Why are some people able to explore a problem space more effectively? We used a simple problem-solving task in which problems can be solved quickly within five moves but with a problem space containing 40-60 distinct states. Problem-solving in this task consists of many attempts to solve the problem, but only one solution works. Across these attempts, participants sometimes repeat an attempt from earlier. Exploratory analyses were conducted on data from 70 problems and six individual difference measures for each of 147 participants. We hypothesized that working memory capacity (WMC) would be associated with a higher proportion of unique attempts. WMC was calculated from two complex span tasks. Higher WMC was associated with a higher proportion of unique attempts and faster solution times. Higher WMC was also associated with more self-terminated solution attempts as opposed to being forced to restart the problem after seven moves by the system.

Individuals can learn differently, even in very simple tasks. In an association learning task, participants learned associations between correct keypresses and sets of categorically related images. Following a delay, participants were then tested on the associations in a surprise test. This task has been used to examine the relationship between reinforcement learning and working memory. However, the strategies that individuals use to explore the associations between keypresses and images as well as the opportunity to take advantage of a simple rule based on the structure of the task without having to retrieve the correct response may both impact test performance. Two experiments were conducted to test these hypotheses. Results showed that the performance difference from the end of the learning phase to the testing phase differed significantly between set size conditions in a way that is more consistent with strategic differences than an interaction between working memory and reinforcement learning.

The way working memory, attention, and long-term memory interact is an important question given the role these cognitive systems play in many tasks. In this paper, we present a study examining prior counterintuitive results that show that prioritization of some stimuli aids learning but hurts performance at a delayed test. In this study, we use eye tracking to measure attention consistency, to examine the effect of prioritization and working memory load on recall accuracy. The goal was to assess two possible explanations of the negative effect of prioritization on a delayed test. Our results indicate that prioritization reduces response time and increases accuracy during learning of associations. However, the negative effect of prioritization on a delayed test is replicated with participants showing higher accuracy for non-prioritized items during testing. Measures of attention shifting and consistency impact learning performance but do not explain the negative prioritization effect at test.

The effect of prioritization on information in working memory has primarily been examined in tasks containing a single type of stimulus and with one item that is prioritized. However, many theories of working memory posit different types of components for the maintenance of verbal or visuospatial information. This study examined differences between prioritized and nonprioritized items as well as word and image stimuli. Participants completed an association learning task in which working memory demands were varied along with the number of items to be prioritized. Following a short delay, retention was tested. Prioritization effects were identified during both the learning and testing phases of the experiment, and the impact of prioritization was moderated by working memory demands of the task. Significant differences in accuracy between word and image stimuli were only observed in the testing phase, with accuracy for verbal information being worse. While prioritization improved accuracy and response times during learning, it led to decreases in the testing phase.

Salient features of a task play an important role in how people create task representations which then influence strategy selection for accomplishing the task. We examined two theories, Represent-Construct-Choose-Learn (RCCL) and Rational Metareasoning (RM), both of which incorporate task features into their models of strategy selection. RCCL theory posits that when a strategy's success rate is low, it indicates that the task representation is not useful and those represented features are irrelevant in this case so people tend to drop these features from the task representation. Conversely, RM theory posits that strategy selection is based on consideration of all available features, with no discrete changes in the features incorporated into the task representation. A study was conducted to examine how participants changed their strategy choices based on the success rate of using a specific task feature. The results showed that neither theory aligned closely with empirical data.

The precise mechanisms precipitating the process of representational change in problem solving have been investigated for nearly a century. One current hypothesis is that analyzing the unchanging elements of previous attempts may facilitate restructuring. We investigated this hypothesis by providing solvers with three common examples of unsuccessful problem attempts, their own problem attempts, or no previous attempts. The prior attempts conditions eliminated the need to rely on working memory to access previous unsuccessful attempts. While there was no evidence for an overall effect of the prior attempts conditions, cognitive reflection was identified as a reliable predictor of restructuring and solving. Eye-tracking data were collected to further investigate the contributions of these systems to fixations while solving. The current study is an exploratory analysis of this data, with analyses focusing on participants' fixations on problem-irrelevant space and unsuccessful attempts.

Humans are highly variable in their ability to learn and executecomplex tasks; however, there are conflicting theories on skillacquisition. This study compared two different explanationsfor how association learning interacts with other cognitiveprocesses: a) reinforcement learning and working memory areseparate, competing processes operating simultaneously onassociation learning; and, b) associations are proceduralizedinto production rules and reinforcement learning acts on thoserules. Participants completed a simple association learning taskfollowed by a delayed test under two conditions designed tocontrast these theories. The results are consistent with aproceduralization account in which reinforcement learning andworking memory are not competitive interfering systems, butthere remain important questions about how these two accountsmay be best integrated.