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Adults are far better at identifying adult talkers than child talkers (e.g., Cooper et al., 2020). Why is this the case? Are child talkers acoustically less distinguishable (e.g., Lee et al., 1999)? Or perhaps adults better identify adult talkers, while children better identify child talkers (e.g., see Anastasi &amp; Rhodes, 2005, for evidence that an own-age advantage exists in face recognition). Here, we test adults (N=72) and 6.5-year-olds (N=71) on a voice identification task featuring single word recordings by 8 children and 8 adults. While all listeners successfully identified all talkers above chance (ps&lt;.05), adults significantly outperformed children with both adult (Madult=0.66 vs. Mchild=0.54; p&lt;.001) and child talkers (Madult=0.64 vs. Mchild=0.57; p&lt;.05). Thus, we find no evidence of an own-age advantage in talker recognition. Rather, we find additional evidence that adults are more skilled than children in voice identification tasks, suggesting that talker recognitions skills take time to fully mature.

CogSci 2022

Is there an Own-Age Advantage in Talker Recognition?

Adults are far better at identifying adult talkers than child talkers (e.g., Cooper et al., 2020). Why is this the case? Are child talkers acoustically less distinguishable (e.g., Lee et al., 1999)? Or perhaps adults better identify adult talkers, while children better identify child talkers (e.g., see Anastasi & Rhodes, 2005, for evidence that an own-age advantage exists in face recognition). Here, we test adults (N=72) and 6.5-year-olds (N=71) on a voice identification task featuring single word recordings by 8 children and 8 adults. While all listeners successfully identified all talkers above chance (ps<.05), adults significantly outperformed children with both adult (Madult=0.66 vs. Mchild=0.54; p<.001) and child talkers (Madult=0.64 vs. Mchild=0.57; p<.05). Thus, we find no evidence of an own-age advantage in talker recognition. Rather, we find additional evidence that adults are more skilled than children in voice identification tasks, suggesting that talker recognitions skills take time to fully mature.

poster

Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
 
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The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





Math talk has implications for the development of numerical concepts. Research suggests that when caregivers talk about natural numbers (1, 2, 3…), it may enhance children’s later math knowledge. Natural numbers have physical quantities that children can observe, yet abstract numerical concepts do not have such observable quantities. In this analysis, we examined how zero occurs in math talk. Using the CHILDES American English corpora (MacWhinney, 2000), we examined the frequency and nature of math talk about zero in naturalistic interactions between 2- to 6-year-olds and other speakers. Input from other speakers increased in frequency and complexity across development. Input with zero in symbolic sentential contexts (e.g., “one and zero make ten”) and cardinal sentential contexts (e.g., “zero means nothing”) increased with development. Children’s production of zero did not change in frequency or context. These results have implications for the concepts about zero children may bring to formal education.

Zero in on This: Children are Exposed to Various Concepts of “Zero” Prior to Age Six

Conditionals have multiple interpretations: the statement "If it rains, Abbie will buy an umbrella" is true when it rains and Abbie buys an umbrella (conjunction), when it doesn’t rain and Abbie doesn’t buy an umbrella (biconditional), and when it doesn’t rain and Abbie buys an umbrella (conditional). Prior research shows that children understand all three interpretations in adolescence, but the reasons for this delay remain unclear. For some researchers, children struggle to represent multiple possibilities due to limited resources. For others, task-related factors underlie children’s difficulties. In a truth-value judgement task, 3-10-year-olds matched a conditional statement with one of two pictures: 1 depicting a false interpretation of the conditional vs. one of the 3 true interpretations. Results showed that children understood conjunction since age 3 but comprehended all three conditional interpretations at age 9, earlier than previously thought. Findings point to a task-related explanation of children’s difficulties.

How do young children interpret conditionals?

Effective practice is key to learning. Yet, it is unclear whether
young children have the ability to make effective and adaptive
training choices. In this project, we investigated 4- to 7-year-old
children’s (n=146) ability to tailor their training strategies
to optimize performance outcomes. Children were presented
with one easy and one difficult guessing game and were asked
to choose which game they wanted to practice. Crucially, before
they chose, they were told that they would eventually be
tested either on the game of their choice (Choice condition) or
on the game the computer would randomly pick (Random condition).
Contrary to our hypotheses, we found that condition
per se did not predict children’s training choices. However, we
found that older children were more likely to make effective
and adaptive training choices than younger children. Overall,
our results indicate that children’s training choices improve
from ages 4 to 7 and inform the development of interventions
to support strategic learning.

Developmental changes in children's training strategies

Learning processes are impacted by individuals’ capabilities (e.g., working memory capacity). Nowadays, the technological improvements like those promised by deep learning methods open new opportunities to adapt learning environments. However, studies using the abilities of these models to individualize learning remain currently rare. The current research project aims to tackle one of the major constraints imposed by video learning related to the transitory aspect of delivered information. The purpose is to individualize the learning, based on learners working memory capacity (WMC).
To this end, several elements need to be taken into account: (1) How to automatically and efficiently evaluate WMC without impacting learning; (2) Analyze in details the actual effect of WMC on video learning; (3) Get the most efficient and acceptable solutions to reduce the effects of WMC.

Adaptive Learning Technology based on Working Memory Capacity

Digital adaptive fact learning systems tailor learning sessions to the individual learner. Most adaptive learning systems assume that the learner has no prior knowledge of the material and therefore use an initial ‘passive rehearsal’ trial for all facts. Here, we test a system that uses active retrieval trials instead of passive study trials when introducing items. This minimizes time loss associated with studying familiar materials and maximizes potential benefits of attempted retrieval before study. We test the system by having participants learn the association between the outlines and names of countries, a domain in which they are likely to have varying degrees of prior knowledge. We show that using attempted retrieval to identify known items is valuable in real-world applications, where partial knowledge of study materials before the start of a learning session is very common.

Test Before Study: Maximizing Adaptive Learning Gains using Prior Knowledge Assessment

Programming is a complex problem-solving domain, requiring the coordination of different types of knowledge and skills. The present study investigates expert and novice programming problem solving by analyzing talk-aloud transcripts and the code generated. Based on this analysis a set of basic goal and step components used by novice and expert programmers are identified, which will inform on the generation of cognitive models in the next phase of this research.

Investigating Expert and Novice Programming Problem Solving

Proficiency in math is critically important given its implications for education and daily life (e.g., finances, health). However, math is a challenging subject, and proficiency requires a complex interplay of content knowledge and general cognitive processes, including Executive Function (EF). In this exploratory study, we used heat maps to examine whether participants' self-reported attention to strategy-specific components of fraction arithmetic equations (i.e., operations, numerators, denominators) was related to their EF and task performance. Our results indicated that participants with stronger EF (indexed by a numerical stroop task) obtained higher fraction arithmetic scores and were also more likely to attend to strategy-specific components in the fraction problems. Additionally, a positive correlation was found between participants’ selection of strategy-specific components and their fraction arithmetic accuracy. 

Keywords: Fraction arithmetic; Strategy Reports; Executive Function; Inhibitory Control; Attention

Inhibition and Fraction Arithmetic: Insights from Heat-map Strategy Reports

Adaptive scheduling systems aim to estimate the ability of an individual in order to prescribe a personalized training schedule. These adaptive systems are often founded on regularities of human memory such as a learning, forgetting, and the spacing effect. One such model which has been developed to both account for regularities of memory and be used in applied contexts is the Predictive Performance Equation (PPE). One limitation of the PPE is that it is only able to account for and incorporate information about a participant’s accuracy on a task and cannot take into account additional performance measures such as reaction time. To expand the PPE, we propose a simple extension to the model, allowing it to account for both accuracy and reaction time measures. Our paper reports the extension to the PPE as well as a formal model comparison to another model of learning and retention (Pavlik and Anderson, 2005). The results of our model comparison reveal that the extended PPE can both better account and predict an individual’s performance than Pavlik and Anderson (2005) model.

Extending the Predictive Performance Equation to Account for Multivariate Performance

A fundamental skill needed to learn to program is code tracing, which involves simulating the steps a computer takes when executing a program to predict how it will behave and what output it. While code tracing helps students understand and write programs, students find this activity challenging. However, to date, little work exists on how to best support novice programmers in this activity. To address this gap, the high-level goal of our research is to investigates the utility of peer-tutoring for learning to trace programs. In our prior work, we conducted a preliminary case study exploring qualitative features of peer tutoring interactions. As the next step, we conducted an experimental study with two conditions: code tracing in a peer tutoring context vs. code-tracing alone. We present results from this study, focusing on quantitative results related to learning outcomes in each condition.

Investigating the utility of peer tutoring for learning the skill of code tracing

Technology has been used in the service of learning for a long time. Nowadays, the use of Artificial Intelligence (AI) is developing but its acceptability among future teachers still needs to be investigated. Moreover, differences between elementary and middle-school teachers could arise, due to the comparison between their role and those of technology involving AI. The current study aims at evaluating the acceptability of technology involving AI among future teachers, using a well-known model and more specifically regarding several tasks. Results show that elementary school teachers expect more performance from technology involving AI, but mainly for a use of content generation (e.g., course content, exercises). Middle-school teachers are more willing to accept technology involving AI for more high added value tasks such as help in writing learning or in diagnosing learning difficulties. Future studies should focus on identifying action levers to favor higher acceptability and actual use.

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Is there an Own-Age Advantage in Talker Recognition?

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