text:
COBI.Bike sport
with universal mount for e-bikes with Bosch motor
The perfect fusion of your smartphone and your e-bike connects you with your digital world and transforms your e-bike into a smart bike. The sport version features all essential functionalities for e-bikes with integrated lights.
Included in this set:
Hub (2. Generation) for e-bikes with Bosch motor from MY 2014
Universal Mount for Android and iPhones
App* for Android/iOS
This set is suitable for most iPhone and Android smartphones with dimensions of 123.8-158.4 mm x 58.6-78.1 mm x 7.1-8.26 mm incl. case, whose total weight (smartphone and case) does not exceed 250g**
Note: The automatic AmbiSense light system is not included. The front and rear lights are available as accessories. The Purion control unit can be converted to the Bosch Intuvia or Nyon display mount and is then compatible with the COBI.Bike system for Bosch eBikes.
*The app can be downloaded from the appropriate App Store at no additional cost. The app can only be used after prior registration and conclusion of a separate usage agreement.
**Please note that the manufacturer's specifications may not correspond to the maximum size. For example, some manufacturers do not take the exposed position of the camera into account.
The Universal Mount supports iPhone 5 and newer. The app is optimized for iOS 12 or better.
Recommended phones:
Samsung Galaxy S7 (edge)
Samsung Galaxy S8 / S8+ / edge
Minimum specifications:
Smartphone width 58.6 to 78.1 mm
CPU: Quad Core 1,4 GHz
> 200MB free memory
Bluetooth Smart (BLE) 4.0
Micro USB connector (type B)
This set is suitable for most iPhone and Android smartphones with dimensions of 123.8-158.4 mm x 58.6-78.1 mm x 7.1-8.26 mm incl. case, whose total weight (smartphone and case) does not exceed 250g. Please note that the manufacturer's specifications may not correspond to the maximum size. For example, some manufacturers do not take the exposed position of the camera into account.
Drive Units Active Line and Performance Line from MY2014, Performance Line CX from MY2016, Active Line Plus from MY2018.
Batteries All Bosch batteries (PowerPacks and PowerTubes), including DualBattery. Please note: For eBikes with PowerTubes, the on/off switch must be accessible if a battery cover is installed so that the system can be switched on even when it is fully discharged.
Handlebar mount Intuvia / Nyon display adapter
Loudspeaker Digital bell, alarm
Wireless Interfaces Bluetooth Smart 4.1, ANT
On-board sensors Accelerometer, barometer, light sensor
Control unit Intuvia / Nyon
Material PC-ABS
Ingress protection IPX5
Dimensions (WxHxD) 91 x 83 x 86 mm
Universal Mount
Smartphone platform iPhone / Android
Smartphone size (incl. case) Length: from 123.8 to 158.4mm
Width: from 58.6 to 78.1 mm
Height: from 7.1 to 8.26mm
Safety note:
Most iPhone models and Android smartphones with 123.8-158.4 mm x 58.6-78.1 mm x 7.1-8.26 mm including cover can be securely fixed to the handlebar using the universal mount. The maximum total weight of the smartphone including the case is 250g.
Please note that the manufacturer's specifications may not correspond to the maximum size. For example, some manufacturers do not take the exposed position of the camera into account.
Mounting Slide-on, secured with two screws
Phone charging connection Micro-USB to Micro-USB, Lightning, Micro-USB to USB-C® adapter cable
Dimensions (WxHxD) 119mm x 70mm x 24mm
Environmental requirement
Storage temperature -10 to 50 °C
Operating (ambient) temperature -5 to 40 °C


text:
Q: Javascript clear listeners and variables I am about to ajaxify my website, but I have observed in some highly ajaxified websites like facebook itself that if you browse for quite some time without refreshing your browser gets slower, respectively more ram is being used and I suppose that is due to javascript "leftovers". It is kind of needless to ask this question if facebook's developers fail to accomplish this but hey, you only lose if you don't try. So the question is "is there a way I can clear variables and listeners from the previous page before loading the new one?". Thank you in advance!

A: That depends on the browser since each browser does its own Garbage Collection. You can however delete variables and HTML elements and if the GC thinks it can reclaim some memory, it will.
I suggest using some developer tools to actually watch the memory usage in real-time. In Chrome you can create a profile timeline and watch the memory usage grow/shrink. So you can actually see in real-time the effects your code have on memory management.
For testing I like to run cleanup code on a button click so I can know exactly when the code ran and how the memory usage was affected by the code.
And hey, since you're in there playing with dev. tools now, check-out the other tools available to you, they are amazingly helpful.

redpajama_set_name: RedPajamaStackExchange


text:
Have Your Say NEDA Data
Council / Board
DPO Australia
NEDA Staff
Key Advocacy Issues
Policy Engagements
Submissions and Policy Documents
NEDA Data
DPOA
Contact Neda
Inclusive Mental Health Reform: Highlighting Issues and Opportunities for Australians From Culturally and Linguistically Diverse Backgrounds
This is a copy of a submission made to the Productivity Commission Inquiry into Mental Health.
This submission has been jointly produced by Mental Health Australia, the Federation of Ethnic Communities' Councils of Australia (FECCA) and the National Ethnic Disability Alliance (NEDA).
Submitted by comms on Thu, 13/06/2019 - 10:48 am
National Ethnic Disability Alliance's Election Platform - April 2019
Submitted by comms on Mon, 06/05/2019 - 10:50 am
Barriers & Exclusions- The support needs of newly arrived refugees with disability
Submitted by rpo on Tue, 05/03/2019 - 10:06 am
Submission to Third Review of the Disability Standards for Accessible Public Transport 2002 (Transport Standards)
In December 2018, Disabled Peoples Organisations Australia (DPO Australia) provided a submission to the Department of Infrastructure, Regional Development and Cities Issues Paper: Third Review of the Disability Standards for Accessible Public Transport 2002 (Transport Standards).
In response to the issues paper, DPO Australia conducted a survey across our membership to capture the public transport experiences of people with disability and incorporated their feedback into our submission.
Submitted by comms on Wed, 19/12/2018 - 11:06 am
NEDA'S CONSTITUTION
This is a copy of NEDA's constitution, updated in November 2018.
Submitted by comms on Tue, 11/12/2018 - 03:34 pm
National Women's Alliances and Disabled People's Organisations Australia Workshop
In October 2018, a workshop was held in Sydney to conduct a review of the National Women's Alliances.
This workshop was organized by Womens With Disability Australia and attended by key decision makers from each of the six Alliances and the four member organisations of Disabled People's Organisations Australia (DPO Australia).
Main findings from the one-day workshop have now been compiled in the report below.
Submitted by comms on Fri, 07/12/2018 - 02:43 pm
Submission to the Australian Human Rights Commission Consultation Paper: Protecting the Human Rights of People Born with Variations in Sex Characteristics in the context of Medical Interventions
In September 2018 DPO Australia made a brief submission to the Australian Human Rights Commission's (AHRC) Consultation Paper, Protecting the Human Rights of People Born with Variations in Sex Characteristics in the context of Medical Interventions.
NEDA Strategic Plan 2018
Submitted by comms on Tue, 11/09/2018 - 11:26 am
DPOA Submission to Senate Inquiry into the need for regulation of Mobility Scooters, also known as motorised wheelchairs.
Submitted by rpo on Wed, 14/03/2018 - 11:02 am
Subscribe to Submissions and Policy Documents
View More ⟶
Royal Commission Terms of Reference are a step towards justice for people with disability
Refugees with disabilities face serious barriers to resettlement, new report finds
Royal Commission vote welcome - now time to make it happen
Subscribe to the NEDA mailing list for all the latest on news, projects, events and more.
Latest Publications See All ⟶
DPOA Submission to the Third Review of the Disability Standards for Accessible Public Transport 2002
National Ethnic Disability Alliance
Civic Square ACT 2608
About NEDA
Policy section
Copyright © National Ethnic Disability Alliance, 2019

redpajama_set_name: RedPajamaCommonCrawl


I bought this set last month and could not believe how easy it was to put up and how useful it is. I don't know how I kept my life organized without it. I have had to re-position them 3 times in the last month and I have not had any issues at all with them not sticking to the wall. I LOVE this purchase!!!
Perfect for my kitchen. I love all 4 pieces & am so glad I purchased.
Just what I wanted and just the right size for the area that I wanted it for.
I purchased the 4 piece set a little over a month ago and I absolutely love it! The only things I could think of that werent perfect was that the tape for the corkboard is a little weak so I used some double sided tape I already owned and the dry erase pieces can be a little awkward because theyre right against the wall which isn't a huge deal. It's just awkward for a writing surface sometimes. I really love the design, the concept and the versatility of this set.
Bought this a few months back. First you cannot really use any other marker except what's provided. That is not good. The adhesive is not very good. It's a great idea but has some execution issues.
My daughter loves it!!!! Makes great room decor too.
Love the concept, but disappointed that the cork board doesn't stay stuck on. There were sticky pads, but no instructions on which side to stick to the cork vs. the mat.
Very crisp clear graphics-- I really like the option to put pieces together how it works for me! Very reasonably priced.
We received the product on time and within moments she had it up on her wall. She uses each board and loves it.
I bought this a couple weeks ago and I absolutely love it, it was super easy to stick it on the wall no problems the only problems I did have is the black marker that was included with the kit is very difficult to wipe off you actually have to use a wet towelette otherwise it will not come off and even then it leaves a little tiny bit of a residue and also for the cork board while the part that sticks to the wall is really sticky and grea, the part that you're supposed to stick on to the corkboard that then sticks on to the wall decal isn't sticky enough so the cork board actually kept falling off. I fixed it myself but actually taking small nails used to hang pictures and nailed it in the four corners but obviously that's not the way it was intended to be used so you be the judge on if that's a deterrent to you.
I bought this for my daughter who is going away to college. It is perfect for use in the dorm. I didn't realize it was 4 separate pieces but that made her love it even more. Functional and cute!
I love that this kit comes with a cork board! It's hard to find a great deal for all this stuff! Everything is just the right size!
I bought these for my office as well as for both my kids' college apartments. They are great. The corkboard is a really nice addition to the usual wall stickers. I would definitely get again.
I kenw exactly what I was getting when I ordered this...how often does that happen any more??
I bought this for my daughter in law to keep track of the kids school activities. SHe loves it. It's a clean crisp appearance on the wall and and a very easy way to keep up with their busy schedule.
I bought this for my collage dorm room since the collage does not allow us to place nail in the wall. It was just what I needed to help keep me orginazed. It was easy to apply and should come down at the end of the year without any damage to the wall.


Today, it's more important than ever to accept payments in the method the customer prefers. Here's what you need to know about omnichannel payments and your software.
When it comes to making purchases, consumers are looking for convenience and a "tailored-to-me" shopping and payment experience. Omnichannel payment acceptance refers to the ability to take payments in a variety of ways without creating a jarring experience – whether a consumer is paying in-store, on the road, online, via recurring invoice or another method.
Today, it's more important than ever to accept payments in the method the customer prefers. We are living in a consumer-driven economy, and as consumers increasingly use "on-demand" digital tools to shop, they now expect to be able to pay how they want and when they want.
Since the consumer's shopping experience is shifting due to technological advances, so must their payment experience. In payment processing, there are primarily two types of credit card acceptance: "card-present," where a card is physically run through a reader, and "card-not-present," where, obviously, a card is not present to be swiped. Within these two overarching categories are multiple ways of accepting payment, including contactless/NFC (near-field communication) transactions, online shopping carts, mPOS (mobile point-of-sale), and traditional POS (point-of-sale) systems, just to name a few.
Payment technologies can facilitate seamless transactions and ultimately help merchants create a tailored-to-me experience for their customers. If a consumer prefers to pay for goods or services via a digital wallet like Apple Pay, then a merchant should be prepared to accept this payment type. Therefore, ISVs should be counseling and offering solutions for their merchants to accept the latest payment methods.
Just as the shopping experience has changed over the past 10-20 years, so have payment technologies. Once upon a time, there were manual credit card imprinters that recorded card information and provided carbon-copy receipts. Now, advanced smart devices that only allow payment through biometric verification offer more secure and seamless experiences.
Omnichannel payment acceptance – being able to take payments, however, whenever and wherever consumers want to pay – improves the consumer's overall experience. This, in turn, helps drive customer and brand loyalty. As an ISV, your goal is to have a comprehensive understanding of how the merchant's business works, so you can create a tailored experience specifically for your merchant's customers.
It's nice to hear about new omnichannel technologies – how they will save consumers time and help merchants grow their business – but what's a real-world example? Many retailers have implemented mPOS solutions into their brick-and-mortar locations. Small card readers, connected to mobile devices that have a payment app installed, enable retailers to create multiple point of sale locations throughout a store, thereby streamlining long lines to a single checkout counter. These mPOS devices can also be used on the road, and their installed payment app can offer centralized, multi-location inventory management as well as a virtual terminal to send invoices, set up recurring billing and accept phone, mail, and online payments from websites, thereby delivering a truly omnichannel payment solution.
Optimizing available technology to create increased sales opportunities, simplify the payment process and improve the consumer's overall experience might seem like a nice idea, but is this truly essential? The answer is yes. As an ISV, if the solution you are providing enables the acceptance of all payment types, you are preparing your merchants for future growth, sustainability and overall success. Take the time to truly understand the merchant's business, where their customer is, and how to make doing business as easy as possible.
For ISVs, a payment processor should be an integration partner who offers a full suite of solutions to help meet consumer demands to pay how they want, when they want and where they want. Making omnichannel payments easy is a key to success, and having the right partner will only enhance what you're able to offer.
A 20-year payment technology veteran, Zavida Mangaru has served in leadership roles at Mastercard and American Express and is the current executive vice president of product strategy and innovation at North American Bancard (NAB). NAB's Velocity provides ISVs, software developers and businesses secure, integrated and customizable payment solutions.


The dedicated AR-45 80% percent lower receiver build accepts .45 ACP and 10mm Glock large frame caliber magazines. Machined from 7075-T6 billet aluminm the Glock .45/10mm AR build lower is compatible with most standard AR-15 components and after market parts.
ANSWER: Because of our mag well design, any jig that attaches to the REAR of the mag well will work for completing this lower. Other jigs can be modified to fit around our mag well and will also work.
ANSWER: YES — After watching several companies try and fail to incorporate a RELIABLE last round bolt hold open (LRBHO) system into their lower receivers we decided to take a different approach and will be incorporating our LRBHO technology (PATENT PENDING) in our matching upper receivers. After rigorous testing and hundreds of hours of research and development, we feel that this is the best option for LRBHO on a Glock magazine AR style firearm.
ANSWER: Although we sell a specific LPK with just the parts needed to build the pistol lower receiver , any standard AR-15 LPK will work. You will not need the magazine catch assembly, bolt catch roll pin, or trigger guard that are normally included with standard LPKs.
QUESTION: What weight buffer do I need for my pistol build AR.
ANSWER (45.CAL): It is advised that you use a 10 oz or heavier buffer. Lighter buffers aren't safe to use in a blowback system with larger calibers such as the .45ACP and 10mm, and they may not feed some types of ammunition reliably without the added weight.
ANSWER (9MM, .40CAL): Although it's not technically required, it is advised that you either use a 9mm buffer (longer and heavier than carbine) or a spacer in your buffer tube before you place your spring in. The reason for this is to limit the BCG travel to the rear so it doesn't come back forward and smash the bolt catch hard enough to break it. The 9mm BCG is shorter than a standard BCG. You can use a standard carbine buffer with a spacer.


text:
Cognition and Behavior
History, Teaching and Public Awareness
Integrative Systems
Neuronal Excitability
Novel Tools and Methods
Sensory and Motor Systems
Research ArticleNew Research, Sensory and Motor Systems
The Temporal Dynamics of Scene Processing: A Multifaceted EEG Investigation
Assaf Harel, Iris I. A. Groen, Dwight J. Kravitz, Leon Y. Deouell and Chris I. Baker
eNeuro 12 September 2016, 3 (5) ENEURO.0139-16.2016; DOI: https://doi.org/10.1523/ENEURO.0139-16.2016
Assaf Harel
1Section on Learning and Plasticity, Laboratory of Brain and Cognition, National Institute of Mental Health/National Institutes of Health, Bethesda, Maryland 20892
Iris I. A. Groen
ORCID record for Iris I. A. Groen
Dwight J. Kravitz
2Department of Psychology, The George Washington University, Washington, DC 20052
Leon Y. Deouell
3Department of Psychology, The Hebrew University of Jerusalem, Jerusalem 91905, Israel
4Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
ORCID record for Leon Y. Deouell
Chris I. Baker
ORCID record for Chris I. Baker
Our remarkable ability to process complex visual scenes is supported by a network of scene-selective cortical regions. Despite growing knowledge about the scene representation in these regions, much less is known about the temporal dynamics with which these representations emerge. We conducted two experiments aimed at identifying and characterizing the earliest markers of scene-specific processing. In the first experiment, human participants viewed images of scenes, faces, and everyday objects while event-related potentials (ERPs) were recorded. We found that the first ERP component to evince a significantly stronger response to scenes than the other categories was the P2, peaking ∼220 ms after stimulus onset. To establish that the P2 component reflects scene-specific processing, in the second experiment, we recorded ERPs while the participants viewed diverse real-world scenes spanning the following three global scene properties: spatial expanse (open/closed), relative distance (near/far), and naturalness (man-made/natural). We found that P2 amplitude was sensitive to these scene properties at both the categorical level, distinguishing between open and closed natural scenes, as well as at the single-image level, reflecting both computationally derived scene statistics and behavioral ratings of naturalness and spatial expanse. Together, these results establish the P2 as an ERP marker for scene processing, and demonstrate that scene-specific global information is available in the neural response as early as 220 ms.
Significance Statement
Humans can process complex scenes very rapidly and efficiently. While recent years have shown great progress in understanding where in the brain scene processing occurs, it is still unknown when in the brain scene-specific processing occurs. We describe a novel electrophysiological signature of scene-selective processing, the P2 event-related potential component. We found that P2, which peaks at ∼220 ms after stimulus onset shows a greater response to scenes than other categories, and distinguishes between scene images based on their global diagnostic properties, such as naturalness and spatial layout. Our findings, therefore, provide critical insight about the time course of scene processing, as they demonstrate that diagnostic scene information can be found as early as 220 ms after stimulus onset.
Real-world visual scenes are rich and complex, containing many different sources of information including spatial layout, local objects, and semantic associations. Despite this complexity, humans recognize scenes easily and very rapidly (Potter, 1976; Joubert et al., 2007; Greene and Oliva, 2009b). Functional magnetic resonance imaging (fMRI) studies have revealed a network of cortical regions engaged by scene processing that exhibit selectively higher responses to scenes than to other categories, such as faces and objects (Epstein, 2008). This cortical specialization likely reflects the unique physical properties and information contained in scenes relative to other visual categories. For example, visual scenes depict heterogeneous real-world environments, which often contain large-scale elements, such as walls, mountains, and buildings, that determine both the spatial layout and scene category (Henderson and Hollingworth, 1999). Consequently, scenes are often recognized based on their global distribution of information (Oliva and Torralba, 2001; Greene and Oliva, 2009a). In comparison, faces are very homogenous, sharing a small set of features organized in a prototypical configuration (Bruce et al., 1998).
Despite a growing understanding from fMRI of the regions that contribute to scene processing and the representations contained therein, its temporal dynamics remain unclear. To date, only a few studies have used electrophysiological measures to study the time course of global scene processing (Sato et al., 1999; Rivolta et al., 2012; Bastin, et al., 2013a; Groen et al., 2013, 2016a; Cichy et al., 2016). The scarcity of electrophysiological studies of scene processing is surprising given the large number of studies in the event-related potential (ERP) literature on face processing, and particularly on the face-selective N170 component (for review, see Rossion and Jacques, 2011). Two previous magnetoencephalography (MEG) studies attempting to find a "scene analog" to the N170 by contrasting responses to faces and scenes have reported inconclusive results. The first study (Sato et al., 1999) reported a stronger response to scenes than faces between 200 and 300 ms after stimulus onset, with an earlier face-responsive signal observed at ∼170 ms. A more recent study (Rivolta et al., 2012) reported an earlier scene response, peaking between 100 and 130 ms after stimulus onset (M100p), with a face-related component manifesting at the same time window, but at different medial-occipital sites.
Note, however, that no strong conclusions can be made from either of these studies, since they contrasted faces and scenes only, leaving open the possibility that simple visual differences between faces and scenes or a decreased response to faces drive the observed differences rather than a preferential response to scenes. Simply showing a greater response to scenes relative to faces does not specify what type of information is being processed at that time that such a categorical difference manifests. Scenes contain multiple sources of information, ranging from "low-level" image statistics (e.g., spatial frequency and contrast) to "high-level" abstract properties (e.g., scene category or spatial layout; for review, see Groen et al., 2016b), and thus the question of what the actual information is that is being indexed by a putative scene-specific ERP still remains unknown.
Therefore, the present study has the following two goals: (1) to establish the earliest time point at which a preferential response to scenes was observed relative to both faces and objects; and (2) to examine whether the responses at that time point convey scene-specific information. In Experiment 1, participants viewed images of scenes, faces, and everyday objects. We found a positive ERP component peaking at ∼220 ms (P2) after stimulus onset that was stronger for scenes relative to both faces and objects. In Experiment 2, we investigated the sensitivity of the P2 to different types of scene information. We recorded participants' ERPs while they viewed a rich set of images of naturalistic scenes varying systematically in their global properties (i.e., spatial expanse, naturalness, and relative distance). In a first set of analyses, we found that the P2 amplitude was sensitive to both naturalness and spatial expanse at a categorical level. To explore to what extent the P2 amplitude was modulated by these properties at the level of individual scenes, in a second independent analysis we quantified the naturalness and spatial layout of each scene using both image statistics and behavioral ratings from independent observers. Both the image statistics and behavioral ratings explained significant variance in the P2 amplitude for individual scenes. Importantly, these modulations by individual image characteristics were present only for the P2, and not for the earlier non-scene-selective P1 or N1 components. Together, these results show the emergence of stronger responses to scenes and the presence of global scene information around 220 ms after stimulus onset.
Human subjects were recruited at the Hebrew University of Jerusalem. Twelve students (8 females; age range, 18–28 years) with normal or corrected-to-normal visual acuity and no history of psychiatric or neurological disorders participated in the reported studies. All participants signed an informed written consent form according to the guidelines of the institutional review board of faculty of social sciences of the Hebrew University of Jerusalem and received due compensation for their participation.
Experiment 1: scene selectivity
The stimuli consisted of 144 grayscale images from the following three visual categories: scenes, faces, and objects (Fig. 1a, stimulus examples). Each visual category contained 48 individual exemplars, spanning multiple subcategories to ensure a wide variety of visual stimulation. The scene stimuli comprised six subcategories, half of them indoor scene categories (churches, concert halls, living rooms), and half of them outdoor scenes (beaches, mountains, deserts). The face stimuli varied in sex and race, comprising Asian and Caucasian, male and female faces presented in front view. The objects consisted of dressers, vases, motorbikes, and roller skates. The mean luminance of all images was equated across categories, with a uniform gray background equated to the mean luminance of the objects. Images were 300 × 300 pixels subtending a square of 8º × 8º at the center of the visual field.
Stimuli and experimental design of Experiment 1 (category selectivity). a, Examples of the stimuli used in Experiment 1. The stimuli consisted of the following three categories: scenes, faces, and objects. Scenes were selected from the following six categories: churches, concert halls, living rooms, beaches, mountains, and deserts (top row, four categories are depicted here). The face stimuli comprised Asian and Caucasian, male and female faces presented in front view (middle row). The objects consisted of dressers, vases, motorbikes, and roller skates (bottom row). Note that in total there were 48 unique exemplars within each visual category. b, Participants viewed the stimuli and performed a simple one-back task, responding whenever the same image was presented twice in a row (in this example, the second presentation of the vase). The stimuli were presented pseudorandomly, with a trial beginning with the presentation of a scene image for 500 ms followed by a blank gray screen for the following 500 ms. c, Schematic representation of the 64 electrode sites from which EEG activity was recorded. The grouped electrodes are those analyzed in the 12 critical regions (see text for details).
Experiment 2: global scene properties
Stimuli for this experiment were images of scenes that had previously been used in a neuroimaging study (Kravitz et al., 2011). The stimulus set comprised 96 individual, highly detailed, and diverse real-world scene images from 16 basic-level scene categories (churches, concert halls, hallways, living rooms, forest canopies, canyons, caves, ice caves, cities, harbors, highways, suburbs, beaches, deserts, hills, mountains), with six exemplars within each category, spanning the following three diagnostic scene properties: spatial expanse (open, closed; the spatial boundary of the scene); relative distance (near, far; distance to the nearest foreground objects); and naturalness (or semantic content; man-made, natural; Fig. 2a, full stimulus set). The images were presented full screen, subtending 27º of visual angle, at a viewing distance of 75 cm. The stimuli were presented using E-Prime presentation software (Psychology Software Tools).
Stimuli and experimental design of Experiment 2 (scene diagnostic properties). a, Full stimulus set. The stimulus set comprised 96 individual, highly detailed, and diverse real-world scene images from 16 basic-level scene categories (churches, concert halls, hallways, living rooms, forest canopies, canyons, caves, ice caves, cities, harbors, highways, suburbs, beaches, deserts, hills, and mountains), with six exemplars within each category spanning the following three diagnostic scene properties: spatial expanse (open, closed; the spatial boundary of the scene); relative distance (near, far; distance to the nearest foreground objects); and naturalness (or semantic content; man-made, natural). b, Participants viewed the stimuli while performing an orthogonal fixation cross task, in which they were required to report whether the horizontal or vertical bar of the central fixation cross lengthened on each trial. Scene stimuli were presented for 500 ms, with a jittered interstimulus interval ranging from 1000 to 3000 ms.
Experimental design and procedure
Participants were tested in both experiments within the same session. A session consisted of 14 blocks: the first and last blocks of each session were the category selectivity experiment (Experiment 1), and the remaining 12 blocks were the diagnostic scene properties experiment (Experiment 2).
Experiment 1
Participants viewed the stimuli and performed a simple one-back memory task (Fig. 1b), responding whenever the same image was presented twice in a row. The stimuli were presented in a pseudorandom order, with a trial beginning with the presentation of an image for 500 ms followed by a gray fixation screen for the following 500 ms. Individual stimuli were presented once within each block.
Participants viewed the 96 scene stimuli, while performing an orthogonal fixation cross task, in which they were required to report whether the horizontal or vertical bar of the central fixation cross lengthened on each trial (Fig. 2b, details). This is the same task used in the prior fMRI study using the same stimuli (Kravitz et al, 2011). The 96 scene stimuli were pseudorandomized within individual blocks and across the 12 blocks. Each scene stimulus was presented once within each block. Scene stimuli were presented for 500 ms with a jittered interstimulus interval ranging from 1000 to 3000 ms.
EEG recording
The EEG analog signals were recorded for the entire length of the experimental session by 64 Ag-AgCl pin-type active electrodes (ActiveTwo, Biosemi) mounted on an elastic cap (ECI) according to the extended 10-20 system, and from two additional electrodes placed at the right and left mastoids, and an electrode on the tip of the nose. All electrodes were referenced to the common mode signal electrode placed between electrodes PO3 and PO4. Eye movements, as well as blinks, were monitored using two pairs of EOG electrodes, one pair attached to the external canthi, and the other to the infraorbital and supraorbital regions of the right eye. Both EEG and EOG were sampled at 256 Hz with a resolution of 24 bits and an active input range of −262 to +262 mV/bit, with on-line low-pass filtering of 51 Hz to prevent aliasing. The digitized EEG was saved and processed off-line.
The data were preprocessed using the FieldTrip toolbox (Oostenveld et al., 2011). The raw data were first high-pass filtered at 1.0 Hz (24 dB) and referenced to the tip of the nose. Eye movements were corrected using an ocular correction ICA procedure (Jung et al., 1998). Remaining artifacts exceeding ±100 mV in amplitude or containing a change of >100 mV in a period of 50 ms were rejected. The preprocessed data was then segmented into epochs ranging from −250 ms before to 500 ms after stimulus onset for all conditions. Further data analysis was conducted using custom scripts written in Matlab (MathWorks).
ERP analysis: experiment 1
Twelve separate "regions of interest" were computed from 48 lateral electrodes, each comprising the mean of 4 electrodes (Fig. 1c). These were based on hemisphere, and within each hemisphere they were grouped along a medial–lateral axis and an anterior–posterior axis (Barber et al., 2011). There were six electrode groups in each hemisphere, with two in each of the anterior, central, and posterior scalp sites; one in the lateral position of the hemisphere; and one in the medial position of the hemisphere, as follows: left anterior lateral (F7, F5, FT7, FC5); left anterior medial (F3, F1, FC3, FC1); left central lateral (T7, C5, TP7, CP5); left central medial (C3, C1, CP3, CP1); left posterior lateral (P7, P5, P9, PO7); left posterior medial (P3, P1, PO3, O1); and similarly for the right hemisphere. In the category localizer study, for each subject the peaks of the P1, N1, and P2 for each separate category in each of the electrode groups were determined as the most positive peak between 80 and 130 ms, the most negative peak between 130 and 200 ms, and the most positive peak between 200 and 320 ms, respectively. Differences between mean peak amplitudes (across subjects) were analyzed using a four-way, within-subject ANOVA with hemisphere (left, right), site (anterior, central, posterior), mediality (medial, central), and category (scenes, objects, faces) as independent factors to test the presence of any category-selective effects on the amplitude of the individually defined peaks of each one of the ERP components. For factors with more than two levels, p values were corrected for nonsphericity using the Greenhouse–Geisser correction (for simplicity, the uncorrected degrees of freedom are presented; Picton et al., 2000).
To avoid a potential bias in our peak selection for Experiment 2, we adopted an ERP "independent localizer" approach, which uses a functional signature from one experiment to determine the latency and loci of the effects of a different experiment (Luck, 2014). Specifically, we defined P1, N1, and P2 time windows for each participant by selecting three time points centered on the peak of the components from Experiment 1 (the time point of the peak, the point prior to it, and the one following it): for P2, the peak was defined as the maximum value between 200 and 320 ms of the difference wave resulting from the subtraction of the object waveform from the scene waveform; for N1, the peak was defined as the maximum value between 130 and 200 ms of the difference wave resulting from the subtraction of the object waveform from the face waveform; and for P1, the peak was defined as the maximum value between 80 and 130 ms of the average waveform formed from averaging the object, scene, and face waveforms. We then extracted the maximal ERP amplitudes in these time windows for each of the conditions of Experiment 2 (the diagnostic scene properties study). Analyses in Experiment 2 were restricted to posterior lateral sites where maximal effects of category were observed. Mean amplitudes were subjected to a four-way ANOVA with hemisphere (right, left), naturalness (man-made, natural), distance (near, far), and spatial expanse (open, closed) as independent variables.
Single-image EEG analysis
Single-image statistics
To investigate the relation between scene properties and image statistics at the level of the individual scenes, we computed two sets of image statistics. The first set of statistics consisted of contrast energy (CE) and spatial coherence (SC). These two parameters are derived from local contrast values (Ghebreab et al., 2009; Scholte et al., 2009) and have previously been shown to predict behavioral performance on man-made versus natural categorization (Groen et al., 2013). In natural scenes, CE and SC typically correlate highly with parameters of a Weibull function fitted to the distribution of contrast values, which reflects the amount of fragmentation in a scene (Simoncelli, 1999; Geusebroek and Smeulders, 2003). CE is a biologically realistic approximation of the distribution width (the scale parameter of the function), whereas SC is an approximation of its shape (the degree to which the function describes a power law or a Gaussian distribution). These two statistics thus capture information about the overall strength of edges in an image (CE) and higher-order correlations between them (SC). Typically, images with high CE values have strong edges due to objects standing out from the background, whereas images with high SC values are cluttered or textured. Here, we computed one CE and one SC value for each scene using the model described previously by Groen et al. (2013).
The second set of statistics consisted of Fourier intercept (FI) and Fourier slope (FS), which are derived from the spatial frequency distributions of individual scenes. These statistics were computed using the procedure described by Oliva and Torralba (2001; i.e., fitting a line to the rotationally averaged power spectrum; but see Groen et al., 2012). These parameters are sensitive to differences in the falloff of the amplitude spectrum of natural scenes and together form a "spectral signature" of an image, which has been shown to be diagnostic of various scene properties (Torralba and Oliva, 2003), including the global scene properties that were manipulated in Experiment 2. To determine whether the global properties were differentially distributed within each of these sets of image statistics, two-sided Kolmogorov–Smirnov tests were conducted for each image parameter (CE, SC, FI, and FS) and global property (naturalness, distance, and spatial expanse) separately.
Single-image behavioral ratings
To quantify the degree of naturalness and spatial expanse of individual scenes, behavioral ratings were obtained from a previous experiment using the same set of stimuli (Kravitz et al., 2011). These ratings reflect the level of naturalness or spatial expanse of each scene relative to the others in the set (Kravitz et al., 2011).
Single-image ERP analysis
To investigate how the variation in image properties affected the evoked neural activity to individual scenes, we ran hierarchical linear regression analyses of single-image ERP amplitude on the image statistics and behavioral ratings. Per image, single-trial amplitudes at the subject-specific peak time points of the P1, N1, and P2 components identified in Experiment 1 were extracted and subsequently averaged over blocks and subjects, resulting in 96 "single-image" amplitude values per component that were subjected to multilinear regression analysis. In separate analyses, ERP amplitude values were entered as the dependent variable, and the image statistics and behavioral ratings were entered as the independent variables. In the first set of analyses, CE, SC, and naturalness ratings were entered either separately or in combination to predict the ERP amplitude, while in the second set the FI, FS, and spatial expanse ratings were entered. Finally, a third set of analyses was conducted that either included all four image statistics, and naturalness and spatial expanse ratings, or image statistics and behavioral ratings combined. Each regression analysis resulted in a measure of explained variance (R 2). These analyses were restricted to the electrodes in the posterior lateral sites, as detailed above (which showed maximal scene selectivity) and were performed on ERP amplitude values averaged across the left and right hemisphere.
Table 1 summarizes the statistical analyses conducted in all experiments (Table 1; superscript letters in Results indicate rows in the table). Observed power was calculated post hoc with GPower version 3.1 (Faul et al., 2007).
Table 1:
Summary of key statistical analyses
Experiment 1: category selectivity
Our primary question was whether there is an ERP component that indexes scene-selective processing. We focus here on the first three visually evoked ERP components: P1, N1, and P2 (Key et al., 2005). We first conducted an omnibus four-way, repeated-measures ANOVA on the amplitude of the individually defined peaks of each one of the ERP components with hemisphere (left, right), site (anterior, central, posterior), mediality (lateral, medial), and category (scenes, objects, faces) as independent factors (Table 2, full results of the ANOVA). This data-driven approach includes the vast majority of electrodes (except for mid-sagittal and anterior prefrontal electrodes; Picton et al., 2000) and was adopted given the very limited knowledge of ERP markers of scene selectivity, which precludes an a priori choice of electrode sites or specific components. Nonetheless, based on the reported locus of face selectivity using ERPs (Rossion and Jacques, 2011) and the close proximity of face- and scene-selective areas measured with fMRI, we expected maximal category effects to manifest in the posterior lateral electrode sites. We next describe each component according to their temporal order.
P1 component
Overall, analysis of the P1 componenta did not show a strong preference for any of the three categories (Table 2, P1 peak amplitudes ANOVA). While category significantly interacted with site [F(2,22) = 5.16, mean squared error (MSE) = 1.18, p = 0.008], and mediality (F(2,22) = 3.55, MSE = 0.74, p = 0.05)a, post hoc analyses of the category effect did not reach significance for either site (anterior: F(2,22) = 2.79, MSE = 0.71, p = 0.08; central: F(2,22) = 1.64, MSE = 1.04, p = 0.21; posterior: F(2,22) = 0.38, MSE = 1.61, p = 0.67)b or mediality (lateral sites: F(2,22) = 0.14, MSE = 0.97, p = 0.86; medial sites: F(2,22) = 1.22, MSE = 1.02, p = 0.31)c levels.
N1/N170 component
As expected, the N1 component showed the well known N170 face effect (Bentin et al., 1996), with its strongest amplitude evoked by images of faces relative to images of objects and scenes (Table 2, N1/170 peak amplitudes ANOVA). This effect was most pronounced in posterior lateral electrodes (Fig. 3a, left), as revealed in a significant category × site × mediality interaction (F(4,44) = 7.71, MSE = 9.107, p = 0.002)d. Follow-up category × site ANOVAe for the lateral sites (posteriorf, centralg, and anteriorh) revealed an N170 effect that was restricted to the posterior lateral sites (F(2,22) = 7.01, MSE = 4.65, p = 0.007)f, showing a stronger amplitude to faces (mean = −4.83 mV, SEM = 0.98) than to objects (mean = −2.39 mV, SEM = 0.82; t(11) = 2.53, p = 0.01)i or scenes (mean = −2.03 mV, SEM = 0.73 t(11) = 3.70, p = 0.002)j, which did not differ in their amplitude (t(11) = 0.52, p = 0.30k; Fig. 3a, left). No significant main effects or interactions were found for the medial sites (Table 2, N1/170 peak amplitudes ANOVA)l.
Experiment 1 results. a, Mean N1/170 and P2 peak amplitudes (left and right column, respectively) in response to scenes (red), faces (blue), and objects (green; peak amplitudes are plotted separately for each hemisphere, for the posterior lateral electrode sites. Error bars indicate the SEM. Significant differences (p < 0.05) between pairs of categories are denoted by asterisk. b, Group-averaged ERPs (n = 12) for the three categories (scenes in red, faces in blue, objects in green) for the left and right hemispheres (data are plotted for the posterior lateral sites). c, ERP difference waveforms depicting face sensitivity (blue, faces-objects) and scene sensitivity (red, scenes-objects) over time for the left and right hemispheres (data are plotted for the posterior lateral sites). The waveforms (solid lines) are presented with across-subjects 95% confidence intervals around them (light blue and red for face and scene sensitivity, respectively).
The first visually evoked component to show a significantly stronger response to scenes compared with objects and faces was the P2 (Fig. 3a, right), peaking at ∼220 ms after stimulus onset (mean = 223 ms, SEM = 3). A main effect of category (Table 2, P2 peak amplitudes ANOVA) was observed (F(2,22) = 10.92, MSE = 12.04, p = 0.001)m, with greater amplitude to scenes (mean = 2.24 mV, SEM = 0.59) relative to objects (mean = 1.37 mV, SEM = 0.60; t(11) = 1.94, p = 0.03)n and faces (mean = 0.41 mV, SEM = 0.73; t(11) = 5.02, p = 0.001)o, which were also lower in amplitude relative to objects (t(11) = 2.70, p = 0.005)p. A general increase in amplitude was noted going from anterior to posterior sites (main effect of site: (F(2,2) = 23.44, MSE = 22.86, p = 0.0001)m, with posterior sites showing a higher amplitude (mean = 2.98 mV, SEM = 0.81) relative to central sites (mean = 0.90 mV, SEM = 0.62; t(11) = 6.07, p = 0.0005)q, which showed a higher amplitude than the frontal sites (mean = 0.13 mV, SEM = 0.48; t(11) = 2.44, p = 0.001)r.
Finally, to directly pit the differential category sensitivity of the N170 and P2 components, we conducted a two-way ANOVA restricted to the posterior lateral sites with category (faces, scenes, objects) and component (P1, N1, P2) as independent variabless followed by post hoc comparisons of the effectst–aa. These analyses confirmed the observed "division of labor" in face and scene selectivity between the N170 and P2, respectively (Fig. 3a), with no selectivity observed at the P1 levelab (consistent with a lack of "low-level" physical stimulus effects). Reflecting the formal statistical tests, the effects of stimulus category, particularly scenes, can be clearly observed in the grand average waveforms evoked by scene, face, and object images during the first 500 ms (plotted for the posterior lateral sites; Fig. 3b,c).
The same analyses were performed on the peak latencies of each of the three components reported above, with no category effects or interaction effects found for any of the components.
Experiment 2: scene diagnostic properties
Average ERP analysis
Having established the existence of an early visually evoked ERP component indexing scene selectivity, the P2, with a temporal locus at ∼220 ms after stimulus onset, we next set out to determine the functional properties of the P2 scene-selective component. We asked whether the P2 amplitude captures diagnostic scene information, and if so, what dimensions it is sensitive to. We focused on global ecological scene properties, such as "naturalness" and "spatial expanse," which have been demonstrated to have psychological reality (Boucart et al., 2013; Groen et al., 2013) as well as neural underpinnings (Kravitz et al., 2011; Park et al., 2011; Harel et al., 2013). Accordingly, we presented naturalistic scene images varying in their spatial expanse, relative distance, and naturalness (Fig. 2), and measured how the amplitude of P2 evoked by these scene images was impacted by changes to each one of these dimensions (for completeness, we also examined the sensitivity of P1 and N1 to these dimensions).
Using the peak P2 window for each participant identified in Experiment 1 (for details, see Materials and Methods), we extracted amplitudes for each of the eight combinations of the three scene dimensions and submitted them to a four-way repeated-measures ANOVAac with hemisphere (left, right), naturalness (man-made, natural), distance (near, far), and spatial expanse (closed, open) as independent variables (Fig. 4, grand average waveforms depicting the three main effects; Table 3, P2 peak amplitudes ANOVA, full details of the ANOVA). We observed a significant main effect of naturalness (F(1,11) = 26.62, MSE = 1.67, p = 0.0005), with natural scenes evoking a greater positive response (mean = 2.85 mV, SEM = 1.05) than man-made scenes (mean = 1.89 mV, SE = 1.02; Fig. 5b). However, this effect was modulated by a significant interaction between spatial expanse and naturalness (F(1,11) = 4.59, MSE = 1.32, p = 0.05). Follow-up post hoc comparisons showed a significant effect of spatial expanse for the natural scenes (t(11) = 2.16, p = 0.05)ad, but not for the man-made scenes (t(11) = −0.53, p = 0.60)ae, with greater positive response for the closed natural scenes relative to the open natural scenes (Fig. 5a).
Group-averaged ERPs (n = 12) for the three diagnostic scene properties tested in Experiment 2, plotted for the left and right posterior lateral sites. Top row, Spatial expanse (open vs closed). Middle row, Naturalness (man-made vs natural). Bottom row, Distance (near vs far).
Grand average ERP analysis results for Experiment 2. a, Mean P2 peak amplitudes in response to open and closed scenes (orange and purple, respectively) presented separately for the man-made and natural scenes (left and right columns respectively). b, Mean P2 peak amplitudes in response to natural (green) and man-made scenes (cyan). c, Mean N1 peak amplitudes in response to natural (green) and man-made scenes (cyan). All data are plotted for the posterior lateral sites. Significant differences (p < 0.05) between pairs of categories are denoted by asterisk (error bars indicate between-subjects SE).
N1 component
N1 amplitude also demonstrated a main effect of naturalness (F(1,11) = 4.88, MSE = 3.38, p = 0.05)af. Man-made scenes evoked a greater negative response (mean = −2.20 mV, SE = 1.43) than the natural scenes (mean = −1.61 mV, SE = 1.57; Fig. 5c). No significant interactions were found for any combination of the scene dimensions (Table 3, N1 peak amplitudes ANOVA).
The P1 component was largely unaffected by the different global scene properties (Table 3, P1 peak amplitudes ANOVA). Interestingly, some interaction effects of distance and hemisphere were notedag. Post hoc testing of the hemisphere × distance interaction revealed a significant effect of distance restricted to the left hemisphere (F(1,11) = 6.26, MSE = 3.08, p = 0.03)ah, with a higher amplitude to the near scenes (mean = 3.41 mV, SE = 1.48) relative to the far scenes (mean = 1.62, SE = 0.83). No significant difference was noted in the right hemisphere (F(1,11) = 2.76, MSE = 7.71, p =0.12)ai. Post hoc testing of the hemisphere × naturalness × distance interactionaj, ak did not reveal any significant effects in either hemisphere (Table 3, P1 peak amplitudes ANOVA: post hoc testing of the hemisphere × naturalness × distance interaction).
The ERP analysis of the scene stimuli revealed that the spatial expanse and the naturalness of the scene have a direct impact on the P2 scene-selective component. Given the averaging involved in ERP analysis, however, it is difficult to assess using a standard ERP analysis how information contained in individual scene images is processed over time. In order to address this question, we conducted a complementary analysis in which we first quantified the variation in individual scene properties, and then asked whether this variation can explain the variance in the ERP amplitude evoked by individual images.
Specifically, we examined the extent to which the observed differences in peak ERP amplitude are related to differences in image summary statistics, as well as behavioral ratings of the naturalness and spatial expanse of individual images. Summary statistics of scenes can be derived computationally from measurements of spatial frequency and local contrast, and have previously been shown to correlate with several global properties, including spatial expanse and naturalness (Oliva and Torralba, 2001; Kravitz et al., 2011; Groen et al., 2013). We first examined whether these image statistics were correlated with these global distinctions in the particular set of scene images used in Experiment 2. We then tested to what degree the variation in these statistics affected the ERP amplitudes at the single-image level, and to what degree these modulations were shared by behavioral ratings of the images or were uniquely driven by the image properties.
Computational analysis of the 96 scenes based on spatial frequency and local contrast (see Materials and Methods) resulted in two pairs of image statistics parameters describing two "feature spaces" in which images clustered differentially by global property (Fig. 6A). Consistent with previous findings, man-made and natural scenes differed significantly in average contrast energy (Kolmogorov–Smirnov test: D = 0.29, p = 0.026)al and spatial coherence (D = 0.29, p = 0.026)al, but not in spatial frequency content (FI: D = 0.21, p = 0.220; FS: D = 0.125, p = 0.822; Fig. 6B) al. Open and closed scenes, on the other hand, differed in both FI (D = 0.44, p = 0.0002) al and FS (D = 0.42, p = 0.0003) al, but not in contrast energy or spatial coherence (D = 0.19, p = 0.33; D = 0.23, p = 0.14, respectively)al. No differences in image statistics were found for near versus far scenes (contrast energy: D = 0.13, p = 0.822; spatial coherence: D = 0.15, p = 0.65; FI: D = 0.19, p = 0.333; FS: D = 0.13, p = 0.822) al. This analysis demonstrates that the global diagnostic scene properties that we found to impact the ERPs at the categorical level can be mapped onto natural image statistics, thereby allowing the quantification of relative variation in this global information between individual scenes.
Image statistics analysis of the 96 scene stimuli used in Experiment 2. A, Local contrast statistics (left) CE and SC, and spatial frequency statistics (right) FI and FS (absolute values are plotted for clarity) for each of the 96 scenes, which are color coded by global categorical distinction. The local contrast and spatial frequency statistics describe two-dimensional spaces in which the scenes cluster by naturalness and spatial expanse, respectively. B, The distributions of man-made vs natural, and open vs closed scenes across the four different computational parameters. *p < 0.05. C, Explained variance (R 2) for a regression model consisting of combinations of local contrast statistics and behavioral naturalness ratings (Nr; left), and a model consisting of spatial frequency statistics and behavioral spatial expanse ratings (Or, right). D, Explained variance for a regression model consisting of all image statistics and all behavioral ratings. Note the change in y-axis compared with that in C.
To examine the extent to which multiple measures of variation in image properties explain modulation of the amplitude of the ERP peaks in which we discerned maximal scene selectivity, we performed a regression analysis of single-image ERP amplitude on the image statistics parameters as well as behavioral ratings of naturalness and spatial expanse obtained previously for these images (see Materials and Methods). We subjected the 96 single-image amplitudes of the scene-selective P2 and the preceding ERP components (P1 and N1) to hierarchical linear regression (see Materials and Methods). Specifically, by comparing the explained variance of models containing both image statistics and behavioral ratings as independent variables ("full models") with regression models containing separate predictors ("reduced models"), we assessed the unique and shared variance explained by each of these measures. Given the clear relationship we found between naturalness and local contrast measures on the one hand, and between spatial expanse and the Fourier spectrum measures on the other, we first tested the role of each set of summary image statistics relative to its respective behavioral measure of the diagnostic properties, namely the subjective ratings of these properties. Finally, we also examined the joint contribution of image statistics and behavioral ratings by combining all measures.
The results indicated no significant contributions of any regression model (combined or reduced) on the N1 and P1 components (all R 2 values <0.075, all p values > 0.12; Table 4, results of all models). Notably, however, for the P2, significant modulations were found for measures of local contrast statistics, spatial frequency, and behavioral ratings (Fig. 6C). In particular, a model consisting of contrast energy, spatial coherence, and naturalness rating explained 22.7% of the P2 amplitude variance (F(3,92) = 8.9, p = 0.00003)am. This variance was partly shared and partly uniquely explained by these three single-image measures. While each measure was capable of explaining some variance in the P2 amplitude in isolation, combining these measures notably improved the performance of the regression model, with naturalness rating and spatial coherence in particular sharing similar variance. A full model of Fourier intercept, Fourier slope, and spatial expanse ratings also explained a substantial amount of the P2 variance (20.7%, F(3,92) = 7.98, p = 0.00008)am, and, again, a combination of measures was required to achieve the highest performance. In particular, FI and FS were predictive only of P2 amplitude when considered in combination, and spatial expanse rating contributed little additional variance beyond that explained by the spatial frequency measures (Table 5, full results of all regression models for P2). Finally, a full model containing all six measures explained 36.6% of P2 variance, which was more than the image statistics and behavioral ratings explained separately (Fig. 6D).
Experiment 2 P1 and N1 multilinear regression analysis
Experiment 2 P2 multilinear regression analysis
The goal of the current study was to establish the electrophysiological markers of scene recognition by (1) identifying a scene-selective ERP marker and (2) gauging its scene information content. Experiment 1 revealed that the first ERP component to display scene selectivity is the P2 ERP component peaking 220 ms after stimulus onset. Experiment 2 demonstrated that the amplitude of the P2 component could be used to index information about diagnostic global scene properties, supporting the role of P2 as an ERP marker of complex scene processing. Finally, single-image analysis using both computational and behavioral assessment of individual images revealed that the P2 components, but not the P1 and N1, are sensitive to variation in global properties between scenes.
We started this investigation by searching for the "scene analog" of the face-selective N170 ERP component. We adopted an fMRI-style approach, which incorporated a "functional localizer" (Saxe et al., 2006) to identify a scene-selective ERP component (Experiment 1), followed by an independent experiment to test the functional properties of this component (Experiment 2). Importantly, the functional localizer approach also has the advantage of avoiding the problem of the use of the same dataset for selection and selective analysis known as "double dipping" in the fMRI literature (Kriegeskorte et al., 2009) and "multiple implicit comparisons" in the ERP literature (Luck, 2014, p. 328). Admittedly, the downside of this approach is that it might miss out on more subtle types of effects, which might manifest "outside" of the focus of interest; that is, at different time windows or electrode sites than the ones examined in the independent experiment. The reason to search for such a scene analog comes from the anatomical proximity of the representations of faces and scenes. In humans, a scene-selective region known as the parahippocampal place area (PPA; Epstein and Kanwisher, 1998) can be found on the ventral surface of occipitotemporal cortex close to the face-selective fusiform face area& (Kanwisher et al., 1997), and on the lateral surface of occipitotemporal cortex a scene-selective occipital place area (Dilks et al., 2013) can be found adjacent to a face-selective occipital face area (Gauthier et al., 2000). A similar spatial arrangement of face- and scene-selective regions has also been reported in the monkey (Nasr et al., 2011; Kornblith et al., 2013). This organization has been suggested to reflect distinctive yet complementary computations, such as visual field biases (Levy et al., 2001; Silson et al., 2015; Verhoef et al., 2015), stimulus rectilinearity (Nasr et al., 2014), and spatial frequency content (Rajimehr et al., 2011). Here we observed that this close relationship between the representations of faces and scenes can also be observed temporally, with close temporal proximity in the visual processing for faces (N170) and scenes (P2).
Very little is known about the P2 component (Luck, 2014). Prior literature focused on the processes underlying the P2 in selective attention, primarily in modulation of nontarget stimuli (for review, see Key et al., 2005). Importantly, the current P2 scene-selective effect is not likely to index attentional processes, as all categories appeared with equal probability, and the orthogonal task we used minimized the possibility that attention was differentially allocated across categories. At the same time, we did not find any category effects on the P1 component, which likely indexes early sensory processing within the extrastriate cortex (Clark et al., 1994). This lack of a P1 effect arguably reflects the wide range of stimuli we used in Experiment 1 (for details, see Materials and Methods), selected so as to minimize the possibility that a single stimulus parameter could drive any potential category effects. The lack of earlier scene selectivity suggests that the selectivity in the P2 cannot be reduced to very basic local visual features (e.g., differences in the retinotopic extent of the full-field scenes relative to isolated objects), although such a contribution cannot be ruled out (for a similar logic applied to the N170, see Bentin et al., 2007). Further support for the idea that scene selectivity manifests at the time window of the P2 comes from two recent intracranial studies (Bastin et al., 2013a,b). These studies reported a scene-selective increase in gamma-band activity between 200 and 500 ms after stimulus onset in posterior parahippocampal electrodes, consistent with our findings, as well as with the scene-selectivity of PPA.
To uncover the underlying processes indexed by the P2, in Experiment 2 we examined how its amplitude is modulated by variations in global scene properties that are known to be diagnostic for scene categorization (Greene and Oliva, 2009a). First, a classic grand average ERP analysis revealed that the P2 amplitude is sensitive to whether the scene is natural or man-made, an effect that was further modified by the spatial expanse of the scene. Second, a single-image ERP analysis established that diagnostic scene information has an impact on the P2 not only at the category level, but also at the individual scene image level. Despite the overt differences between these two analyses, the results of the single-image analysis are consistent with the average-based ERP analysis in pointing to the P2 as the critical time window for the integration of information about multiple scene dimensions. We found that two types of natural image statistics (derived from local contrast and the image power spectrum, respectively) could predict the P2 amplitude elicited by single-scene images, and that the variance explained by these factors was partly shared with behavioral ratings of naturalness and spatial expanse of the scenes. Thus, these analyses converge to emphasize the significance of the P2 time window—at ∼220 ms after stimulus onset—for processing of scene-diagnostic information. Notably, converging with our current findings, a recent MEG study reported a marker of real-world scene size ∼250 ms after stimulus onset (Cichy et al., 2016).
The fact that P2 amplitude is sensitive to both scene naturalness and spatial expanse establishes its potential utility for understanding the usage of diagnostic information for scene recognition, as both are considered key global properties for scene categorization (Torralba et al., 2006). Naturalness (or semantic content), in particular, has been suggested to play a central role in scene categorization (Oliva and Torralba, 2001; Greene and Oliva, 2009a; Groen et al., 2013; Berman et al., 2014). For example, the discrimination between man-made and natural scenes occurs rapidly, and precedes categorization based on the basic-level category of the scene (Joubert et al., 2007; Loschky and Larson, 2010; Kadar and Ben-Shahar, 2012; Banno and Saiki, 2015). Indeed, in the current study naturalness already had an effect on the N1 component, albeit only at the grand average analysis level. The N1 time window (150-180 ms) has been highlighted by several works that have looked at categorization of natural scene images using behavioral and electrophysiological measures (for review, see Fabre-Thorpe, 2011). Specifically, an anterior ERP waveform carried information about natural and man-made objects and scenes as early as 150 ms after stimulus onset (Thorpe et al., 1996). This raises the possibility that scene-specific processing, particularly as it relates to the distinction between natural and man-made scenes, can be observed earlier than the P2 level. Note, however, that these studies are typically focused on the recognition of a specific object in a scene, rather than on processing the global diagnostic properties of scenes, as was the focus of the current study. It should also be noted that in contrast to the grand average level analysis, at the single-image level analysis, none of the global statistics we tested were found to have a significant effect on the N1 amplitude. Earlier studies examining the effects of global scene statistics along the entire ERP time course (on a time point-by-time point basis; Groen et al., 2016a) reported that across the whole scalp, naturalness could be decoded as early as 100 ms after stimulus onset. However, consistent with the present results those studies showed that man-made/natural differences only appeared at 200 ms at the posterior lateral sites in which scene selectivity was observed here.
Future work on the significance of P2 as a marker of scene recognition processes will enable to determine how higher-order image properties are combined to form the basis for scene categorization, and how this information varies relative to other visual categories. Given their direct link with the P2 amplitude, the summary image statistics we used here have the potential to provide valuable insights regarding the time course of visual categorization. Specifically, it seems reasonable to assume that the low-level properties that these summary image statistics are derived from (contrast, spatial frequency) are common to all stimulus types (faces, objects, scenes). However, it may be argued that the variation in these image properties is unique to scenes, as these second-order statistics are diagnostic only for scene-related categorization tasks. Put differently, faces and objects might be considered as very particular types of "scenes," with a very narrow range of contrast and spatial frequency distributions, whereas real-world scenes, in contrast, vary over a much larger range of this information. It is exactly these variations in statistical properties that might be picked up by specialized scene-selective mechanisms. Along these lines, PPA has been reported to respond preferentially not only to real-world scenes, but also to surface textures and object ensembles (Cant and Goodale, 2007; Cant and Xu, 2012). What is common to these seemingly distinct types of stimuli? It has been suggested that the processing of all three types of stimuli requires the extraction of summary statistics without encoding each repeating element in great detail (Cant and Xu, 2012). Thus, some aspects of scene perception may be achieved by a general mechanism for extracting summary image statistics from multiple sources, computing the variation across the entire visual field without necessarily encoding the detailed local features (Oliva and Torralba, 2001; Torralba and Oliva, 2003; Wolfe et al., 2011). At this point, the current conjecture is still speculative and requires more research before establishing strong conclusions. However, one fruitful direction might be the generation of artificial stimuli varying in their summary image statistics (Groen et al., 2012) and assessing how they impact the P2 magnitude.
In two experiments we have identified the posterior visual P2 ERP component as the earliest marker of scene selectivity, peaking 220 ms after stimulus onset. The scene-selective P2 effect reflects the processing of diagnostic scene information as we found it to be modulated by two global scene properties: spatial expanse and naturalness. Further, image statistics diagnostic of the global scene properties and behavioral ratings of individual images were predictive of the P2 response. Together, these results suggest that higher-order scene properties become maximally represented ∼220 ms after stimulus onset, and establish the P2 as an ERP marker for scene processing.
Acknowledgments: We thank Maram Abofoul and Talia Shrem for help in data collection.
This work was supported by the Intramural Program of the National Institute of Mental Health (Grant ZIA-MH-002909). IIAG is supported by a Rubicon Fellowship from the Netherlands Organization of Scientific Research (NWO).
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
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The decision was a result of the Reviewing Editor Lila Davachi and the peer reviewers coming together and discussing their recommendations until a consensus was reached. A fact-based synthesis statement explaining their decision and outlining what is needed to prepare a revision is listed below. The following reviewer(s) agreed to reveal their identity: H.Steven Scholte
This is a very complete set of studies on the temporal dynamics of scene processing, filling a gap in the literature regarding scene processing. The set of two experiments are technically sounded, and the results strong: spatial layout properties modulating the P2 (∼ 220 msec) is a very neat novel result for the field of cognitive neuroscience.
I did read the entire paper, and I must say: this same paper could be cut by 40-50%, with lots of statistics (like comparison with image statistics models/algorithms) that could go in supplement. the paper is lengthy and difficult to read, because it does repeat a lot the same conclusions, findings, etc. My main revision would be to reduce the paper greatly, so readers can enjoy it more. The message is direct and straightforward: a P2 spatial scene component ∼ 220 msec.
The authors first isolate moments in time / locations at which scenes differ from objects and faces. Next they use there moments of interest to explore a second dataset which contains images on multiple axis relevant for scene recognition.
The authors show that the P2 coincides with a moment at which both a different in open / closed and manmade / natural can be found. From this they conclude that the P2 relevant for scene recognition and might be a homologue to the face N170. The paper presented an interesting and nice read. I think the authors are selling themselves short with the actual presentations.
Major comments
•The authors could provide a somewhat gentler introduction to the problem of scene statistics vs. scene recognition. In the end the reasoning in the discussion, that a true scene recognition component should contain sensitivity to multiple scene statistics elements could be made more explicit in the introduction. E.g. the authors should establish in the introduction what a litmus test is for 'true' scene specific information. Now they ignore the problem.
•The moments in time approach gives a clear focus, however, some elements of the data get blurred. For instance at page 17 the authors state there are no low-level physical stimulus effects at the P1. This is of course correct but they could be (and probably are) present in the data as a whole. The authors could provide somewhat more meta info about the general (strong) approach but also note the weakness in the discussion (e.g. what is not found in exp1 is not reported in exp 2).
•The correlations between Ce and Sc and Fi and Fs appear to be very high. But this can be related to the specific image set. Is it? How are the correlations between Ce/Cs vs. Fi/Fs?
•The authors observe that Fi/Fs is related to open / close (following Kravitz) and Ce/Cs related to manmage / natural (following Groen). They find correlations of both in the P2. It should be possible to make the point of this relationship more directly. Can these values be used, as mediation parameters, to reduce the manmade / natural and open / close effects reported in fig 5?
•What is the combined explained variance of Fi+ Fs and Ce + Sc? If both play a different role the combined value should be higher.
Minor comments
•I miss to what degree the authors use mcc for exp 1. This is not necessary if it only pertains to ROI selection but the authors also report the data from exp 1 as a finding by itself. Please clarify.
•P8. For inspection during recording you want to select an actual reference to get the full CMRR (not relevant for recording in the past but might be sensible for the future).
•Include the nose reference (if used) as a measured electrode in EEG recording.
•Bottom p29, one of the Groen papers shows sensitivity to manmande / natural during the N1 amplitude (at least in terms of time) and for SC or CE.
•Some of the references are not in the literature list
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Assaf Harel, Iris I. A. Groen, Dwight J. Kravitz, Leon Y. Deouell, Chris I. Baker
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Insulin Potentiation Therapy (IPT) as an Immune Support Therapy: Scientific Basis of Fudge Factors
by Simon Yu, MD | Apr 15, 2016 | Cancer, Chronic Disease, History of Medicine, Therapies
The 10:1 ratio of bacteria living in the human body to human cells has been promoted like the Gospel. Nobody had questioned this ratio until recently. Calculations of human microbiomes suggest a very different ratio (Science News, Feb 2016 by Tina Hesman Saey).
This new research suggests the average person is composed of about 40 trillion bacteria and 30 trillion human cells for a 1.3:1 ratio of bacteria to human cells. In 2014 issue of Microbe, Judah Rosner, a molecular biologist at the National Institute of Diabetes and Digestive and Kidney Disease in Bethesda, MD, called the 10:1 ratio a "fake fact" with too many fudge factors.
A breakdown of human cells in the body shows 84% red blood cells and 16% for all other human cells. By weight, muscle and fat are the heavy hitters, making up 75% of cell mass. But those cells tend to be big and represent only about 0.2% of human cell numbers.
Fake data and fudge factors in medical science are much more common problems than you may think. Physicians have to rely on their lifetime experiences for their clinical decisions and need an awareness of the hypes and artifacts in medical science.
Other researchers point out that the old calculation considered only bacteria, while viruses, fungi, archaea, and other microbes, including parasites, are excluded. Let them argue. I am not interested in this fruitless academic intellectual squabble over fudge factors.
This latest debunking of the current belief system about the human microbiome reminds me of what is happening with Insulin Potentiation Therapy (IPT). I wrote an article, "Insulin Therapy for Other Than Diabetes," about five years ago describing IPT as a new promising forgotten therapy. Insulin Potentiation Therapy was developed in 1932 by a Mexican military surgeon, Donato Perez Garcia, Sr., M.D.
Dr. Garcia was able to cure many medical conditions including asthma, psoriasis, migraines, neurosyphilis, lupus, multiple sclerosis, and even cancers. In 1944, Time magazine covered the story of Dr. Garcia's therapy and called it Insulin Shock Therapy. Despite the coverage by the main media, his idea was never fully accepted in the United States. For more details of the history, do an internet search for Insulin Potentiation Therapy by Donato Perez Garcia, M.D. or read my article.
IPT was brought to the United States by the Canadian-American physician Steven Ayer, M.D. in collaboration with Donato Perez Garcia, M.D., grandson of Donato Perez Garcia, Sr, M.D. There is a small group of American physicians who embrace Dr. Donato Perez Garcia's IPT, independent of his updated protocol. They are using IPT with low dose chemotherapy (typically using 10% of the standard dose) for cancer treatment. They are promoting IPT as a safer, cheaper alternative to high dose chemotherapy.
Although, I have been trained by an American IPT medical group on how to use IPT, I have been refusing to use any chemo agents, even low dose of chemo agents, for a philosophical reason. I do not treat cancer. I treat the underlying medical problems. I believe cancer is a metabolic disease associated with unrecognized, infectious diseases, as if cancer is a metabolic parasite. Hidden dental infections and unresolved emotional conflicts may play an important role for why these patients are not responding to standard or alternative therapies. I've written many articles on this subject of cancer and the parasite/dental infection/emotional conflict connection.
IPT can be used as an independent Immune Support Therapy without chemo agents. You may add antiviral, antibacterial, antifungal, and antiparasitic medications with IPT to reduce the total body infectious burden and boost one's immune system. I have seen some dramatic self-healing responses (Accidental Cure) regardless of the symptoms, syndromes, or diagnosis.
Recently, I did visit Dr. Donato Perez Garcia, grandson of the original developer, at the Hospital Angeles in Tijuana, Mexico. I went to see him to pay my respect for the three generations of the Garcia family who developed and have been using IPT. They have over one hundred years of combined experiences using IPT.
One of the new insights I gained from visiting and learning directly from Dr. Donato Garcia was that he has been using only about 10 % of the standard insulin dose as compared to what I learned five years ago. Smaller insulin doses induce gentler hypoglycemic reactions and milder adrenergic responses with less resistance to chemo therapy and better outcomes. Most American physicians using IPT with low dose chemo are not aware of this valuable information.
My experience with IPT, and this new way of calculating insulin dose, reminds me of the story of the human microbiome. The latest microbiome data disputes the old held idea of a 10:1 ratio of bacterial cells to human cells as a true scientific fact. These are two examples of why old scientific "facts" sometimes need new calculations.


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