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Posted this originally as a comment to this post, https://www.reddit.com/r/TheSilphRoad/comments/6z01me/my_ex_raid_experience/ , and I got a recommendation to give it it's own post due to the prior post's age, so that will explain references to 'OP.' Here we go:
I was an observer at the Columbus Circle ('CC') raid (a handful of people without invites made it into the police barricade) and want to just highlight a few things based on what OP mentioned.
- Silph Road team members, who were acting as a street team of sorts for Niantic, confirmed raids go up to only 40 people as far as they know, and as we saw in CC and in the OP's post. Silph Road team also seemed to confirm Niantic is looking for gyms in parks that can hold people (i.e. not sidewalk gyms, which are the most common in NYC).
- The coordination part OP mentions is key. People tried to get the different teams to reconcile after Instinct locked down the gym and the hour started (people were definitely angry with the team of the gym), but everyone just sort of jumped in thinking they needed the full 20 in the raid (there were two raid teams). Only a few people were Mystic and some ended up with six balls and didn't catch. Dividing people up into smaller teams is totally key to increase balls. Moveset was likely Confusion/Psychic, so Mewtwo was defeated fairly quickly with TTars. No one tested for moveset - everyone was too eager to get in.
- One person experienced an error 2/3s of the way through the raid battle and needed to restart the app. It affected his damage and thus number of balls.
- There were no spoofers invited to the raid as far as I could tell (based on numbers in the battle and people in person) except for one individual who was a 'local spoofer' i.e. lives in the city and plays there as well, but spoofed a raid at this specific gym a few days before. Even though Columbus Circle is loaded with spoofers (I did a Raikou raid myself after at a nearby gym and was only the 'real' person in the raid when I went in with 15 others and a 20 person raid had just gone through with one 'real'), no spoofers were invited to the EX Raid. These people were either handpicked and/or Niantic knows how to identify 'long distance spoofers' and chooses not to act against them in most cases.
- It's been confirmed before, but to reiterate - any level of raid qualifies for a possible EX raid pass.
- Consider playing after the raid. Not sure what caused it (could have been increased spoofer activity in the area causing better spawns, could just be RNG, or could be EX Raid related), but the nearby spawns for the next three hours after were incredible. Within a half hour of the end of the raid hour a Togetic and a Dragonite (93IV) spawned a block over from CC. Within two or three hours after many Mareep, another Togetic and an Unown all spawned within the same biome (a grid of 4 avenues by about 15 streets). Needless to say, not normal for the area (at least as far as I know and I play in that area often).
Edit: Clarity
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Abstract
Autism spectrum disorder (ASD) and obsessive-compulsive disorder (OCD) often share phenotypes of repetitive behaviors, possibly underpinned by abnormal decision-making. To compare neural correlates underlying decision-making between these disorders, brain activation of boys with ASD (N = 24), OCD (N = 20) and typically developing controls (N = 20) during gambling was compared, and computational modeling compared performance. Patients were unimpaired on number of risky decisions, but modeling showed that both patient groups had lower choice consistency and relied less on reinforcement learning compared to controls. ASD individuals had disorder-specific choice perseverance abnormalities compared to OCD individuals. Neurofunctionally, ASD and OCD boys shared dorsolateral/inferior frontal underactivation compared to controls during decision-making. During outcome anticipation, patients shared underactivation compared to controls in lateral inferior/orbitofrontal cortex and ventral striatum. During reward receipt, ASD boys had disorder-specific enhanced activation in inferior frontal/insular regions relative to OCD boys and controls. Results showed that ASD and OCD individuals shared decision-making strategies that differed from controls to achieve comparable performance to controls. Patients showed shared abnormalities in lateral-(orbito)fronto-striatal reward circuitry, but ASD boys had disorder-specific lateral inferior frontal/insular overactivation, suggesting that shared and disorder-specific mechanisms underpin decision-making in these disorders. Findings provide evidence for shared neurobiological substrates that could serve as possible future biomarkers.
Autism Spectrum Disorder, computational modeling, decision-making, fMRI, obsessive-compulsive disorder
Introduction
Autism Spectrum Disorder (ASD) is characterized by social and communication difficulties and restricted, repetitive behaviors (American Psychiatric Association 2013) and affects 0.6–2.0% of the population, with a higher prevalence in males (Blumberg et al. 2013). Obsessive-Compulsive Disorder (OCD) is identified by recurrent and intrusive distressing thoughts (obsessions) and repetitive rituals (compulsions) (American Psychiatric Association 2013) and has a prevalence of 1–3%, with a slightly higher incidence in males in pediatric samples (Ruscio et al. 2010). These highly heterogeneous and frequently comorbid disorders can sometimes be clinically difficult to separate, as symptoms such as repetitive behaviors in ASD can often resemble OCD-related compulsions (Russell et al. 2005). Such overlap has been attributed to shared genetic risk and biological mechanisms as well as diagnostic mislabelling (Russell et al. 2016), highlighting a need to understand the distinct and overlapping underlying neurobiological mechanisms of both disorders.
Executive functions (EF) are higher-order cognitive functions important for goal-directed behavior and can be conceptualized dichotomously as “cool” EF, referring to nonemotional functions including inhibition and working memory, and “hot” EF, referring to functions with reward-based motivation including gambling and reward learning (Zelazo and Müller 2007). Cool EF has been widely investigated in ASD and OCD (for reviews, see (Zelazo and Müller 2007; van Velzen et al. 2014; Carlisi, Norman, Lukito et al. 2016; Norman et al. 2016)). However, relatively less is known about the mechanisms underlying reward-related hot EF processes in these disorders, as evidence to date has been inconsistent.
Impaired decision-making has been implicated in both ASD and OCD (Cavedini et al. 2006; Luke et al. 2012). The Iowa Gambling Task (IGT) (Bechara et al. 1994) has been widely used in typically developing populations to measure reward-based decision-making and temporal foresight impairments under conditions of ambiguity, as it requires reinforcement learning to distinguish between choices that yield large immediate gains but even larger losses (risky options) leading to long-term financial losses and decks that give small gains but even smaller losses, leading to long-term financial gains at the end of the game (safe options).
There have been only 5 studies in ASD using the IGT (Johnson et al. 2006; Yechiam et al. 2010; South et al. 2014; Mussey et al. 2015; Zhang et al. 2015), showing mixed results. A relatively consistent finding in both children/adolescents (Johnson et al. 2006; Yechiam et al. 2010) and adults (Mussey et al. 2015) is that ASD individuals shift more frequently between choices, possibly due to difficulties with implicit learning (Johnson et al. 2006) or exploration-focused learning strategies (Yechiam et al. 2010). Another study in adults with ASD found that the ASD group had worse performance, preferring disadvantageous decks (Zhang et al. 2015). However, one study (South et al. 2014) in children/adolescents found superior performance in ASD adolescents relative to typically developing controls, explained by a “loss-avoidance” style of decision-making in the ASD group in contrast to a “reward-seeking” style often observed among typically developing adolescents (Smith et al. 2012).
There have been relatively more studies using the IGT in adults with OCD (e.g., (Purcell et al. 1998; Cavedini et al. 2002, 2010; Cavallaro et al. 2003; Olley et al. 2007; Starcke et al. 2010; Rocha et al. 2011; Grassi et al. 2015; Kim et al. 2015)). The majority show impaired decision-making in patients relative to controls, with patients preferring large immediate rewards and not learning from losses, although there have also been negative findings (Nielen et al. 2002; Lawrence et al. 2006; Krishna et al. 2011). Only one study was conducted in children with OCD using the IGT which found that patients performed worse relative to controls and that this was related to symptom severity during the most severe period of illness (Kodaira et al. 2012).
The IGT taps a range of cognitive processes including reward-related decision-making, reward sensitivity, loss aversion, temporal foresight, inhibitory control (to inhibit the contextual “thrill” of immediate gains), and exploratory behavior. Thus, to clarify IGT performance impairments (or lack thereof) in both clinical groups, it is important to investigate these cognitive and motivational factors on a more nuanced level to better characterize task performance, and computational modeling is a useful tool for this (Huys et al. 2016).
Similar performance deficits could also be mediated by different underlying neurofunctional networks. No functional magnetic resonance imaging (fMRI) studies, however, have yet investigated the neural correlates of decision-making under ambiguity in ASD or OCD using the IGT. In typically developing individuals, the IGT activates dorsolateral and ventromedial prefrontal, orbitofrontal, insular, posterior cingulate, and ventral striatal regions during the various stages of the decision-making process (Li et al. 2010). In light of a dearth of evidence in ASD and OCD specifically on the IGT, evidence can be compiled from studies examining related reward-based decision-making processes; during tasks of temporal discounting (Chantiluke, Christakou et al. 2014) and reversal-learning (Chantiluke, Barrett, Giampietro, Brammer et al. 2015), adolescents with ASD have shown abnormalities in related fronto-temporo-limbic systems mediating executive processes (Carlisi, Norman, Lukito et al. 2016) and ventromedial/fronto-limbic regions important for reward-related functions, especially those involving monetary gain/loss (Kohls et al. 2013). OCD has traditionally been conceptualized as a disorder of abnormalities in ventral affective systems including (orbito)fronto-striato-thalamo-cortical networks as well as in lateral orbitofrontal-striatal systems important for cognitive/inhibitory control (Zelazo and Müller 2007; Menzies et al. 2008; Carlisi, Norman, Lukito et al. 2016). fMRI studies involving reward-related decision-making support evidence for abnormalities in both motivation control as well as cognitive control regions by showing that OCD patients relative to controls have hyperactivity in ventromedial prefrontal, orbitofrontal and anterior cingulate cortex (ACC) regions projecting to ventral striatum and medio-dorsal thalamus, and underactivation in cortico-striato-thalamic regions including dorsolateral prefrontal cortex (DLPFC), temporal and parietal cortices, and basal ganglia (Menzies et al. 2008; Brem et al. 2012).
The relative lack of consistent findings in ASD and OCD on the IGT highlights a need for a better understanding of neurocognitive phenotypes of reward-based decision-making in these disorders. Recent efforts such as the Research Domain Criteria (RDoC; (Insel et al. 2010)) stress the importance of investigating trans-diagnostic phenotypes which may be underpinned by shared and/or disorder-specific neurofunctional mechanisms. Thus, we compared adolescents with ASD to those with OCD and typically developing controls to investigate shared and disorder-specific brain function abnormalities during the IGT and compared reinforcement learning models to examine fine-grained differences in behavioral factors that might underlie overall decision-making. We hypothesized that both patient groups would be impaired on some aspect of task performance. Specifically, we hypothesized that OCD adolescents would show increased risky decision-making on the IGT compared to typically developing controls as evidenced by previous studies (Starcke et al. 2010; Grassi et al. 2015). Moreover, we hypothesized that OCD boys would show more brain-based impairments during loss and negative outcome based on the literature in this patient group of impaired error monitoring (Fitzgerald et al. 2005) and the clinical literature of the prototypical feeling that things need to be “just right” which often characterizes individuals with OCD (Coles et al. 2003). For ASD boys, we hypothesized this group would show lower choice consistency compared to typically developing control participants (Johnson et al. 2006; Yechiam et al. 2010) and OCD patients. We tested whether differences were due to more nuanced shared or disorder-specific differences in decision-making styles. Based on evidence from IGT studies in typically developing individuals showing that reward-based decision-making may be driven by dorsolateral and ventromedial/orbitofronto-striato-limbic function (Li et al. 2010; Christakou, Gershman et al. 2013), we hypothesized that both groups would show abnormalities in these networks (Christakou et al. 2011; Brem et al. 2012). Furthermore, based on prior evidence of neurofunctional reward-related deficits in the 2 disorders, we hypothesized that both disorders would show abnormal reward processing in ventromedial-fronto-temporo-limbic (Kohls et al. 2013) regions important for reward-based decision-making and temporal foresight required by the task (Menzies et al. 2008). However, we also expected disorder-specific stronger deficits in OCD in orbitofrontal regions and in ASD in ventral striatal and anterior cingulate regions based on respective deficits in these regions observed in each disorder (Menzies et al. 2008; Kohls et al. 2013).
Materials and Methods
Participants
64 right-handed (Oldfield 1971) boys (20 typically developing control boys, 24 boys with ASD, 20 boys with OCD), 11–17 years-old, IQ ≥ 70 (Wechsler 1999) participated. Medication-naïve ASD boys were recruited from local clinics. Clinical ASD diagnosis was made by a consultant psychiatrist using ICD-10 research diagnostic criteria (WHO 1992) and confirmed using the Autism Diagnostic Interview-Revised (ADI-R (Lord et al. 1994)). The Autism Diagnostic Observation Schedule (ADOS (Lord et al. 1989)) was also completed. All ASD boys reached clinical thresholds in all domains on the ADI-R (social, communication, restricted/stereotyped behavior) and ADOS (communication, social). Parents of ASD boys also completed the Social Communication Questionnaire (SCQ; (Rutter et al. 2003)) and the Strengths and Difficulties Questionnaire (SDQ; (Goodman and Scott 1999)). ASD participants had a physical examination to exclude comorbid medical disorders and any abnormalities associated with ASD. Individuals with comorbid psychiatric conditions, including OCD and ADHD, were not included.
OCD boys were recruited from the Maudsley Hospital National & Specialist OCD clinic. Diagnosis was made by a consultant clinician using ICD-10 criteria and confirmed with the Children's Yale-Brown Obsessive-Compulsive Scale (CY-BOCS; (Goodman et al. 1989)) and ancillary symptom checklist. Parents of OCD boys also completed the SDQ. OCD patients with comorbid psychiatric or neurological conditions, including ASD and ADHD, were excluded. Four boys were prescribed stable doses of antidepressants (see Supplement).
Twenty age- and handedness-matched typically developing control boys were recruited locally by advertisement. Controls did not meet clinical thresholds on the SDQ and SCQ for any disorder and did not have a current or lifetime history of any psychiatric condition.
Exclusion criteria for all subjects were comorbid psychiatric/medical disorders affecting brain development (e.g., epilepsy/psychosis), drug/alcohol dependency, history of head injury, genetic conditions associated with autism, abnormal structural MRI scans, and MRI contraindications. Controls also participated in our fMRI study examining maturation of decision-making on the IGT, published previously (Christakou, Gershman et al. 2013). Most ASD and control participants also participated in additional fMRI tasks during their visit, published elsewhere (Christakou et al. 2011; Christakou, Murphy et al. 2013; Chantiluke, Barrett et al. 2014; Murphy et al. 2014; Chantiluke, Barrett, Giampietro, Brammer et al. 2015; Chantiluke, Barrett, Giampietro, Santosh et al. 2015; Carlisi, Chantiluke et al. 2016).
This study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the local Research Ethics Committee (05/Q0706/275). Study details were explained to participants and guardians. Written, informed assent/consent was obtained for all participants, and individuals were compensated for their time and travel expenses.
Iowa Gambling Task
The fMRI version of the IGT used in this study is described in detail elsewhere (Christakou et al. 2009; Christakou, Gershman et al. 2013). Briefly, on each of 80 trials, participants were presented with 4 card decks (A/B/C/D) on a screen and instructed to choose any deck by pressing the corresponding button with the right hand on an MR-compatible 5-button response box. They were instructed to win as much money as possible by the end of the task. They were only told that sometimes they would win money and sometimes they would lose money, and that some decks might be better than others. They were also told that their final amount won on the task would determine how much of a maximum £30 they would receive as compensation (in reality, all subjects received £30).
Decks A and B were termed disadvantageous or “risky” decks because they returned relatively large gains (£190/£200/£210) but even larger losses (£240/£250/£260), leading to an overall net loss, whereas decks C and D were advantageous or “safe” because they returned small gains (£90/£100/£110) but even smaller losses (£40/£50/£60), resulting in a net gain. There was a 50% probability of winning or losing on each deck.
Task performance is summarized by the ratio of advantageous choices to total choices or, the number of cards picked from decks C + D divided by the total number of cards picked (A + B + C + D). This ratio is proportional to the “net score” ((C + D) − (A + B)) frequently used when quantifying performance on the IGT (Bechara et al. 1994) without giving negative values. Ratios above 0.5 denote preference for safe relative to risky decks, while a ratio below 0.5 implies perseveration on risky choices despite accumulating losses. Responses where reaction time (RT) was less than 200 ms were considered “premature” and these trials were not included in analyses (Thorpe et al. 1996).
This IGT adaptation differs from other fMRI versions (e.g., (Lawrence et al. 2009)) in that choice was temporally separated from its outcome, haemodynamically decoupling choice and outcome evaluation, allowing separate examination of each. Subjects were given 3 s to respond. Following each choice, the chosen deck was superimposed with a 12-segment wheel ticking down every 0.5 s for a total 6 s until outcome presentation. If no response was made, the trial progressed directly to a blank screen for 9 s. Positive (win) and negative (loss) outcomes were indicated by a happy or sad face presented below the deck and the amount won or lost indicated on the card (Fig. 1). Outcomes were presented for 3 s. Trials lasted 15 s, ending with a blank screen after outcome presentation serving as an implicit baseline in the fMRI analysis. Omitted trials were excluded from analyses. The length of each inter-trial interval (ITI) was determined by the RT, which jittered trial events so as to maintain a 15 s total trial duration. As these manipulations lengthened trial and task duration compared to other behavioral variants, this version of the task included 80 trials rather than the typical 100 trials (Bechara et al. 1994; Lawrence et al. 2009). Total task time was 21 min. Before testing, participants practiced the task in a mock scanner, where 10 test trials presented equal payoffs across decks.
Computational Modeling
The IGT requires decision-making based on the learned outcomes of previous choices. Performance on the IGT can be influenced by a range of factors including learning rates, reward and loss sensitivity, or inconsistent responding (Ahn et al. 2014). Thus, computational approaches are especially useful for understanding the processes underlying IGT performance. We used hierarchical Bayesian analysis (HBA) implemented within the hBayesDM R package (https://cran.r-project.org/web/packages/hBayesDM/index.html) for computational modeling of IGT performance (Ahn et al. 2016). For further details of the methods, rationale and advantages of HBA over other modeling methods (e.g., maximum likelihood estimation), see Supplement and (Lee 2011). HBA involves preparation of trial-by-trial task data for each participant, model fitting and comparison of 3 commonly used and validated models of the IGT: the “Prospect Valence Learning (PVL)-Decay Reinforcement Learning (RI) model, the PVL-Delta model and the Value-Plus-Perseverance (VPP)“ model (Worthy, Pang et al. 2013; Ahn et al. 2014; Steingroever et al. 2014).
The PVL models focus on 4 parameters based on learning theory: α represents feedback sensitivity,