Biology subjects

Lowe, B. G.

Publications and source records attributed to Lowe, B. G..

2 recordsLinked to original sources

bioRxiv2025

The Latency of a Domain-General Visual Surprise Signal is Attribute Dependent

Predictions concerning upcoming visual input play a key role in resolving percepts. Sometimes input is surprising, under which circumstances the brain must calibrate erroneous predictions so that perception is veridical. Despite the extensive literature investigating the nature of prediction error signalling, it is still unclear how this process interacts with the functionally segregated nature of the visual cortex, particularly within the temporal domain. Here, we recorded electroencephalography (EEG) from humans whilst they viewed static image trajectories containing a bound object that sequentially changed along different visual attribute dimensions (shape and colour). Crucially, the context of this change was designed to appear random (and unsurprising) or violate the established trajectory (and cause a surprise). Event-related potential analysis found no effects of surprise after controlling for cortical adaptation. However, multivariate pattern analyses found whole-brain neural representations of visual surprise that overlapped between attributes, albeit at distinct, attribute-specific latencies. These findings suggest that visual surprise results in whole-brain, generalised (i.e., attribute-agnostic) prediction error responses that conform to an attribute-dependent temporal hierarchy.

neuroscience↗

bioRxiv2025

Using EEG to Detect Lapses in Sustained Attention to Moving Stimuli

Sustaining attention is effortful but crucial for daily life. Despite this, attentional lapses are common and can have fatal consequences (e.g., when driving). The spontaneous nature of these lapses make studying their underlying phenomena elusive. As such, methods capable of determining when lapses have occurred may be fruitful research tools, with the potential to save lives if implemented within real world settings. Here, we capitalised on a recent hierarchical classification method, which uses multivariate decoding to index how well human observers sustain their attention within a dynamic visual environment. We asked whether this method could be used to anticipate behavioural errors based on neural activity measured with electroencephalography (EEG). We first decoded patterns of EEG activity that systematically correlated with critical aspects of a Multiple Object Monitoring (MOM) task. The extent to which we could decode this information depended on whether a stimulus was relevant for behaviour, which was lower before participants failed to detect (or missed) target stimuli, presumably due to attentional lapses. Here, we exploited this drop in neural decodability to predict whether errors were about to occur on each trial. The results form a foundation for sensitive and specific methods to objectively detect lapses in sustained attention based on patterns of brain activity.

neuroscience↗