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Goal-directed behavior requires the ability to flexibly switch between task sets with changing environmental demands. Switching between tasks generally comes at the cost of slower and less accurate responses. Compared with adults, children often show greater switch costs, presumably reflecting the protracted development of the ability to flexibly update task-set representations. To test whether the distinctiveness of neural task-set representations is more strongly affected by a task switch in children compared with adults, we examined multivoxel patterns of fMRI activation in 88 children (8–11 years, 49 girls, 39 boys) and 52 adults (20–30 years, 27 women, 25 men) during a task-switching paradigm. Using multivariate pattern analysis (MVPA), we investigated whether task-set representations were less distinct on switch than on repeat trials across frontoparietal, cingulo-opercular, and temporo-occipital regions. Children and adults showed lower accuracy and longer response times on switch than on repeat trials. Switch costs were similar across groups. Decoding accuracy was lower on switch than repeat trials, suggesting that switching reduces the distinctiveness of task-set representations. Reliable age differences in switch-related reductions of decoding accuracy were absent. More nuanced analyses using probability measures indicated that the distinctiveness of task sets was more affected by switch demand in children than in adults in a subset of frontal, cingulate, and temporal regions. These results point to a remarkable degree of maturity of neural representations of task-relevant information in late childhood along with more subtle region-specific age differences in the effects of task switching on rule representation.

Task-Switch Related Reductions in Neural Distinctiveness in Children and Adults: Commonalities and Differences

Pre-trained large language models (LLMs) have garnered significant attention for their ability to generate human-like text and responses across various domains. This study delves into examines the social and strategic behavior of the commonly used LLM GPT-3.5 by investigating its suggestions in well-established behavioral economics paradigms. Specifically, we focus on social preferences, including altruism, reciprocity, and fairness, in the context of two classic economic games: the Dictator Game (DG) and the Ultimatum Game (UG). Our research aims to answer three overarching questions: (1) To what extent do GPT-3.5 suggestions reflect human social preferences? (2) How do socio-demographic features of the advisee and (3) technical parameters of the model influence the suggestions of GPT-3.5? We present detailed empirical evidence from extensive experiments with GPT-3.5, analyzing its responses to various game scenarios while manipulating the demographics of the advisee and the model temperature. Our findings reveal that, in the DG Dictator Game, model suggestions are more altruistic than in humans. We further show that it also picks up on more subtle aspects of human social preferences: fairness and reciprocity. This research contributes to the ongoing exploration of AI-driven systems’ alignment with human behavior and social norms, providing valuable insights into the behavior of pre-trained LLMs and their implications for human-AI interactions. Additionally, our study offers a methodological benchmark for future research examining human-like characteristics and behaviors in language models.

GPT-3.5 altruistic advice is sensitive to reciprocal concerns but not to strategic risk - Scientific Reports

Sara Bonati

Hey there 👋 I’m a Data Ops Engineer with a background in Cognitive Neuroscience and Data Science. 

I design, build, and deploy internal tools and data pipelines that help teams move faster and smarter.