Prior probability modulates anticipatory activity in category-specific areas

Abstract

Bayesian models are currently a dominant framework for describing human information processing. However, it is not clear yet how major tenets of this framework can be translated to brain processes. In this study, we addressed the neural underpinning of prior probability and its effect on anticipatory activity in category-specific areas. Before fMRI scanning, participants were trained in two behavioral sessions to learn the prior probability and correct order of visual events within a sequence. The events of each sequence included two different presentations of a geometric shape and one picture of either a house or a face, which appeared with either a high or a low likelihood. Each sequence was preceded by a cue that gave participants probabilistic information about which items to expect next. This allowed examining cue-related anticipatory modulation of activity as a function of prior probability in category-specific areas (fusiform face area and parahippocampal place area). Our findings show that activity in the fusiform face area was higher when faces had a higher prior probability. The finding of a difference between levels of expectations is consistent with graded, probabilistically modulated activity, but the data do not rule out the alternative explanation of a categorical neural response. Importantly, these differences were only visible during anticipation, and vanished at the time of stimulus presentation, calling for a functional distinction when considering the effects of prior probability. Finally, there were no anticipatory effects for houses in the parahippocampal place area, suggesting sensitivity to stimulus material when looking at effects of prediction.

Evaluating the neurophysiological evidence for predictive processing as a model of perception

“…showed that a representation of the expected stimulus could be decoded from neural activity before stimulus onset using multivariate analyses and forward‐modeling, respectively. However, it should be noted that the prestimulus expectation effects observed by Trapp et al 82 . and Kok et al 53 .…”

“…As with RS, ES has been consistently reported in human brain imaging studies across sensory modalities and noninvasive neural recording techniques 45,46,[74][75][76][77][78][79][80][81] (but see Ref. 82) and a number of human studies have demonstrated that the magnitude of stimulus-evoked responses is inversely proportional to the degree to which the stimulus was expected 75,76,83,84 (see Refs. 47 and 85 for corresponding evidence from neuronal recordings in rodents and monkeys, respectively).…”

“…Indeed, as mentioned above, many studies have reported that stimulus-evoked responses are inversely proportional to the degree to which the stimulus was expected, 47,75,76,[83][84][85]98 although this effect is not universally observed. 79,82 In PP, stimulus salience and attention are considered emergent properties of this precisionweighting mechanism. 9,28 Here, attending to a stimulus feature or to a part of the visual field is equivalent to predicting high-precision information from the associated error units and hence upweighting their error signals' influence in subsequent revisions of perceptual hypotheses.…”

P300 amplitude variations, prior probabilities, and likelihoods: A Bayesian ERP study

“…In the context of the dopaminergic system, another line of research has shown that this system conveys stochastic parameters of upcoming rewards, such as reward uncertainty (Fiorillo, Tobler, & Schultz, 2003;Preuschoff, Bossaerts, & Quartz, 2006;Preuschoff, Quartz, & Bossaerts, 2008). Trapp, Lepsien, Kotz, and Bar (2016) addressed the neural underpinnings of prior probability in category-specific areas (i.e., the fusiform face area and the parahippocampal place area). Their findings showed that hemodynamic activity in the fusiform face area was higher when faces had higher prior probability.…”

Predicting Affective Information – An Evaluation of Repetition Suppression Effects

“…Decreased neural responses for predictable sensory events are considered as the hallmark of predictive processing, and indeed they have been reported in numerous studies (e.g., Alink et al, 2010; Todorovic et al, 2011; de Gardelle et al, 2013). Predictions about sensory events can be generated by cues, temporal and spatial regularities, and via semantic, contextual, and associative processes (Bar, 2007; Turk-Browne et al, 2010; Kimura et al, 2012; Kok et al, 2014; Bendixen et al, 2015; Jessen and Kotz, 2015; Trapp et al, 2016). But expectations can also emerge as a consequence of repetitive exposure.…”