The temporal dynamics of animacy-related neural representations are robust to controversial training
Animal bias refers to the human tendency to prioritise the neural processing of animate over inanimate stimuli, a phenomenon observed across multiple cognitive domains, such as attention, perception, memory, and language. Lookalikes (also referred to as zoomorphic objects), inanimate objects that visually resemble animals, tend to be interpreted as animate regardless of their inanimate identity. We examined whether this animal bias can be modified through learning to understand how experience and plasticity shape visual object representations. We tested whether feedback-based categorisation training in which lookalikes had to be categorised as inanimate could reduce the animal bias. Thirty healthy participants completed three weekly sessions of animacy categorisation training, followed by an EEG recording session during which they performed an animacy task. Repeated-measures ANOVA on behavioural data revealed that categorisation efficiency, measured as the inverse efficiency score, improved both within and across sessions, but there was no evidence that this improvement was specific to lookalikes. Time-resolved multivariate pattern analysis and representational similarity analysis on EEG data obtained after this training showed a descriptively smaller animal bias relative to the no-training dataset from \citet{Chen2023}, but this reduction was not statistically significant in any analysis. Moreover, a significant animal bias was still present in participants who underwent training. Thus, neural reorganisation related to the animal bias is limited even after several training sessions to overcome this bias.