Rule-Based Category Learning in Children: The Role of Inhibitory Control

The present study examined category learning in relation to inhibitory control and working memory in children and adults. Results revealed that categorization performance improved with age. Young children struggled with rule learning, many older children were successful at rule learning, and most adults had no difficulty with the task. Model-based analyses suggested that performance differences were due to young children’s inability to inhibit the salient, but irrelevant rule. Interestingly, when the analyses focused only on older children and adults who used the task appropriate strategy, the age-related rule-based deficit disappeared. Also, results revealed that successful performance on the categorization task was associated with better inhibitory control for older children, whereas successful performance on the categorization task was associated with greater working memory in young children. These findings suggest that the ability to learn categories varies with age and it may be partially dependent on inhibitory control and working memory

Category Learning in Older Adulthood: Understanding and Reducing Age-Related Deficits

Executive functions are important for learning rule-based (RB) categories, as well as non-rule-based (NRB) categories (e.g., categories learned implicitly, without a verbal rule). However, executive functioning is known to decline with age, leading to age-related deficits in category learning. The current thesis examines RB and NRB category learning in older adults using category sets that vary in difficulty (e.g., rule complexity, number of stimulus dimensions, salience of stimulus dimensions). In Chapter 2, older adults and younger adults completed three category sets (simple single-dimensional RB, disjunctive RB, and NRB). Older adults learned the simple, single-dimensional rules quite well. In contrast to younger adults, older adults found complex disjunctive RB categories harder to learn than NRB categories because of the executive functioning demands associated with complex rule learning. In Chapter 3, I introduced a pre-training procedure prior to the disjunctive RB and NRB categorization task used in Chapter 2. This was done in an effort to reduce task demands, as to minimize age-related categorization deficits. Both RB and NRB category learning improved among older adults following pre-training, but the improvements to RB learning were more drastic, suggesting that executive functioning plays a heavier role in RB learning. In Study 1 of Chapter 4 I used a difficult, single-dimensional RB category set (i.e., the correct rule is based on the less salient stimulus dimension) and a NRB category set to further examine category learning in normal aging and to better understand the types of strategies used by older adults. Relative to younger adults, older adults struggled with learning both the RB and NRB category set because they used suboptimal rules during the RB task and a RB strategy during the NRB task. In Study 2 of Chapter 4, I used a pre-training procedure to familiarize older adults with the stimulus dimensions of the RB category set, reducing the executive function demands of the task. Pre-training improved RB accuracy and the consistency with which older adults applied the rule. Across all studies, executive functioning abilities were associated with RB and NRB category learning. Overall, the results from this thesis help to better understand the locus of age-related categorization deficits and offer a method of reducing these deficits

Rule-based category learning in children: the role of age and executive functioning.

Rule-based category learning was examined in 4-11 year-olds and adults. Participants were asked to learn a set of novel perceptual categories in a classification learning task. Categorization performance improved with age, with younger children showing the strongest rule-based deficit relative to older children and adults. Model-based analyses provided insight regarding the type of strategy being used to solve the categorization task, demonstrating that the use of the task appropriate strategy increased with age. When children and adults who identified the correct categorization rule were compared, the performance deficit was no longer evident. Executive functions were also measured. While both working memory and inhibitory control were related to rule-based categorization and improved with age, working memory specifically was found to marginally mediate the age-related improvements in categorization. When analyses focused only on the sample of children, results showed that working memory ability and inhibitory control were associated with categorization performance and strategy use. The current findings track changes in categorization performance across childhood, demonstrating at which points performance begins to mature and resemble that of adults. Additionally, findings highlight the potential role that working memory and inhibitory control may play in rule-based category learning

Category learning in older adulthood: A study of the Shepard, Hovland, and Jenkins (1961) tasks.

Shepard, Hovland, and Jenkins (1961) examined the categorization abilities of younger adults using tasks involving single-dimensional rule learning, disjunctive rule learning, and family resemblance learning. The current study examined category learning in older adults using this well-known category set. Older adults, like younger adults, found category tasks with a single relevant dimension the easiest to learn. In contrast to younger adults, older adults found complex disjunctive rule-based categories harder to learn than family resemblance based categories. Disjunctive rule-based category learning appeared to be the most difficult for older adults to learn because this category set placed the heaviest demands on working memory, which is known to be a cognitive function that declines with normal aging. The authors discuss why complex rule-based category learning is considered more difficult for older adults to learn relative to younger adults, drawing parallels to developmental research

Ego depletion interferes with rule-defined category learning but not non-rule-defined category learning.

Considerable research on category learning has suggested that many cognitive and environmental factors can have a differential effect on the learning of rule-defined (RD) categories as opposed to the learning of non-rule-defined (NRD) categories. Prior research has also suggested that ego depletion can temporarily reduce the capacity for executive functioning and cognitive flexibility. The present study examined whether temporarily reducing participants\u27 executive functioning via a resource depletion manipulation would differentially impact RD and NRD category learning. Participants were either asked to write a story with no restrictions (the control condition), or without using two common letters (the ego depletion condition). Participants were then asked to learn either a set of RD categories or a set of NRD categories. Resource depleted participants performed more poorly than controls on the RD task, but did not differ from controls on the NRD task, suggesting that self regulatory resources are required for successful RD category learning. These results lend support to multiple systems theories and clarify the role of self-regulatory resources within this theory

Cognitive changes in conjunctive rule-based category learning: An ERP approach

When learning rule-based categories, sufficient cognitive resources are needed to test hypotheses, maintain the currently active rule in working memory, update rules after feedback, and to select a new rule if necessary. Prior research has demonstrated that conjunctive rules are more complex than unidimensional rules and place greater demands on executive functions like working memory. In our study, event-related potentials (ERPs) were recorded while participants performed a conjunctive rule-based category learning task with trial-by-trial feedback. In line with prior research, correct categorization responses resulted in a larger stimulus-locked late positive complex compared to incorrect responses, possibly indexing the updating of rule information in memory. Incorrect trials elicited a pronounced feedback-locked P300 elicited which suggested a disconnect between perception, and the rule-based strategy. We also examined the differential processing of stimuli that were able to be correctly classified by the suboptimal single-dimensional rule (“easy” stimuli) versus those that could only be correctly classified by the optimal, conjunctive rule (“difficult” stimuli). Among strong learners, a larger, late positive slow wave emerged for difficult compared with easy stimuli, suggesting differential processing of category items even though strong learners performed well on the conjunctive category set. Overall, the findings suggest that ERP combined with computational modelling can be used to better understand the cognitive processes involved in rule-based category learning

Learning categories via rules and similarity: comparing adults and children.

Two experiments explored the different strategies used by children and adults when learning new perceptual categories. Participants were asked to learn a set of categories for which both a single-feature rule and overall similarity would allow for perfect performance. Other rules allowed for suboptimal performance. Transfer stimuli (Experiments 1 and 2) and single features (Experiment 2) were presented after training to help determine how the categories were learned. In both experiments, we found that adults made significantly more optimal rule-based responses to the test stimuli than children. Children showed a variety of categorization styles, with a few relying on the optimal rules, many relying on suboptimal single-feature rules, and only a few relying on overall family resemblance. We interpret these results within a multiple systems framework, and we argue that children show the pattern they do because they lack the necessary cognitive resources to fully engage in hypothesis testing, rule selection, and verbally mediated category learning

Better mood and better performance. Learning rule-described categories is enhanced by positive mood.

Theories of mood and its effect on cognitive processing suggest that positive mood may allow for increased cognitive flexibility. This increased flexibility is associated with the prefrontal cortex and the anterior cingulate cortex, both of which play crucial roles in hypothesis testing and rule selection. Thus, cognitive tasks that rely on behaviors such as hypothesis testing and rule selection may benefit from positive mood, whereas tasks that do not rely on such behaviors should not be affected by positive mood. We explored this idea within a category-learning framework. Positive, neutral, and negative moods were induced in our subjects, and they learned either a rule-described or a non-rule-described category set. Subjects in the positive-mood condition performed better than subjects in the neutral- or negative-mood conditions in classifying stimuli from rule-described categories. Positive mood also affected the strategy of subjects who classified stimuli from non-rule-described categories

Category learning performance for children and adults across 80 trials.

<p>Error bars denote standard error of the mean.</p