RESUMEN
Many recent artificial intelligence (AI) systems take inspiration from biological episodic memory. Here, we ask how these 'episodic-inspired' AI systems might inform our understanding of biological episodic memory. We discuss work showing that these systems implement some key features of episodic memory while differing in important respects and appear to enjoy behavioural advantages in the domains of strategic decision-making, fast learning, navigation, exploration and acting over temporal distance. We propose that these systems could be used to evaluate competing theories of episodic memory's operations and function. However, further work is needed to validate them as models of episodic memory and isolate the contributions of their memory systems to their behaviour. More immediately, we propose that these systems have a role to play in directing episodic memory research by highlighting novel or neglected hypotheses as pursuit-worthy. In this vein, we propose that the evidence reviewed here highlights two pursuit-worthy hypotheses about episodic memory's function: that it plays a role in planning that is independent of future-oriented simulation, and that it is adaptive in virtue of its contributions to fast learning in novel, sparse-reward environments. This article is part of the theme issue 'Elements of episodic memory: lessons from 40 years of research'.
Asunto(s)
Inteligencia Artificial , Memoria Episódica , Humanos , Aprendizaje/fisiología , Toma de Decisiones/fisiologíaRESUMEN
Research into the newly-coined 'condition' of 'aphantasia', an individual difference involving the self-reported absence of voluntary visual imagery, has taken off in recent years, and more and more people are 'self-diagnosing' as aphantasic. Yet, there is no consensus on whether aphantasia should really be described as a 'condition', and there is no battery of psychometric instruments to detect or 'diagnose' aphantasia. Instead, researchers currently rely on the Vividness of Visual Imagery Questionnaire (VVIQ) to 'diagnose' aphantasia. We review here fundamental and methodological problems affecting aphantasia research stemming from an inadequate focus on how we should define aphantasia, whether aphantasia is a pathological condition, and the extensive use of VVIQ as a 'diagnostic test' for aphantasia. Firstly, we draw attention to 'literature blindness' for visual imagery research from the 1960s-1990s concerning individual differences in visual imagery vividness. Secondly, despite aphantasia being defined as a 'condition' where voluntary visual imagery is absent as indicated by the lowest score on the VVIQ, aphantasia studies inconsistently employ samples comprised of a mixture of participants with no visual imagery and low visual imagery, and we argue that this hinders the uncovering of the underlying cause of aphantasia. Thirdly, the scores used to designate the boundary between aphantasia and non-aphantasia are arbitrary and differ between studies, compromising the possibility for cross-study comparison of results. Fourthly, the problems of 'diagnosing' aphantasia are not limited to the academic sphere, as one can 'self-diagnose' online, for example by using the variant-VVIQ on the Aphantasia Network website. However, the variant-VVIQ departs from the original in ways likely to impact validity and accuracy, which could lead people to falsely believe they have been 'diagnosed' with aphantasia by a scientifically-validated measure. Fifthly, we discuss the hypothesis that people who believe they have been 'diagnosed' with aphantasia might be vulnerable to health anxiety, distress, and stigma. We conclude with a discussion about some fundamental aspects of how to classify a disorder, and suggest the need for a new psychometric measure of aphantasia.