Attention, Perception, & Psychophysics ( IF 1.7 ) Pub Date : 2021-03-31 , DOI: 10.3758/s13414-021-02256-7 Árni Kristjánsson , Dejan Draschkow
Research within visual cognition has made tremendous strides in uncovering the basic operating characteristics of the visual system by reducing the complexity of natural vision to artificial but well-controlled experimental tasks and stimuli. This reductionist approach has for example been used to assess the basic limitations of visual attention, visual working memory (VWM) capacity, and the fidelity of visual long-term memory (VLTM). The assessment of these limits is usually made in a pure sense, irrespective of goals, actions, and priors. While it is important to map out the bottlenecks our visual system faces, we focus here on selected examples of how such limitations can be overcome. Recent findings suggest that during more natural tasks, capacity may be higher than reductionist research suggests and that separable systems subserve different actions, such as reaching and looking, which might provide important insights about how pure attentional or memory limitations could be circumvented. We also review evidence suggesting that the closer we get to naturalistic behavior, the more we encounter implicit learning mechanisms that operate “for free” and “on the fly.” These mechanisms provide a surprisingly rich visual experience, which can support capacity-limited systems. We speculate whether natural tasks may yield different estimates of the limitations of VWM, VLTM, and attention, and propose that capacity measurements should also pass the real-world test within naturalistic frameworks. Our review highlights various approaches for this and suggests that our understanding of visual cognition will benefit from incorporating the complexities of real-world cognition in experimental approaches.
中文翻译:
保持真实:超越视觉认知的能力极限
视觉认知方面的研究通过将自然视觉的复杂性降低到人工但控制良好的实验任务和刺激中,在揭示视觉系统的基本操作特性方面取得了长足的进步。例如,这种简化派方法已用于评估视觉注意力,视觉工作记忆(VWM)容量和视觉长期记忆(VLTM)逼真度的基本限制。这些限制的评估通常是纯粹的意识,与目标,行动和先验无关。尽管找出视觉系统面临的瓶颈很重要,但我们在这里集中讨论如何克服这些限制的示例。最近的发现表明,在更自然的任务中,处理能力可能会比还原主义者的研究建议的要高,并且可分离的系统会分担不同的动作,例如伸手可及的距离,这可能会提供有关纯净程度的重要见解可以避免注意力或记忆力的限制。我们还审查了证据,这些证据表明,我们越接近自然主义行为,就会遇到越多的隐性学习机制,这些机制“免费”和“即时”运行。这些机制提供了令人惊讶的丰富视觉体验,可以支持容量受限的系统。我们推测自然任务是否可能对VWM,VLTM和注意力的局限性产生不同的估计,并建议能力度量也应通过自然主义框架内的实际测试。我们的综述重点介绍了各种方法,并暗示我们对视觉认知的理解将受益于将现实世界认知的复杂性纳入实验方法中。