Biology subjects

Griffiths, T. L.

Publications and source records attributed to Griffiths, T. L..

4 recordsLinked to original sources

bioRxiv2026

Cognitive capacity and control in the evolution of intelligence

A diversity of intelligences arises from the constraints under which animals evolve. However, characterizing how constraints shape intelligence is challenging because it requires relating the restrictions on cognitive mechanisms to those that affect their evolution. We demonstrate the potentially complex interaction between constraints by considering the case study of working memory. Here, information-processing capability is limited by the storage capacity available to hold representations, and the degree of control over those representations. We present an evolutionary model that is mechanistically detailed enough to capture the interactions between capacity and control. This allows us to make quantitative predictions about the distinct patterns of information processing that might be observed across animals. Further, our model's cognitive detail allows us to fit recall performance on the retro-cue task, illustrating how model predictions can be tested by comparing humans and rhesus monkeys (Macaca mulatta). We find that capacity and control are synergistic and amplify each other's effects. However, evolution prioritizes investment in capacity because it is required for control to be effective. The strength of synergy varies due to interactions between these cognitive components depending on task complexity, cue reliability, and the availability of metabolic energy. Consequently, our model predicts diversity in investment in capacity and control across animals, and identifies a small number of regimes into which lineages could evolve. We discuss how the computational structure of tasks exerts selection on cognitive designs.

animal behavior and cognition↗

bioRxiv2023

Musical pitch has multiple psychological geometries

Pitch perception is central for both speech and music, and the representation of musical pitch has intrigued scholars for centuries. In his seminal work, Roger Shepard proposed a series of increasingly complex geometrical models to approximate the psychological representation of pitch, most famously the pitch helix which is often used in textbooks. The pitch helix represents the logarithmic scaling of the periodicity of tones and the similarity between tones separated by an octave. However, support for geometrical models of musical pitch derives from studies that are small-scale and sometimes contradictory. Moreover, research suggests that the representation of pitch is influenced by task context and expertise raising the question of whether any single integrated geometric approximation is sufficient. Using multi-dimensional scaling analysis, we revisit this problem through nine experiments involving participants with varied levels of musical expertise (N = 592) and paradigms covering both perception and production, as well as implicit and explicit measures of perceptual similarity. We show that, depending on task and musical experience, the best geometrical approximation can exhibit an array of structures ranging from linear to double-helical structures, providing strong evidence that a simple helical model, or in fact any fixed geometrical model, cannot explain the data. To explain these large variations, we then show that they can be captured by different reweightings of a small set of perceptual factors suggesting a composite representation. These findings highlight the importance of examining diverse tasks and populations to address the classic question of how perceptual representations are organized.

neuroscience↗

bioRxiv2022

Modeling human eye movements during immersive visual search

The nature of eye movements during visual search has been widely studied in psychology and neuroscience. Virtual reality (VR) paradigms provide an opportunity to test whether computational models of search can predict naturalistic search behavior. However, existing ideal observer models are constrained by strong assumptions about the structure of the world, rendering them impractical for modeling the complexity of environments that can be studied in VR. To address these limitations, we frame naturalistic visual search as a problem of allocating limited cognitive resources, formalized as a meta-level Markov decision process (meta-MDP) over a representation of the environment encoded by a deep neural network. We train reinforcement learning agents to solve the meta-MDP, showing that the agents optimal policy converges to a classic ideal observer model of search developed for simplified environments. We compare the learned policy with human gaze data from a visual search experiment conducted in VR, finding a qualitative and quantitative correspondence between model predictions and human behavior. Our results suggest that gaze behavior in naturalistic visual search is consistent with rational allocation of limited cognitive resources.

neuroscience↗

bioRxiv2022

Reconstructing the cascade of language processing in the brain using the internal computations of a transformer-based language model

Humans use complex linguistic structures to transmit ideas to one another. The brain is thought to deploy specialized computations to process these structures. Recently, a new class of artificial neural networks based on the Transformer architecture has revolutionized the field of language modeling, attracting attention from neuroscientists seeking to understand the neurobiology of language in silico. Transformers integrate information across words via multiple layers of structured circuit computations, forming increasingly contextualized representations of linguistic content. Prior work has focused on the internal representations (the "embeddings") generated by these circuits. In this paper, we instead analyze the circuit computations directly: we deconstruct these computations into functionally-specialized "transformations" to provide a complementary window onto linguistic computations in the human brain. Using functional MRI data acquired while participants listened to naturalistic spoken stories, we first verify that the transformations account for considerable variance in brain activity across the cortical language network. We then demonstrate that the emergent syntactic computations performed by individual, functionally-specialized "attention heads" differentially predict brain activity in specific cortical regions. These heads fall along gradients corresponding to different layers, contextual distances, and syntactic dependencies in a low-dimensional cortical space. Our findings indicate that large language models and the cortical language network may converge on similar trends of functional specialization for processing natural language.

neuroscience↗