As I have discussed numerous times on this blog, our brains did not evolve to be optimal precise perceivers and processors of information. Here is an infographic showing 188 documents cognitive biases. These biases are not all bad – they are tradeoffs. Evolutionary forces care only about survival, and so the idea is that many of these biases are more adaptive than accurate. We may, for example, overcall risk because avoiding risk has an adaptive benefit. Not all of the biases have to be adaptive. Some may be epiphenomena, or themselves tradeoffs – a side effect of another adaptation. Our visual perception is rife with such tradeoffs, emphasizing movements, edges, and change at the expense of accuracy and the occasional optical illusion.

One interesting perceptual bias is called serial dependence bias – what we see is influenced by what we recently saw (or heard). It’s as if one perception primes us and influences the next. It’s easy to see how this could be adaptive. If you see a wolf in the distance, your perception is now primed to see wolves. This bias may also benefit in pattern recognition, making patterns easier to detect. Of course, pattern recognition is one of the biggest perceptual biases in humans. Our brains are biased towards detecting potential patterns, way over calling possible patterns, and then filtering out the false positives at the back end. Perhaps serial perceptual bias is also part of this hyper-pattern recognition system.

Psychologists have an important question about serial dependence bias, however. Does this bias occur at the perceptual level (such as visual processing) or at a higher cognitive level? A recently published study attempted to address this question. They exposed subjects to an image of coins for half a second (the study is Japanese, so both the subjects and coins were Japanese). They then asked subjects to estimate the number of coins they just saw and their total monetary value. The researchers wanted to know what had a greater effect on the subjects – the previous amount of coins they had just viewed or their most recent guess. The idea is that if serial dependence bias is primarily perceptual, then the amount of coins will be what affects their subsequent guesses. If the bias is primarily a higher cognitive phenomenon, then their previous guesses will have a greater effect than the actual amount they saw. To help separate the two (because higher guesses would tend to align with greater amounts) they had subjects estimate the number and value of coins on only every other image. Therefore their most recent guess would be different than the most recent image they saw.

What they found is that the most recent guess had a much greater influence on subsequent estimate than the most recent image. This suggests that serial dependence bias is more of a cognitive bias than a lower-level perceptual bias. It does not affect what we see, just what we think about what we see. This was a crucial step in trying to understand the phenomenon at a deeper level. Future research can now try to untangle how this cognitive bias works on a neurological level.

Understanding serial dependence bias has real world implications. One study, for example showed that radiologists display serial dependence bias when reading radiographs. The researchers added simulated lesions to real radiographs. This allowed them to control for the properties of the lesions – are they light or dark, for example. They found that serial dependence bias pulled the radiologists in the direction of finding similar lesions by 13%. This is a large effect size in this context. Finding a dark lesion on a film primed the radiologists to better detect similar dark lesions on subsequent films.

Serial dependence bias can also be either attractive or repulsive – it can pull later perception toward the previous stimuli or push it away. In one study, for example, they looked at estimates of direction heading and found a repulsive serial dependence bias. This seems to favor change rather than consistency. This implies that serial dependence bias is context dependent, which makes sense if it is a higher cognitive phenomenon.

We can potentially compensate for this bias if we are aware of it. Knowing that once you are primed to see a thing you will begin to see it everywhere can help us make sense of the world, and avoid spurious conclusions. It might even be possible to use serial dependence bias to reduce errors in things like reading medical imaging. Perhaps radiologists can calibrate themselves by looking at standard images showing different types of common lesions before starting a session of reading. For critical applications it may also make sense to have more than one person confirm a reading or evaluation, so that their biases are less likely to align. Of course, increasingly we are using AI as that mechanism of bias and error reduction.