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A good deal of recent research has examined children’s norm learning across a wide range of novel contexts. The typical interpretation of these findings is that children’s norm learning is driven by group-based biases. In this paper, we present an alternative interpretation and corresponding meta-analyses that cast the current body of evidence in a rather different light. First, we argue the extant literature uses an ill-suited standard for assessing bias. Rather than comparing children’s judgments to what is expected under random chance (a ‘random standard’), bias is better assessed by comparing children’s judgments to what is most probable, given their total evidence (an ‘evidential standard’). Next, we report a meta-analysis of the known findings to date (k = 40 effect sizes; N = 1,369 in total; ages 4- to 13-years-old) to compare children’s norm learning against an appropriate evidential standard. Meta-analytic estimates reveal that children’s norm learning is not restrictively biased toward narrow-scope inferences on account of group-based factors. Rather, the findings to date are consistent with children’s norm learning being rational (i.e., statistically appropriate, given their evidence) or even inclusively biased toward making the wide-scope inference that a novel norm applies to everyone in a population. We conclude with brief discussion of implications for current understanding and future research on norm acquisition.

CogSci 2021

Is children&#39;s norm learning rational? A meta-analysis

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**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

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The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

Is children's norm learning rational? A meta-analysis

poster

When given unrestricted time to process an image, people are faster and more accurate at making categorical decisions about a depicted object (e.g., Labrador) if it is close in sensorimotor and linguistic distributional experience to its target category concept (e.g., dog). In this preregistered study, we examined whether sensorimotor and linguistic distributional information affect object categorisation differently as a function of time available for perceptual processing. Using an ultrarapid categorisation paradigm with backwards masking, we systematically varied onset timing (SOA) of a post-stimulus mask (17-133ms) following a briefly displayed (17ms) object. Preliminary results suggest that linguistic distributional distance between concept and category (e.g., Labrador → dog), but not sensorimotor distance, affects categorisation accuracy and RT even in rapid categorisation, and that these effects do not vary systematically by SOA. These findings support the role of a linguistic shortcut (i.e., using linguistic distributional instead of sensorimotor information) in rapid object categorisation.

Linguistic distributional information about object labels affects ultrarapid object categorization

Remembering names is generally considered a difficult task. The face-name mnemonic strategy enhances encoding of face-name associations using a name transformation, a prominent facial feature, and interactive imagery. Name recall involves mentally retracing these steps. However, the components of the face-name mnemonic strategy are relatively unexplored. The present study tested the effectiveness of variants of the face-name mnemonic strategy in comparison to an uninstructed group for learning face-name associations. Experiment 1 demonstrated a significant advantage of the name-transformation mnemonic strategy compared to the uninstructed approach. Experiment 2 further examines the name-transformation mnemonic strategy with young adults. This study demonstrates the effectiveness of a variation of the face-name mnemonic strategy with implications for memory rehabilitation interventions.

The effectiveness of face-name mnemonics on name recall

It has been shown that in hand-written transcription tasks temporal micro-behavioural chunk signals hold promise as measures of competence in various domains (e.g., Cheng, 2014). But data capture under that an approach requires the use of graphics tablets which are relatively uncommon. In this theoretical paper we propose and explore an alternative method – Competence Assessment by Stimulus Matching (CASM). This new method uses simple mouse-driven interfaces to produce temporal chunk signals as measures of learner’s ability. However, it is not obvious what features of CASM will produce effective competence measures and the design space of CASM tasks is large. Thus, this paper uses GOMS modelling in order to explore the design space to find factors that will maximize the discrimination of chunk measures of competence. The modelling results of this paper show that CASM has potential in using chunk signals to measure competence in the domain of English language.

Competence Assessment by Stimulus Matching: An Application of GOMS to Assess Chunks in Memory

Representing a location in space requires two things: an anchor point, and a code (or coordinate system) to define other locations relative to that anchor point. Recent work has shed light on the latter, providing evidence that the default ‘format’ of visuospatial representation may be polar coordinates (i.e., angle/distance relations). Yet the former remains a topic of debate. For example, a classic distinction in the realm of spatial navigation research pits representation relative to landmarks against representation relative to boundaries. Here, we exploit the polar format of spatial representations to propose a new method for assessing the locus of spatial representation. Specifically, we show that from simple localization errors we can infer the anchor point from which observers localized a target point. We highlight a few basic demonstrations of this method and discuss possible applications for further research on spatial representation.

‘Decoding’ the locus of spatial representation from simple localization errors

We explore the dynamic coordination of perception, decision, and action underpinning perceptual choices by recording cursor movements during a binary response task. Stimuli were presented sequentially to control the time-course of perception, and we utilized a Hidden Markov Model (HMM) to relate measured movements of the mouse cursor to latent cognitive processes. Stimuli were simple perceptual objects comprised of two features, one of which was fully diagnostic of the correct response, while the other provided a probabilistic cue. The order of their arrival varied across trials, allowing us to manipulate the order of feature processing. The model builds upon response time methods and makes predictions about when individual features were perceived and the accumulation of evidence towards a response, every 10 milliseconds of each trial.

Dynamic Perception Revealed by Cursor Movements and Hidden Markov Modeling

Studies have shown that video game players exhibit superior performance to non-video game players on a number of cognitive tasks. Methods to compare the groups have
mainly involved using measures of central tendency such as the mean without examining intra-individual differences in performance. In the present study, top-ranking Tetris players and novice Tetris players completed a reaction time cognitive task. Results show that the top-ranking players exhibit faster reaction times compared to novice players. Beyond the mean RT, we used the ex-Gaussian modeling technique and found differences in variability and attention between the two groups. Future studies can use modeling techniques such as the exGaussian distribution to analyze the whole distribution at an
individual level beyond measures of central tendency and further examine the behavioral differences between video game players and non-video game players.

Need for speed: Applying ex-Gaussian modeling techniques to examine intra-individual reaction time variability in expert Tetris players

When learning to bind visual symbols to sounds, to what extent do beginning readers track seemingly irrelevant information such as a symbol’s position within a visual display? In this study, we used adult typical readers’ own webcams to track their eye movements during a paired associate learning task that arbitrarily bound unfamiliar characters with monosyllabic pseudowords. Overall, participants’ error rate in recognition (Phase 1) decreased as a function of exposure, but was not modulated by the episodic memory-based effect of ‘looking-at-nothing’. Moreover, participants’ lowest error rate in both recognition and recall (Phases 1 and 2) was associated with item consistency across multiple exposures, in terms of spatial and contextual properties (i.e., stimulus’ screen location and co-occurrences with specific distractor items during encoding). Taken together, our findings suggest that normally developing readers extract statistical regularities in the input during visual-phonological associative learning, leading to rapid acquisition of these pre-orthographic representations.

Episodic Memory Cues in Acquisition of Novel Visual-Phonological Associations: a Webcam-Based Eye-Tracking Study

To hold someone responsible, we need to assess what causal role their action played in bringing about the outcome. Causality, in turn, can be understood in terms of difference-making -- C caused E if E would have been different had C been different. In previous work, the counterfactual of “C being different” is often construed as “C being absent”. Here, we explore how in social situations, this counterfactual can be alternatively construed by imagining how a different person would have acted in the same situation. We propose a computational model that formalizes this idea of counterfactual replacement. The model compares what actually happened with what would have happened had the person of interest been replaced by someone else, and then predicts responsibility to the extent that the replacement person would likely have changed the outcome. We test our model against people’s responsibility judgments in a variety of scenarios across several experiments.

The role of counterfactual reasoning in responsibility judgments

In collaborative tasks, humans can make better joint decisions by aggregating individual information in proportion to their communicated confidence (Bahrami et al., 2010). However, if people blindly rely on their partner’s confidence expressions, they could easily reach suboptimal solutions when their collaborator's confidence judgments are not calibrated to their performance, but for instance exhibit an overconfidence bias. Given that calibrated advisers are rated as more credible (Sah et al., 2013), we propose that prior experience with a collaborator will lead to a recalibration of their confidence judgements before incorporating their advice. In an online experiment, participants first viewed two other fictitious participants, one calibrated and one biased, perform a categorization task. Following this, participants completed a similar task by taking advice from just one of the two previously observed advisers on a given trial. We tested whether participants chose the adviser who had the trial-by-trial highest expressed or recalibrated confidence.

Do humans recalibrate the confidence of advisers?

In this experiment, we investigated the role of dimensional distinctiveness on the generalization of novel names for unfamiliar objects. In a comparison design, we manipulated the sequence of trials difficulty, starting either with more difficult trials or with easier trials. To achieve this, we manipulated the dimensional distinctiveness of the first comparison trials and of the, later, transfer trials. Results showed that high-distinctiveness (easy) stimuli increased children’s later performance in the low-distinctiveness (difficult) condition whereas low-distinctiveness early training led to no later improvement in easier trials. Last, a correct answer for the first trial in the first learning part predicted the level of performance in the second learning part. We interpret these findings in terms of differential costs of comparison for varying levels of distinctiveness and level of abstraction from one condition to another.

Is children's norm learning rational? A meta-analysis

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Linguistic distributional information about object labels affects ultrarapid object categorization

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