Animate and Inanimate Words Demonstrate Equivalent Retrieval Dynamics Despite the Occurrence of the Animacy Advantage

People demonstrate a memory advantage for animate (living) concepts over inanimate (nonliving) concepts in a variety of memory tasks, including free recall, but we do not know the mechanism(s) that produces this effect. We compared the retrieval dynamics (serial-position effects, probability of first recall, output order, categorical clustering, and recall contiguity) of animate and inanimate words in a typical free recall task to help elucidate this effect. Participants were more likely to recall animate than inanimate words, but we found few, if any, differences in retrieval dynamics by word type. The animacy advantage was obtained across serial position, including occurring in both the primacy and recency regions of the lists. Participants were equally likely to recall an animate or inanimate word first on the tests and did not prioritize recalling words of one type earlier in retrieval or demonstrate strong clustering by animacy at recall. Participants showed some greater contiguity of recall for inanimate words, but this outcome ran counter to the animacy effect. Together, the results suggest that the animacy advantage stems from increased item-specific memory strength for animate over inanimate words and is unlikely to stem from intentional or strategic differences in encoding or retrieval by word type, categorical strategies, or differences in temporal organization. Although the present results do not directly support or refute any current explanations for the animacy advantage, we suggest that measures of retrieval dynamics can help to inspire or constrain future accounts for this effect and can be incorporated into relevant hypothesis testing.

The Effect of Strategic Instruction for Categorical Clustering on Preschoolers’ Recall Accuracy

Categorical clustering involves the grouping of stimuli into meaningful categories when encoding or retrieving to-be-learned information during memory tasks. The current study measured the categorical clustering behavior of 40 three- to four-year-olds who completed a spatial memory task requiring them to remove and return toys to their original locations within a box. After completing the spatial memory task without strategic instruction, participants were randomly assigned to receive instructions to categorically cluster while removing (encoding) or returning the toys (retrieval) to the box, or receive no strategic instruction (control). Results showed increases in clustering behaviors following strategic instruction, suggesting that participants were able to successfully produce clustering behavior following instruction to do so. Although instruction to categorically cluster during encoding and retrieval generated a significant increase in clustering behavior when removing and returning the toys to the box, respectively, it only improved recall for those instructed to categorically cluster when retrieving the toy locations. Increased engagement in categorical clustering when encoding the toy locations negatively impacted recall, resulting in a utilization deficiency for participants instructed to remove the toys according to their category membership. No changes in clustering behavior or recall accuracy were observed in the participants who completed the task a second time without strategic instruction. Implications for the current understanding of utilization deficiencies observed among preschoolers and future directions for research are discussed.

A Novel Consensus Fuzzy K-Modes Clustering Using Coupling DNA-Chain-Hypergraph P System for Categorical Data

In this paper, a data clustering method named consensus fuzzy k-modes clustering is proposed to improve the performance of the clustering for the categorical data. At the same time, the coupling DNA-chain-hypergraph P system is constructed to realize the process of the clustering. This P system can prevent the clustering algorithm falling into the local optimum and realize the clustering process in implicit parallelism. The consensus fuzzy k-modes algorithm can combine the advantages of the fuzzy k-modes algorithm, weight fuzzy k-modes algorithm and genetic fuzzy k-modes algorithm. The fuzzy k-modes algorithm can realize the soft partition which is closer to reality, but treats all the variables equally. The weight fuzzy k-modes algorithm introduced the weight vector which strengthens the basic k-modes clustering by associating higher weights with features useful in analysis. These two methods are only improvements the k-modes algorithm itself. So, the genetic k-modes algorithm is proposed which used the genetic operations in the clustering process. In this paper, we examine these three kinds of k-modes algorithms and further introduce DNA genetic optimization operations in the final consensus process. Finally, we conduct experiments on the seven UCI datasets and compare the clustering results with another four categorical clustering algorithms. The experiment results and statistical test results show that our method can get better clustering results than the compared clustering algorithms, respectively.