Evidence of memory from brain data.
Emily R D MurphyJesse RissmanPublished in: Journal of law and the biosciences (2020)
Much courtroom evidence relies on assessing witness memory. Recent advances in brain imaging analysis techniques offer new information about the nature of autobiographical memory and introduce the potential for brain-based memory detection. In particular, the use of powerful machine-learning algorithms reveals the limits of technological capacities to detect true memories and contributes to existing psychological understanding that all memory is potentially flawed. This article first provides the conceptual foundation for brain-based memory detection as evidence. It then comprehensively reviews the state of the art in brain-based memory detection research before establishing a framework for admissibility of brain-based memory detection evidence in the courtroom and considering whether and how such use would be consistent with notions of justice. The central question that this interdisciplinary analysis presents is: if the science is sophisticated enough to demonstrate that accurate, veridical memory detection is limited by biological, rather than technological, constraints, what should that understanding mean for broader legal conceptions of how memory is traditionally assessed and relied upon in legal proceedings? Ultimately, we argue that courtroom admissibility is presently a misdirected pursuit, though there is still much to be gained from advancing our understanding of the biology of human memory.
Keyphrases
- working memory
- machine learning
- resting state
- white matter
- public health
- label free
- randomized controlled trial
- loop mediated isothermal amplification
- real time pcr
- systematic review
- deep learning
- photodynamic therapy
- social media
- physical activity
- health information
- brain injury
- big data
- risk assessment
- depressive symptoms