Prediction errors in surface segmentation are reflected in the visual mismatch negativity, independently of task and surface features

Oxner, Matt; Rosentreter, Eric T.; Hayward, William G.; Corballis, Paul M.

doi:10.1167/19.6.9

Cited by 5 publications

(5 citation statements)

References 74 publications

(97 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, PHT matching low level sensory features, such as gratings (Kok et al, 2012), to hypotheses about such low level features should occur at earlier stages in the processing hierarchy. These PHT processes maybe reflected in other ERP components, such as the visual mismatch negativity (Oxner et al, 2019), or even earlier ERP components that index expected features, like the P1 (Boutonnet & Lupyan, 2015).…”

Section: The N300 Indexes Perceptual Hypothesis Testingmentioning

confidence: 99%

The N300: An Index For Predictive Coding Of Complex Visual Objects and Scenes

Kumar

Federmeier

Beck

2020

Preprint

View full text Add to dashboard Cite

The bulk of support for predictive coding models has come from the models' ability to simulate known perceptual or neuronal phenomena, but there have been fewer attempts to identify a reliable neural signature of predictive coding. Here we propose that the N300 component of the event-related potential (ERP), occurring 250-350 ms post-stimulus-onset, may be such a signature of perceptual hypothesis testing operating at the scale of whole objects and scenes. We show that N300 amplitudes are smaller to representative ('good exemplars') compared to less representative ('bad exemplars') items from natural scene categories. Integrating these results with patterns observed for objects, we establish that, across a variety of visual stimuli, the N300 is responsive to statistical regularity, or the degree to which the input is 'expected' (either explicitly or implicitly) by the system based on prior knowledge, with statistically regular images, which entail reduced prediction error, evoking a reduced response. Moreover, we show that the measure exhibits context-dependency; that is, we find the N300 sensitivity to category representativeness only when stimuli are congruent with and not when they are incongruent with a category pre-cue, suggesting that the component may reflect the ease with which an image matches the current hypothesis generated by the visual system. Thus, we argue that the N300 ERP component is the best candidate to date for an index of perceptual hypotheses testing, whereby incoming sensory information for complex visual objects and scenes is accessed against contextual predictions generated in mid-level visual areas.

show abstract

Section: The N300 Indexes Perceptual Hypothesis Testingmentioning

confidence: 99%

The N300: An Index For Predictive Coding Of Complex Visual Objects and Scenes

Kumar

Federmeier

Beck

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Two articles in the Special Issue provide evidence for high-level predictive representations using EEG. Oxner et al (2019) show that prediction errors in surface segmentation are associated with visual mismatch negativity and with the P2 wave in the event-related potential. Based on amplitude differences in the error-related negativity, Maurer et al (2019) suggest that the brain generates error predictions that can dissociate relevant from irrelevant errors and that relevant errors lead to larger behavioral adjustments than irrelevant ones.…”

Section: Predicting Sensory Changes In the Worldmentioning

confidence: 86%

Introduction to special issue on “Prediction in Perception and Action”

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In a study of MMN, Clifford et al proposed a visual mismatching negative wave (vMMN) when investigating the changes of brain in the process of color perception, providing possible evidence for the classification and coding of color pre-attention [40]. Oxner et al proposed that changes in the layout of an interface would cause changes in MMN, P2-N2, and P300 [41]. However, MMN is mainly caused by hearing, and the classical MMN differential wave can be obtained in the classical Oddball paradigm.…”

Section: Eeg Technologymentioning

confidence: 99%

“…Firstly, the amplitude of P300 was analyzed, and the absolute values of the average amplitude of each color in the blue series followed the order of: blue 4 P300 generally occurs in the parietal lobe. In this study, P3, P4, CP1, CP2, Pz, and POz were chosen based on the selection of electrodes in the experimental study of P300 conducted by our predecessors [24,41,42] and the significance of the amplitude of each electrode in the EEG.…”

Section: Blue Seriesmentioning

confidence: 99%

Research on the Colors of Military Symbols in Digital Situation Maps Based on Event-Related Potential Technology

Niu

Xue

et al. 2020

IJGI

View full text Add to dashboard Cite

Under the trend of increasingly informationalized military operations and the increasing maneuverability of combat units, military commanders have put forward higher requirements for the accuracy and promptness of information on battlefield situation maps. Based on the sea battlefield, this paper studies the pros and cons of the color matching of military symbols on sea situation maps. Fifteen colors, where each Hue had five colors, were chosen using the Munsell Color System according to Chroma axis and the Value axis on a span of 2 and 4. By collecting and analyzing the P300 EEG data, reaction time data, and accuracy data of 20 subjects, a better color matching selection of military symbols on pure color (L = 85, a = −10, and b = −23) sea situation maps is put forward, and the conclusions are as follows: (1) the different colors all cause the P300 component in EEG experiment. Among them, the P300 amplitude that is caused by military symbols with lower Chroma is smaller and the latency is shorter, indicating that the user experience and efficiency of low Chroma color symbols will be better than those with high Chroma color symbols. (2) High Value color map military symbols cause higher P300 amplitude and longer latency. According to the results above, this paper puts forward three optimized colors, namely, blue (L = 39, a = 20, and b = −49), green (L = 80, a = −72, and b = 72), and red (L = 20, a = 41, and b = 28). Additionally, three map interfaces were designed to confirm the validity of these colors. By means of applying the NASA-TLX (Task Load Index) scale to evaluate the task load of the confirmation interfaces, it can be concluded that these three optimized colors are preferred by users who are skilled in GIS and interface design. Therefore, the research conclusion of this paper can provide important reference values for military map design, which is helpful in shortening the identification and judgment time during the use of situation maps and it can improve users’ operation performance.

show abstract

Prediction errors in surface segmentation are reflected in the visual mismatch negativity, independently of task and surface features

Abstract: visual surface segmentation leads to observable electrophysiological signals of prediction error that are dissociable from stimulus-specific encoding and lowerlevel perceptual processing.

Cited by 5 publications

References 74 publications

The N300: An Index For Predictive Coding Of Complex Visual Objects and Scenes

The N300: An Index For Predictive Coding Of Complex Visual Objects and Scenes

Introduction to special issue on “Prediction in Perception and Action”

Research on the Colors of Military Symbols in Digital Situation Maps Based on Event-Related Potential Technology

Contact Info

Product

Resources

About