Electrophysiological correlates of incidentally learned expectations in human vision

Statistical Learning of Incidental Perceptual Regularities Induces Sensory Conditioned Cortical Responses

Statistical learning of sensory patterns can lead to predictive neural processes enhancing stimulus perception and enabling fast deviancy detection. Predictive processes have been extensively demonstrated when environmental statistical regularities are relevant to task execution. Preliminary evidence indicates that statistical learning can even occur independently of task relevance and top-down attention, although the temporal profile and neural mechanisms underlying sensory predictions and error signals induced by statistical learning of incidental sensory regularities remain unclear. In our study, we adopted an implicit sensory conditioning paradigm that elicited the generation of specific perceptual priors in relation to task-irrelevant audio-visual associations, while recording Electroencephalography (EEG). Our results showed that learning task-irrelevant associations between audio-visual stimuli resulted in anticipatory neural responses to predictive auditory stimuli conveying anticipatory signals of expected visual stimulus presence or absence. Moreover, we observed specific modulation of cortical responses to probabilistic visual stimulus presentation or omission. Pattern similarity analysis indicated that predictive auditory stimuli tended to resemble the response to expected visual stimulus presence or absence. Remarkably, Hierarchical Gaussian filter modeling estimating dynamic changes of prediction error signals in relation to differential probabilistic occurrences of audio-visual stimuli further demonstrated instantiation of predictive neural signals by showing distinct neural processing of prediction error in relation to violation of expected visual stimulus presence or absence. Overall, our findings indicated that statistical learning of non-salient and task-irrelevant perceptual regularities could induce the generation of neural priors at the time of predictive stimulus presentation, possibly conveying sensory-specific information about the predicted consecutive stimulus.

Prior probability cues bias sensory encoding with increasing task exposure

When observers have prior knowledge about the likely outcome of their perceptual decisions, they exhibit robust behavioural biases in reaction time and choice accuracy. Computational modelling typically attributes these effects to strategic adjustments in the criterion amount of evidence required to commit to a choice alternative - usually implemented by a starting point shift - but recent work suggests that expectations may also fundamentally bias the encoding of the sensory evidence itself. Here, we recorded neural activity with EEG while participants performed a contrast discrimination task with valid, invalid, or neutral probabilistic cues across multiple testing sessions. We measured sensory evidence encoding via contrast-dependent steady-state visual-evoked potentials (SSVEP), while a read-out of criterion adjustments was provided by effector-selective mu-beta band activity over motor cortex. In keeping with prior modelling and neural recording studies, cues evoked substantial biases in motor preparation consistent with criterion adjustments, but we additionally found that the cues produced a significant modulation of the SSVEP during evidence presentation. While motor preparation adjustments were observed in the earliest trials, the sensory-level effects only emerged with extended task exposure. Our results suggest that, in addition to strategic adjustments to the decision process, probabilistic information can also induce subtle biases in the encoding of the evidence itself.

Stimulus expectations do not modulate visual event-related potentials in probabilistic cueing designs

Humans and other animals can learn and exploit repeating patterns that occur within their environments. These learned patterns can be used to form expectations about future sensory events. Several influential predictive coding models have been proposed to explain how learned expectations influence the activity of stimulus-selective neurons in the visual system. These models specify reductions in neural response measures when expectations are fulfilled (termed expectation suppression) and increases following surprising sensory events. However, there is currently scant evidence for expectation suppression in the visual system when confounding factors are taken into account. Effects of surprise have been observed in blood oxygen level dependent (BOLD) signals, but not when using electrophysiological measures. To provide a strong test for expectation suppression and surprise effects we performed a predictive cueing experiment while recording electroencephalographic (EEG) data. Participants (n=48) learned cue-face associations during a training session and were then exposed to these cue-face pairs in a subsequent experiment. Using univariate analyses of face-evoked event-related potentials (ERPs) we did not observe any differences across expected (90% probability), neutral (50%) and surprising (10%) face conditions. Across these comparisons, Bayes factors consistently favoured the null hypothesis throughout the time-course of the stimulus-evoked response. When using multivariate pattern analysis we did not observe above-chance classification of expected and surprising face-evoked ERPs. By contrast, we found robust within- and across-trial stimulus repetition effects. Our findings do not support predictive coding-based accounts that specify reduced prediction error signalling when perceptual expectations are fulfilled. They instead highlight the utility of other types of predictive processing models that describe expectation-related phenomena in the visual system without recourse to prediction error signalling.

Evaluating the evidence for expectation suppression in the visual system

Reports of expectation suppression have shaped the development of influential predictive coding-based theories of visual perception. However recent work has highlighted confounding factors that may mimic or inflate expectation suppression effects. In this review, we describe four confounds that are prevalent across experiments that tested for expectation suppression: effects of surprise, attention, stimulus repetition and adaptation, and stimulus novelty. With these confounds in mind we then critically review the evidence for expectation suppression across probabilistic cueing, statistical learning, oddball, action-outcome learning and apparent motion designs. We found evidence for expectation suppression within a specific subset of statistical learning designs that involved weeks of sequence learning prior to neural activity measurement. Across other experimental contexts, whereby stimulus appearance probabilities were learned within one or two testing sessions, there was inconsistent evidence for genuine expectation suppression. We discuss how an absence of expectation suppression could inform models of predictive processing, repetition suppression and perceptual decision-making. We also provide suggestions for designing experiments that may better test for expectation suppression in future work.

Visual mismatch responses index surprise signalling but not expectation suppression

The ability to distinguish between commonplace and unusual sensory events is critical for efficient learning and adaptive behaviour. This has been investigated using oddball designs in which sequences of often-appearing (i.e., expected) stimuli are interspersed with rare (i.e., surprising) deviants. Resulting differences in electrophysiological responses following surprising compared to expected stimuli are known as visual mismatch responses (VMRs). VMRs are thought to index co-occurring contributions of stimulus repetition effects, expectation suppression (that occurs when one's expectations are fulfilled), and expectation violation (i.e., surprise) responses; however, these different effects have been conflated in existing oddball designs. To better isolate and quantify effects of expectation suppression and surprise, we adapted an oddball design based on Fast Periodic Visual Stimulation (FPVS) that controls for stimulus repetition effects. We recorded electroencephalography (EEG) while participants (N = 48) viewed stimulation sequences in which a single face identity was periodically presented at 6 Hz. Critically, one of two different face identities (termed oddballs) appeared as every 7th image throughout the sequence. The presentation probabilities of each oddball image within a sequence varied between 10 and 90%, such that participants could form expectations about which oddball face identity was more likely to appear within each sequence. We also included 'expectation neutral' 50% probability sequences, whereby consistently biased expectations would not be formed for either oddball face identity. We found that VMRs indexed surprise responses, and effects of expectation suppression were absent. That is, ERPs were more negative-going at occipitoparietal electrodes for surprising compared to neutral oddballs, but did not differ between expected and neutral oddballs. Surprising oddball-evoked ERPs were also highly similar across the 10-40% appearance probability conditions. Our findings indicate that VMRs which are not accounted for by repetition effects are best described as an all-or-none surprise response, rather than a minimisation of prediction error responses associated with expectation suppression.

Decisional space modulates visual categorization - Evidence from saccadic reaction times

Manual and saccadic reaction times (SRTs) have been used to determine the minimum time required for different types of visual categorizations. Such studies have demonstrated extremely rapid detection of faces within natural scenes, whereas increasingly complex decisions (i.e. levels of processing) require longer processing times. We reasoned that visual categorization speed is not only dependent on the processing level, but is further affected by decisional space constraints. In the context of two different tasks, observers performed choice saccades towards female (gender categorization) or personally familiar (familiarity categorization) faces. Additionally, familiarity categorizations were completed with stimulus sets that differed in the number of individuals presented (3 vs. 7 identities) to investigate the effect of decisional space constraints. We observe an inverse relationship between visual categorization proficiency and decisional space. Observers were most accurate for categorization of gender, which could be achieved in as little as 140 ms. Categorization of highly predictable targets was more error-prone and required an additional ∼100 ms processing time. Our findings add to an increasing body of evidence demonstraing that pre-activation of identity-information can modulate early visual processing in a top-down manner. They also emphasize the importance of considering procedural aspects, as well as terminology when aiming to characterize cognitive processes.