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Reposted by Andrew Wang

Manikya Alister @manikyaalister.bsky.social · 09/06/2026

One of the first studies from my PhD is out now in JEP:G 🥳We tested whether people can infer the truth from teachers who were either helpful, misleading, or randomly sampling. With Keith Ransom and @perfors.net psycnet.apa.org/fulltext/202...

Title of paper: When a Helpful Bias Is Unhelpful: Limitations in Reasoning
About Random and Deliberately Misleading Evidence
Abstract: Social information aids learning: By making assumptions about other people’s knowledge and intentions,
people can draw strong and accurate inferences from limited data. In this study, we systematically tested
people’s ability to reason from information providers with different intentions. The task was an adaptation of
Shafto et al.’s (2014) rectangle game, where learners guessed a rectangle’s size and location based on
provided clues. We examined reasoning based on information from four types of providers: a helpful
provider, a provider who sampled randomly, and two misleading providers (who could mislead but not lie).
We also varied whether people were given a cover story describing the provider in advance or whether they
could infer how helpful a provider was based on what the provider shared. Participants learned efficiently
from helpful providers, aligning closely with the predictions of a normative Bayesian model, even without a
cover story. However, while people usually recognized unhelpful providers, they struggled to identify and
respond appropriately to misleading strategies. Overall, our results suggest a helpful bias: In our task,
participants assumed helpful intent unless given explicit feedback, and even then, they did not fully adjust in
line with Bayesian predictions. People also struggled to overcome this bias when learning from randomly
sampled information, especially when they had experience being an information provider themselves
(Experiment 3).

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Andrew Wang @andreww3-wang.bsky.social · 17/06/2026

📄 Another new paper! Together with Marc Brysbaert and Fritz Günther, we collected norms for over 80,000 English words and expressions to find out which people generally find most important/useful, and see which lexical properties predict a word’s *utility*. 🔗 link.springer.com/article/10.3...

link.springer.com

Adding volition to word processing: Expected utility norms for 80,000 English words and multiword expressions - Behavior Research Methods

This study examined the concept of word usefulness by analyzing expected utility ratings for over 80,000 English words and multiword expressions. Participants used best–worst ratings to indicate how u...

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Andrew Wang @andreww3-wang.bsky.social · 28/04/2026

📄 Long overdue paper announcement: Core Vocabulary in Language Representation and Processing was published late last year in Cognitive Science. The work explores novel approaches of quantifying vocabulary centrality and tests them in an empirical task. 🔗 onlinelibrary.wiley.com/share/author...

onlinelibrary.wiley.com

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Andrew Wang @andreww3-wang.bsky.social · 28/04/2026

I'm Andrew, an emerging cognitive scientist at @unimelb.edu.au @psychunimelb.bsky.social, and soon at National University Singapore, broadly studying topics in the area of language. I'll be posting stuff here now I guess. idk how this thing works yet.

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Andrew Wang @andreww3-wang.bsky.social · 28/04/2026

*taps mic* 🎤 is this thing on?

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