SBS DTP PhD student Nathaniel Farndale Wright has co‑authored a new Commentary article in Behavioural and Brain Sciences (Volume 48), offering a novel perspective on how sleight‑of‑hand magic can help reveal the brain’s unseen information‑processing mechanisms. The work responds to the recent Target Article by Coombs and Trestman on the evolution of brains and cognition across the animal kingdom.
Using Magic to Understand the Mind
In their Commentary, On bodies, brains, and behaviour (and a little bit of magic), Farndale Wright and Professor Nicola S. Clayton FRS propose that magic illusions, particularly those involving skilled manual actions, offer a powerful method for investigating the predictive algorithms the brain uses to navigate complex and changing environments. By examining how both humans and non‑human animals experience prediction errors during sleight‑of‑hand effects, the authors argue that magic can act as a window into the hidden mechanisms underlying sensorimotor processing.
Target Article: Coombs, S., & Trestman, M. (2025). A multi-trait embodied framework for the evolution of brains and cognition across animal phyla.Behavioural and Brain Sciences, 48, e77. doi:10.1017/S0140525X24000335
Access the Commentary (Open Access): https://doi.org/10.17863/CAM.118146
About the Research
Understanding how the brain integrates vast quantities of sensorimotor information to make predictions and guide behaviour remains one of the major challenges in contemporary neuroscience. Leading theories suggest the brain uses efficient “shortcuts,” drawing on prior experiences to form predictions that are updated through sensory feedback.
Breakdowns in these systems are thought to play a role in neurodevelopmental and psychiatric conditions including OCD, schizophrenia, and dyslexia, yet the neural pathways responsible often remain inaccessible.
Recent comparative work in the psychology and biology of magic has opened up new opportunities for investigating these pathways. By studying how different species respond to cognitive illusions, researchers can explore how embodiment, morphology, and experience shape expectations about others’ actions.
Nathaniel’s PhD research aims to extend this line of inquiry into machine learning and robotics. By training artificial agents to experience and respond to cognitive illusions, the project seeks to replicate and examine the information‑processing strategies that produce such effects in biological organisms. This offers a promising, non‑invasive approach to modelling some of the brain’s most elusive computational processes.
Nathaniel’s PhD Project
Unlocking the secrets of the brain is often considered one of the grand scientific challenges of our time. One aspect of this challenge involves uncovering how the brain processes the vast quantities of sensorimotor information endemic to embodiment in order to inform cognition and produce appropriate reactions to dynamic environmental conditions (Friston, 2010). Leading theories postulate that significant “shortcuts” are used to facilitate such processing, through which predictions relating to the changing environment are formed using prior experiences and updated through sensorimotor feedback (Friston 2010; Yon and Frith, 2021). It is believed that many neuropsychiatric and neurodevelopmental disorders may result from errors in the formation of these elegant pathways, such as OCD, schizophrenia and dyslexia (Harding et al., 2024; Sterzer et al., 2018; Sigurdardottir et al., 2017). However, such pathways remain a cognitive “black box,” limiting potential treatment options.
The comparative psychology and biology of magic have recently offered early insights into the role of embodied information in perceptions and expectations surrounding physical actions performed by dexterous agents, using cognitive illusions as a powerful violation‑of‑expectation paradigm (Garcia‑Pelegrin et al., 2021, 2023, 2024). These investigations have explored cross‑species differences in prediction errors relating to manual action expectations in primate species with varying hand morphologies, using sleight‑of‑hand magic tricks that rely on distinct biomechanical processes. This work offers an exciting insight into sensorimotor processing and prediction, with significant scope for further investigation.
Today, researchers can build more sophisticated machine‑learning systems than ever before (Piloto et al., 2022; Gemini Team Google, 2023). These systems have the capacity to act as a test‑bed through which to empirically explore the role of sensorimotor representations of environmental information upon behaviour. Using machine‑learning models, it is possible to approximate subunits of biological learning in an artificial context and measure the impact of different information acquisition and transmission pathways on artificial agents’ behaviours (e.g., Bell and Lawrence, 2022). As these artificial agents are trainable and modifiable by design, their use presents the opportunity to examine the role of different information streams upon behavioural outputs with a granularity and specificity that is nearly impossible to achieve in the biological context without extensive invasive neurobiological investigations—such as lesioning studies to prevent information transfer between brain regions. Subsequently, these proposed methods offer an exciting new avenue through which to shed light on the brain’s most mysterious information‑processing pathways.
By replicating cognitive illusions in artificial agents, researchers hope to reveal the information‑processing systems responsible for their presence in biological agents and, in doing so, begin to map the heuristics that govern biological agents’ cognitive processes.
About the Authors
Professor Nicola S. Clayton FRS, FRSA, FSB, FAPS, C.Psychol
Professor of Comparative Cognition at the University of Cambridge, internationally recognised for her pioneering research into the evolution and development of cognition in both human and non‑human animals.
Nathaniel Farndale Wright
PhD student in Professor Clayton’s Comparative Cognition Lab at the University of Cambridge. His doctoral research investigates how embodiment shapes cognition and decision‑making, with a particular interest in applications to machine learning and robotics.
Full Citations
Target Article: Coombs, S., & Trestman, M. (2025). A multi-trait embodied framework for the evolution of brains and cognition across animal phyla. Behavioural and Brain Sciences, 48, e77. doi:10.1017/S0140525X24000335
Commentary: Farndale Wright, N. R., & Clayton, N. S. (2025). On bodies, brains, and behaviour (and a little bit of magic).Behavioural and Brain Sciences, 48, e97. doi:10.1017/S0140525X25100666