A Study of Individual Differences in Categorization with Redundancy

Modality-specific impacts of distractors on visual and auditory categorical decision-making: an evidence accumulation perspective

Our brain constantly processes multisensory inputs to make decisions and guide behaviors, but how goal-relevant processes are influenced by irrelevant information is unclear. Here, we investigated the effects of intermodal and intramodal task-irrelevant information on visual and auditory categorical decision-making. In both visual and auditory tasks, we manipulated the modality of irrelevant inputs (visual vs. auditory vs. none) and used linear discrimination analysis of EEG and hierarchical drift-diffusion modeling (HDDM) to identify when and how task-irrelevant information affected decision-relevant processing. The results revealed modality-specific impacts of irrelevant inputs on visual and auditory categorical decision-making. The distinct effects on the visual task were shown on the neural components, with auditory distractors amplifying the sensory processing whereas visual distractors amplifying the post-sensory process. Conversely, the distinct effects on the auditory task were shown in behavioral performance and underlying cognitive processes. Visual distractors facilitate behavioral performance and affect both stages, but auditory distractors interfere with behavioral performance and impact on the sensory processing rather than the post-sensory decision stage. Overall, these findings suggested that auditory distractors affect the sensory processing stage of both tasks while visual distractors affect the post-sensory decision stage of visual categorical decision-making and both stages of auditory categorical decision-making. This study provides insights into how humans process information from multiple sensory modalities during decision-making by leveraging modality-specific impacts.

The role of posterior parietal cortex in detecting changes in feedback contingency

Well-practiced or learned behaviors are extremely resilient. For example, it is extremely difficult for a trained typist to forget how to use a keyboard configuration that they are familiar with. While they can be trained on a new keyboard configuration, the original skill quickly comes back when the old keyboard configuration is used again. This resiliency of learned skills is both a blessing and a curse. It makes useful skills durable, but it also makes maladaptive behaviors difficult to extinguish. Crossley et al. (2013) proposed a computational model and behavioral paradigm aimed at unlearning skills using various feedback contingency manipulations during an extinction phase. They showed that partially-valid feedback during extinction removed evidence for fast reacquisition, which they interpreted as evidence for unlearning. In this article, we replicated the Crossley et al. paradigm using fMRI. Univariate analyses showed differences in BOLD signals between the different experiment phases in the frontoparietal attention network. The superior and inferior parietal lobules (SPL and IPL, respectively) showed the largest cluster differences both between experimental phases and between extinction conditions. In contrast, the prefrontal cortex only showed differences in cluster of activities between extinction conditions. Multivariate pattern analysis was also used with seeds in the SPL and IPL. The results showed that these brain areas were critical in detecting changes in experimental phases. Overall, the fMRI results found mixed evidence for the Crossley et al. model and suggest that while unlearning prevents fast reacquisition, the absence of fast reacquisition does not necessarily implies that unlearning occurred.

Stable, flexible, common, and distinct behaviors support rule-based and information-integration category learning

The ability to organize variable sensory signals into discrete categories is a fundamental process in human cognition thought to underlie many real-world learning problems. Decades of research suggests that two learning systems may support category learning and that categories with different distributional structures (rule-based, information-integration) optimally rely on different learning systems. However, it remains unclear how the same individual learns these different categories and whether the behaviors that support learning success are common or distinct across different categories. In two experiments, we investigate learning and develop a taxonomy of learning behaviors to investigate which behaviors are stable or flexible as the same individual learns rule-based and information-integration categories and which behaviors are common or distinct to learning success for these different types of categories. We found that some learning behaviors are stable in an individual across category learning tasks (learning success, strategy consistency), while others are flexibly task-modulated (learning speed, strategy, stability). Further, success in rule-based and information-integration category learning was supported by both common (faster learning speeds, higher working memory ability) and distinct factors (learning strategies, strategy consistency). Overall, these results demonstrate that even with highly similar categories and identical training tasks, individuals dynamically adjust some behaviors to fit the task and success in learning different kinds of categories is supported by both common and distinct factors. These results illustrate a need for theoretical perspectives of category learning to include nuances of behavior at the level of an individual learner.