ANR EXPER

Imagine driving on a foggy day. The other cars in front of you cannot be seen well and just appear as blurry shapes. Yet, you have no difficulties in interpreting these blurry shapes as cars. This is because based on your prior experiences of driving, you know that in this context (a road), this is the most likely interpretation.

When visual objects are ambiguous, our prior knowledge about associations between objects and their context (what we call “contextual associations”) can thus influence how we interpret what we see. But can they also influence how well we see? For example, can our expectations about these blurry objects being cars make them subjectively appear sharper than they actually are?

In a series of experiments, we showed that blurred objects that can be expected based on their context (e.g., a car in a highway scene) are indeed subjectively perceived as sharper than the exact same blurred object that cannot (for example, a car in a meaningless context). Furthermore, we showed that expectations based on objects can reciprocally sharpen the perception of their context. A blurred scene context containing an intact context is perceived as sharper than the same context without a meaningful object. Finally, we showed that our prior knowledge and expectations not only influence subjective perception of objects and their contexts but also how we perceive entire scenes (for example, a blurred scene presented upright – which conforms to our prior experience of scenes – is perceives as sharper than the exact same scene presented upside-down). 

These results indicate that the content of our perception can be strongly influenced by what we know and what we expect in our visual environment. But is it always the case? While relying on our expectations may help us see objects that are ambiguous better, it may be less useful when objects are clear and unambiguous. In the latter case, biasing our percepts toward what we expect could even be counterproductive as it could lead us to wrongly perceive objects that are unexpected (if a cow suddenly crosses the road on a sunny day, it would make no sense to perceive it as a car). In subsequent studies, we found that, fortunately, our visual system flexibly adapts to these constraints. When visual objects are ambiguous (blurred) we tend to rely on our expectations to make what we expect appear clearer and the effects of expectations on perception scale with the strength of our expectations. However, when visual objects are clear and unambiguous (or when we do not have strong expectations about them), their perception is dampened to the benefit of unexpected objects that may carry more relevant information to adapt our behavior. 

What is the point of this research?

At a fundamental level, this research addresses a long-standing question in the field of cognitive psychology but also philosophy about the boundaries between perception and cognition. While it is well accepted that what we see influences what we know whether what we know reciprocally constrains what or how we see has been a  matter of intense debate. Our findings therefore provide evidence in favor of this view by showing that objectively identical stimuli can be subjectively perceived as different depending on knowledge we have about them. 

Addressing this question also has clinical implications. Visual sensory deficits are widely spread, especially in older adults who face pathologies such as age-related macular degeneration (loss of vision in the central visual field, predominantly used to process objects in details) or glaucoma (loss of vision in the peripheral visual field, predominantly used to process scene context). Understanding how our prior knowledge and expectations can influence how we see is therefore important to better understand how sensory loss can be somehow compensated by relying on these mechanisms. 

What’s next? We are currently running studies to further characterize the extent to which what we know influences what we see and the neural correlates of these mechanisms. Stay tuned for updates!