Dynamic saccade context triggers more stable object-location binding.

Our visual systems rapidly perceive and integrate information about object identities and locations. There is long-standing debate about if and how we achieve world-centered (spatiotopic) object representations across eye movements, with many studies reporting persistent retinotopic (eye-centered) effects even for higher level object-location binding. But these studies are generally conducted in fairly static experimental contexts. Might spatiotopic object-location binding only emerge in more dynamic saccade contexts? In the present study, we investigated this using the spatial congruency bias paradigm in healthy adults. In the static (single-saccade) context, we found purely retinotopic binding, as before. However, robust spatiotopic binding emerged in the dynamic saccade context (multiple frequent saccades and saccades during stimulus presentation). We further isolated specific factors that modulate retinotopic and spatiotopic binding. Our results provide strong evidence that dynamic saccade context can trigger more stable object-location binding in ecologically relevant spatiotopic coordinates, perhaps via a more flexible brain state that accommodates improved visual stability in the dynamic world. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The development of visual cognition: The emergence of spatial congruency bias.

In adults, spatial location plays a special role in visual object processing. People are more likely to judge two sequentially presented objects as being identical when they appear in the same location compared to in different locations (a phenomenon referred to as Spatial Congruency Bias [SCB]). However, no comparable Identity Congruency Bias (ICB) is found, suggesting an asymmetric location-identity relationship in object binding. What gives rise to this asymmetric congruency bias? This paper considered two possible hypotheses. Hypothesis 1 suggests that the asymmetric congruency bias results from an inherently special role of location in the visual system. In contrast, Hypothesis 2 suggests that the asymmetric congruency bias is a product of development, reflecting people's experience with the world. To distinguish the two hypotheses, we tested both adults' and 5-year-old children's SCB and ICB by Identity Judgment Experiments and Spatial Judgment Experiments, respectively. The study found that adults only exhibited a SCB, but no ICB. However, young children exhibited both SCB and ICB, suggesting a symmetric congruency bias and reciprocal influences between location and identity in early development. The results indicate that the asymmetric location-identity relationship develops as object identity's influence on location gets pruned away, while location's influence on identity is preserved, possibly due to people's gained experiences with regularities of the world. RESEARCH HIGHLIGHTS: Adults exhibit Spatial Congruency Bias-an asymmetric location-identity relationship with location biasing their judgment of object identities, but not vice versa. Asymmetric congruency bias may result from an inherently special role of location in visual system (Hypothesis 1) or accumulated experiences with the world (Hypothesis 2). To distinguish the two hypotheses, the study investigated the Spatial Congruency Bias and Identity Congruency Bias in both adults and 5-year-old children. Unlike adults who exhibited only Spatial Congruency Bias, 5-year-old children exhibited both Spatial Congruency Bias and Identity Congruency Bias.

Achieving more human brain-like vision via human EEG representational alignment.

Despite advancements in artificial intelligence, object recognition models still lag behind in emulating visual information processing in human brains. Recent studies have highlighted the potential of using neural data to mimic brain processing; however, these often rely on invasive neural recordings from non-human subjects, leaving a critical gap in understanding human visual perception. Addressing this gap, we present, for the first time, 'Re(presentational)Al(ignment)net', a vision model aligned with human brain activity based on non-invasive EEG, demonstrating a significantly higher similarity to human brain representations. Our innovative image-to-brain multi-layer encoding framework advances human neural alignment by optimizing multiple model layers and enabling the model to efficiently learn and mimic human brain's visual representational patterns across object categories and different modalities. Our findings suggest that ReAlnet represents a breakthrough in bridging the gap between artificial and human vision, and paving the way for more brain-like artificial intelligence systems.