Melbourne School of Psychological Sciences - Research Publications
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ItemPosition representations of moving objects align with real-time position in the early visual response(eLIFE SCIENCES PUBL LTD, 2023-01-19)When interacting with the dynamic world, the brain receives outdated sensory information, due to the time required for neural transmission and processing. In motion perception, the brain may overcome these fundamental delays through predictively encoding the position of moving objects using information from their past trajectories. In the present study, we evaluated this proposition using multivariate analysis of high temporal resolution electroencephalographic data. We tracked neural position representations of moving objects at different stages of visual processing, relative to the real-time position of the object. During early stimulus-evoked activity, position representations of moving objects were activated substantially earlier than the equivalent activity evoked by unpredictable flashes, aligning the earliest representations of moving stimuli with their real-time positions. These findings indicate that the predictability of straight trajectories enables full compensation for the neural delays accumulated early in stimulus processing, but that delays still accumulate across later stages of cortical processing.
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ItemNo Preview AvailableDecoding continuous variables from event-related potential (ERP) data with linear support vector regression using the Decision Decoding Toolbox (DDTBOX)(FRONTIERS MEDIA SA, 2022-11-03)Multivariate classification analysis for event-related potential (ERP) data is a powerful tool for predicting cognitive variables. However, classification is often restricted to categorical variables and under-utilises continuous data, such as response times, response force, or subjective ratings. An alternative approach is support vector regression (SVR), which uses single-trial data to predict continuous variables of interest. In this tutorial-style paper, we demonstrate how SVR is implemented in the Decision Decoding Toolbox (DDTBOX). To illustrate in more detail how results depend on specific toolbox settings and data features, we report results from two simulation studies resembling real EEG data, and one real ERP-data set, in which we predicted continuous variables across a range of analysis parameters. Across all studies, we demonstrate that SVR is effective for analysis windows ranging from 2 to 100 ms, and relatively unaffected by temporal averaging. Prediction is still successful when only a small number of channels encode true information, and the analysis is robust to temporal jittering of the relevant information in the signal. Our results show that SVR as implemented in DDTBOX can reliably predict continuous, more nuanced variables, which may not be well-captured by classification analysis. In sum, we demonstrate that linear SVR is a powerful tool for the investigation of single-trial EEG data in relation to continuous variables, and we provide practical guidance for users.
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ItemVisual mismatch responses index surprise signalling but not expectation suppression( 2020-06-24)Abstract 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 10-90%, 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. Highlights -We used a recently-developed oddball design that controls for repetition effects -We found effects of surprise but not expectation suppression on ERPs -Surprise responses did not vary by stimulus appearance probability
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ItemNo Preview AvailableDecoding explicit and implicit representations of health and taste attributes of foods in the human brain( 2021)Obesity has become a significant problem word-wide and is strongly linked to poor food choices. Even in healthy individuals, taste perceptions often drive dietary decisions more strongly than healthiness. This study tested whether health and taste representations can be directly decoded from brain activity, both when explicitly considered, and when implicitly processed for decision-making. We used multivariate support vector regression for event-related potentials (as measured by the electroencephalogram) occurring in the first second of food cue processing to predict ratings of tastiness and healthiness. In Experiment 1, 37 healthy participants viewed images of various foods and explicitly rated their tastiness and healthiness, whereas in Experiment 2, 89 healthy participants indicated their desire to consume snack foods, with no explicit instruction to consider tastiness or healthiness. In Experiment 1 both attributes could be decoded, with taste information being available earlier than health. In Experiment 2, both dimensions were also decodable, and their significant decoding preceded the decoding of decisions (i.e., desire to consume the food). However, in Experiment 2, health representations were decodable earlier than taste representations. These results suggest that health information is activated in the brain during the early stages of dietary decisions, which is promising for designing obesity interventions aimed at quickly activating health awareness.
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ItemDecoding continuous variables from event-related potential (ERP) data with linear support vector regression (SVR) using the Decision Decoding Toolbox (DDTBOX)( 2021-06)Background: Multivariate classification analysis for event-related potential (ERP) data is a powerful tool for predicting cognitive variables. However, classification is often restricted to categorical variables and under-utilises continuous data, such as response times, response force, or subjective ratings. An alternative approach is Support Vector Regression (SVR), which uses single-trial data to predict continuous variables of interest. New Method: In this tutorial-style paper, we demonstrate how SVR is implemented in the Decision Decoding Toolbox (DDTBOX). To illustrate in more detail how results depend on specific toolbox settings and data features, we report results from two simulation studies resembling real EEG data, and one real ERP-data set, in which we predicted continuous variables across a range of analysis parameters. Results: Across all studies, we demonstrate that SVR is effective for analysis windows ranging from 2 ms – 100 ms, and relatively unaffected by temporal averaging. Prediction is still successful when only a small number of channels encode true information, and the analysis is robust to temporal jittering of the relevant information in the signal. Comparison with existing Methods: Our result show that SVR as implemented in DDTBOX can reliably predict continuous, more nuanced variables, which may not be well-captured by classification analysis. Conclusions In sum, we demonstrate that linear SVR is a powerful tool for the investigation of single-trial EEG data in relation to continuous variables, and we provide practical guidance for users.
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ItemPosition representations of moving objects align with real-time position in the early visual response( 2022-06-29)Abstract: When localising a moving object, the brain receives outdated sensory information about its position, due to the time required for neural transmission and processing. The brain may overcome these fundamental delays through predictively encoding the position of moving objects using information from their past trajectories. In the present study, we evaluated this proposition using multivariate analysis of high temporal resolution electroencephalographic data. We tracked neural position representations of moving objects at different stages of visual processing, relative to the real-time position of the object. During early stimulus-evoked activity, position representations of moving objects were activated substantially earlier than the equivalent activity evoked by unpredictable flashes, aligning the earliest representations of moving stimuli with their real-time positions. These findings indicate that the predictability of straight trajectories enables full compensation for the neural delays accumulated early in stimulus processing, but that delays still accumulate across later stages of cortical processing.
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ItemPredictions drive neural representations of visual events ahead of incoming sensory information(NATL ACAD SCIENCES, 2020-03-31)The transmission of sensory information through the visual system takes time. As a result of these delays, the visual information available to the brain always lags behind the timing of events in the present moment. Compensating for these delays is crucial for functioning within dynamic environments, since interacting with a moving object (e.g., catching a ball) requires real-time localization of the object. One way the brain might achieve this is via prediction of anticipated events. Using time-resolved decoding of electroencephalographic (EEG) data, we demonstrate that the visual system represents the anticipated future position of a moving object, showing that predictive mechanisms activate the same neural representations as afferent sensory input. Importantly, this activation is evident before sensory input corresponding to the stimulus position is able to arrive. Finally, we demonstrate that, when predicted events do not eventuate, sensory information arrives too late to prevent the visual system from representing what was expected but never presented. Taken together, we demonstrate how the visual system can implement predictive mechanisms to preactivate sensory representations, and argue that this might allow it to compensate for its own temporal constraints, allowing us to interact with dynamic visual environments in real time.
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ItemVisual mismatch responses index surprise signalling but not expectation suppression(ELSEVIER MASSON, CORP OFF, 2021-01)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.
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ItemThe time-course of prediction formation and revision in human visual motion processing(ELSEVIER MASSON, CORP OFF, 2021-05)Establishing the real-time position of a moving object poses a challenge to the visual system due to neural processing delays. While sensory information is travelling through the visual hierarchy, the object continues moving and information about its position becomes outdated. By extrapolating the position of a moving object along its trajectory, predictive mechanisms might effectively decrease the processing time associated with these objects. Here, we use time-resolved decoding of electroencephalographic (EEG) data from an apparent motion paradigm to demonstrate the interaction of two separate predictive mechanisms. First, we reveal predictive latency advantages for position representations as soon as the second object in an apparent motion sequence - even before the stimulus contains any physical motion energy. This is consistent with the existence of omni-directional, within-layer waves of sub-threshold activity that bring neurons coding for adjacent positions closer to their firing threshold, thereby reducing the processing time of the second stimulus in one of those positions. Second, we show that an additional direction-specific latency advantage emerges from the third sequence position onward, once the direction of the apparent motion stimulus is uniquely determined. Because the receptive fields of early visual areas are too small to encompass sequential apparent motion positions (as evidenced by the lack of latency modulation for the second stimulus position), this latency advantage most likely arises from descending predictions from higher to lower visual areas through feedback connections. Finally, we reveal that the same predictive activation that facilitates the processing of the object in its expected position needs to be overcome when the object's trajectory unexpectedly reverses, causing an additional latency disadvantage for stimuli that violate predictions. Altogether, our results suggest that two complementary mechanisms interact to form and revise predictions in visual motion processing, modulating the latencies of neural position representations at different levels of visual processing.
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ItemDecoding explicit and implicit representations of health and taste attributes of foods in the human brain(PERGAMON-ELSEVIER SCIENCE LTD, 2021-11-12)Obesity has become a significant problem word-wide and is strongly linked to poor food choices. Even in healthy individuals, taste perceptions often drive dietary decisions more strongly than healthiness. This study tested whether health and taste representations can be directly decoded from brain activity, both when explicitly considered, and when implicitly processed for decision-making. We used multivariate support vector regression for event-related potentials (as measured by the electroencephalogram) to estimate a regression model predicting ratings of tastiness and healthiness for each participant, based on their neural activity occurring in the first second of food cue processing. In Experiment 1, 37 healthy participants viewed images of various foods and explicitly rated their tastiness and healthiness. In Experiment 2, 89 healthy participants completed a similar rating task, followed by an additional experimental phase, in which they indicated their desire to consume snack foods with no explicit instruction to consider tastiness or healthiness. In Experiment 1 both attributes could be decoded, with taste information being available earlier than health. In Experiment 2, both dimensions were also decodable, and their significant decoding preceded the decoding of decisions (i.e., desire to consume the food). However, in Experiment 2, health representations were decodable earlier than taste representations. These results suggest that health information is activated in the brain during the early stages of dietary decisions, which is promising for designing obesity interventions aimed at quickly activating health awareness.