Helping is an inherently cooperative behavior, but the cognitive mechanisms underlying this behavior remain relatively underexplored. In this paper, we introduce a novel gamified paradigm for understanding a variety of cognitive behaviors associated with helping. Principals are assigned secret goals in a block-based grid (e.g., move all blue blocks to room C), and helpers can either pass their turn or make a move that could help the principal. We show that principals make useful and pragmatic first moves and helpers accumulate evidence over time before initiating a helpful move. We also introduce a preliminary set of computational models based on recursive pragmatic inference and utility maximization that attempt to account for these behavioral findings.

Context-sensitive communication not only requires speakers to choose relevant utterances from alternatives, but also to retrieve and evaluate the relevant utterances from memory in the first place. In this work, we compared different proposals about how underlying semantic representations work together with higher-level selection processes to enable individuals to flexibly utilize context to guide their language use. We examined speaker and guesser performance in a two-player iterative language game based on Codenames, which asks speakers to choose a single `clue' word that allows their partner to select a pair of target words from a context of distractors. The descriptive analyses indicated that speakers were sensitive to the shared semantic neighborhood of the target word pair and were able to use guesser feedback to shift their clues closer to the unguessed word. We also formulated a series of computational models combining different semantic representations with different selection processes. Model comparisons suggested that a model which integrated contextualized lexical representations based on association networks with a contextualized model of pragmatic reasoning was better able to predict behavior in the game compared to models that lacked context at either the representational or process level. Our findings suggest that flexibility in communication is driven by context-sensitivity at at the level of both representations and processes.

Failing to come up with a word or name is a fairly common experience that is exacerbated in older adulthood and among populations with language impairments, and yet the mechanisms underlying lexical retrieval remain fairly understudied. In this work, we introduce and evaluate a series of nested computational models of lexical retrieval that combine semantic representations derived from a distributional semantic model with a process model to account for behavioral performance in a primed lexical retrieval task. The models were tested on a behavioral data set where participants attempted to retrieve answers to descriptions of low-frequency words and were provided a semantically and/or phonologically related prime word before the retrieval attempt. Model comparisons indicated that a model that emphasized semantic activations from the description and phonological activations from the prime word best accounted for the overall data. Additionally, incorrect responses and metacognitive judgments indicating that participants had other words in mind were associated with models that instead emphasized semantic activations from the prime word. Taken together, these results identify the locus of lexical retrieval failures and offer the opportunity to investigate broader questions about semantic memory retrieval.

It is presently unclear how localized, word association networkrepresentations compare to distributed, spatial representationsin representing distant concepts and accounting for primingeffects. We compared and contrasted 4 models of representingsemantic knowledge (5018-word directed and undirected stepdistance networks, an association-correlation network andword2vec spatial representations) to predict semantic primingperformance for distant concepts. In Experiment 1, responselatencies for relatedness judgments for word-pairs followed aquadratic relationship with network path lengths and spatialcosines, replicating and extending a pattern recently reportedby Kenett, Levi, Anaki, and Faust (2017) for an 800-wordHebrew network. In Experiment 2, response latencies toidentify a word through progressive demasking showed a lineartrend for path lengths and cosines, suggesting that simpleassociation networks can capture distant semanticrelationships. Further analyses indicated that spatial modelsand correlation networks are less sensitive to directassociations and likely represent more higher-levelrelationships between words.

When concepts are retrieved from memory, this process occurs within a rich search space where multiple sources of information interact with each other. Although the mapping from wordform to meaning is generally considered to be arbitrary, there is recent evidence to suggest that form and meaning may be correlated in natural language, and semantic and phonological cues may interact during retrieval. However, whether phonology interacts with meaning-related information in deeper semantic retrieval tasks, and whether this interaction has broader implications for how we conceptualize semantic retrieval remains relatively understudied. We examined these questions within the framework of the semantic fluency task, where individuals were asked to retrieve as many exemplars as they could from a given category (e.g., animals) within a fixed period of time. Responses were more phonologically similar during later stages of retrieval, and greater phonological similarity across responses was associated with greater number of items produced. We formulated a nested set of optimal foraging models to evaluate the combined influence of semantic and phonological information on retrieval likelihood. Model comparisons revealed that a model that combined frequency, semantic, and phonological information locally to make within-category transitions but relied on only frequency as a global cue to make between-category transitions produced the best explanation of the behavioral data.

Humans engage in a wide variety of search behaviors during their lifetime. These search behaviors may be external such as foraging for food in the wild, or searching for mates, or internal, such as searching for concepts in memory. Decades of work on search processes among humans has suggested that these two types of search behaviors share common characteristics and physiological mechanisms. Despite this progress, understanding the dynamics of internal search is an ongoing challenge in the field.

Communication involves searching for optimal utterances within memory and then evaluating those utterances against a target goal. This task is substantially harder when informa- tion about multiple concepts has to be communicated, such as describing how music and tides are similar. Whether the search process for this challenging communicative task con- verges onto the optimal response relatively quickly, or involves more strategic decision-making to evaluate different candi- dates remains understudied. In this work, speakers gener- ated single word “clues” that would enable a listener to cor- rectly identify a pair of words among several distractor words. Speakers and listeners generated candidates before producing final responses. Each player was biased towards the first can- didate(s) they generated, even when this candidate was sub- optimal compared to other candidates, as was the case for less related concepts. Furthermore, straying away from the initial semantic “patch” of responses decreased accuracy in the game. Overall, these findings suggest that individuals tend to identify the relevant semantic cluster early on during semantic search, and are likely to employ the “take-the-first” strategy for select- ing utterances in ambiguous, ill-defined semantic contexts.

Searching through memory is mediated by complex interactions between the underlying mental lexicon and the processes that operate on this lexicon. However, these interactions are difficult to study due to the effortless manner in which neurotypical individuals perform cognitive tasks. In this work, we examine these interactions within a sample of prelingually deaf individuals with cochlear implants and normal hearing individuals who were administered the verbal fluency task for the "animals" category. Specifically, we tested how different candidates for underlying mental lexicons and processes account for search behavior within the verbal fluency task across the two groups. The models learned semantic representations from different combinations of textual (word2vec) and speech-based (speech2vec) information. The representations were then combined with process models of memory search based on optimal foraging theory that incorporate different lexical sources for transitions within and between clusters of items produced in the fluency task. Our findings show that semantic, word frequency, and phonological information jointly influence search behavior and highlight the delicate balance of different lexical sources that produces successful search outcomes.