CorsiBot: A Cognitive Robotic Model of Serial Spatial Recall

Humans can reproduce a recently observed sequence of locations by selecting its items in order. The Corsi block-tapping task is widely used to test this ability and measures visuospatial working memory span.Most studies of this task have explored how span depends on sequence length and spatial layout, and have treated recall as a readout of the stored sequence. However, recall in this task is carried out through action. Whether and how the visual feedback from actions takes part in step-by-step retrieval remains unclear. In this research, we introduce CorsiBot, an embodied cognitive robot model that observes pointing sequences and recalls them through successive selections, allowing the visual feedback after each selection to be systematically varied. CorsiBot reproduces key features of human recall, including a decline in accuracy with increasing sequence length and characteristic error patterns. The results show that the model's internal state carries a stable signal of the next location to be recalled, and that altering only the timing of the visual feedback of a selection, with the network and visual content unchanged, shifts both this signal and the next choice, while the model still retains which location it has just selected. We found that the visual feedback of action do not merely record what has been done but shape which location is retrieved next. These findings suggest that step-by-step retrieval from visuospatial working memory may be coupled to the sensory feedback of human actions. In addition, CorsiBot provides an embodied robotic platform for testing how action shapes sequential memory retrieval.