Bradley Love @ProfData Twitter profile

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Bradley Love

1 year

Introducing , a large language model (LLM) to assist neuroscience research. BrainGPT is trained on the vast neuroscience literature and can be used to optimize study design, detect anomalous results, and evaluate models against broad data patterns. (1/3)

BrainGPT

This is the homepage for BrainGPT, a Large Language Model tool to assist neuroscientific research.

braingpt.org

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Bradley Love

@ProfData

6 months

"Large language models surpass human experts in predicting neuroscience results" w @ken_lxl and . LLMs integrate a noisy yet interrelated scientific literature to forecast outcomes. 1/6

Large language models surpass human experts in predicting...

Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution....

arxiv.org

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Bradley Love

@ProfData

4 years

Introducing the embedding space you didn't know you needed: Human similarity judgments for the entire ImageNet (50k images) validation set. Perfect for evaluating representations, including unsupervised models. It's already bearing fruit, w @BDRoads (1/3)

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Bradley Love

@ProfData

4 years

This new preprint is a review of the many ways to link models and brain measures. It will appear in the 2nd edition of the "Introduction to model-based cognitive neuroscience" book, but is free here. I could see this working for a course.

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Bradley Love

@ProfData

4 years

New preprint, "A Too-Good-to-be-True Prior to Reduce Shortcut Reliance". If it's too good to be true, it probably is and that holds for deep learning as well. To generalize broadly, models need to learn invariants but instead are fooled by shortcuts. (1/4)

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Bradley Love

@ProfData

2 years

It can be exciting relating aspects of models, including deep networks, to the brain, but what does it mean to say a model layer and brain region correspond? The field has adopted "correlation implies correspondence", but is that valid? w @nickjsexton 1/3

Reassessing hierarchical correspondences between brain and deep networks through direct interface

Model-brain correspondences established by substituting parts of models with brain activity explain brain function.

www.science.org

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Bradley Love

@ProfData

3 years

New work w Daniel Barry, "A neural network account of memory replay and knowledge consolidation", tests some ideas regarding replay's role in learning at ImageNet Scale. Generative replay is akin to dreaming, training the network w novel murky images (1/3)

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Bradley Love

@ProfData

4 years

Interested in a PhD that combines aspects of computational neuroscience, cogsci, real-world behaviour, and deep learning? Please get in touch. I post bc potential students contact me, which is a biased sample. I hope to broaden and diversify, so feel free to get in touch. 1/2

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Bradley Love

@ProfData

2 years

I just listened to a brilliant neuroscientist explain how deep learning is scientifically useless because it's a black box that is too complex to be understood, then in the next beat said the key to understanding the brain is recording from more neurons. #BiggerGreyBox

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Bradley Love

@ProfData

5 years

New paper w @BDRoads , "Learning as the Unsupervised Alignment of Conceptual Systems". Supervised learning tasks can be solved by purely unsupervised means by exploiting correspondences across systems (e.g., text, images, etc.). 1/5

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Bradley Love

@ProfData

4 years

Now out, "Levels of Biological Plausibility", covering how notions of mechanism, reduction, and emergence are tied to levels, and how claims of biological plausibility are empty at best. Instead, specify relevant data and findings for model selection.

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Bradley Love

@ProfData

7 years

Heuristics work not because they are simple, but because they approximate Bayesian inference under an extreme prior.

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Bradley Love

@ProfData

5 years

"A non-spatial account of place and grid cells..." led by @Rob_Mok is out. So called place and grid cells may arise from general learning rules that are not specifically spatial. E.g., Grid response is a consequence of study designs, not core to brain.

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Bradley Love

@ProfData

4 years

Thanks to @pouget_alex , @KordingLab , and the @neuromatch community for the engaging debate and discussion yesterday on the role of Bayesian models in neuroscience. Below is a link to replay the event and my conclusion slide.

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Bradley Love

@ProfData

7 months

"The inevitability and superfluousness of cell types in spatial cognition" w @ken_lxl @Rob_Mok Whether place, border, head direction, Jennifer Aniston, or whatever cells, are we fooling ourselves? Are these intuitive findings scientific discoveries? 1/6

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Bradley Love

@ProfData

6 years

"Estimating the functional dimensionality of neural representations" is published open access in NeuroImage. Easy to use method with Matlab and Python code online. Examples are for fMRI, but suitable to other data types.

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Bradley Love

@ProfData

5 years

Out today, "Ventromedial prefrontal cortex compression during concept learning" w @mmack @preston_lab . vmPFC compresses information during learning in a goal-directed manner. Its timecourse is unique and parallels attention weights from learning models.

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Bradley Love

@ProfData

4 years

New blog, "A neuroscience-inspired approach to transfer learning" w @ken_lxl @BDRoads . We add goal-directed attention to the middle of a deep convolutional network and find it better adapts to new tasks than retraining the top layer as is standard in ML.

A neuroscience-inspired approach to transfer learning — Bradley C. Love

Inspired by the brain, we find a goal-directed attention approach to feature reuse bests a commonly used machine learning strategy (Luo et al., 2020). In particular, attentional modulation of...

bradlove.org

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Bradley Love

@ProfData

4 years

New paper with @adamnhornsby "How decisions and the desire for coherency shape subjective preferences over time". We find people's preferences align to match their choices, which in turn guides future decisions. 1/n

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Bradley Love

@ProfData

3 years

"Bidirectional influences of information sampling and concept learning" w @braunlich_k is out in Psychological Review. What you know affects what you see which affects what you know. We offer a simple account that consists of two model components. (1/6)

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Bradley Love

@ProfData

6 years

New preprint with @Rob_Mok , "A Non-spatial Account of Grid and Place cells". So called place and grid cells may arise from general learning rules that are not specifically spatial. E.g., Grid response is a consequence of study designs, not core to brain.

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Bradley Love

@ProfData

5 years

New preprint "The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks" (DCNNs). Continuing the virtuous cycle between neuroscience and ML/AI by incorporating top-down, goal-directed attention into DCNNs. w @BDRoads @ken_lxl

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Bradley Love

@ProfData

5 years

Here's a quick walk through our new preprint that aims to make deep learning more human-like by incorporating top-down goal-directed attention. 1/n

Bradley Love

@ProfData

5 years

New preprint "The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks" (DCNNs). Continuing the virtuous cycle between neuroscience and ML/AI by incorporating top-down, goal-directed attention into DCNNs. w @BDRoads @ken_lxl

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Bradley Love

@ProfData

4 years

Rosa Cao's paper on Predictive Processing is BRUTAL. Traditional and predictive processing accounts of the brain are informationally equivalent and only differ in whether one arbitrarily likes to view the "upward" signal as conveying error or the stimulus.

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Bradley Love

@ProfData

5 years

New preprint, "Levels of Biological Plausibility", covering how understanding of mechanism, reduction, and emergence are tied to levels, as well as how biological plausibility is an incoherent concept under a levels of mechanism view.

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Bradley Love

@ProfData

5 years

Our new blog "Model-based fMRI giveth and taketh away" shares our experiences as one of the 70 labs participating in the project on reproducibility in fMRI analyses. @seb_bobadilla , @o_guest , and I did 2 analyses and found (1/n)

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Bradley Love

@ProfData

7 years

"How a CogSci undergrad invented PageRank three years before Google" is a blog highlighting what cognitive science can contribute to machine learning, touching on my first project and pub way back in 1995.

How a CogSci undergrad invented PageRank three years before Google — Bradley C. Love

Before Google, search engines, like AltaVista, often retrieved spurious web pages. Out of all the possible pages to return how does one determine which ones are the most relevant? One key to Google’s...

bradlove.org

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Bradley Love

@ProfData

3 years

How can words shape meaning? We ( @ken_lxl @nickjsexton ) offer "A deep learning account of how language affects thought" that goes from images to meaning to words. Words are a discriminative signal that shapes meaning, like how actions and decisions can.

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Bradley Love

@ProfData

4 years

My experience getting up to speed with variational Bayesian inference last week:

Aleksandar Ivanov 🇺🇦

@phant0msp1k3

4 years

How to be more impressive? What a lot of current Neuroscience papers feel like.

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Bradley Love

@ProfData

3 years

When does unsupervised training help or hurt human learners? Work with Franziska Bröker and Peter Dayan on getting semi-supervised learning right by aligning a learner's internal representations with unsupervised training.

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Bradley Love

@ProfData

4 years

Our ( @BDRoads ) TiCS spotlight "Similarity as a Window on the Dimensions of Object Representation" discusses exciting work led by @martin_hebart on inferring semantic representations from human similarity ratings. (1/2)

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Bradley Love

@ProfData

3 years

New preprint w @nickjsexton "Directly interfacing brain and deep networks exposes non-hierarchical visual processing". Instead of correlating model and brain activity, a stricter test of correspondence is driving model behavior with brain activity. (1/6)

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Bradley Love

@ProfData

6 years

Open is good right? In response to @KriegeskorteLab 's open review of our work on neural similarity, I offer you "An open review of Niko Kriegeskorte", which is a less tedious read that touches on how difficult it is do something novel in this field.

An Open Review of Niko Kriegeskorte — Bradley C. Love

Imagine you think and work carefully on an ambitious paper for a few years, trying to answer fundamental questions the field has overlooked. Now, imagine after a very long wait you receive negative...

bradlove.org

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Bradley Love

@ProfData

4 years

1000th tweet. (1) New profile pic for the pandemic (2) brief review paper on "Model-based fMRI analysis of memory". The paper covers the role cognitive models can play and how they mesh with other approaches.

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Bradley Love

@ProfData

1 year

BrainGPT aims to tame the vast neuroscience literature to assist scientists in their research. Sign-up at to get involved. The video below provides background and motivation for the project. Our benchmarking effort is also covered.

Bradley Love | Advancing Neuroscience Using Large Language Models

*Apply to join Foresight Neurotech program:* https://foresight.org/neurotech-improving-cognition-program/ A group of neuroscience researchers, entrepreneurs,...

www.youtube.com

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Bradley Love

@ProfData

1 year

When people do "semantic foraging" from memory (e.g., list all the animals you know) they tend to generate clumps of related items (e.g., pets, then African animals, then farm animals, etc.). ChatGPT shows this same behavior for a bunch of prompts, 1/2

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Bradley Love

@ProfData

3 years

I am struck by neuroscientists often conflating "all our constructs arise from lower-level entities" with naive reductionism, degenerating to name calling at times. How the physical world operates and how to best study and understand minds need not align.

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Bradley Love

@ProfData

6 years

It might be time to go farther and break away from the content heavy training in the traditional psych degree with a new degree track that emphasizes mathematical and computational methods more as these are the skills/knowledge increasingly needed in research and many jobs.

Olivia Guest · Ολίβια Γκεστ

@o_guest

6 years

New blog post! 👩🏻‍💻👨🏿‍💻👩🏾‍💻👨🏽‍💻👩🏼‍💻👩🏿‍💻👨🏻‍💻 Why women in psychology can't program "About two months ago my brother, who works in a data science on social psychology data, asked me why his colleagues, who are women and have PhDs in psychology, cannot code"

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Bradley Love

@ProfData

4 years

I uploaded my first youtube video; watch your back PewDiePie! The video is my CNS talk from last month. "Category Learning as Compression", which is joint work with @mmack , @preston_lab , @braunlich_k , and others.

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Bradley Love

@ProfData

2 years

System alignment is a different, complementary, way to view learning. Rather than learning from events, entire systems (e.g., visual and linguistic modalities) are aligned based on similarity relations within each system. (1/2)

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Bradley Love

@ProfData

5 months

Here's a recent talk I gave on the project, touching on findings from here, . The video is sectioned for those wishing to skip the bits on explanatory vs. predictive approaches.

"Taming the neuroscience literature with explanatory and predictive...

Chapters:0:00 Introduction3:30 Process models (theoretical, explanatory)16:18 Models as tools (BrainGPT, predictive)39:03 ConclusionsModels can help scienti...

www.youtube.com

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Bradley Love

@ProfData

5 years

New preprint with @Rob_Mok , "Abstract neural representations of category membership beyond information coding stimulus or response". For flexibility, the brain represents categories in a format orthogonal to sensory-motor codes, akin to an a amodal symbol

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Bradley Love

@ProfData

9 months

How predictable is neuroscience? Can LLMs outperform humans? Please participate in the survey to help us find out. You choose between two versions of a neuro abstract: the original vs. one with altered results. Which is which? 1/2

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Bradley Love

@ProfData

5 years

New preprint with @BDRoads , "Learning as the Unsupervised Alignment of Conceptual Systems". Because items similar in one system (images) are also in other systems (text, audio), supervised tasks can be solved by unsupervised learning.

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Bradley Love

@ProfData

6 years

What makes two brain states similar? New preprint from my lab by @seb_bobadilla @mehrotrabhinav tests competing similarity measures. Same measures do well across brain regions, but change across tasks. Pearson corr, the de facto standard, never does well.

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Bradley Love

@ProfData

5 years

New preprint "The neural link between subjective value and decision entropy" w @seb_bobadilla @o_guest . Subj value and confidence, measured as inverse entropy, are intertwined throughout the brain; tied to the desirability of actions. No pure value areas!

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Bradley Love

@ProfData

9 months

"Adaptive stretching of representations across brain regions and deep learning model layers": Using monkey multi-unit recording data, we find representations across cortex (PFC, FEF, LIP, IT) adaptively stretch along task relevant stimulus dimensions. 1/7

Adaptive stretching of representations across brain regions and deep learning model layers

Prefrontal cortex (PFC) is known to modulate the visual system to favor goal-relevant information by accentuating task-relevant stimulus dimensions. Does the brain broadly re-configures itself to...

www.biorxiv.org

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Bradley Love

@ProfData

5 years

Two postdocs in computational cognitive neuroscience wanted. We are open and supportive of people coming from either the psych/neuro or the compsci side. One push is making deep learning more human-like using brain and behavioral data. Submitted ad, (1/2)

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Bradley Love

@ProfData

3 years

New work with Daniel Barry, "Human learning follows the dynamics of gradient descent". Artificial neural networks are everywhere in psych/neuro, but few have considered whether humans and networks share common dynamics when learning new tasks. (1/4)

Human learning follows the dynamics of gradient descent

Artificial neural networks (ANNs) have achieved near human-level performance on many tasks and can account for human behavioural and brain measures in a number of domains. Although a principal...

osf.io

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Bradley Love

@ProfData

6 years

"Bidirectional Influences of Information-Sampling and Concept Learning". New preprint from lab w @braunlich_k that casts categorization as an active sampling process in which what you know affects what you see. From eye movements to information bubbles!

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Bradley Love

@ProfData

2 years

Work w @Rob_Mok , "A multi-level account of hippocampal function from behaviour to neurons". How can high-level cognitive constructs like symbols, clusters, and so forth be implemented in neurons? That's a gap not addressed by model-based neuroscience. 1/8

A multi-level account of hippocampal function from behaviour to neurons

A complete neuroscience requires multi-level theories that address phenomena ranging from higher-level cognitive behaviors to activities within a cell. A levels-of-mechanism approach that decomposes...

www.biorxiv.org

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Bradley Love

@ProfData

5 months

In the preprint, , critics worried there were precursors to our BrainBench test items in the scientific literature that helped LLMs predict results. Here's an additional check to rule that out. 1/2

Large language models surpass human experts in predicting...

Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution....

arxiv.org

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Bradley Love

@ProfData

6 years

PhD studentship in AI available NOW. Just got word of funding for a project on unsupervised deep learning. Ideal for students interested in human cognition who take some inspiration from the brain. Please get in touch () ASAP!

Bradley C. Love

I am Professor of Cognitive and Decision Sciences in Experimental Psychology at UCL and a fellow at The Alan Turing Institute for data science. My lab's research centers around human learning and...

bradlove.org

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Bradley Love

@ProfData

5 years

New work with @adamnhornsby on how people form subjective preferences through discriminative learning to promote self-consistency. While standard reinforcement learning (RL) can master Atari games, Go, etc., real life doesn't serve up points to maximise.

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Bradley Love

@ProfData

5 years

New preprint with @o_guest , "Levels of Representation in a Deep Learning Model of Categorization" The convolutional network (think ventral stream) provides representations to support novel concept learning (think hippocampus, mPFC). (1/4)

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Bradley Love

@ProfData

6 years

Updated preprint, "Measures of Neural Similarity" For non cogsci folks, we explain similarity≠classification, show sim. generalizes beyond fixed classes, information theory basis for decoding, and how CNNs work with diff. sim. functions. @seb_bobadilla

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Bradley Love

@ProfData

7 years

Good news, this preprint has been accepted for publication at Cognitive Psychology with no revisions, which is a first for the Editor (and me)!

Bradley Love

@ProfData

7 years

Heuristics work not because they are simple, but because they approximate Bayesian inference under an extreme prior.

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Bradley Love

@ProfData

4 years

One upshot is that there are no value areas per se in the brain because wherever there is value there is also a confidence signal that combines to provide a post decision evaluation of the choice option.

Sebastian Bobadilla Suarez

@seb_bobadilla

4 years

We find a systematic relationship between subjective value (SV) and inverse decision entropy (iDE, similar to decision confidence) in the medial cortex (and other areas). 2/n

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Bradley Love

@ProfData

6 months

Neuroscientists should read some psychology to learn that experts are not necessarily the best at prediction. Expertise is more than that.

Kording Lab 🦖

@KordingLab

6 months

Are neuroscience experts like me about to be outperformed at predicting our own experiments by BrainGPT or similar ( @profdata )?

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Bradley Love

@ProfData

5 years

"Measures of Neural Similarity" led by @seb_bobadilla is out. We used a decoding approach to determine what makes two brain states similar. Findings were striking: the same similarity measures flourished across brain regions, but not across tasks. (1/2)

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Bradley Love

@ProfData

7 years

Your brain is Bayesian on Amazon; integrating value and confidence judgments according to their reliability.

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Bradley Love

@ProfData

3 years

New preprint with @adamnhornsby "Sequential consumer choice as multi-cued retrieval". How do you make a choice when the options are virtually unlimited, like in online grocery shopping? One possibility is that our last choice cues the next in memory. 1/6

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Bradley Love

@ProfData

3 years

New preprint, "A Deep Learning Account of How Language Affects Thought", w @ken_lxl @nickjsexton . Shared labels highlight commonalities between concepts whereas contrasting labels make differences apparent. We offer a straightforward account, (1/2)

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Bradley Love

@ProfData

6 years

Read about an alternative timeline where I don't exist, "To learn how cognition is implemented in the brain, we must build computational models that can perform cognitive tasks, and test such models with brain and behavioral experiments" @NKriegeskorte

Cognitive computational neuroscience

To learn how cognition is implemented in the brain, we must build computational models that can perform cognitive tasks, and test such models with brain and behavioral experiments. Cognitive...

arxiv.org

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Bradley Love

@ProfData

6 years

Useful R package for those wanting to work with models of human category learning.

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Bradley Love

@ProfData

2 years

New work, "A controller-peripheral architecture and costly energy principle for learning", by @ken_lxl @Rob_Mok @BDRoads We offer a framework for how brain regions coordinate to complete a task and apply the approach to concept learning from images. 1/4

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Bradley Love

@ProfData

6 years

@tallinzen @o_guest Too much to say on twitter, but just look at the conference. it's a ton of parallel tracks of hit-and-miss quality to suck in sufficient numbers of junior researchers into a pyramid scheme in which they pay tribute to baby boomer club members awarding each other prizes.

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Bradley Love

@ProfData

5 years

SFN, tomorrow afternoon, "The neural link between subjective value and decision entropy" by @seb_bobadilla and @o_guest . Value and inverse entropy (confidence) are deeply intertwined across the brain, co-occurring with a +/+ and -/- pairing. No pure value areas. (608.03/BB54)

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Bradley Love

@ProfData

2 years

Thread on new paper "Sequential consumer choice as multi-cued retrieval" w @adamnhornsby @ScienceAdvances . We use multiple embedding spaces to explain how people make open-ended sequential decisions. How do you choose from an unlimited set of options? 1/6

Sequential consumer choice as multi-cued retrieval

Five million sequential consumer choices are predicted by their similarity across multiple systems in memory.

www.science.org

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Bradley Love

@ProfData

11 months

Can learning occur offline with asyncrhonous inputs? Yes, in systems alignment entire embeddings (e.g., visual and linguistic) are mapped in an unsupervised manner. Systems alignment appears to support children's word learning. w @kaarinaho @BDRoads (1/3)

Signatures of cross-modal alignment in children’s early concepts | PNAS

Whether supervised or unsupervised, human and machine learning is usually characterized as event-based. However, learning may also proceed by syste...

www.pnas.org

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Bradley Love

@ProfData

7 years

For model-based and multivariate fMRI analyses, this can tell what an account is missing, "Estimating the functional dimensionality of neural representations",

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Bradley Love

@ProfData

1 year

Benchmark time! In search of collaborators. To properly develop BrainGPT and other approaches, we need to make and publish a decent neuroscience benchmark. We are looking for 20 or so co-authors to contribute several test cases each (instructions will be provided). (1/2)

Bradley Love

@ProfData

1 year

Introducing , a large language model (LLM) to assist neuroscience research. BrainGPT is trained on the vast neuroscience literature and can be used to optimize study design, detect anomalous results, and evaluate models against broad data patterns. (1/3)

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Bradley Love

@ProfData

6 years

New blog, "Fast food science is a shit sandwich". Are we expediting science by engaging in real-time, interactive debate or are we instead making deep thought and inquiry that much more difficult?

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Bradley Love

@ProfData

5 years

The world as a laboratory for studying human decision making. With @adamnhornsby @london_rosie @GilesPavey @gershbrain @RaBhui @nj_blair , and many more.

Do supermarkets know more about us than we do?

Product loyalty is not solely based on value, quality or choice but also on personal attachment, studies suggest.

www.bbc.co.uk

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Bradley Love

@ProfData

7 years

Less-is-more can be true at the algorithmic level, but is false at the computational level; sim + proof in preprint

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Bradley Love

@ProfData

6 years

Postdoc wanted, any nationality. There will be a formal job description advertised in the coming weeks, but this is a heads up for anyone interested!

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Bradley Love

@ProfData

4 years

I've seen related disasters in Psych/Neuro/CogSci. Be weary of complex models when the code is not freely available and does not generate the published results. Pages of magic constants and if-then special cases are red flags. Learning models should be trainable from scratch.

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Bradley Love

@ProfData

6 years

Topic modelling on consumer activity reveals semantic categories organised by goals, not shared features. Big data from supermarket offers alternative to text corpora analysis. For concepts, You are what you eat! Preprint with @adamnhornsby @london_rosie

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Bradley Love

@ProfData

1 year

Note: does not summarize nor retrieve articles. In such cases, LLMs often confabulate, which can be harmful. Instead, BrainGPT stitches together existing knowledge too vast for human comprehension to assist humans in expanding scientific frontiers. (3/3)

BrainGPT

This is the homepage for BrainGPT, a Large Language Model tool to assist neuroscientific research.

braingpt.org

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Bradley Love

@ProfData

6 years

Bridging levels of analysis, cognitive models reflect individual differences in learned attention for both behavioral and neural measures. Now in press at JEP: General,

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Bradley Love

@ProfData

7 years

New work from @mmack , @preston_lab and me shows mPFC compresses info during learning in a goal-directed manner (1/2)

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Bradley Love

@ProfData

6 years

Version 1.0 of the Matlab and Python code for "Estimating the functional dimensionality of neural representations" is up thanks to the heroic efforts of @o_guest , @seb_bobadilla , and @braunlich_k in lab. Please give the code a try, available on github,

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Bradley Love

@ProfData

4 years

I had a great discussion with @pgmid on his Brain Inspired podcast, touching on concept learning, cognitive models, the hippocampus, deep nets, emergence, levels of analysis, and more. It was super fun. (1/2)

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Bradley Love

@ProfData

6 years

@tyrell_turing @aidanhorner @J_A_Quent It's like people lost the plot a bit, like why they got into psychology/neuroscience/science in the first place.

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Bradley Love

@ProfData

5 years

For folks excited by this recent Nature paper on dimensionality and smoothness of neural representations, here's a robust method to estimate () dimensionality with code (). For representational smoothness,

What the success of brain imaging implies about the neural code

For brain imaging to be useful despite its limitations in measuring neural activity, the neural code must be smooth both in a traditional sense and functionally.

elifesciences.org

Carsen Stringer @[email protected]

@computingnature

5 years

"High-dimensional geometry of population responses in visual cortex" now with 27-page math supplement! with @marius10p , @kennethd_harris , @MatteoCarandini , @SteinmetzNeuro

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Bradley Love

@ProfData

2 years

We ( @kaarinaho @BDRoads ) offer a new viewpoint on learning. Rather than master (x, y) pairs (e.g., stimulus, category), we propose entire systems are mapped back-and-forth. E.g., from X (e.g., images) to Y (e.g., words) and from Y to X. People do this! 1/3

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Bradley Love

@ProfData

6 years

"Model comparison, not model falsification", I think a lot of thorny problems are minimized by model comparison, rather than debate over optimality, etc. I look forward to reading the other commentaries to this enjoyable BBS article.

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Bradley Love

@ProfData

6 years

Good advice for those restricted to bad practices. For students serious about #openscience and avoiding errors in 2018, the data analysis should be done by a script (R, Python, whatever) on the raw data files. Even better, properly comment and use version control (e.g., github).

Dorothy Bishop

@deevybee

6 years

This is possibly one of the most informative and useful abstracts I have ever read. People: do this!

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Bradley Love

@ProfData

5 years

Good morning SFN attendees, start your day with 170.10 / AA18 - "An abstract neural representation of category membership beyond information coding stimulus or response" by @Rob_Mok . The title says it all, surprising, like the brain constructed an amodal symbol.

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Bradley Love

@ProfData

6 years

Neuro folks, many are confused: Last week, behavior was key. This week it's philosophy. Oscillate between neural networks are the savior to they are implausible. Build new ontologies from data, but theory is key. Etc. All of this confusion is largely from same circle of people.

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Bradley Love

@ProfData

6 years

Whoa, that was the nicest interview ever; it reminded me that work is a fun intellectual journey. It's probably time to finally join the BNA ( @BritishNeuro ),

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Bradley Love

@ProfData

5 years

For the politically minded, new work with @adamnhornsby shows voters readily adopt any position their chosen candidate takes, particularly those from a certain US party. The drive to be coherent in terms of past choices (including votes) is powerful.

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Bradley Love

@ProfData

4 years

The "The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks" is now out in Computational Brain and Behavior. The quote tweet below briefly walks through the preprint. The published version includes an added bonus, (1/2)

Bradley Love

@ProfData

5 years

People use top-down attention, such as when searching for one's keys. One idea is that prefrontal cortex helps reconfigure our visual system to be more sensitive to features relevant to our goals. This has costs and benefits. 2/n

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Bradley Love

@ProfData

3 years

This is a different way of viewing learning. In addition to trial-by-trial correction, people may align entire conceptual systems to facillitate learning and zero shot generalization. For example, people might align an embedding space for visual objects with one for words. 1/3

Kaarina Aho

@kaarinaho

3 years

New paper w @BDRoads & @ProfData : "System alignment supports cross-domain learning and zero-shot generalisation" (). We find that alignable systems accelerate learning for cross-system mappings, and facilitate generalisation to completely novel stimuli.

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Bradley Love

@ProfData

9 months

Do you think neuroscientists or large language models (LLMs) will perform better on the benchmark? (Neuroscientists, please participate in the benchmark. Here's the survey, 11 questions, 15-20 minutes, )

Neuroscientists

70

LLMs

51

Show me the results

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Bradley Love

@ProfData

6 years

Thanks to everyone for the fruitful discussions on open and closed reviewing. I learned a lot. Open review is clearly not one thing and it's not simply a question of how open, but of how well conceived and executed. E.g., platform used, consent,etc. Not a seat-of-the-pants thing

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Bradley Love

@ProfData

7 years

Cognitive models are "real" at both behavioral and neural levels, linking individual differences in attention across levels. Preprint with @braunlich_k , "Occipitotemporal Representations Reflect Individual Differences in Conceptual Knowledge"

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Bradley Love

@ProfData

6 years

Check out @Rob_Mok 's poster "A Non-spatial Account of Grid and Place cells". We think place and grid cells are poorly named bc they arise from general learning rules that are not specifically spatial. E.g., Grid response is a consequence of study designs, not core to brain.

Mona Garvert

@mona_garvert

6 years

The grid cell meeting is starting in a few hours and we will be sharing most of the event with the whole wide world! Link to the livestream on our website from 9am BST: #gridmeeting18

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Bradley Love

@ProfData

3 years

Great to hear @SaxeLab and @talia_konkle have critical comments about pushing "biological plausibility" on this podcast. I have the paper for you devoted to this topic and related issues in emergence and reduction, "Levels of biological plausibility",

Levels of biological plausibility | Philosophical Transactions of the Royal Society B: Biological...

Notions of mechanism, emergence, reduction and explanation are all tied to levels of analysis. I cover the relationship between lower and higher levels, suggest a level of mechanism approach for...

royalsocietypublishing.org

Paul Middlebrooks

@pgmid

3 years

Once we tackle these 26 ideas and assumptions holding us back (and befriend more crackpots, per @gershbrain ), we'll sing... Don't you know I'm still standing better than I ever did Looking like a true survivor, feeling like a little kid. anyone...?

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Bradley Love

@ProfData

4 years

And thanks to @socmathpsych for the best paper award in @CompBrainBeh (a journal full of amazing papers). There's a lot that can be done examining behaviour outside the lab at scale to better understand the human mind.

Adam Hornsby

@adamnhornsby

5 years

*Paper published with @ProfData and @dunnhumby at @CompBrainBeh !* . We used data from ~1.3m till receipts to evaluate how people semantically represent objects in-the-wild. Our model revealed that real-world categories are organised around goals👇

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