Nagle Fintan, Johnston Alan
CoMPLEX, University College London, London, WC1E 6BT, UK.
Imperial College, Exhibition Road, London, SW7 2AZ, UK.
Sci Rep. 2021 May 19;11(1):10566. doi: 10.1038/s41598-021-89453-4.
Encoding and recognising complex natural sequences provides a challenge for human vision. We found that observers could recognise a previously presented segment of a video of a hearth fire when embedded in a longer sequence. Recognition performance declined when the test video was spatially inverted, but not when it was hue reversed or temporally reversed. Sampled motion degraded forwards/reversed playback discrimination, indicating observers were sensitive to the asymmetric pattern of motion of flames. For brief targets, performance increased with target length. More generally, performance depended on the relative lengths of the target and embedding sequence. Increased errors with embedded sequence length were driven by positive responses to non-target sequences (false alarms) rather than omissions. Taken together these observations favour interpreting performance in terms of an incremental decision-making model based on a sequential statistical analysis in which evidence accrues for one of two alternatives. We also suggest that prediction could provide a means of providing and evaluating evidence in a sequential analysis model.
对人类视觉而言，编码和识别复杂的自然序列是一项挑战。我们发现，观察者能够识别 hearth fire（此处可能有误，推测为hearth fire即壁炉火焰）视频中先前呈现的片段，当该片段嵌入更长的序列中时。当测试视频进行空间反转时，识别性能下降，但当进行色调反转或时间反转时则不然。采样运动降低了正向/反向播放的辨别能力，这表明观察者对火焰运动的不对称模式很敏感。对于简短目标，性能随目标长度增加。更一般地说，性能取决于目标和嵌入序列的相对长度。嵌入序列长度增加时错误增多是由对非目标序列的肯定反应（误报）而非遗漏所致。综合这些观察结果，支持基于顺序统计分析的增量决策模型来解释性能，在该模型中，证据累积支持两种选择之一。我们还建议，预测可以为顺序分析模型提供提供和评估证据的一种方式。
