forager: a Python package and web interface for modeling mental search.

Analyzing data from the verbal fluency task (e.g., "name all the animals you can in a minute") is of interest to both memory researchers and clinicians due to its broader implications for memory search and retrieval. Recent work has proposed several computational models to examine nuanced differences in search behavior, which can provide insights into the mechanisms underlying memory search. A prominent account of memory search within the fluency task was proposed by Hills et al. (2012), where mental search is modeled after how animals forage for food in physical space. Despite the broad potential utility of these models to scientists and clinicians, there is currently no open-source program to apply and compare existing foraging models or clustering algorithms without extensive, often redundant programming. To remove this barrier to studying search patterns in the fluency task, we created forager, a Python package ( https://github.com/thelexiconlab/forager ) and web interface ( https://forager.research.bowdoin.edu/ ). forager provides multiple automated methods to designate clusters and switches within a fluency list, implements a novel set of computational models that can examine the influence of multiple lexical sources (semantic, phonological, and frequency) on memory search using semantic embeddings, and also enables researchers to evaluate relative model performance at the individual and group level. The package and web interface cater to users with various levels of programming experience. In this work, we introduce forager's basic functionality and use cases that demonstrate its utility with pre-existing behavioral and clinical data sets of the semantic fluency task.

A Critical Review of Network-Based and Distributional Approaches to Semantic Memory Structure and Processes.

Some of the earliest work on understanding how concepts are organized in memory used a network-based approach, where words or concepts are represented as nodes, and relationships between words are represented by links between nodes. Over the past two decades, advances in network science and graph theoretical methods have led to the development of computational semantic networks. This review provides a modern perspective on how computational semantic networks have proven to be useful tools to investigate the structure of semantic memory as well as search and retrieval processes within semantic memory, to ultimately model performance in a wide variety of cognitive tasks. Regarding representation, the review focuses on the distinctions and similarities between network-based (based on behavioral norms) approaches and more recent distributional (based on natural language corpora) semantic models, and the potential overlap between the two approaches. Capturing the type of relation between concepts appears to be particularly important in this modeling endeavor. Regarding processes, the review focuses on random walk models and the degree to which retrieval processes demand attention in pursuit of given task goals, which dovetails with the type of relation retrieved during tasks. Ultimately, this review provides a critical assessment of how the network perspective can be reconciled with distributional and machine-learning-based perspectives to meaning representation, and describes how cognitive network science provides a useful conceptual toolkit to probe both the structure and retrieval processes within semantic memory.

Semantic Memory Search and Retrieval in a Novel Cooperative Word Game: A Comparison of Associative and Distributional Semantic Models.

Considerable work during the past two decades has focused on modeling the structure of semantic memory, although the performance of these models in complex and unconstrained semantic tasks remains relatively understudied. We introduce a two-player cooperative word game, Connector (based on the boardgame Codenames), and investigate whether similarity metrics derived from two large databases of human free association norms, the University of South Florida norms and the Small World of Words norms, and two distributional semantic models based on large language corpora (word2vec and GloVe) predict performance in this game. Participant dyads were presented with 20-item word boards with word pairs of varying relatedness. The speaker received a word pair from the board (e.g., exam-algebra) and generated a one-word semantic clue (e.g., math), which was used by the guesser to identify the word pair on the board across three attempts. Response times to generate the clue, as well as accuracy and latencies for the guessed word pair, were strongly predicted by the cosine similarity between word pairs and clues in random walk-based associative models, and to a lesser degree by the distributional models, suggesting that conceptual representations activated during free association were better able to capture search and retrieval processes in the game. Further, the speaker adjusted subsequent clues based on the first attempt by the guesser, who in turn benefited from the adjustment in clues, suggesting a cooperative influence in the game that was effectively captured by both associative and distributional models. These results indicate that both associative and distributional models can capture relatively unconstrained search processes in a cooperative game setting, and Connector is particularly suited to examine communication and semantic search processes.

Semantic memory: A review of methods, models, and current challenges.

Adult semantic memory has been traditionally conceptualized as a relatively static memory system that consists of knowledge about the world, concepts, and symbols. Considerable work in the past few decades has challenged this static view of semantic memory, and instead proposed a more fluid and flexible system that is sensitive to context, task demands, and perceptual and sensorimotor information from the environment. This paper (1) reviews traditional and modern computational models of semantic memory, within the umbrella of network (free association-based), feature (property generation norms-based), and distributional semantic (natural language corpora-based) models, (2) discusses the contribution of these models to important debates in the literature regarding knowledge representation (localist vs. distributed representations) and learning (error-free/Hebbian learning vs. error-driven/predictive learning), and (3) evaluates how modern computational models (neural network, retrieval-based, and topic models) are revisiting the traditional "static" conceptualization of semantic memory and tackling important challenges in semantic modeling such as addressing temporal, contextual, and attentional influences, as well as incorporating grounding and compositionality into semantic representations. The review also identifies new challenges regarding the abundance and availability of data, the generalization of semantic models to other languages, and the role of social interaction and collaboration in language learning and development. The concluding section advocates the need for integrating representational accounts of semantic memory with process-based accounts of cognitive behavior, as well as the need for explicit comparisons of computational models to human baselines in semantic tasks to adequately assess their psychological plausibility as models of human semantic memory.

Attempted prime retrieval is a double-edged sword: Facilitation and disruption in repeated lexical retrieval.

The phenomenological experience of lexical retrieval often involves repeated, active attempts to retrieve phonologically and/or semantically related information. However, the influence of these multiple retrieval attempts on subsequent lexical retrieval is presently unknown. We investigated the influence of passively viewing or actively retrieving different types of information at the critical moment preceding lexical retrieval through a novel priming paradigm. Participants attempted to retrieve target words (e.g., abdicate) from low-frequency descriptions (e.g., to formally renounce a throne). Target retrieval was preceded by passive viewing (Experiment 1), or active retrieval of the prime word (Experiments 2-6). Primes were either "both" semantically and phonologically related (e.g., abandon), only phonologically related (e.g., abdomen), only semantically related (e.g., resign), or unrelated (e.g., obvious) to the target word. When primes were passively viewed, phonological facilitation in target retrieval accuracy was observed. In contrast, when participants actively attempted to retrieve primes from their definitions, no phonological facilitation was observed. Successful retrieval of semantic and both primes facilitated subsequent target retrieval, whereas, failure to retrieve semantic and both primes inhibited subsequent target retrieval. These facilitatory and inhibitory influences of prime retrieval for semantic and both primes were independent of feedback on retrieval performance (Experiment 4) and participants' overall knowledge of the primes and targets (Experiment 5), and also did not extend to retrieval from episodic memory (Experiment 6). The findings are consistent with ongoing retrospective processes during target retrieval, which reengage prime retrieval success or failure and consequently produce benefits and costs during repeated retrieval from semantic memory. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Distant connectivity and multiple-step priming in large-scale semantic networks.

We examined 3 different network models of representing semantic knowledge (5,018-word directed and undirected step distance networks, and an association-correlation network) to predict lexical priming effects. In Experiment 1, participants made semantic relatedness judgments for word pairs with varying path lengths. Response latencies for judgments followed a quadratic relationship with network path lengths, replicating and extending a recent pattern reported by Kenett, Levi, Anaki, and Faust (2017) for an 800-word association-correlation network in Hebrew. In Experiment 2, participants identified target words in a progressive demasking task, immediately following a briefly presented prime (120 ms). Response latencies to identify the target showed a linear trend for all network path lengths. Importantly, there were statistically significant differences between relatively distant words in the step distance networks, for example, path lengths 4 and beyond, suggesting that association networks can indeed capture distant functional semantic relationships. Additional comparisons with 2 distributional models (LSA and word2vec) suggested that distributional models also successfully predicted response latencies, although there appear to be fundamental differences in the types of semantic relationships captured by the different models. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Converging semantic and phonological information in lexical retrieval and selection in young and older adults.

The present experiments investigated the influence of combined phonological and semantic information on lexical retrieval, metacognitive retrieval states, and selection in an immediate multiple-choice task. Younger and older adults attempted to retrieve words (e.g., abdicate) from low-frequency word definitions. Retrieval was preceded by primes that were "both" semantically and phonologically related (e.g., abandon), phonologically related (e.g., abdomen), semantically related (e.g., resign), or unrelated (e.g., pink). Younger and older adults benefited from phonological primes in retrieval, and also showed reduced, but reliable, facilitation from "both" primes. Younger and older adults also indicated that they were likely to "know" the answer more often after any related primes compared with unrelated primes. Because there was no facilitation in actual retrieval after semantic primes, this reflects a false "knowing" response. After each retrieval attempt, participants were given the correct answer along with the 4 primes in a multiple-choice test. Both younger and older adults were likely to false alarm to the "both" and semantic alternatives. When instructed that the prime was not the answer, younger adults decreased their false alarms, but not the older adults. With masked, briefly presented primes, younger adults mimicked the false alarms shown by older adults, suggesting that the high false alarm rates in older adults reflect an inability to discriminate the source of activation. The present experiments provide strong evidence for age-invariant phonological facilitation, and also suggest that overlapping semantic information moderates the facilitatory effect of phonological information on retrieval, and also produces age-related differences on an immediate multiple-choice task. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Autobiographical recall of personally familiar names and temporal information in e-mails: An automatic analytic approach using e-mail communications.

An important question that arises from autobiographical memory research is whether the variables that influence memory in the laboratory also drive memory for autobiographical episodes in real life. We explored this question within the context of e-mail communications and investigated the variables that influence recall for personally familiar names and temporal information in e-mails. We designed a Web-based program that analyzed each participant's year-old sent e-mail archive and applied textual analysis algorithms to identify a set of sentences likely to be memorable. These sentences were then used as the stimuli in a cued recall task. Participants saw two sentences from their sent e-mail as a cue and attempted to recall the name of the e-mail recipient. Participants also rated the vividness of recall for the e-mail conversation and estimated the month in which they had written the e-mail. Linear mixed-effect analyses revealed that recipient name recall accuracy decreased with longer retention intervals and increased with greater frequency of contact with the recipient. Also, with longer retention intervals, participants dated e-mails as being more recent than their actual month. This telescoping error was moderately larger for e-mails with greater sentiment. These findings suggest that memory for personally familiar names and temporal information in e-mails closely follows the patterns for autobiographical memory and proper-name recall found in laboratory settings. This study introduces an innovative, Web-based experimental method for studying the cognitive processes related to autobiographical memories using ecologically valid, naturalistic communications.

Lineage-specific cell death in postembryonic brain development of Drosophila.

The Drosophila central brain is composed of thousands of neurons that derive from approximately 100 neuroblasts per hemisphere. Functional circuits in the brain require precise neuronal wiring and tight control of neuronal numbers. How this accurate control of neuronal numbers is achieved during neural development is largely unclear. Specifically, the role of programmed cell death in control of cell numbers has not been studied in the central brain neuroblast lineages. Here, we focus on four postembryonic neuroblast lineages in the central brain identified on the basis that they express the homeobox gene engrailed (en). For each lineage, we determine the total number of adult-specific neurons generated as well as number and pattern of en-expressing cells. We then demonstrate that programmed cell death has a pronounced effect on the number of cells in the four lineages; approximately half of the immature adult-specific neurons in three of the four lineages are eliminated by cell death during postembryonic development. Moreover, we show that programmed cell death selectively affects en-positive versus en-negative cells in a lineage-specific manner and, thus, controls the relative number of en-expressing neurons in each lineage. Furthermore, we provide evidence that Notch signaling is involved in the regulation of en expression. Based on our findings, we conclude that lineage-specific programmed cell death plays a prominent role in the generation of neuronal number and lineage diversity in the Drosophila brain.

Arborization pattern of engrailed-positive neural lineages reveal neuromere boundaries in the Drosophila brain neuropil.

The Drosophila brain is a highly complex structure composed of thousands of neurons that are interconnected in numerous exquisitely organized neuropil structures such as the mushroom bodies, central complex, antennal lobes, and other specialized neuropils. While the neurons of the insect brain are known to derive in a lineage-specific fashion from a stereotyped set of segmentally organized neuroblasts, the developmental origin and neuromeric organization of the neuropil formed by these neurons is still unclear. In this study we used genetic labeling techniques to characterize the neuropil innervation pattern of engrailed-expressing brain lineages of known neuromeric origin. We show that the neurons of these lineages project to and form most arborizations, in particular all of their proximal branches, in the same brain neuropil compartments in embryonic, larval and adult stages. Moreover, we show that engrailed-positive neurons of differing neuromeric origin respect boundaries between neuromere-specific compartments in the brain. This is confirmed by an analysis of the arborization pattern of empty spiracles-expressing lineages. These findings indicate that arborizations of lineages deriving from different brain neuromeres innervate a nonoverlapping set of neuropil compartments. This supports a model for neuromere-specific brain neuropil, in which a given lineage forms its proximal arborizations predominantly in the compartments that correspond to its neuromere of origin.