You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: Attention promotes the neural encoding of prediction errors}
Article:
Abstract:
The encoding of sensory information in the human brain is thought to be optimised by two principal processes: ‘prediction’ uses stored information to guide the interpretation of forthcoming sensory events, and ‘attention’ prioritizes these events according to their behavioural relevance. Despite the ubiquitous contributions of attention and prediction to various aspects of perception and cognition, it remains unknown how they interact to modulate information processing in the brain. A recent extension of predictive coding theory suggests that attention optimises the expected precision of predictions by modulating the synaptic gain of prediction error units. Because prediction errors code for the difference between predictions and sensory signals, this model would suggest that attention increases the selectivity for mismatch information in the neural response to a surprising stimulus. Alternative predictive coding models propose that attention increases the activity of prediction( or ‘representation’) neurons and would therefore suggest that attention and prediction synergistically modulate selectivity for ‘feature information’ in the brain. Here, we applied forward encoding models to neural activity recorded via electroencephalography( EEG) as human observers performed a simple visual task to test for the effect of attention on both mismatch and feature information in the neural response to surprising stimuli. Participants attended or ignored a periodic stream of gratings, the orientations of which could be either predictable, surprising, or unpredictable. We found that surprising stimuli evoked neural responses that were encoded according to the difference between predicted and observed stimulus features, and that attention facilitated the encoding of this type of information in the brain. These findings advance our understanding of how attention and prediction modulate information processing in the brain, as well as support the theory that attention optimises precision expectations during hierarchical inference by increasing the gain of prediction errors.
Introduction:
Perception is believed to arise from a process of active inference[1], during which the brain retrieves information from past experiences to build predictive models of likely future occurrences and compares these predictions with incoming sensory evidence[2, 3]. In support of the idea that prediction increases the efficiency of neural encoding, previous studies have demonstrated that predicted visual events typically evoke smaller neural responses than surprising events( e. g., evoked activity measured in terms of changes in electrical potential or blood oxygen level dependent[BOLD] response; for a review, see[4]). Recent studies have shown that selective attention can increase[5] or reverse[6] the suppressive effect of prediction on neural activity, suggesting that attention and prediction facilitate perception[7] via synergistic modulation of bottom-up sensory signals[8–11]. It remains unclear, however, what type of information is modulated in the interaction between attention and prediction. This question is important because different predictive coding models make distinct predictions about how information is transmitted through the cortical hierarchy[3, 8, 12, 13]. Here, we used forward encoding models to assess selectivity for two distinct types of information in the neural response to surprising stimuli—feature and mismatch information—and to test the effect of attention on these two informational codes. A prominent version of predictive coding theory claims that top-down prediction signals ‘cancel out’ bottom-up sensory signals that match the predicted content, leaving only the remaining prediction error to propagate forward and update a model of the sensory environment[2, 8, 9]. Because error propagation is thought to be associated with superficial pyramidal cells[9], and these cells are thought to be primarily responsible for generating EEG signals[14, 15], this theory predicts that surprising events will increase the selectivity of EEG responses to the difference between predicted and observed stimulus features, i. e., mismatch information. Furthermore, a recent extension of this theory suggests that selective attention optimises the expected precision of predictions by modulating the synaptic gain( postsynaptic responsiveness) of prediction error units[8]—i. e., neurons coding for behaviourally relevant prediction errors should be more responsive than those coding for irrelevant prediction errors. On this account, attention should further increase selectivity for mismatch information in the neural response to surprising stimuli relative to unsurprising stimuli. Here, we call this account the ‘mismatch information model’. Alternative predictive coding models[12, 13, 16] propose that predictions—as opposed to prediction errors—are propagated forward through the visual hierarchy, and it is these prediction signals that are modulated by attention. For example, the model proposed by Spratling[12] simulates the common physiological finding that attention to a stimulus enhances the firing rate of neurons tuned to specific stimulus features( e. g., orientation or colour for visual neurons) and has been shown to be mathematically equivalent to the biased competition model of attention[17–20]. In line with these alternative models, we investigated a second hypothesis—here termed the ‘feature information model’—which proposes that the interaction between attention and prediction at the level of neural responses is driven by changes in feature-specific information in the brain. Here, we tested whether the feature information model or the mismatch information model provides a better account of the neural coding of surprising stimuli in the human brain and examined the influence of selective attention on each of these two neural codes. Participants attended to, or ignored, periodic streams of visual gratings, the orientations of which were either predictable, surprising, or unpredictable. We applied forward encoding models to whole-brain neural activity measured using EEG to quantify the neural selectivity for information related to the grating orientation and the mismatch between the predicted and observed grating orientations. We show that surprising stimuli evoke neural responses that contain information related to the difference between predicted and observed stimulus features, consistent with the mismatch information model. Crucially, we also find that attention increases the selectivity for mismatch information in the neural response to surprising stimuli, supporting the hypothesis that attention increases the gain of prediction errors[8].
Discussion:
Here we set out to determine what type of information is modulated in the interaction between attention and prediction[8]. To achieve this, we used forward encoding models of EEG data to quantify the selectivity for orientation and mismatch information in the neural responses to surprising and unpredictable stimuli in the well-established roving oddball paradigm[21, 37]. Relative to unpredictable stimuli( controls), we found that EEG responses to surprising stimuli( deviants) were equally selective for orientation information, but more selective for information related to the difference between predicted and observed stimulus features. These results are consistent with the mismatch information model and support the idea that top-down prediction signals ‘cancel out’ matching bottom-up sensory signals and leave only the remaining prediction error to propagate forward[2, 3, 8, 9]. Crucially, we also found that attention increased the selectivity for mismatch information in neural responses to surprising but not control stimuli. This finding demonstrates that attention boosts mismatch information evoked by surprising stimuli( putative prediction errors) and is consistent with a recent version of predictive coding theory that proposes attention optimises the expected precision of predictions by increasing the gain of prediction errors[8]. We found no difference between orientation response profiles evoked by surprising and unpredictable stimuli( a prediction of the feature information model), suggesting that the increase in EEG activity that is typically observed with surprise is not coded according to stimulus features. This finding contradicts predictive coding models in which predictions( or ‘representations’) of stimulus features are passed up the visual hierarchy[12, 16, 17]. Because feedforward connections largely originate primarily from superficial pyramidal cells and it is this activity that is measured with EEG[9, 14, 15], these models would predict that surprise changes the feature selectivity of EEG responses: a finding we do not observe here. This finding might also seem to contradict a recent study that demonstrated greater selectivity for orientation information in early visual cortex BOLD activity following presentation of a predicted grating, relative to a surprising grating[38]. Since BOLD activity indirectly measures the activity patterns of heterogenous populations of neurons, however, this change in feature selectivity could have reflected a change in either of the two neuronal populations proposed to underlie predictive coding—predictions or prediction errors. The latter interpretation is inconsistent with the results of the present study, which suggests that prediction errors are encoded according to the mismatch between predicted and observed stimulus features, and not the features themselves. The former interpretation( i. e., that predictions are coded according to the stimulus features) fits well with a recent study that showed prediction induces feature-specific templates immediately prior to stimulus onset[31]. Thus, a parsimonious account of the literature to date suggests that predictions and prediction errors are represented in the brain via distinct neural codes: whereas predictions are represented according to stimulus features, prediction errors are represented according to the mismatch between predicted and observed stimulus features. In a recent study by our group[39], we observed a decrease in orientation selectivity in the neural response to predicted stimuli, relative to surprising stimuli, shortly after stimulus onset( 79–185 ms). Here, we observed a similar( but nonsignificant) trend in the same direction( standards < deviants) at approximately the same time( 94–145 ms, S2C Fig, cluster not shown). Close inspection of the present results, however, suggests that some orientation information evoked by the previous standard was still present in the brain at the onset of the subsequent standard( indicated by the above-zero amplitude of the orientation response to standards at stimulus onset, t = 0 ms, S2C Fig), which may have obscured detection of the early effect reported in Tang and colleagues[39]. The present results revealed a late effect of prediction( standards < deviants, 324–550 ms, S2C and S2D Fig) that was not observed in our previous work[39]. Since a critical difference between the two studies was the number of times identical stimuli could be presented consecutively( no more than twice in the previous study), we speculate that the late effect observed here might reflect the minimal amount of model updating required after the presentation of a precisely predicted stimulus. We also found that attention increased the amplitude of orientation response profiles( Fig 3A and 3B), consistent with previous studies that applied forward encoding models to human functional MRI( fMRI)[34, 40] and time-frequency-resolved EEG data[29]. The present study replicates and extends these studies with the application of forward encoding models to time-resolved EEG recordings( resulting in <30 ms temporal resolution after smoothing), demonstrating that attention increases feature selectivity in the human brain from approximately 200 ms after stimulus onset. Crucially, we also tested the interactive effects of attention and prediction on information processing in the brain. There was a large and significant effect of attention on mismatch response profiles in response to surprising but not unpredictable stimuli( beginning around 150 ms after stimulus onset and reaching significance from about 350 ms). This finding demonstrates that attention boosts mismatch prediction errors evoked by surprising stimuli and is consistent with a recent iteration of predictive coding theory according to which attention optimises the expected precision of prediction errors[8]. Previous studies have found evidence for an interaction between attention and prediction in both the auditory[5] and visual[6, 41] modalities. These studies used activation-based analyses to compare differences between predicted and unpredicted stimuli at the level of overall neural activity but did not investigate what type of information is modulated in the interaction between attention and prediction. In contrast, the present study used information-based analyses[42] to identify specific patterns of neural activity that are associated with orientation-mismatch information in the brain, and showed that selectivity for this type of information( but not feature information) is increased with attention. Thus, the present study provides clear support for the hypothesis that attention boosts the gain of prediction errors[8]. It will be important for future research to investigate whether the interactive effects of attention and prediction on mismatch information is contingent on the type of attention( e. g., feature-based versus spatial attention) or prediction( e. g., rule-based versus multimodal cue-stimulus predictions;[31, 43]). We found that the magnitude of mismatch response profiles correlated with the number of preceding standards( Fig 4A and 4B). Previous work in the auditory domain demonstrated that successive repetitions of the standard evoke progressively increased responses to a subsequent attended deviant[35]. Here, we find a corollary for this effect in the visual domain and demonstrate that the neural activity modulated by the number of preceding standards is likely encoded as mismatch information. This finding is also consistent with the notion that repeating the standard allows a more precise prediction to be generated, which results in a larger prediction error to a subsequent surprising stimulus[44]. We also found that mismatch response profiles increased with the magnitude of the mismatch between predicted and observed stimulus features( Fig 4C). Previous work in the auditory domain has demonstrated a correlation between deviation magnitude and the amplitude of the neural response to deviants( i. e., the mismatch negativity)[45]. Here, we demonstrate a relationship between deviation magnitude and selectivity for mismatch information( as opposed to activation levels) in the visual domain, suggesting that the magnitude of mismatch information might be used by the brain to guide updating of the predictive model. Since the present study investigated mismatch signals with respect to a continuous and circular feature dimension( i. e., orientation), it will be important for future research to extend the current line of research to noncircular( e. g., luminance, auditory frequency) and categorical( e. g., facial emotions) feature dimensions. There was a lateral shift in the response profile of individual mismatch channels toward the orthogonal( 90°) channel( Fig 4D). The extent of this effect depended on the deviation magnitude, with large deviations( ±40°–80°) being predominantly stacked over the 90° channel and smaller deviations( ±20°) being more closely aligned with their veridical mismatch angle( Fig 4D). We speculate that this might indicate a qualitative difference in the way that small and large prediction errors were treated by the brain in the present study. Small deviations may have resulted in updating and retention of the current model( via a near-veridical mismatch signal), whereas large deviations may have resulted in the wholesale rejection of the current model( via a generic mismatch signal) in favour of an alternative model that represents the deviant stimulus. In the latter case, the magnitude of the( orthogonal) mismatch channel response might represent an efficient code that the brain utilises to select from a number of likely alternative models. A number of recent studies failed to find an interaction between the effects of attention and prediction on stimulus information in the brain[31, 38, 46]. If predictions are encoded according to stimulus features, as we argue above, these null findings contradict the theory that attention boosts predictions[47]. In contrast, we show that prediction errors, represented according to the mismatch between predicted and observed stimulus features, are enhanced with attention. Although the present study cannot speak to the activity of single neurons, we note that the emerging picture is consistent with the notion that predictions and prediction errors are represented in distinct populations of neurons[2] that encode two distinct types of information and are differentially influenced by attention. Under this framework, feature information encoded by prediction units would be immune to attention, whereas mismatch information encoded by prediction error units would be enhanced by attention. Future research could test these hypotheses at the single-cell level, for example by using single-unit electrode recordings or 2-photon calcium imaging to assess whether different neurons within a given cortical area satisfy these constraints.
The human brain is theorised to operate like a sophisticated hypothesis tester , using past experience to generate a model of the external world , testing predictions of this model against incoming sensory evidence , and generating a ‘prediction error’ signal that updates the model when predictions and sensory evidence do not match . In addition to predicting the content of sensory signals , an optimal system should also predict the reliability ( or ‘precision’ ) of those signals to minimise the influence of unreliable sensory information . It has been proposed that attention optimises this process by boosting prediction error signals , which are coded as the difference ( or ‘mismatch’ ) between predicted and observed stimulus features . Accordingly , this theory predicts that attention should increase the selectivity for mismatch information in the neural response to surprising stimuli . We tested this hypothesis in human participants by training a decoding algorithm to identify ‘mismatch information’ in the brain , recorded by electroencephalography ( EEG ) , following the presentation of surprising stimuli that were either attended or ignored . We found that attention did indeed increase the selectivity for mismatch information in the neural response , supporting the notion that attention and prediction are intricately related processes .