Doby Rahnev

Stimulus reliability but not boundary distance manipulations violate the folded-X pattern of confidence The folded-X pattern has been identified as a critical signature of confidence: as conditions become easier, confidence increases for correct trials b…

Our new paper is out in Cognition! What determines whether confidence follows the classic "folded-X" pattern vs. the "double-increase" pattern? The answer lies in the type of stimulus manipulation. Big thanks to my advisor Doby @dobyrahnev.bsky.social and co-first author @herrickfung.bsky.social !

More evidence for ‘task-defining’ vs. ‘auxiliary’ stimulus manipulations, each with distinct effects on confidence.

Check out my new paper with @dobyrahnev.bsky.social and @kaixue98.bsky.social, now in Cognition. Also check out our earlier sister paper on this matter!

Kai's thread 👇

a while back i threatened to share this. finally online

for detection tasks we often systematically estimates sensitivity wrong. we need to control for unequal variance in models, but we often don't coz it needs extra data

now there's a virtually 'free' way to do it

www.cell.com/iscience/pdf...

Turns out that individual differences in accuracy, confidence, and RT among ANNs that only differ in their random initialization mimic the individual differences in humans.

It may be time for NeuroAI to take individual differences even more seriously.

Check out Herrick's thread 👇

🚨 New preprint on individual differences in artificial neural networks and human behavior.

We show that individual differences among ANN instances trained with different random initializations capture the individual differences in human behavior.

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This paper started almost a decade ago in collaboration with the amazing @racheldenison.bsky.social. Marshall Green and Mingjia Hu did the actual work.

These results don't mean that ANNs are a good model of internal evidence for all visual tasks (far from it), but they do show that this is likely to be the case for simple visual spaces.

Critically, artificial neural networks (ANNs) trained on the orientation task reproduced both the fine- and coarse scale results as emergent properties, without any special training or fine-tuning. This was the same for 3-, 4-, and 5-layer networks.

At the same time, increasing the stimulus tilt in coarse-scale increments had a highly non-linear transformation with a plateau beyond 14 degrees. This difference between fine- and coarse-scale results isn't predicted a priori from most standard models.

In a task where subjects judged if Gabors were tilted clockwise or counterclockwise, we examined how orientation is transformed into internal evidence. We found that increasing the stimulus tilt in fine-scale increments resulted in a linear increase in sensitivity.

Using Artificial Neural Networks to Relate External Sensory Features to Internal Decisional Evidence Abstract. All theories of perceptual decision-making postulate that external sensory information is transformed into the internal evidence that is used to judge the identity of the stimulus. However, ...

How do you know how visual stimuli are represented internally for decision making? This is perhaps the central question in perceptual decision making. In a new paper, we show that one can use artificial neural networks to crack this problem. #NeuroAi #VisionScience

direct.mit.edu/opmi/article...

Great work by the whole team: Medha Shekhar, @herrickfung.bsky.social, Krish Saxena, and Farshad Rafiei. Code and data posted as always.

More generally, our work represents the power of ANNs to uncover how humans represent and operate on perceptual information.

We found clear evidence that the Top2Diff model provided the best quantitative and qualitative fits to the data, suggesting that it most closely mimics the human confidence computation.

We then compared 7 confidence strategies: positive evidence (PE), Bayesian Confidence Hypothesis (BCH), Top-2 Difference in raw evidence (Top2Diff) or probability (ProbTop2Diff), Top Minus Average (ProbAvgRes), Entropy and Softmax. These are all the main competitors for multi-alternative decisions.

Human subjects performed an 8-choice digit categorization task based on noisy MNIST images. We used RTNet - a network we developed recently that is known to show the signature of human perceptual decisions (Rafiei et al., 2024, Nat Hum Beh) - to model the internal activation produced by each image.

Using artificial neural networks to reveal the human confidence computation Author summary Human decisions are accompanied by a sense of confidence which reflects the decision accuracy. Conventionally, human confidence has been studied using two-choice tasks with simple stimu...

How do people compute a sense of confidence? This question is usually addressed using very simple images because we don't know how complex stimuli are represented internally. In a new paper, we addressed this question using artificial neural networks (ANNs).

journals.plos.org/ploscompbiol...

It won't actually exist for another month or so, but because it now 'exists' on amazon, I'll humbly observe that, after working through this book, your student/trainee would be able to read and understand all but two or three papers in this week's J. Neurosci. Check it out:

New preprint: Confidence-accuracy dissociations in perceptual decision making. A review I was supposed to write 3 years ago for my VSS Young Investigator Award. Better late than never 😅 I tried to organize the literature and explore the likely mechanisms. Feedback welcome!

osf.io/preprints/ps...

Transcranial Focused Ultrasound for Identifying the Neural Substrate of Conscious Perception Identifying what aspects of brain activity are responsible for conscious perception remains one of the most challenging problems in science. While pro…

New paper: Transcranial Focused Ultrasound for Identifying the Neural Substrate of Conscious Perception. With Dan Freeman, @brianodegaard.bsky.social, and Seung-Schik Yoo. www.sciencedirect.com/science/arti...

Congrats Chaz!!! Also, lovely kids :)

Human-like individual differences emerge from random weight initializations in neural networks Much of AI research targets the behavior of an average human, a focus that traces to Turing's imitation game. Yet, no two human individuals behave exactly alike. In this study, we show that artificial...

No two humans behave exactly alike. But what about neural networks? We found early evidence that human-like individual differences in behavior emerge from networks trained with different initializations. Here’s a peek at our results—to be presented at UniReps & DBM @NeurIPS. Full paper on the way!

My lab at Boston University has open positions for a postdoc and PhD students. We study visual perception, attention, and decision making with a focus on temporal dynamics. Check out our recent work here sites.bu.edu/denisonlab/ and email me if you're interested in learning more

📣 BIG NEWS EVERYONE. I am so excited to announce…

🎉 I’m moving to University College London @ucl.ac.uk to join the Experimental Psychology department in @uclpals.bsky.social! 🎉

The big move happens in spring/summer. So I’m already exploring recruiting staff & students at UCL for fall 2026!

It looks like stimulus manipulations can be divided into "task-defining" and "auxiliary". The manipulations from each group have very different effects on accuracy vs. confidence. And all auxiliary manipulations seem to work in basically the same way. Really cool stuff by @herrickfung.bsky.social.

Similarities and differences in the effects of different stimulus manipulations on accuracy and confidence Visual stimuli can vary in multiple dimensions that affect accuracy and confidence in a perceptual decision-making task. However, previous studies hav…

Glad to see my first-year project is out!

In two experiments, we manipulated multiple stimulus features in a perception task, yet their effects on confidence and accuracy fell into just two distinct behavioral patterns, offering a way to predict the effects of novel stimulus manipulations.

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Internal World Models as Imagination Networks in Cognitive Agents What is the computational objective of imagination? While classical interpretations suggest imagination is useful for maximizing rewards, recent findings challenge this view. In this study, we propose...

World models are a highly speculative topic in AI as well as in cognitive science. I’m excited to share my manuscript on investigating internal world models using imagination networks in humans and LLMs! 🧵1/n

arxiv.org/abs/2510.04391

Introducing hMFC: A Bayesian hierarchical model of trial-to-trial fluctuations in decision criterion! Now out in @plos.org Comp Bio.
led by Robin Vloeberghs with @anne-urai.bsky.social Scott Linderman

Paper: desenderlab.com/wp-content/u... Thread ↓↓↓

#PsychSciSky #Neuroscience #Neuroskyence

Do you ever compute d' in detection tasks? Because of unequal variance, d' is biased in such tasks and you can't fix the bias without confidence ratings. In this preprint led by Kiyo Miyoshi and @hakwan.bsky.social, we show how to use RT instead of confidence to fix the d'.

osf.io/preprints/ps...

Our work showing human-like individual differences in perceptual decisions emerge from random weight initializations in deep neural networks has been accepted in two NeurIPS workshops! 🎉 Awesome job by my student @herrickfung.bsky.social in collaboration with the amazing @apurvaratan.bsky.social.