People often choose what information they want to gather. This kind of learning is known as active learning, and has been the subject of intense study in recent years in several fields. Although there are many questions to ask about active learning, perhaps the most pressing question about active learning is this: how and why do people seek the particular information they seek? These theories propose that people search for information optimally; that is, they generate queries that provide the most information possible. The rational account of active learning has been successfully tested in many domains in psychology , however, one challenge for the rational account of active learning involves the role of semantic similarity in memory search. Optimal search often requires asking questions that are dissimilar to each other, as asking the same question repeatedly will usually provide the same information. Consider, for example, a task in which the learner has to determine how much of a new nutrient there is in different food items. The learner can ask questions about each item sequentially and must retrieve each item from memory prior to the query. As similar items usually have similar properties, for the questions to be maximally informative,plastic seedling pots the queried items must be as different to each other as possible. It is much better to follow up a query about strawberry with a query about egg than a query about blueberry.This optimal search strategy is the opposite of what researchers have observed in most recall tasks.

Typically, when asked to retrieve items from memory, people generate sequences of semantically similar items, an effect known as semantic congruence. The semantic congruence effect is remarkably robust, and emerges across a variety of tasks including free association , free recall from lists , semantic memory search , and memory-based decision making . This is due to the associative structure of memory . Retrieved items cue successive items based on their strength of association. Items that are similar are more associated with each other, which is why the retrieval of strawberry is more likely to cue blueberry than egg. How is this conflict resolved in naturalistic active learning tasks? Are people able to search optimally and retrieve sequences of dissimilar items, or are they fundamentally constrained by the associative memory processes that lead to semantic congruence in other recall tasks? Contrary to the popular optimality hypothesis that claims that people can ask efficient questions that maximize expected information gain, we found that subjects failed to generate optimal inquiries and that the suboptimality was largely due to associative memory search . Additional pre-registered experiments showed that subjects’ querying behavior was no more optimal – or less similarity driven – in our active learning task than a traditional semantic search task , and no more optimal or less similarity driven when directly told to query more optimally by querying dissimilar items , suggesting that memory based active learning is at the mercy of extremely stubborn memory constraints, which are difficult to alleviate by task instructions. A final experiment showed that subjects can distinguish between the more and less optimal query sets, suggesting that subjects understand what optimality entails, but that memory constraints make the spontaneous generation of optimal queries from memory difficult.

Our results stand in stark contrast with the large body of work that finds optimal search in active learning. The theory that people acquire information optimally has been very successful in explaining human inquiry in several domains. However, most prior studies use fairly simple, artificial stimuli, and do not require subjects to generate queries from memory. We thus suggest that the scope of the optimality hypothesis in explaining human active learning may be more limited than previously thought. Indeed, we suspect that any setting in which subjects must formulate sequences of queries in natural language will probably be constrained by memory processes, particularly the similarity-driven associative memory search. Although associative memory processes curtail optimal active learning, that does not mean that people’s memory processes are inherently flawed. Rather, memory serves multiple cognitive functions and the associative biases documented in this paper may reflect optimal trade offs between diverging task demands. Indeed, many researchers have argued that association or similarity-driven memory search is part of an optimal system for semantic memory retrieval . Related work has shown that associative memory processes implicated in judgment and decision biases are adaptive in that they often lead to accurate inference and generalization with minimal cognitive cost . Regulating these processes in active learning tasks may be too effortful, and people may be optimally trading off performance with the cognitive cost required to succeed in our task . This theory predicts that even though we were unable to reduce semantic congruence and increase optimal search through coaching, performance may improve with higher incentives or practice. Testing these predictions is an important topic for future work. Other future directions include the refinement of our memory and learning models.

For example, subjects in our study learned about novel target properties. Yet they came into the experiments with idiosyncratic knowledge about food items or animals. Thus, it is likely they held different prior belief about the novel target properties. Since prior belief is not the focus of this paper, we assumed all subjects held the same prior belief in the experiments. In future work, the shape of prior belief can be set as free parameters and the same framework can be used to derive the prior representation of target properties in a given domain. Individual differences in this regard can be revealed. The Bayesian learning model also assumes that subjects maintain a distribution of belief over multiple hypotheses . However, other research suggests that in a closely related – and not even as complex – active category learning setting, subjects maintain a single hypothesis at a time . Previous research also reveals other simple heuristics, such as the split-half heuristic and the likelihood difference heuristic , in active learning tasks. It is possible that such heuristics play a role in the query search in our active learning tasks and, therefore, can be considered in the modeling of algorithmic processes in future research. Our work contributes to the emerging body of research that offers researchers a naturalistic search domain to study active learning. Additionally, our computational models integrate insights from several fields, and are able to jointly describe both algorithmic memory search processes as well as the optimality or suboptimality of these search processes for active learning. In this way,container size for raspberries our paper presents a powerful new research paradigm for naturalistic active learning. There has been an increasing interest in porting computational cognitive models beyond abstract lab stimuli, to attempt to describe everyday cognition. This has been driven by the availability of new machine learning models that offer quantitative representations for natural entities , as well as the growing demand from policy makers and practitioners for theory-driven behavioral and cognitive insights. Our research is part of this trend, and we look forward to future work that applies established algorithmic and rational theories of cognition to rich stimuli sets to better understand human cognition and behavior in the wild. Pomegranate is an ancient, deciduous fruit tree crop in the family Lythraceae . However, it is still often classified in its own family, Punicaceae, with the only other known species of the same genus, Punica protopunica . Punica protopunica is only known to exist in the natural world on Socotra Island, off the coast of Yemen . Pomegranate has been cultivated by many cultures over thousands of years and its origins are presently believed to encompass the Asiatic regions of present day Iran to the northern Himalayan Mountains . An exact location or region of origin has not been confirmed. As a crop, it is commercially grown in tropical and subtropical arid and semi-arid regions on every continent except Antarctica, but it is often described as best suited to a Mediterranean climate, with hot summers and cool winters . Pomegranate is prized for its fruit, flowers, bark, and leaves, long believed by many cultures to have medicinal properties .

Recent developments in technology have allowed for the discovery, identification, and quantification of bioactive phytochemical compounds in pomegranate fruit with putative health benefits for humans . Many of these are polyphenolic compounds which serve as antioxidants that have been demonstrated to have higher levels of radical-scavenging activity than those present in most other commercial fruit, red wines or green tea infusions . Clinical studies relating to the health benefits associated with pomegranate fruit have focused on ‘Wonderful,’ the industry standard . The United States Department of Agriculture-Agricultural Research Service, National Clonal Germplasm Repository has a large collection of pomegranate cultivars with diverse horticultural traits that have yet to be evaluated for grower suitability. In addition, there exist ornamental cultivars with potential for drought tolerant landscape and floriculture applications, rootstock breeding, especially dwarfing, or as a source of genes for altering fruit and juice parameters . Accurate statistics for current total global pomegranate production are unavailable because the Food and Agriculture Organization of the United Nations presently does not keep records on pomegranate production for any country in its database , 2016). Consequently, there is disagreement in the literature as to which country is the leading producer of the fruit and this is likely because records from some countries are incomplete or unavailable and the two top producing countries are in close competition. Recent estimates have reported global production to be around 1.5 million t∙yr-1 . This estimate reported that the primary pomegranate producing country in the world was Iran, followed by India and China, which were the second and third highest producers, respectively. The United States was ranked fourth in pomegranate production in this estimate . The study stated Iran had 65,000 ha of plantings with a national yield of about 600,000 t∙yr-1 . Iran is also the leading exporter of pomegranates in the world, with estimates of their exports ranging from 10% to 30% of their total crop . India produces 500,000 t∙yr-1 on 55,000 ha of land with only 22,000 t being exported. Data for China is sparse, but they were reported in this study to produce 260,000 t∙yr-1 of fruit . In another estimate, total global production was calculated to be 1.857 million t∙yr-1 and it lists India as the primary producer of pomegranate, followed by Iran and the United States as the second and third highest producers, respectively . Wolfe et al. reported that India, Iran and the United States produced approximately 900,000, 700,000 and 127,000 t∙yr-1 , respectively, with these three countries representing the majority of the global market. Turkey and Spain were ranked the fourth and fifth highest producers of pomegranate, producing 80,000 and 50,000 t∙yr-1 , respectively. China was not included in this ranking, probably due to lack of data. Most recently, Iran’s Ministry of Agriculture reported that Iran’s pomegranate production increased 10% in 2016 to approximately 1.1 million t of pomegranate fruit compared to 1 million t in 2015, making them the top ranked pomegranate producer in the world, followed by India, China, Turkey and the United States . Pomegranate has been successfully cultivated in California since 1769, when Spanish missionaries planted trees from the Old World at the missions along the coast of California for a food source . Some of these trees exist today despite decades of neglect. Today, California leads the United States in pomegranate production. The number of farms producing pomegranate increased significantly from 369 farms in 2002 to 599 farms in 2007, increasing from 3,859 ha to 9,922 ha , 2007. By 2012, the number of farms with pomegranates increased to 783, totaling more than 13,041 ha of pomegranate plantings . Most recent reports put total production of pomegranate in California at over 280,000 tons valued at $115.4 million per year, with an average yield of 25.9 tons per ha , 2015. In addition, the juice market has been valued at $91 million . In terms of diversity, commercial pomegranate production in California is largely dominated by a single cultivar, Wonderful , due to its high quality fruit and juice . Other cultivars are grown commercially to a lesser extent. These cultivars are considered “early” cultivars because they mature much sooner than Wonderful.