Gary Lupyan

@glupyan.bsky.social

Are the mental contents underlying implicit evaluations accessible to conscious introspection? Prior studies show high predictive accuracy, leading to a growing consensus that people can introspectively access their implicit biases. New paper out in JPSP! 🧵🧵🧵

The tide is turning against a major idea in social psychology: that implicit evaluations reflect mental content that lies beyond conscious awareness. This view is being reconsidered in light of mounting evidence that people can predict their own implicit evaluations with high accuracy. However, there are reasons to question whether such predictive accuracy reflects introspective access. First, prior studies have relied almost exclusively on familiar targets (e.g., racial groups), allowing predictions to be informed by background knowledge (e.g., knowing that a group is stigmatized) rather than introspection. Second, implicit and explicit evaluations have been highly correlated in prior work, enabling accurate predictions simply by assuming that implicit evaluations mirror explicit ones. Here, we report eight experiments (five pre-registered; N = 6,794) designed to minimize these nonintrospective routes to predictive accuracy. We introduced participants to novel targets and shifted implicit and explicit evaluations of these targets in opposite directions, rendering explicit evaluations an unreliable cue. Under these conditions, predictive accuracy ranged from low to nonexistent; participants frequently anticipated shifts in their implicit evaluations in the opposite direction of the actual change. These results generalized across two learning paradigms (impression formation and attribute conditioning), two implicit evaluation measures (Implicit Association Test and evaluative priming task), and between-participant and within-participant designs. We consider multiple interpretations of these findings, including the possibility that implicit evaluations reflect mental content that is largely or even entirely inaccessible to conscious awareness.

Most of the federal agency science budgets goes into the salaries of students and trainees, and the decision to take that money and instead give it to industry and already rich venture capitalists is fundamentally a decision to have many fewer scientists and do far less science in the future.

Psychologists should found “participant island,” where we strand a large representative sample of people who must complete our tasks to earn airdrops of food and water. If you’re really good you get to temporarily board the Neuroimaging yacht

As someone who increasingly uses AI (coding, exploring connections b/w ideas, summarizing, outlining, editing..) I find LLM-generated text to be clean and efficient. LLMs know how to communicate. But I am literally nauseated by the idea of putting any LLM-written text into the public sphere.

Great piece. It may just be the algorithm but it does seem like there’s a recent vibe shift where a new group of people are having a “ohhh this is real” moment that others had a long time ago. Agentic LLMs workflows seem to have a lot to do with it. Others are reacting by digging into their denial.

Aeon Magazine@aeon.co · 2mo ago

Adopting a cautiously optimistic perspective on AI, the literary critic and editor Martin Puchner proposes that we share something fundamental with the machines: the use of language. To explore this, he draws on a rich tapestry of theory, literature and life experience in this Essay

What's so poignant about this heart-wrenching thread is that the students know AI can't be brushed off. This is a Copernican moment for human cognition and so many academics seem to be stuck on it-can't-even-spell-spell-strawberry level denial.

Jeff Sharlet@jeffsharlet.bsky.social · 2mo ago

At the end of the term I asked my college creative wriing students to submit anonymous thoughts on AI. No real surprises: Mood ranges from resignation to despair, capitulation from embittered erosion of standards to total, feelings of betrayal from deep to furious. 1/

I used to explicitly teach exactly this at university of bath computer science in a course called “research project preparation” How to read science articles was in the same lecture as how to not plagiarise / how to cite; iirc Good to have a reading for @hertieschool.bsky.social students, thx Pat

Pat Savage@patrickesavage.bsky.social · 2mo ago

My hot take: we faculty bear some blame for failing to teach students how to read efficiently. Reading every word was never practical, and LLMs tempt students we haven’t taught otherwise. Here’s what I wrote to teach this - please use with your own students if helpful! doi.org/10.31234/osf...