A confirmation bias in perceptual decision-making due to hierarchical approximate inference

Abstract

1Human decisions are known to be systematically biased. A prominent example of such a bias 2 occurs when integrating a sequence of sensory evidence over time. Previous empirical studies di↵er 3 in the nature of the bias they observe, ranging from favoring early evidence (primacy), to favoring 4 late evidence (recency). Here, we present a unifying framework that explains these biases and 5 makes novel psychophysical and neurophysiological predictions. By explicitly modeling both the 6 approximate and the hierarchical nature of inference in the brain, we show that temporal biases 7 depend on the balance between "sensory information" and "category information" in the stimulus. 8Finally, we present new data from a human psychophysics task that confirm that temporal biases 9 can be robustly changed within subjects as predicted by our models. 10Imagine a doctor trying to infer the cause of a patient's symptoms from an x-ray image. Unsure 12 about the evidence in the image, she asks a radiologist for a second opinion. If she tells the 13 radiologist her suspicion, she may bias his report. If she does not, he may not detect a faint 14 diagnostic pattern. As a result, if the evidence in the image is hard to detect or ambiguous, 15 the radiologist's second opinion, and hence the final diagnosis, may be swayed by the doctor's 16 initial hypothesis. The problem faced by these doctors exemplifies the di culty of hierarchical 17 inference: each doctor's suspicion both informs and is informed by their collective diagnosis. If 18 they are not careful, their diagnosis may fall prey to circular reasoning. The brain faces a similar 19 problem during perceptual decision-making: any decision-making area combines sequential signals 20 from sensory brain areas, not directly from sensory input, just as the doctors' consensus is based 21 on their individual diagnoses rather than on the evidence per se. If sensory signals in the brain 22 themselves reflect inferences that combine both prior expectations and sensory evidence, we suggest 23 that this can then lead to an observable perceptual confirmation bias (Nickerson, 1998). 24 We formalize this idea in the context of approximate Bayesian inference and classic evidence-25 integration tasks in which a range of biases has been observed and for which a unifying explanation 26 1 is currently lacking. Evidence-integration tasks require subjects to categorize a sequence of inde-27 pendent and identically distributed (iid) draws of stimuli (Gold and Shadlen, 2007; Bogacz et al., 28 2006). Previous normative models of evidence integration hinge on two quantities: the amount of 29 information available on a single stimulus draw and the total number of draws. One might expect, 30 then, that temporal biases should have some canonical form in tasks where these quantities are 31 matched. However, existing studies are heterogeneous, reporting one of three distinct motifs: some 32 find that early evidence is weighted more strongly (a primacy e↵ect) (Kiani et al., 2008; Nienborg 33 and Cu...

Choices change the temporal weighting of decision evidence

“…Some studies found stronger weighting of early evidence ("primacy"; 6-9), others stronger weighting of late evidence ("recency"; [10][11][12], and yet others even nonmonotonic weighting profiles (13,14). These distinct temporal weighting profiles may inform about differences in the mechanisms underlying decision formation (14)(15)(16)(17). Yet, mechanistic inferences are limited by the fact that most of the above studies were conducted in different subjects and used various different stimuli and tasks (with evidence varying on different timescales).…”

Noise correlations for faster and more robust learning

“…The context dependent noise correlations that speed learning (figure 7), however, would not arise through simple Hebbian learning. Such correlations could potentially be produced through selective topdown signals from the choice neurons, as has been previously proposed (Wimmer et al, 2015;Haefner et al, 2016;Bondy et al, 2018;Lange et al, 2018). Moreover, top-down input may selectively target neuronal ensembles produced through Hebbian learning (Collins and Frank, 2013).…”

“…In particular, noise reduction in task irrelevant dimensions might be considered in the same light that is often cast on suppression of task irrelevant dimensions by attentional mechanisms (Zanto and Gazzaley, 2009), in particular for purposes of accurate credit assignment (Akaishi et al, 2016;Leong et al, 2017). One possibility is that compressed low-dimensional task representations in higherorder decision regions (Mack et al, 2019) may pass accumulated decision related information back to sensory regions in order to approximate Bayesian inference (Haefner et al, 2016;Bondy et al, 2018;Lange et al, 2018). As task relevant features are learned, such a process would promote noise correlations between neurons coding those relevant features.…”

Flexible categorization in perceptual decision making

“…Recently, two studies have proposed alternative models that can explain the differences of PK time-courses found across subjects and experiments. In the first model, based on approximate Bayesian inference, the primacy effect produced by bottom-up vs. top-down hierarchical dynamics, was modulated by the stimulus properties which could yield different PK time-courses, a prediction that was tested in a visual discrimination task 41 . The second study proposed a model that can produce different PK time-courses by adjusting the time scales of a divisive normalization mechanism, which yields primacy, and a leak mechanism, which promotes recency 42 .…”