standard LMM and the LMM with MI methods are more appropriate because they allow for a more adequate modeling of the covariance structure. The standard LMM has the advantage that it is easier to conduct and that it does not require a Monte Carlo component. The LMM with MI, on the other hand, has the advantage that it can flexibly deal with missing responses and missing covariate values at the same time. The different normative methods are illustrated on the basis of the data of a large longitudinal study in which a cognitive test (the Stroop Color Word Test) was administered at four measurement occasions (i.e., at baseline and 3, 6, and 12 years later). The results are discussed and suggestions for future research are provided.     

Modeling external events in the three-level analysis of multiple-baseline across-participants designs: A simulation study

In this study, we focus on a three-level meta-analysis for combining data from studies using multiple-baseline across-participants designs. A complicating factor in such designs is that results might be biased if the dependent variable is affected by not explicitly modeled external events, such as the illness of a teacher, an exciting class activity, or the presence of a foreign observer. In multiple-baseline designs, external effects can become apparent if they simultaneously have an effect on the outcome score(s) of the participants within a study. This study presents a method for adjusting the three-level model to external events and evaluates the appropriateness of the modified model. Therefore, we use a simulation study, and we illustrate the new approach with real data sets. The results indicate that ignoring an external event effect results in biased estimates of the treatment effects, especially when there is only a small number of studies and measurement occasions involved. The mean squared error, as well as the standard error and coverage proportion of the effect estimates, is improved with the modified model. Moreover, the adjusted model results in less biased variance estimates. If there is no external event effect, we find no differences in results between the modified and unmodified models.     

Semantic priming in number naming

The issue of semantic and non-semantic conversion routes for numerals is still debated in numerical cognition. We report two number-naming experiments in which the target numerals were preceded by another numeral (prime). The primes and targets could be presented either in arabic (digit) notation or in verbal (alphabetical) notation. The results reveal a semantically related distance effect: Latencies are fastest when the prime has the same value as the target and increase when the distance between prime and target increases. We argue that the present results are congruent with the idea that the numerals make access to an ordered semantic number line common to all notations, as the results are the same for within-notation priming (arabic-arabic or verbal- verbal) and between-notations priming (arabic-verbal or verbal-arabic). The present results also point to a rapid involvement of semantics in the naming of numerals, also when the numerals are words. As such, they are in line with recent claims of rapid semantic mediation in word naming.     

Dissecting the symbolic distance effect: Comparison and priming effects in numerical and nonnumerical orders

When participants are asked to compare two stimuli, responses are slower for stimuli close to each other on the relevant dimension than for stimuli further apart. Previously, it has been proposed that this comparison distance effect originates from overlap in the representation of the stimuli. This idea is generally accepted in numerical cognition, where it is assumed that representational overlap of numbers on a mental number line accounts for the effect (e.g., Cohen Kadosh et al., 2005). In contrast, others have emphasized the role of response-related processes to explain the comparison distance effect (e.g., Banks, 1977). In the present study, numbers and letters are used to show that the comparison distance effect can be dissociated from a more direct behavioral signature of representational overlap, the priming distance effect. The implication is that a comparison distance effect does not imply representational overlap. An interpretation is given in terms of a recently proposed model of quantity comparison (Verguts, Fias, & Stevens, 2005).     

Cognitive control acts locally

Cognitive control adjusts information processing to momentary needs and task requirements. We investigated conflict adaptation when participants are performing two tasks, a Simon task and a SNARC task. The results indicated that one congruency effect (e.g., Simon) was reduced after conflict in the other task (e.g., SNARC), but only when both tasks used identical relevant information. On the other hand, when both tasks used different relevant information, a larger congruency effect was observed after conflict in the other task. The results are explained in terms of a local control mechanism.     

Hebbian learning of cognitive control: dealing with specific and nonspecific adaptation

The conflict monitoring model of M. M. Botvinick, T. S. Braver, D. M. Barch, C. S. Carter, and J. D. Cohen (2001) triggered several research programs investigating various aspects of cognitive control. One problematic aspect of the Botvinick et al. model is that there is no clear account of how the cognitive system knows where to intervene when conflict is detected. As a result, recent findings of task-specific and context-specific (e.g., item-specific) adaptation are difficult to interpret. The difficulty with item-specific adaptation was recently pointed out by C. Blais, S. Robidoux, E. F. Risko, and D. Besner (2007), who proposed an alternative model that could account for this. However, the same problem of where the cognitive system should intervene resurfaces in a different shape in this model, and it has difficulty in accounting for the Gratton effect, a hallmark item-nonspecific effect. The authors of the current article show how these problems can be solved when cognitive control is implemented as a conflict-modulated Hebbian learning rule.     

Bayesian Procedures for Identifying Aberrant Response-Time Patterns in Adaptive Testing

In order to identify aberrant response-time patterns on educational and psychological tests, it is important to be able to separate the speed at which the test taker operates from the time the items require. A lognormal model for response times with this feature was used to derive a Bayesian procedure for detecting aberrant response times. Besides, a combination of the response-time model with a regular response model in an hierarchical framework was used in an alternative procedure for the detection of aberrant response times, in which collateral information on the test takers’ speed is derived from their response vectors. The procedures are illustrated using a data set for the Graduate Management Admission Test^® (GMAT^®). In addition, a power study was conducted using simulated cheating behavior on an adaptive test.     

On the Non-Existence of Optimal Solutions and the Occurrence of “Degeneracy” in the CANDECOMP/PARAFAC Model

The CANDECOMP/PARAFAC (CP) model decomposes a three-way array into a prespecified number of R factors and a residual array by minimizing the sum of squares of the latter. It is well known that an optimal solution for CP need not exist. We show that if an optimal CP solution does not exist, then any sequence of CP factors monotonically decreasing the CP criterion value to its infimum will exhibit the features of a so-called “degeneracy”. That is, the parameter matrices become nearly rank deficient and the Euclidean norm of some factors tends to infinity. We also show that the CP criterion function does attain its infimum if one of the parameter matrices is constrained to be column-wise orthonormal.