BAP 2024 round up!

As the year’s meetings go, BAP 2024 was a lively one for the PaL Lab, with a strong line-up of talks and posters showcasing our work on uncertainty, learning, and affect.

• Tom Murray explored how acute stress alters social learning under uncertainty, showing slower adaptation to social cues, especially in those high in compulsivity and social withdrawal.

• Bowen (Eddie) Xiao examined volatility processing and valence biases across anxiety and autism in a preregistered online study, revealing subtle differences in how people update beliefs about rewards and punishments.

• Benji Illingworth presented work on information sampling in autism, showing increased sampling and reduced efficiency consistent with an enhanced drive to reduce uncertainty.

• Nazia Jassim linked autistic traits to occipital GABA using 7T MRS, connecting inhibitory tone with individual differences in visual perception.

• Tim Sandhu discussed how stress and social withdrawal shape aversive learning and perceived uncertainty, using computational models to tease apart latent processes.

• Calum Guinea presented showing that when learning is less reliable, participants are less willing to expend effort, especially those higher in anxiety or low mood.

• Rebecca Lawson closed the loop with a talk titled: Computations of uncertainty in infancy: unlocking potential for early intervention, highlighting new findings from high-density optical imaging showing how infants process prediction errors, and how early differences in uncertainty computation might inform future approaches to mental health.

  
  

A cracking set of presentations from the lab spanning stress, autism, anxiety, motivation, and development - all united by our shared fascination with how brains learn about an uncertain world.