Guido Biele

• Borge, Tiril Cecilie; Biele, Guido Philipp Emmanuel; Papadopoulou, Eleni Zoumpoulia; Andersen, Lene Frost; Jacka, Felice; Eggesbø, Merete Åse; Caspersen, Ida Henriette; Aase, Heidi; Meltzer, Helle Margrete & Brantsæter, Anne Lise (2021). The associations between maternal and child diet quality and child ADHD - findings from a large Norwegian pregnancy cohort study. BMC Psychiatry.  ISSN 1471-244X.  21, s 1- 14 . doi: 10.1186/s12888-021-03130-4 Full text in Research Archive.
• Choi, Giehae; Villanger, Gro Dehli; Drover, Samantha S.M.; Sakhi, Amrit Kaur; Thomsen, Cathrine; Nethery, Rachel C.; Zeiner, Pål; Knudsen, Gun Peggy Strømstad; Reichborn-Kjennerud, Ted; Øvergaard, Kristin Romvig; Herring, Amy H.; Skogan, Annette Holth; Biele, Guido; Aase, Heidi & Engel, Stephanie M. (2021). Prenatal phthalate exposures and executive function in preschool children. Environment International.  ISSN 0160-4120.  149 . doi: 10.1016/j.envint.2021.106403 Full text in Research Archive.
• Jones, Terry C.; Biele, Guido; Mühlemann, Barbara; Veith, Talitha; Schneider, Julia; Beheim-Schwarzbach, Jörn; Bleicker, Tobias; Tesch, Julia; Schmidt, Marie Luisa; Sander, Leif Erik; Kurth, Florian; Menzel, Peter; Schwarzer, Rolf; Zuchowski, Marta; Hofmann, Jörg; Krumbholz, Andi; Stein, Angela; Edelmann, Anke; Corman, Victor Max & Drosten, Christian (2021). Estimating infectiousness throughout SARS-CoV-2 infection course. Science.  ISSN 0036-8075.  373(6551), s 1- 21 . doi: 10.1126/science.abi5273
• Skogheim, Thea Steen; Weyde, Kjell Vegard Fjeldheim; Engel, Stephanie M.; Aase, Heidi; Suren, Pål; Øie, Merete Glenne; Biele, Guido; Reichborn-Kjennerud, Ted; Caspersen, Ida Henriette; Hornig, Mady; Haug, Line Småstuen & Villanger, Gro Dehli (2021). Metal and essential element concentrations during pregnancy and associations with autism spectrum disorder and attention-deficit/hyperactivity disorder in children. Environment International.  ISSN 0160-4120.  152, s 1- 14 . doi: 10.1016/j.envint.2021.106468 Full text in Research Archive.
• Dort, Martina; Strelow, Anna Enrica; French, Blandine; Groom, Madeleine; Luman, Marjolein; Thorell, Lisa B.; Biele, Guido & Christiansen, Hanna (2020). Bibliometric review: Classroom management in ADHD-Is there a communication gap concerning knowledge between the scientific fields psychiatry/psychology and education?. Sustainability.  ISSN 2071-1050.  12(17), s 1- 15 . doi: 10.3390/SU12176826
• Jore, Toril; Oppedal, Brit & Biele, Guido (2020). Social anxiety among unaccompanied minor refugees in Norway.The association with pre-migration trauma and post-migration acculturation related factors. Journal of Psychosomatic Research.  ISSN 0022-3999.  136 . doi: 10.1016/j.jpsychores.2020.110175 Full text in Research Archive.
• Biele, Guido; Gustavson, Kristin; Czajkowski, Nikolai Olavi; Nilsen, Roy Miodini; Reichborn-Kjennerud, Ted; Magnus, Per; Stoltenberg, Camilla & Aase, Heidi (2019). Bias from self selection and loss to follow-up in prospective cohort studies. European Journal of Epidemiology (EJE).  ISSN 0393-2990.  34(10), s 927- 938 . doi: 10.1007/s10654-019-00550-1
• Borge, Tiril Cecilie; Brantsæter, Anne Lise; Caspersen, Ida Henriette; Meltzer, Helle Margrete; Brandlistuen, Ragnhild Eek; Aase, Heidi & Biele, Guido Philipp Emmanuel (2019). Estimating the Strength of Associations Between Prenatal Diet Quality and Child Developmental Outcomes: Results From a Large Prospective Pregnancy Cohort Study. American Journal of Epidemiology.  ISSN 0002-9262.  188(11), s 1902- 1912 . doi: 10.1093/aje/kwz166 Full text in Research Archive. Show summary

  Our aim in this study was to estimate the strength of associations between prenatal diet quality and child behavioral, language, and motor functions in the Norwegian Mother and Child Cohort Study (1999–2008). We created a prenatal diet quality index (PDQI) based on adherence to Norwegian dietary guidelines. Child outcomes were defined as sum scores on the Child Behavior Checklist, the Ages and Stages Questionnaire, and the Child Development Index at ages 18, 36, and 60 months. Using a longitudinal cohort study design and Bayesian hierarchical modeling, we estimated association strengths using inverse probability weighting to account for selection bias. In total, 27,529 mother-child pairs were eligible for inclusion. A 1–standard-deviation increase in PDQI score was associated with an absolute reduction in outcome sum scores of 0.02–0.21 and a 3%–7% relative decrease, with larger decreases seen for language and motor functions than for behavioral functions. PDQI scores were inversely associated with all child functions, but the estimated strength of each association was low. The results indicate that the observed variations in PDQI scores in an industrialized Western society may not profoundly influence the child functions studied.

• Skogheim, Thea Steen; Villanger, Gro Dehli; Weyde, Kjell Vegard Fjeldheim; Engel, Stephanie M.; Suren, Pål; Øie, Merete Glenne; Skogan, Annette Holth; Biele, Guido; Zeiner, Pål; Øvergaard, Kristin Romvig; Haug, Line Småstuen; Sabaredzovic, Azemira & Aase, Heidi (2019). Prenatal exposure to perfluoroalkyl substances and associations with symptoms of attention-deficit/hyperactivity disorder and cognitive functions in preschool children. International Journal of Hygiene and Environmental Health.  ISSN 1438-4639.  223(1), s 80- 92 . doi: 10.1016/j.ijheh.2019.10.003 Full text in Research Archive.
• Øvergaard, Kristin Romvig; Ørbeck, Beate; Friis, Svein; Biele, Guido; Pripp, Are Hugo; Aase, Heidi & Zeiner, Pål (2019). Screening With an ADHD-Specific Rating Scale in Preschoolers: A Cross-Cultural Comparison of the Early Childhood Inventory-4. Psychological Assessment.  ISSN 1040-3590.  31(8), s 985- 994 . doi: 10.1037/pas0000722
• Suren, Pål; Thorstensen, Anne Gjertrud; Tørstad, Marit; Emhjellen, Petter Espeseth; Furu, Kari; Biele, Guido; Aase, Heidi; Stoltenberg, Camilla; Zeiner, Pål; Bakken, Inger Johanne & Reichborn-Kjennerud, Ted (2018). Diagnostikk av hyperkinetisk forstyrrelse hos barn i Norge. Tidsskrift for Den norske legeforening.  ISSN 0029-2001. . doi: 10.4045/tidsskr.18.0418 Full text in Research Archive.
• Øvergaard, Kristin Romvig; Ørbeck, Beate; Friis, Svein; Pripp, Are Hugo; Biele, Guido; Aase, Heidi & Zeiner, Pål (2018). Attention-Deficit/Hyperactivity Disorder in Preschoolers: The Accuracy of a Short Screener. Journal of the American Academy of Child and Adolescent Psychiatry.  ISSN 0890-8567.  57(6), s 428- 435 . doi: 10.1016/j.jaac.2018.03.008 Full text in Research Archive.
• Borge, Tiril Cecilie; Aase, Heidi; Brantsæter, Anne Lise & Biele, Guido (2017). The importance of maternal diet quality during pregnancy on cognitive and behavioural outcomes in children: a systematic review and meta-analysis. BMJ Open.  ISSN 2044-6055.  7(9) . doi: 10.1136/bmjopen-2017-016777 Full text in Research Archive.
• Eikemo, Marie Helene; Biele, Guido; Willoch, Frode; Thomsen, Lotte & Leknes, Siri (2017). Opioid modulation of value-based decision-making in healthy humans. Neuropsychopharmacology.  ISSN 0893-133X.  42(9), s 1833- 1840 . doi: 10.1038/npp.2017.58 Full text in Research Archive.
• Mowinckel, Athanasia Monika; Alnæs, Dag; Pedersen, Mads Lund; Ziegler, Sigurd; Fredriksen, Mats; Kaufmann, Tobias; Sonuga-Barke, Edmund J.S.; Endestad, Tor; Westlye, Lars Tjelta & Biele, Guido (2017). Increased default-mode variability is related to reduced task-performance and is evident in adults with ADHD. NeuroImage: Clinical.  ISSN 2213-1582.  16, s 369- 382 . doi: 10.1016/j.nicl.2017.03.008 Full text in Research Archive.
• Skåtun, Kristina Cecilie; Kaufmann, Tobias; Brandt, Christine Lycke; Doan, Nhat Trung; Alnæs, Dag; Tønnesen, Siren; Biele, Guido; Vaskinn, Anja; Melle, Ingrid; Agartz, Ingrid; Andreassen, Ole Andreas & Westlye, Lars Tjelta (2017). Thalamo-cortical functional connectivity in schizophrenia and bipolar disorder. Brain Imaging and Behavior.  ISSN 1931-7557. . doi: 10.1007/s11682-017-9714-y
• Berg, Nathan; Biele, Guido & Gigerenzer, Gerd (2016). Consistent Bayesians Are No More Accurate Than Non-Bayesians: Economists Surveyed About PSA. Review of Behavioral Economics.  ISSN 2326-6198.  3(2), s 189- 219 . doi: 10.1561/105.00000034
• Caspersen, Ida Henriette; Aase, Heidi; Biele, Guido; Brantsæter, Anne Lise; Haugen, Alice Margaretha; Kvalem, Helen Engelstad; Skogan, Annette Holth; Zeiner, Pål; Alexander, Jan; Meltzer, Helle Margrete & Knutsen, Helle Katrine (2016). The influence of maternal dietary exposure to dioxins and PCBs during pregnancy on ADHD symptoms and cognitive functions in Norwegian preschool children. Environment International.  ISSN 0160-4120.  94, s 649- 660 . doi: 10.1016/j.envint.2016.06.033
• Pedersen, Mads Lund; Frank, Michael J. & Biele, Guido (2016). The drift diffusion model as the choice rule in reinforcement learning. Psychonomic Bulletin & Review.  ISSN 1069-9384. . doi: 10.3758/s13423-016-1199-y
• Skogan, Annette Holth; Øvergaard, Kristin Romvig; Baumgartner, Nina; Urnes, Anne-Margrethe; Ørbeck, Beate; Aase, Heidi; Biele, Guido; Zeiner, Pål & Egeland, Jens (2016). Eksekutiv funksjon i tidlig førskolealder-funn fra den norske ADHD-studien. Nevropsykologi : Medlemsblad for Norsk Nevropsykologisk Forening.  ISSN 1500-8347.  18(1), s 4- 13
• Skåtun, Kristina Cecilie; Kaufmann, Tobias; Tønnesen, Siren; Biele, Guido; Melle, Ingrid; Agartz, Ingrid; Alnæs, Dag; Andreassen, Ole Andreas & Westlye, Lars Tjelta (2016). Global brain connectivity alterations in persons with schizophrenia and bipolar spectrum disorders. Journal of Psychiatry & Neuroscience.  ISSN 1180-4882.  41(5), s 331- 341 . doi: 10.1503/jpn.150159 Full text in Research Archive.
• Ziegler, Sigurd; Pedersen, Mads Lund; Mowinckel, Athanasia Monika & Biele, Guido (2016). Modelling ADHD: A review of ADHD theories through their predictions for computational models of decision-making and reinforcement learning. Neuroscience and Biobehavioral Reviews.  ISSN 0149-7634.  71, s 633- 656 . doi: 10.1016/j.neubiorev.2016.09.002
• Mowinckel, Athanasia Monika; Pedersen, Mads Lund; Eilertsen, Espen Moen & Biele, Guido (2015). A Meta-Analysis of Decision-Making and Attention in Adults With ADHD. Journal of Attention Disorders.  ISSN 1087-0547.  19(5), s 355- 367 . doi: 10.1177/1087054714558872 Show summary

  Abstract Objective: Deficient reward processing has gained attention as an important aspect of ADHD, but little is known about reward-based decision-making (DM) in adults with ADHD. This article summarizes research on DM in adult ADHD and contextualizes DM deficits by comparing them to attention deficits. Method: Meta-analytic methods were used to calculate average effect sizes for different DM domains and continuous performance task (CPT) measures. Results: None of the 59 included studies (DM: 12 studies; CPT: 43; both: 4) had indications of publication bias. DM and CPT measures showed robust, small to medium effects. Large effect sizes were found for a drift diffusion model analysis of the CPT. Conclusion: The results support the existence of DM deficits in adults with ADHD, which are of similar magnitude as attention deficits. These findings warrant further examination of DM in adults with ADHD to improve the understanding of underlying neurocognitive mechanisms

• Pedersen, Mads Lund; Endestad, Tor & Biele, Guido (2015). Evidence Accumulation and Choice Maintenance Are Dissociated in Human Perceptual Decision Making. PLOS ONE.  ISSN 1932-6203.  10(10) . doi: 10.1371/journal.pone.0140361 Full text in Research Archive.
• Suter, Renata S.; Pachur, Thorsten; Hertwig, Ralph; Endestad, Tor & Biele, Guido (2015). The neural basis of risky choice with affective outcomes. PLOS ONE.  ISSN 1932-6203.  10(4) . doi: 10.1371/journal.pone.0122475 Full text in Research Archive.
• Biele, Guido; Zeiner, Pål & Aase, Heidi (2014). Convergent and discriminant validity pf psychiatric symptoms reported in The Norwegian Mother and Child Cohort Study at age 3 years with independent clinical assessment in the Longitudinal ADHD Cohort Study. Norsk Epidemiologi.  ISSN 0803-2491.  24(1-2), s 169- 176 . doi: 10.5324/nje.v24i1-2.1819 Full text in Research Archive.
• Blank, Helen; Biele, Guido; Heekeren, Hauke R. & Philiastides, Marios G. (2013). Temporal Characteristics of the Influence of Punishment on Perceptual Decision Making in the Human Brain. Journal of Neuroscience.  ISSN 0270-6474.  33(9), s 3939- 3952 . doi: 10.1523/JNEUROSCI.4151-12.2013
• Leknes, Siri Graff; Berna, Chantal; Lee, Michael; Snyder, Gregory; Biele, Guido & Tracey, Irene (2013). The importance of context: When relative relief renders pain pleasant. Pain.  ISSN 0304-3959.  154(3), s 402- 410 . doi: 10.1016/j.pain.2012.11.018
• Green, Nikos; Biele, Guido & Heekeren, Hauke R (2012). Changes in Neural Connectivity Underlie Decision Threshold Modulation for Reward Maximization. Journal of Neuroscience.  ISSN 0270-6474.  32(43), s 14942- 14950 . doi: 10.1523/JNEUROSCI.0573-12.2012
• Meshi, Dar; Biele, Guido; Korn, Christoph W. & Heekeren, Hauke R. (2012). How Expert Advice Influences Decision Making. PLOS ONE.  ISSN 1932-6203.  7(11) . doi: 10.1371/journal.pone.0049748
• Biele, Guido; Rieskamp, Jörg; Krugel, Lea K. & Heekeren, Hauke R. (2011). The Neural Basis of Following Advice. PLoS Biology.  ISSN 1544-9173.  9 . doi: 10.1371/journal.pbio.1001089 Show summary

  Learning by following explicit advice is fundamental for human cultural evolution, yet the neurobiology of adaptive social learning is largely unknown. Here, we used simulations to analyze the adaptive value of social learning mechanisms, computational modeling of behavioral data to describe cognitive mechanisms involved in social learning, and model-based functional magnetic resonance imaging (fMRI) to identify the neurobiological basis of following advice. One-time advice received before learning had a sustained influence on people's learning processes. This was best explained by social learning mechanisms implementing a more positive evaluation of the outcomes from recommended options. Computer simulations showed that this "outcome-bonus" accumulates more rewards than an alternative mechanism implementing higher initial reward expectation for recommended options. fMRI results revealed a neural outcome-bonus signal in the septal area and the left caudate. This neural signal coded rewards in the absence of advice, and crucially, it signaled greater positive rewards for positive and negative feedback after recommended rather than after non-recommended choices. Hence, our results indicate that following advice is intrinsically rewarding. A positive correlation between the model's outcome-bonus parameter and amygdala activity after positive feedback directly relates the computational model to brain activity. These results advance the understanding of social learning by providing a neurobiological account for adaptive learning from advice.

• Basten, Ulrike; Biele, Guido; Heekeren, Hauke R. & Fiebach, Christian J. (2010). How the brain integrates costs and benefits during decision making. Proceedings of the National Academy of Sciences of the United States of America.  ISSN 0027-8424.  107(50), s 21767- 21772 . doi: 10.1073/pnas.0908104107 Show summary

  When we make decisions, the benefits of an option often need to be weighed against accompanying costs. Little is known, however, about the neural systems underlying such cost-benefit computations. Using functional magnetic resonance imaging and choice modeling, we show that decision making based on cost-benefit comparison can be explained as a stochastic accumulation of cost-benefit difference. Model-driven functional MRI shows that ventromedial and left dorsolateral prefrontal cortex compare costs and benefits by computing the difference between neural signatures of anticipated benefits and costs from the ventral striatum and amygdala, respectively. Moreover, changes in blood oxygen level dependent (BOLD) signal in the bilateral middle intraparietal sulcus reflect the accumulation of the difference signal from ventromedial prefrontal cortex. In sum, we show that a neurophysiological mechanism previously established for perceptual decision making, that is, the difference-based accumulation of evidence, is fundamental also in value-based decisions. The brain, thus, weighs costs against benefits by combining neural benefit and cost signals into a single, difference-based neural representation of net value, which is accumulated over time until the individual decides to accept or reject an option.

• Graef, Susanne; Biele, Guido; Krugel, Lea K.; Marzinzik, Frank; Wahl, Michael; Wotka, Johann; Klostermann, Fabian & Heekeren, Hauke R. (2010). Differential influence of levodopa on reward-based learning in Parkinson's disease. Frontiers in Human Neuroscience.  ISSN 1662-5161.  4 . doi: 10.3389/fnhum.2010.00169 Show summary

  The mesocorticolimbic dopamine (DA) system linking the dopaminergic midbrain to the prefrontal cortex and subcortical striatum has been shown to be sensitive to reinforcement in animals and humans. Within this system, coexistent segregated striato-frontal circuits have been linked to different functions. In the present study, we tested patients with Parkinson’s disease (PD), a neurodegenerative disorder characterized by dopaminergic cell loss, on two reward-based learning tasks assumed to differentially involve dorsal and ventral striato-frontal circuits. 15 non-depressed and non-demented PD patients on levodopa monotherapy were tested both on and off medication. Levodopa had beneficial effects on the performance on an instrumental learning task with constant stimulus-reward associations, hypothesized to rely on dorsal striato-frontal circuits. In contrast, performance on a reversal learning task with changing reward contingencies, relying on ventral striato-frontal structures, was better in the unmedicated state. These results are in line with the “overdose hypothesis” which assumes detrimental effects of dopaminergic medication on functions relying upon less affected regions in PD. This study demonstrates, in a within-subject design, a double dissociation of dopaminergic medication and performance on two reward-based learning tasks differing in regard to whether reward contingencies are constant or dynamic. There was no evidence for a dose effect of levodopa on reward-based behavior with the patients’ actual levodopa dose being uncorrelated to their performance on the reward-based learning tasks.

• Mohr, Peter N.C.; Biele, Guido & Heekeren, Hauke R (2010). Neural processing of risk. Journal of Neuroscience.  ISSN 0270-6474.  30(19), s 6613- 6619 . doi: 10.1523/JNEUROSCI.0003-10.2010 Show summary

  In our everyday life, we often have to make decisions with risky consequences, such as choosing a restaurant for dinner or choosing a form of retirement saving. To date, however, little is known about how the brain processes risk. Recent conceptualizations of risky decision making highlight that it is generally associated with emotions but do not specify how emotions are implicated in risk processing. Moreover, little is known about risk processing in non-choice situations and how potential losses influence risk processing. Here we used quantitative meta-analyses of functional magnetic resonance imaging experiments on risk processing in the brain to investigate (1) how risk processing is influenced by emotions, (2) how it differs between choice and non-choice situations, and (3) how it changes when losses are possible. By showing that, over a range of experiments and paradigms, risk is consistently represented in the anterior insula, a brain region known to process aversive emotions such as anxiety, disappointment, or regret, we provide evidence that risk processing is influenced by emotions. Furthermore, our results show risk-related activity in the dorsolateral prefrontal cortex and the parietal cortex in choice situations but not in situations in which no choice is involved or a choice has already been made. The anterior insula was predominantly active in the presence of potential losses, indicating that potential losses modulate risk processing.

• Mohr, Peter N.C.; Biele, Guido; Krugel, Lea K.; Li, Shu-Chen & Heekeren, Hauke R. (2010). Neural foundations of risk-return trade-off in investment decisions. NeuroImage.  ISSN 1053-8119.  49(3), s 2556- 2563 . doi: 10.1016/j.neuroimage.2009.10.060 Show summary

  Many decisions people make can be described as decisions under risk. Understanding the mechanisms that drive these decisions is an important goal in decision neuroscience. Two competing classes of risky decision making models have been proposed to describe human behavior, namely utility-based models and risk-return models. Here we used a novel investment decision task that uses streams of (past) returns as stimuli to investigate how consistent the two classes of models are with the neurobiological processes underlying investment decisions (where outcomes usually follow continuous distributions). By showing (a) that risk-return models can explain choices behaviorally and (b) that the components of risk-return models (value, risk, and risk attitude) are represented in the brain during choices, we provide evidence that risk-return models describe the neural processes underlying investment decisions well. Most importantly, the observed correlation between risk and brain activity in the anterior insula during choices supports risk-return models more than utility-based models because risk is an explicit component of risk-return models but not of the utility-based models.

• Philiastides, Marios G.; Biele, Guido; Vavatzanidis, Niki; Kazzer, Philipp & Heekeren, Hauke R. (2010). Temporal dynamics of prediction error processing during reward-based decision making. NeuroImage.  ISSN 1053-8119.  53, s 221- 232 . doi: 10.1016/j.neuroimage.2010.05.052 Show summary

  Adaptive decision making depends on the accurate representation of rewards associated with potential choices. These representations can be acquired with reinforcement learning (RL) mechanisms, which use the prediction error (PE, the difference between expected and received rewards) as a learning signal to update reward expectations. While EEG experiments have highlighted the role of feedback-related potentials during performance monitoring, important questions about the temporal sequence of feedback processing and the specific function of feedback-related potentials during reward-based decision making remain. Here, we hypothesized that feedback processing starts with a qualitative evaluation of outcome-valence, which is subsequently complemented by a quantitative representation of PE magnitude. Results of a model-based single-trial analysis of EEG data collected during a reversal learning task showed that around 220ms after feedback outcomes are initially evaluated categorically with respect to their valence (positive vs. negative). Around 300ms, and parallel to the maintained valence-evaluation, the brain also represents quantitative information about PE magnitude, thus providing the complete information needed to update reward expectations and to guide adaptive decision making. Importantly, our single-trial EEG analysis based on PEs from an RL model showed that the feedback-related potentials do not merely reflect error awareness, but rather quantitative information crucial for learning reward contingencies.

• Philiastides, Marios G; Biele, Guido & Heekeren, Hauke R (2010). A mechanistic account of value computation in the human brain. Proceedings of the National Academy of Sciences of the United States of America.  ISSN 0027-8424.  107(20) . doi: 10.1073/pnas.1001732107 Show summary

  To make decisions based on the value of different options, we often have to combine different sources of probabilistic evidence. For example, when shopping for strawberries on a fruit stand, one uses their color and size to infer-with some uncertainty-which strawberries taste best. Despite much progress in understanding the neural underpinnings of value-based decision making in humans, it remains unclear how the brain represents different sources of probabilistic evidence and how they are used to compute value signals needed to drive the decision. Here, we use a visual probabilistic categorization task to show that regions in ventral temporal cortex encode probabilistic evidence for different decision alternatives, while ventromedial prefrontal cortex integrates information from these regions into a value signal using a difference-based comparator operation.

• Biele, Guido; Erev, Ido & Ert, Eyal (2009). Learning, risk attitude and hot stoves in restless bandit problems. Journal of Mathematical Psychology.  ISSN 0022-2496.  3, s 155- 167 . doi: 10.1016/j.jmp.2008.05.006 Show summary

  This research examines decisions from experience in restless bandit problems. Two experiments revealed four main effects. (1) Risk neutrality: the typical participant did not learn to become risk averse, a contradiction of the hot stove effect. (2) Sensitivity to the transition probabilities that govern the Markov process. (3) Positive recency: the probability of a risky choice being repeated was higher after a win than after a loss. (4) Inertia: the probability of a risky choice being repeated following a loss was higher than the probability of a risky choice after a safe choice. These results can be described with a simple contingent sampler model, which assumes that choices are made based on small samples of experiences contingent on the current state. (C) 2008 Elsevier Inc. All rights reserved.

• Biele, Guido; Rieskamp, Jörg & Gonzalez, Richard (2009). Computational Models for the Combination of Advice and Individual Learning. Cognitive Science.  ISSN 0364-0213.  33(2), s 206- 242 . doi: 10.1111/j.1551-6709.2009.01010.x Show summary

  Decision making often takes place in social environments where other actors influence individuals' decisions. The present article examines how advice affects individual learning. Five social learning models combining advice and individual learning-four based on reinforcement learning and one on Bayesian learning-and one individual learning model are tested against each other. In two experiments, some participants received good or bad advice prior to a repeated multioption choice task. Receivers of advice adhered to the advice, so that good advice improved performance. The social learning models described the observed learning processes better than the individual learning model. Of the models tested, the best social learning model assumes that outcomes from recommended options are more positively evaluated than outcomes from nonrecommended options. This model correctly predicted that receivers first adhere to advice, then explore other options, and finally return to the recommended option. The model also predicted accurately that good advice has a stronger impact on learning than bad advice. One-time advice can have a long-lasting influence on learning by changing the subjective evaluation of outcomes of recommended options.

• Krugel, Lea K.; Biele, Guido; Mohr, Peter N.C.; Li, Shu-Chen & Heekeren, Hauke R (2009). Genetic variation in dopaminergic neuromodulation influences the ability to rapidly and flexibly adapt decisions. Proceedings of the National Academy of Sciences of the United States of America.  ISSN 0027-8424.  106(42), s 17951- 17956 . doi: 10.1073/pnas.0905191106 Show summary

  The ability to rapidly and flexibly adapt decisions to available rewards is crucial for survival in dynamic environments. Reward-based decisions are guided by reward expectations that are updated based on prediction errors, and processing of these errors involves dopaminergic neuromodulation in the striatum. To test the hypothesis that the COMT gene Val(158)Met polymorphism leads to interindividual differences in reward-based learning, we used the neuromodulatory role of dopamine in signaling prediction errors. We show a behavioral advantage for the phylogenetically ancestral Val/Val genotype in an instrumental reversal learning task that requires rapid and flexible adaptation of decisions to changing reward contingencies in a dynamic environment. Implementing a reinforcement learning model with a dynamic learning rate to estimate prediction error and learning rate for each trial, we discovered that a higher and more flexible learning rate underlies the advantage of the Val/Val genotype. Model-based fMRI analysis revealed that greater and more differentiated striatal fMRI responses to prediction errors reflect this advantage on the neurobiological level. Learning rate-dependent changes in effective connectivity between the striatum and prefrontal cortex were greater in the Val/Val than Met/Met genotype, suggesting that the advantage results from a downstream effect of the prefrontal cortex that is presumably mediated by differences in dopamine metabolism. These results show a critical role of dopamine in processing the weight a particular prediction error has on the expectation updating for the next decision, thereby providing important insights into neurobiological mechanisms underlying the ability to rapidly and flexibly adapt decisions to changing reward contingencies.

• Biele, Guido; Rieskamp, Jörg & Czienskowski, Uwe (2008). Explaining cooperation in groups: Testing models of reciprocity and learning. Organizational Behavior and Human Decision Processes.  ISSN 0749-5978.  106, s 89- 105 . doi: 10.1016/j.obhdp.2008.01.001 Show summary

  What are the underlying cognitive processes of cooperation in groups? The question is addressed by examining how well a reciprocity model, two learning models, and social value orientation can predict cooperation in two iterated n-person social dilemmas with continuous contributions. In the public goods dilemma, participants repeatedly decided about contributions to one public good. In the social dilemma network, the public good was divided into multiple two-person public goods, thus allowing selective cooperation. In the experiment participants had to search actively for information about other players’ past behaviors on a computerized information board. The reciprocity model was more successful than the competing models in predicting participants’ information search and contributions. As predicted by the reciprocity model, participants cooperated substantially more in the social dilemma network. Cooperation in repeatedly interacting groups seems to be determined more by reciprocity than by reinforcement learning or stable social motivation.

• Pachur, Thorsten & Biele, Guido (2007). Forecasting from ignorance: the use and usefulness of recognition in lay predictions of sports events. Acta Psychologica.  ISSN 0001-6918.  125(1), s 99- 116 . doi: 10.1016/j.actpsy.2006.07.002 Show summary

  Whereas previous studies on how people make forecasts of sports events focused primarily on experts, we examined how laypeople do this task. In particular, we (a) tested the recognition heuristic [Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: the recognition heuristic. Psychological Review, 109, 75-90], which requires partial ignorance, against four alternative mechanisms in describing laypeople's forecasts for the European Soccer Championships 2004; (b) evaluated how well recognition predicted the outcomes of the matches compared to direct indicators of team strength (e.g., past performance, rankings); and (c) studied the less-is-more effect--the phenomenon that knowing less leads to more correct forecasts than knowing more--which can occur when the recognition heuristic is used. Two groups of participants (laypeople, experts) made forecasts for the first-round matches of the tournament. Of the five candidate mechanisms, the recognition heuristic predicted laypeople's forecasts best: when applicable, it accounted for 90% of the forecasts. The recognition heuristic correctly predicted the actual winner of the matches substantially better than chance but did not achieve the accuracy of direct indicators of team strength. The experts made more correct forecasts than the laypeople. Moreover, we found no benefit of ignorance among the group of laypeople, although the conditions for a less-is-more effect specified by Goldstein and Gigerenzer were fulfilled.

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• Øvergaard, Kristin Romvig; Ørbeck, Beate; Friis, Svein; Biele, Guido; Aase, Heidi & Zeiner, Pål (2018). Inattention in pre-School ADHD- are there gender differences?.
• Øvergaard, Kristin Romvig; Ørbeck, Beate; Friis, Svein; Pripp, Are Hugo; Biele, Guido; Aase, Heidi & Zeiner, Pål (2018). Dr Øvergaard et al. Reply. Journal of the American Academy of Child and Adolescent Psychiatry.  ISSN 0890-8567.  57(9), s 701- 702 . doi: 10.1016/j.jaac.2018.05.021
• Ørstavik, Ragnhild; Gustavson, Kristin; Rohrer-Baumgartner, Nina; Biele, Guido; Furu, Kari; Karlstad, Øystein; Reichborn-Kjennerud, Ted; Borge, Tiril Cecilie & Aase, Heidi (2016). ADHD i Norge: en statusrapport. rapport Nasjonalt folkehelseinstitutt. 2016:4. Full text in Research Archive.
• Leknes, Siri; Berna, Chantal; Lee, M; Snyder, G.; Biele, Guido & Tracey, Irene (2013). Response to the commentary “Multiple potential mechanisms for context effects on pain”. Pain.  ISSN 0304-3959.  154(8), s 1485- 1486 . doi: 10.1016/j.pain.2013.04.002
• Hammerer, D.; Biele, Guido; Philiastides, M.; Schroeder, S; Müller, V.; Heekeren, H.; Lindenberger, U. & Li, S.C. (2011). Lifespan Differences in Electrophysiological Correlates of Early Monitoring and Late Evaluative Processes During Reinforcement Learning. Journal of Psychophysiology.  ISSN 0269-8803.  25, s 14- 15

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