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Do children learn language from the words that they produce themselves? Because children know that they have imperfect knowledge of language, they could simply ignore their own productions. However, children could also learn from their productions -- using what they say and how their caregivers respond to update their language models. Using irregular plurals as a case study, we conducted a large-scale corpus analysis and two experimental studies to understand the role of children&#39;s productions and caregivers&#39; responses in language learning. We demonstrate that children do learn from their own production, with errorful utterances leading to more errors. However, at least in some contexts, children can use implicit corrections from parents to offset the negative effects of their errors. Children thus appear to learn not only from their caregivers&#39; productions, but also from their own productions and from the relationship between the two.

CogSci 2022

Of mouses and mans: the role of production and feedback in language learning

Do children learn language from the words that they produce themselves? Because children know that they have imperfect knowledge of language, they could simply ignore their own productions. However, children could also learn from their productions -- using what they say and how their caregivers respond to update their language models. Using irregular plurals as a case study, we conducted a large-scale corpus analysis and two experimental studies to understand the role of children's productions and caregivers' responses in language learning. We demonstrate that children do learn from their own production, with errorful utterances leading to more errors. However, at least in some contexts, children can use implicit corrections from parents to offset the negative effects of their errors. Children thus appear to learn not only from their caregivers' productions, but also from their own productions and from the relationship between the two.

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According to cross-situational learning, infants aggregate statistical information across naming events to resolve ambiguous word-referent mappings. While lab experiments show that learners are sensitive to these statistics, in studies using naturalistic stimuli, adults often fail to infer the correct referent. Here, we examined how young learners' input “in the wild” differs from laboratory experiments. We analyzed the temporal and spatial regularities of parent naming events in a naturalistic dataset of parent-child play and tested their effect on infants' visual attention. Parents were less likely to name the same toy twice than to name two different toys in sequence, except at short lags (0>t>5s). Most of the visual scenes accompanying naming events were composed of several toys of approximately equal (small) size. Child-attention to the target toy appeared to be modulated primarily by size. These results underscore the importance of quantifying naturalistic statistical regularities for understanding the mechanisms of word learning.

Quantifying cross-situational statistics during parent-child toy play

Iconicity refers to a resemblance between word form and meaning. Previous work has shown that iconic words are learned earlier and processed faster. Here we examined whether iconicity would also affect a recognition memory task. We also manipulated the level at which items were encoded—with a focus on either their meaning or their form—in order to gain insight into the mechanism by which iconicity would affect memory. In comparison with non-iconic words, iconic words were associated with a higher false alarm rate, a lower d’ score, and a lower criterion C. We did not observe any interaction between iconicity and encoding condition. We also conducted an analysis of recognition memory megastudy data and found that iconicity was predictive of higher false alarms and a lower criterion C across 1,646 items. We interpret these results as suggesting that iconicity leads to a feeling of familiarity in recognition memory.

Effects of Iconicity in Recognition Memory

A hallmark of human memory is the ability to integrate discrete experiences into cognitive maps. A fundamental form of this integration is transitive inference (TI), in which overlapping premises (A < B, B < C) are integrated into a unified representation of a relational hierarchy (A < B < C). Few existing theories provide a mechanistic account of this construction of relational knowledge and how it is shaped by different training conditions. This study builds on recent findings that TI is facilitated by chaining of overlapping premises, with a new behavioral experiment confirming an advantage over non-overlapping sequences matched for premise frequency and spacing. A subsequent simulation study shows that the chaining effect is captured by a particle filter which performs approximate Bayesian inference about the latent hierarchy. These results provide a better understanding of how chaining shapes the construction of relational knowledge in the face of uncertainty and forgetting.

Modeling the effect of chained study in transitive inference

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.

Mouse-mole-vole: The inconspicuous benefit of phonology during retrieval from semantic memory

Previous studies have shown that representationally complex referents are encoded slower into working memory (WM) but are retrieved faster (Hofmeister, 2011; Karimi & Ferreira, 2016). However, the cost of maintaining complex representations is still not well understood. Through two self-paced reading experiments, we investigated the cost of encoding, maintaining and retrieving complex representations in WM. While we replicated the facilitatory effect during retrieval, the slowdown during encoding was not consistent across our experiments. More critically, for the first time, our experiments demonstrated that maintaining complex representations in WM is less costly than maintaining their simple counterparts. Furthermore, we found that WM maintenance cost is reduced because complex target noun phrases are more distinct from other competing referents in WM than simple ones. Overall, our results showed that the semantic elaboration of complex representations can reduce maintenance cost and provided new perspectives into this understudied WM process.

Noun phrase representational complexity reduces maintenance cost in working memory by increasing distinctiveness between referents

Knowledge of cause and effect allows people to navigate and understand the complex systems of the world. Despite the importance of causal knowledge to everyday reasoning, little is known about how people transfer causal knowledge learned in one situation to novel contexts. In two experiments, we examine when people choose to generalize two types of causal knowledge, causal mechanisms (Experiment 1) and causal strength (Experiment 2), across various domains. We find that people willingly transfer causal knowledge to novel contexts when the entities in those contexts share high categorical relatedness with the source of the causal knowledge. The extent to which people are willing to transfer causal knowledge decreases as category similarity decreases. We discuss future research that could delineate the boundaries of causal transfer.

Transferring Novel Causal Knowledge

Self-directed learners are often described as ‘intuitive scientists’, yet they also tend to struggle in assessments of their scientific reasoning. We investigate a novel explanation for this apparent gap between formal and informal scientific inquiry. Specifically, we consider whether learners’ documented failure to correctly apply the control of variables strategy might stem from a mismatch between their causal intuitions and task presentation. Children (7- and 9-year-olds) and adults were tested on a version of a traditional multivariate reasoning task (Tschirgi, 1980) that we modified to clarify ambiguous elements of the causal logic. A significant majority of participants in all age groups selected informative experiments on this modified task, avoiding confounded actions with positive tangible outcomes. This finding contrasts with the longstanding claim that learners do not correctly employ control of variables without extensive training and suggests that self-directed scientific inquiry may be intuitively suited to support causal learning goals.

Clarifying the Causal Logic of a Classic Control of Variables Task

Suppose we observe something happen in an interaction be- tween two objects A and B. Can we then predict what will hap- pen in an interaction between A and C, or between B and C? Recent research, inspired by work on the “causal asymmetry”, suggests that people use cues to causal agency to guide object- based generalization decisions, even in relatively abstract set- tings. When object A possesses cues to causal agency (e.g. it moves, remains stable throughout the interaction), people tend to predict that what happened will probably also occur in an interaction between A and C, but not between B and C. Here we replicate and extend this work, with the goal of identify- ing the cues that people use to determine that an object is a causal agent. In four experiments, we manipulate three prop- erties of the agent and recipient objects. We find that people anchor their inductive generalizations around the agent object when that object possesses all three cues to causal agency, but removing either cue abolishes the asymmetry.

Dissecting causal asymmetries in inductive generalization

The same causal system can be accurately described in many ways. What governs the evaluation of these choices? We propose a novel, formal account of causal evaluation according to which evaluations of causal claims reflect the joint demands of maximal informativeness and maximal compression. Across two experiments, we show that evaluations of more and less compressed causal claims are sensitive to the amount of information lost by choosing the more compressed causal claim over a less compressed one, regardless of whether the compression is realized by coarsening a single variable or by eliding a background condition. This offers a unified account of two dimensions along which causal claims are evaluated (proportionality and stability), and contributes to a more general picture of human cognition according to which the capacity to create compressed (causal) representations plays a central role.

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Of mouses and mans: the role of production and feedback in language learning

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