scispace - formally typeset
Open accessJournal ArticleDOI: 10.1016/J.NEUROIMAGE.2021.117896

Perceptual difficulty modulates the direction of information flow in familiar face recognition.

02 Mar 2021-NeuroImage (Academic Press)-Vol. 233, pp 117896-117896
Abstract: Humans are fast and accurate when they recognize familiar faces. Previous neurophysiological studies have shown enhanced representations for the dichotomy of familiar vs. unfamiliar faces. As familiarity is a spectrum, however, any neural correlate should reflect graded representations for more vs. less familiar faces along the spectrum. By systematically varying familiarity across stimuli, we show a neural familiarity spectrum using electroencephalography. We then evaluated the spatiotemporal dynamics of familiar face recognition across the brain. Specifically, we developed a novel informational connectivity method to test whether peri-frontal brain areas contribute to familiar face recognition. Results showed that feed-forward flow dominates for the most familiar faces and top-down flow was only dominant when sensory evidence was insufficient to support face recognition. These results demonstrate that perceptual difficulty and the level of familiarity influence the neural representation of familiar faces and the degree to which peri-frontal neural networks contribute to familiar face recognition.

... read more

Citations
  More

10 results found


Open accessJournal ArticleDOI: 10.7554/ELIFE.60563
08 Apr 2021-eLife
Abstract: There are many monitoring environments, such as railway control, in which lapses of attention can have tragic consequences. Problematically, sustained monitoring for rare targets is difficult, with more misses and longer reaction times over time. What changes in the brain underpin these ‘vigilance decrements’? We designed a multiple-object monitoring (MOM) paradigm to examine how the neural representation of information varied with target frequency and time performing the task. Behavioural performance decreased over time for the rare target (monitoring) condition, but not for a frequent target (active) condition. This was mirrored in neural decoding using magnetoencephalography: coding of critical information declined more during monitoring versus active conditions along the experiment. We developed new analyses that can predict behavioural errors from the neural data more than a second before they occurred. This facilitates pre-empting behavioural errors due to lapses in attention and provides new insight into the neural correlates of vigilance decrements.

... read more

Topics: Vigilance (psychology) (56%), Neural decoding (50%)

8 Citations


Open accessPosted ContentDOI: 10.1101/2020.09.02.279042
27 Oct 2020-bioRxiv
Abstract: Humans are remarkably efficent at recognizing objects. Understanding how the brain performs object recognition has been challenging. Our understanding has been advanced substantially in recent years with the development of multivariate decoding methods. Most start-of-the-art decoding procedures, make use of the ‘mean’ neural activation to extract object category information, which overlooks temporal variability in the signals. Here, we studied category-related information in 30 mathematically distinct features from electroencephalography (EEG) across three independent and highly-varied datasets using multivariate decoding. While the event-related potential (ERP) components of N1 and P2a were among the most informative features, the informative original signal samples and Wavelet coefficients, selected through principal component analysis, outperformed them. The four mentioned informative features showed more pronounced decoding in the Theta frequency band, which has been suggested to support feed-forward processing of visual information in the brain. Correlational analyses showed that the features, which were most informative about object categories, could predict participants’ behavioral performance (reaction time) more accurately than the less informative features. These results suggest a new approach for studying how the human brain encodes object category information and how we can read them out more optimally to investigate the temporal dynamics of the neural code. The codes are available online at https://osf.io/wbvpn/.

... read more

5 Citations


Open accessJournal ArticleDOI: 10.1038/S42003-021-02109-X
Jade B. Jackson1, Eva Feredoes2, Anina N. Rich3, Michael Lindner2  +2 moreInstitutions (3)
17 May 2021-
Abstract: Dorsolateral prefrontal cortex (dlPFC) is proposed to drive brain-wide focus by biasing processing in favour of task-relevant information. A longstanding debate concerns whether this is achieved through enhancing processing of relevant information and/or by inhibiting irrelevant information. To address this, we applied transcranial magnetic stimulation (TMS) during fMRI, and tested for causal changes in information coding. Participants attended to one feature, whilst ignoring another feature, of a visual object. If dlPFC is necessary for facilitation, disruptive TMS should decrease coding of attended features. Conversely, if dlPFC is crucial for inhibition, TMS should increase coding of ignored features. Here, we show that TMS decreases coding of relevant information across frontoparietal cortex, and the impact is significantly stronger than any effect on irrelevant information, which is not statistically detectable. This provides causal evidence for a specific role of dlPFC in enhancing task-relevant representations and demonstrates the cognitive-neural insights possible with concurrent TMS-fMRI-MVPA.

... read more

3 Citations


Open accessJournal ArticleDOI: 10.1162/NECO_A_01436
12 Oct 2021-Neural Computation
Abstract: How does the human brain encode visual object categories? Our understanding of this has advanced substantially with the development of multivariate decoding analyses. However, conventional electroencephalography (EEG) decoding predominantly uses the mean neural activation within the analysis window to extract category information. Such temporal averaging overlooks the within-trial neural variability that is suggested to provide an additional channel for the encoding of information about the complexity and uncertainty of the sensory input. The richness of temporal variabilities, however, has not been systematically compared with the conventional mean activity. Here we compare the information content of 31 variability-sensitive features against the mean of activity, using three independent highly varied data sets. In whole-trial decoding, the classical event-related potential (ERP) components of P2a and P2b provided information comparable to those provided by original magnitude data (OMD) and wavelet coefficients (WC), the two most informative variability-sensitive features. In time-resolved decoding, the OMD and WC outperformed all the other features (including the mean), which were sensitive to limited and specific aspects of temporal variabilities, such as their phase or frequency. The information was more pronounced in the theta frequency band, previously suggested to support feedforward visual processing. We concluded that the brain might encode the information in multiple aspects of neural variabilities simultaneously such as phase, amplitude, and frequency rather than mean per se. In our active categorization data set, we found that more effective decoding of the neural codes corresponds to better prediction of behavioral performance. Therefore, the incorporation of temporal variabilities in time-resolved decoding can provide additional category information and improved prediction of behavior.

... read more

Topics: Decoding methods (50%)

1 Citations


Open accessJournal ArticleDOI: 10.1093/CERCOR/BHAB366
Alexia Dalski1, Alexia Dalski2, Alexia Dalski3, Gyula Kovács1  +1 moreInstitutions (3)
09 Oct 2021-Cerebral Cortex
Abstract: We explored the neural signatures of face familiarity using cross-participant and cross-experiment decoding of event-related potentials, evoked by unknown and experimentally familiarized faces from a set of experiments with different participants, stimuli, and familiarization-types. Human participants of both sexes were either familiarized perceptually, via media exposure, or by personal interaction. We observed significant cross-experiment familiarity decoding involving all three experiments, predominantly over posterior and central regions of the right hemisphere in the 270-630 ms time window. This shared face familiarity effect was most prominent across the Media and the Personal, as well as between the Perceptual and Personal experiments. Cross-experiment decodability makes this signal a strong candidate for a general neural indicator of face familiarity, independent of familiarization methods, participants, and stimuli. Furthermore, the sustained pattern of temporal generalization suggests that it reflects a single automatic processing cascade that is maintained over time.

... read more

Topics: Set (psychology) (50%)

References
  More

87 results found


Open accessJournal ArticleDOI: 10.1023/A:1022627411411
Corinna Cortes1, Vladimir Vapnik1Institutions (1)
15 Sep 1995-Machine Learning
Abstract: The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

... read more

Topics: Feature learning (63%), Active learning (machine learning) (62%), Feature vector (62%) ... read more

35,157 Citations


Journal ArticleDOI: 10.1163/156856897X00357
01 Jan 1997-Spatial Vision
Abstract: The Psychophysics Toolbox is a software package that supports visual psychophysics. Its routines provide an interface between a high-level interpreted language (MATLAB on the Macintosh) and the video display hardware. A set of example programs is included with the Toolbox distribution.

... read more

Topics: Visual Psychophysics (64%), Toolbox (55%), Psychophysics (52%)

15,313 Citations


Journal ArticleDOI: 10.1163/156856897X00366
Denis G. Pelli1Institutions (1)
01 Jan 1997-Spatial Vision
Abstract: The VideoToolbox is a free collection of two hundred C subroutines for Macintosh computers that calibrates and controls the computer-display interface to create accurately specified visual stimuli. High-level platform-independent languages like MATLAB are best for creating the numbers that describe the desired images. Low-level, computer-specific VideoToolbox routines control the hardware that transforms those numbers into a movie. Transcending the particular computer and language, we discuss the nature of the computer-display interface, and how to calibrate and control it.

... read more

Topics: Interface (computing) (53%), Visual Psychophysics (51%), Software (51%)

9,169 Citations


Open accessJournal ArticleDOI: 10.1038/S41598-016-0028-X
Michiru Nishita1, Seung-Yeol Park2, Tadashi Nishio1, Koki Kamizaki1  +8 moreInstitutions (5)
26 Jan 2017-Scientific Reports
Abstract: Signaling through the Ror2 receptor tyrosine kinase promotes invadopodia formation for tumor invasion. Here, we identify intraflagellar transport 20 (IFT20) as a new target of this signaling in tumors that lack primary cilia, and find that IFT20 mediates the ability of Ror2 signaling to induce the invasiveness of these tumors. We also find that IFT20 regulates the nucleation of Golgi-derived microtubules by affecting the GM130-AKAP450 complex, which promotes Golgi ribbon formation in achieving polarized secretion for cell migration and invasion. Furthermore, IFT20 promotes the efficiency of transport through the Golgi complex. These findings shed new insights into how Ror2 signaling promotes tumor invasiveness, and also advance the understanding of how Golgi structure and transport can be regulated.

... read more

Topics: Golgi apparatus (58%), Invadopodia (56%), Intraflagellar transport (51%) ... read more

8,752 Citations


Open accessJournal ArticleDOI: 10.1093/CERCOR/1.1.1
01 Jan 1991-Cerebral Cortex
Abstract: In recent years, many new cortical areas have been identified in the macaque monkey. The number of identified connections between areas has increased even more dramatically. We report here on (1) a summary of the layout of cortical areas associated with vision and with other modalities, (2) a computerized database for storing and representing large amounts of information on connectivity patterns, and (3) the application of these data to the analysis of hierarchical organization of the cerebral cortex. Our analysis concentrates on the visual system, which includes 25 neocortical areas that are predominantly or exclusively visual in function, plus an additional 7 areas that we regard as visual-association areas on the basis of their extensive visual inputs. A total of 305 connections among these 32 visual and visual-association areas have been reported. This represents 31% of the possible number of pathways if each area were connected with all others. The actual degree of connectivity is likely to be closer to 40%. The great majority of pathways involve reciprocal connections between areas. There are also extensive connections with cortical areas outside the visual system proper, including the somatosensory cortex, as well as neocortical, transitional, and archicortical regions in the temporal and frontal lobes. In the somatosensory/motor system, there are 62 identified pathways linking 13 cortical areas, suggesting an overall connectivity of about 40%. Based on the laminar patterns of connections between areas, we propose a hierarchy of visual areas and of somatosensory/motor areas that is more comprehensive than those suggested in other recent studies. The current version of the visual hierarchy includes 10 levels of cortical processing. Altogether, it contains 14 levels if one includes the retina and lateral geniculate nucleus at the bottom as well as the entorhinal cortex and hippocampus at the top. Within this hierarchy, there are multiple, intertwined processing streams, which, at a low level, are related to the compartmental organization of areas V1 and V2 and, at a high level, are related to the distinction between processing centers in the temporal and parietal lobes. However, there are some pathways and relationships (about 10% of the total) whose descriptions do not fit cleanly into this hierarchical scheme for one reason or another. In most instances, though, it is unclear whether these represent genuine exceptions to a strict hierarchy rather than inaccuracies or uncertainities in the reported assignment.

... read more

7,279 Citations