Scientific advances across a range of disciplines hinge on the ability to make inferences about unobservable theoretical entities on the basis of empirical data patterns. Accurate inferences rely on both discovering valid, replicable data patterns and accurately interpreting those patterns in terms of their implications for theoretical constructs. The replication crisis in science has led to widespread efforts to improve the reliability of research findings, but comparatively little attention has been devoted to the validity of inferences based on those findings. Using an example from cognitive psychology, we demonstrate a blinded-inference paradigm for assessing the quality of theoretical inferences from data. Our results reveal substantial variability in experts’ judgments on the very same data, hinting at a possible inference crisis.

There is great interest in relating individual differences in cognitive processing to activation of neural systems. The general process involves relating measures of task performance like reaction times or accuracy to brain activity to identify individual differences in neural processing. One limitation of this approach is that measures like reaction times can be affected by multiple components of processing. For instance, some individuals might have higher accuracy in a memory task because they respond more cautiously, not because they have better memory. Computational models of decision making, like the drift–diffusion model and the linear ballistic accumulator model, provide a potential solution to this problem. They can be fitted to data from individual participants to disentangle the effects of the different processes driving behavior. In this sense the models can provide cleaner measures of the processes of interest, and enhance our understanding of how neural activity varies across individuals or populations. The advantages of this model-based approach to investigating individual differences in neural activity are discussed with recent examples of how this method can improve our understanding of the brain–behavior relationship.

The attentional network test (ANT) uses flanker stimuli with different cue conditions to quantify differences in attentional processing. However, it is unclear precisely how the alerting and orienting cues in the task affect different decision processes. The present study leveraged computational modeling to identify the relationship between attentional cues and decision components. ANT data from a large sample of 156 participants were analyzed using the spotlight diffusion model, which quantifies decision components for response caution, motor/encoding time, perceptual processing, and attentional control. The spotlight analysis showed that the attentional cues had multiple effects on decision processing. Compared to the no cue condition, an alerting cue led to faster encoding/motor speed, improved perceptual processing, and increased attentional focusing. The orienting cue further led to a decrease in response caution and increased encoding/motor speed and attentional focusing to reduce interference from incompatible flankers. This analysis demonstrates that alerting and orienting cues have complex effects on decision processes that are not captured by simple differences in RTs, and that model-based analyses can delineate such effects to allow researchers to identify precisely how attentional processing varies across individuals or conditions in tasks like the ANT.

Background Individuals with Down syndrome (DS) appear to perform at a level that is commensurate with developmental expectations on simple tasks of selective attention. In this study, we examine how their selective attention is impacted by target changes that unfold over both time and space. This increased complexity reflects an attempt at greater ecological validity in an experimental task, as a steppingstone for better understanding attention among persons with DS in real-world environments. Methods A modified flanker task was used to assess visual temporal and spatial filtering among persons with DS (n = 14) and typically developing individuals (n = 14) matched on non-verbal mental age (mental age = 8.5 years). Experimental conditions included varying the stimulus onset asynchronies between the onset of the target and flankers, the distances between the target and flankers, and the similarity of the target and flankers. ResultsBoth the participants with DS and the typically developing participants showed slower reaction times and lower accuracy rates when the flankers appeared closer in time and/or space to the target. Conclusion No group differences were found on a broad level, but the findings suggest that dynamic stimuli may be processed differently by those with DS. Implications of the findings are discussed in relation to the developmental approach to intellectual disability originally articulated by Ed Zigler.

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