Our lab investigates the neural and cognitive mechanisms underlying perceptual, reward-based and voluntary decision-making, preference and belief formation, health choices, change-of-mind decisions, decision errors, and related cognitive processes, using methods from psychology, finance, and cognitive neuroscience.
Workshop day at the Abbotsford Convent
We used this great location for an in-depth discussion of all new projects, as well as a Bitbucket workshop and a coding hackathon, organised by Dr Daniel Feuerriegel. Thanks to everyone for participating!Announcement
Vivian's last day
Vivian had her last day today, and we celebrated her time in the lab Melbourne-style with coffee and cake. Thank you, Vivian, for your hard work, and all the best for your future career!Announcement
The new "Change-of-Mind" team has started!
From left to right: Will Turner (PhD student), Daniel Feuerriegel (Postdoc), Vivian Jiayi Luo (Masters student), Milan Andrejevic (PhD student)Announcement
Katharina Voigt's new paper shows how previous choices change preferences
The forthcoming paper by Katharina Voigt, Carsten Murawski and Stefan Bode in the Journal of Experimental Psychology: Learning, Memory and Cognition demonstrates the exogenous formation of preferences. After making decisions between equally valued snack foods, people were less willing to pay for initially rejected snacks, and more willing to pay for initially chosen snacks. Check out the paper for details!Announcement
The Decision Neuroscience Lab is located at The University of Melbourne:
- Faculty of Medicine, Dentistry and Health Sciences
- Melbourne School of Psychological Sciences
Our research staff are located in the Melbourne School of Psychological Sciences.
Head of Lab
Melbourne School of Psychological Sciences
+61 3 9035 3849
PhD Student (primary supervisor: Dr Simon Laham)
Melbourne School of Psychological Sciences /
Department of Finance
+61 3 8344 4185
Melbourne School of Psychological Sciences /
Department of Finance
+61 3 8344 4185
Melbourne School of Psychological Sciences
+61 3 8344 4446
Melbourne School of Psychological Sciences
+61 3 8344 4446
Melbourne School of Psychological Sciences
+61 3 8344 4185
PhD Student (primary supervisor: Dr Trevor Chong)
Melbourne School of Psychological Sciences
+61 3 8344 4185
Melbourne School of Psychological Sciences
Melbourne School of Psychological Sciences
Melbourne School of Psychological Sciences
+61 3 8344 0221
Melbourne School of Psychological Sciences
Melbourne School of Psychological Sciences
Melbourne School of Psychological Sciences
Melbourne School of Psychological Sciences
+61 3 8344 4185
Associate Investigators / Visiting Academics
- Carsten Murawski PhD (associate investigator / co-head of lab)
- Carmen Morawetz PhD (visiting scientist)
- Prof Jutta Stahl PhD (visiting scientist)
Postdocs / PhD Students
- Elaine Corbett PhD (post-doc)
- Daniel Bennett PhD (PhD student)
Honours / Masters Students
- Ariel Goh
- Tamir Goldberg
- Karen Sasmita
- Maia Tarrell
- Joanna Thio
- Aidan Jago
- Maja Brydevall
- Sebastian Speer
- Alyssa Ng
- William Turner
- Patrick Summerell
- Phillip Johnston
- Vivian Jiayi Luo
- Hayley Warren
- Christina Van Heer
- Maggie Webb
- Alex Kline
- Rebekah Street
- Maja Brydevall
- Amanda Ng
- Tracey Wang
- Megan Edelman
- Jessica Paul
- Marina Saade
- Della Averill
- Kashmira Daruwalla
- Sophia Bock
- Pola Held
- Nicole Stefanac
- Hayley McFadyen
- Kathleen De Boer
- Jamila Bahrami
We are looking for participants to take part in our experiments.
Become a test participant
Most of our experiments are conducted on the University of Melbourne campus in Parkville and generally last between 45 minutes and 1 hour (some might last longer). Our functional magnetic resonance imaging (fMRI) studies take place at the Monash Biomedical Imaging (MBI) centre in Clayton. Participants are compensated for their time.
If you would like to take part in one of our experiments, please click on the 'Register Here' button below, and our team will be in touch with you shortly.
If you want to find out more about our experiments, please contact us via email (firstname.lastname@example.org).
Publications by current members of the Decision Neuroscience Lab
Barke A, Bode S, Dechent P, Schmidt-Samoa C, Van Heer C, Stahl J (2017). To err is (perfectly) human: behavioural and neural correlates of error processing and perfectionism. Social Cognitive and Affective Neuroscience, in press.
Barke A, Bode S, Dechent P, Schmidt-Samoa C, Van Heer C, Stahl J
Chan YM, Pianta MJ, Bode S, McKendrick AM (2017). Neural correlates of audiovisual synchrony judgements in older adults. Neurobiology of Aging, 55, 38-48.
Chan YM, Pianta MJ, Bode S, McKendrick AM
Fung BJ, Bode S, Murawski C (2017). Consumption of caloric rewards decreases temporal persistence. Proceedings of the Royal Society B: Biological Sciences, 284:20162759.
Fung BJ, Bode S, Murawski C
Fung BJ, Crone D, Bode S, Murawski C (2017). Cardiac signals are independently associated with temporal discounting and time perception. Frontiers in Behavioral Neuroscience, 11:1.
Fung BJ, Crone D, Bode S, Murawski C
Fung BJ, Murawski C, Bode S (2017). Caloric primary rewards systematically alter time perception. Journal of Experimental Psychology: Human Perception & Performance, in press.
Fung BJ, Murawski C, Bode S
Morawetz C, Bode S, Baudewig J, Heekeren HR (2017). Effective amygdala-prefrontal connectivity predicts individual differences in successful emotion regulation. Social Cognitive and Affective Neuroscience, 12(4), 569-585.
Morawetz C, Bode S, Baudewig J, Heekeren HR
Morawetz C, Bode S, Derntl B, Heekeren HR (2017). The effect of strategies, goals and stimulus material on the neural mechanisms of emotion regulation: A meta-analysis of fMRI studies. Neuroscience & Biobehavioral Reviews, 72, 11-128.
Morawetz C, Bode S, Derntl B, Heekeren HR (2017)
Voigt K, Murawski C, Bode S (2017). Endogenous formation of preferences: choices systematically change willingness-to-pay for goods. Journal of Experimental Psychology: Learning, Memory and Cognition, in press.
Voigt K, Murawski C, Bode S
Bennett D, Bode S, Brydevall M, Warren HA, & Murawski C (2016). Intrinsic Valuation of Information in Decision Making under Uncertainty. PLoS Comput Biol, 12(7), e1005020.
In a dynamic world, an accurate model of the environment is vital for survival, and agents ought regularly to seek out new information with which to update their world models. This aspect of behaviour is not captured well by classical theories of decision making, and the cognitive mechanisms of information seeking are poorly understood. In particular, it is not known whether information is valued only for its instrumental use, or whether humans also assign it a non-instrumental intrinsic value. To address this question, the present study assessed preference for non-instrumental information among 80 healthy participants in two experiments. Participants performed a novel information preference task in which they could choose to pay a monetary cost to receive advance information about the outcome of a monetary lottery. Importantly, acquiring information did not alter lottery outcome probabilities. We found that participants were willing to incur considerable monetary costs to acquire payoff-irrelevant information about the lottery outcome. This behaviour was well explained by a computational cognitive model in which information preference resulted from aversion to temporally prolonged uncertainty. These results strongly suggest that humans assign an intrinsic value to information in a manner inconsistent with normative accounts of decision making under uncertainty. This intrinsic value may be associated with adaptive behaviour in real-world environments by producing a bias towards exploratory and information-seeking behaviour. Read this publication.
Bennett D, Dluzniak A, Cropper SJ, Partos T, Sundram S, Carter O (2016). Selective impairment of global motion integration, but not global form detection, in schizophrenia and bipolar affective disorder. Schizophrenia Research: Cognition, 3, 11-14.
Recent evidence suggests that schizophrenia is associated with impaired processing of global visual motion, but intact processing of global visual form. This project assessed whether preserved visual form detection in schizophrenia extended beyond low-level pattern discrimination to a naturalistic form-detection task. We assessed both naturalistic form detection and global motion detection in individuals with schizophrenia spectrum disorder, bipolar affective disorder, and healthy controls. Individuals with schizophrenia spectrum disorder and bipolar affective disorder were impaired relative to healthy controls on the global motion task, but not the naturalistic form-detection task. Results indicate that preservation of visual form detection in these disorders extends beyond configural forms to naturalistic object processing. Read this publication.
Morawetz C, Bode S, Baudewig J, Jacobs AM, Heekeren HR (2016). Neural representation of emotion regulation goals. Human Brain Mapping, 37(2), 600-620.
The use of top–down cognitive control mechanisms to regulate emotional responses as circumstances change is critical for mental and physical health. Several theoretical models of emotion regulation have been postulated; it remains unclear, however, in which brain regions emotion regulation goals (e.g., the downregulation of fear) are represented. Here, we examined the neural mechanisms of regulating emotion using fMRI and identified brain regions representing reappraisal goals. Using a multimethodological analysis approach, combining standard activation-based and pattern-information analyses, we identified a distributed network of lateral frontal, temporal, and parietal regions implicated in reappraisal and within it, a core system that represents reappraisal goals in an abstract, stimulus-independent fashion. Within this core system, the neural pattern-separability in a subset of regions including the left inferior frontal gyrus, middle temporal gyrus, and inferior parietal lobe was related to the success in emotion regulation. Those brain regions might link the prefrontal control regions with the subcortical affective regions. Given the strong association of this subsystem with inner speech functions and semantic memory, we conclude that those cognitive mechanisms may be used for orchestrating emotion regulation. Read this publication.
Morawetz C, Bode S, Baudewig J, Kirilina E, Heekeren HR (2016). Changes in effective connectivity between dorsal and ventral prefrontal regions moderate emotion regulation. Cerebral Cortex, 26(5), 1923-1937.
Reappraisal, the cognitive reevaluation of a potentially emotionally arousing event, has been proposed to be based upon top-down appraisal systems within the prefrontal cortex (PFC). It still remains unclear, however, how different prefrontal regions interact to control and regulate emotional responses. We used fMRI and dynamic causal modeling (DCM) to characterize the functional interrelationships among dorsal and ventral PFC regions involved in reappraisal. Specifically, we examined the effective connectivity between the inferior frontal gyrus (IFG), dorsolateral PFC (DLPFC), and other reappraisal-related regions (supplementary motor area, supramarginal gyrus) during the up- and downregulation of emotions in response to highly arousing extreme sports film clips. Read this publication.
Bennett D (2015). The neural mechanisms of Bayesian belief updating. The Journal of Neuroscience, 35(50), 16300-16302.
Bennett D, Murawski C, Bode S (2015). Single-trial event-related potential correlates of belief updating. eNeuro: 2(5).
Bennett D, Murawski C, Bode S
Belief updating—the process by which an agent alters an internal model of its environment—is a core function of the central nervous system. Recent theory has proposed broad principles by which belief updating might operate, but more precise details of its implementation in the human brain remain unclear. In order to address this question, we studied how two components of the human event-related potential encoded different aspects of belief updating. Participants completed a novel perceptual learning task while electroencephalography was recorded. Participants learned the mapping between the contrast of a dynamic visual stimulus and a monetary reward, and updated their beliefs about a target contrast on each trial. A Bayesian computational model was formulated to estimate belief states at each trial and used to quantify two variables: belief update size and belief uncertainty. Robust single-trial regression was used to assess how these model-derived variables were related to the amplitudes of the P3 and the stimulus-preceding negativity (SPN), respectively. Results showed a positive relationship between belief update size and P3 amplitude at one fronto-central electrode, and a negative relationship between SPN amplitude and belief uncertainty at a left central and a right parietal electrode. These results provide evidence that belief update size and belief uncertainty have distinct neural signatures that can be tracked in single trials in specific ERP components. This, in turn, provides evidence that the cognitive mechanisms underlying belief updating in humans can be described well within a Bayesian framework. Read this publication.
Bossaerts P, Murawski C (2015). From behavioural economics to neuroeconomics to decision neuroscience: the ascent of biology in research on human decision making. Current Opinion in Behavioral Sciences, 4:37-42.
Here, we briefly review the evolution of research on human decision-making over the past few decades. We discern a trend whereby biology moves from subserving economics (neuroeconomics), to providing the data that advance our knowledge of the nature of human decision-making (decision neuroscience). Examples illustrate that the integration of behavioural and biological models is fruitful especially for understanding heterogeneity of choice in humans. Read this publication.
Simmank J, Bode S, Murawski C, Horstmann A (2015). Incidental rewarding cues influence economic decision-making in obesity. Frontiers in Behavioral Neuroscience, 9:278.
Recent research suggests that obesity is linked to prominent alterations in learning and decision-making. This general difference may also underlie the preference for immediately consumable, highly palatable but unhealthy and high-calorie foods. Such poor food-related inter-temporal decision-making can explain weight gain; however, it is not yet clear whether this deficit can be generalized to other domains of inter-temporal decision-making, for example financial decisions. Further, little is known about the stability of decision-making behavior in obesity, especially in the presence of rewarding cues. To answer these questions, obese and lean participants (n = 52) completed two sessions of a novel priming paradigm including a computerized monetary delay discounting task. Read this publication.
Bode S, Bennett DB, Stahl J, Murawski, C (2014). Distributed patterns of event-related potentials predict subsequent ratings of abstract stimulus attributes. PLoS ONE 9(10): e109070.
Exposure to pleasant and rewarding visual stimuli can bias people's choices towards either immediate or delayed gratification. We hypothesised that this phenomenon might be based on carry-over effects from a fast, unconscious assessment of the abstract ‘time reference’ of a stimuli, i.e. how the stimulus relates to one's personal understanding and connotation of time. Here we investigated whether participants' post-experiment ratings of task-irrelevant, positive background visual stimuli for the dimensions ‘arousal’ (used as a control condition) and ‘time reference’ were related to differences in single-channel event-related potentials (ERPs) and whether they could be predicted from spatio-temporal patterns of ERPs. Participants performed a demanding foreground choice-reaction task while on each trial one task-irrelevant image (depicting objects, people and scenes) was presented in the background. Conventional ERP analyses as well as multivariate support vector regression (SVR) analyses were conducted to predict participants' subsequent ratings. We found that only SVR allowed both ‘arousal’ and ‘time reference’ ratings to be predicted during the first 200 ms post-stimulus. This demonstrates an early, automatic semantic stimulus analysis, which might be related to the high relevance of ‘time reference’ to everyday decision-making and preference formation. Read this publication.
Bode S, Murawski C, Soon CS, Bode P, Stahl J, Smith P (2014).Demystifying "free will": The role of contextual information in explaining predictive brain activity for internal decisions. Neuroscience & Biobehavioral Reviews, 47:636-645.
Novel multivariate pattern classification analyses have enabled the prediction of decision outcomes from brain activity prior to decision-makers’ reported awareness. These findings are often discussed in relation to the philosophical concept of “free will”. We argue that these studies demonstrate the role of unconscious processes in simple free choices, but they do not inform the philosophical debate. Moreover, these findings are difficult to relate to cognitive decision-making models, due to misleading assumptions about random choices. We review evidence suggesting that sequential-sampling models, which assume accumulation of evidence towards a decision threshold, can also be applied to free decisions. If external evidence is eliminated by the task instructions, decision-makers might use alternative, subtle contextual information as evidence, such as their choice history, that is not consciously monitored and usually concealed by the experimental design. We conclude that the investigation of neural activity patterns associated with free decisions should aim to investigate how decisions are jointly a function of internal and external contexts, rather than to resolve the philosophical “free will” debate. Read this publication.
Bode S, Stahl J (2014). Predicting errors from patterns of event-related potentials preceding an overt response. Biological Psychology, 103, 357-369.
Everyday actions often require fast and efficient error detection and error correction. For this, the brain has to accumulate evidence for errors as soon as it becomes available. This study used multivariate pattern classification techniques for event-related potentials to track the accumulation of error-related brain activity before an overt response was made. Upcoming errors in a digit-flanker task could be predicted after the initiation of an erroneous motor response, ∼90 ms before response execution. Channels over motor and parieto-occipital cortices were most important for error prediction, suggesting ongoing perceptual analyses and comparisons of initiated and appropriate motor programmes. Lower response force on error trials as compared to correct trials was observed, which indicates that this early error information was used for attempts to correct for errors before the overt response was made. In summary, our results suggest an early, automatic accumulation of error-related information, providing input for fast correction processes. Read this publication.
Woolgar A, Golland P, Bode S (2014). Coping with confounds in multi-voxel analysis: what should we do about reaction time differences? A comment on Todd, Nystrom & Cohen 2013. Neuroimage, 98, 73-80.
Multivoxel pattern analysis (MVPA) is a sensitive and increasingly popular method for examining differences between neural activation patterns that cannot be detected using classical mass-univariate analysis. Recently, Todd et al. ("Confounds in multivariate pattern analysis: Theory and rule representation case study", 2013, NeuroImage 77: 157-165) highlighted a potential problem for these methods: high sensitivity to confounds at the level of individual participants due to the use of directionless summary statistics. Unlike traditional mass-univariate analyses where confounding activation differences in opposite directions tend to approximately average out at group level, group level MVPA results may be driven by any activation differences that can be discriminated in individual participants. In Todd et al.'s empirical data, factoring out differences in reaction time (RT) reduced a classifier's ability to distinguish patterns of activation pertaining to two task rules. This raises two significant questions for the field: to what extent have previous multivoxel discriminations in the literature been driven by RT differences, and by what methods should future studies take RT and other confounds into account? We build on the work of Todd et al. and compare two different approaches to remove the effect of RT in MVPA. We show that in our empirical data, in contrast to that of Todd et al., the effect of RT on rule decoding is negligible, and results were not affected by the specific details of RT modelling. We discuss the meaning of and sensitivity for confounds in traditional and multivoxel approaches to fMRI analysis. We observe that the increased sensitivity of MVPA comes at a price of reduced specificity, meaning that these methods in particular call for careful consideration of what differs between our conditions of interest. We conclude that the additional complexity of the experimental design, analysis and interpretation needed for MVPA is still not a reason to favour a less sensitive approach. Read this publication.
Bode S, Bogler C, Haynes JD (2013). Similar neural mechanisms for guesses and free decisions. Neuroimage, 65(2), 456-465.
When facing perceptual choices under challenging conditions we might believe to be purely guessing. But which brain regions determine the outcome of our guesses? One possibility is that higher-level, domain-general brain regions might help break the symmetry between equal-appearing choices. Here we directly investigated whether perceptual guesses share brain networks with other types of free decisions. We trained an fMRI-based pattern classifier to distinguish between two perceptual guesses and tested whether it was able to predict the outcome of similar non-perceptual choices, as in conventional free choice tasks. Activation patterns in the medial posterior parietal cortex cross-predicted free decisions from perceptual guesses and vice versa. This inter-changeability strongly speaks for a similar neural code for both types of decisions. The posterior parietal cortex might be part of a domain-general system that helps resolve decision conflicts when no choice option is more or less likely or valuable, thus preventing behavioural stalemate. Read this publication.
Bogler C, Bode S, Haynes JD (2013). Orientation pop-out processing in human visual cortex. Neuroimage. 81, 73-80.
Visual stimuli can "pop out" if they are different to their background. There has been considerable debate as to the role of primary visual cortex (V1) versus higher visual areas (esp. V4) in pop-out processing. Here we parametrically modulated the relative orientation of stimuli and their backgrounds to investigate the neural correlates of pop-out in visual cortex while subjects were performing a demanding fixation task in a scanner. Whole brain and region of interest analyses confirmed a representation of orientation contrast in extrastriate visual cortex (V4), but not in striate visual cortex (V1). Thus, although previous studies have shown that human V1 can be involved in orientation pop-out, our findings demonstrate that there are cases where V1 is "blind" and pop-out detection is restricted to higher visual areas. Pop-out processing is presumably a distributed process across multiple visual regions. Read this publication.
Burnett J, Davis K, Murawski C, Wilkins R, Wilkinson N (2013). Measuring retirement savings adequacy in Australia. JASSA, 4:28-35.
We present two new metrics to assess the adequacy of retirement savings and estimate these metrics for a representative sample of the Australian population aged 40 to 64. Our estimates support the widely held belief that most individuals are not 'on track' to achieve a comfortable standard of living in retirement, although couples appear better prepared than singles. We also estimate the relative expected contributions of the various 'pillars' of retirement income. The metrics presented here may provide a better way to communicate adequacy to individuals, and encourage increased saving. Read this publication.
Gibson R, Murawski C (2013). Margining in derivatives markets and the stability of the banking sector. Journal of Banking & Finance, 37(4),1119-1132.
We investigate the effects of margining, a widely-used mechanism for attaching collateral to derivatives contracts, on derivatives trading volume, default risk, and on the welfare in the banking sector. First, we develop a stylized banking sector equilibrium model to develop some basic intuition of the effects of margining. We find that a margin requirement can be privately and socially sub-optimal. Subsequently, we extend this model into a dynamic simulation model that captures some of the essential characteristics of over-the-counter derivatives markets. Contrarily to the common belief that margining always reduces default risk, we find that there exist situations in which margining increases default risk, reduces aggregate derivatives’ trading volume, and has an ambiguous effect on welfare in the banking sector. The negative effects of margining are exacerbated during periods of market stress when margin rates are high and collateral is scarce. We also find that central counterparties only lift some of the inefficiencies caused by margining. Read this publication.
Krüger D, Klapötke S, Bode S, Mattler U (2013). Neural correlates of control operations in inverse priming with relevant and irrelevant masks.Neuroimage, 64(1),197-208.
The inverse priming paradigm can be considered one example which demonstrates the operation of control processes in the absence of conscious experience of the inducing stimuli. Inverse priming is generated by a prime that is followed by a mask and a subsequent imperative target stimulus. With "relevant" masks that are composed of the superposition of both prime alternatives, the inverse priming effect is typically larger than with "irrelevant" masks that are free of task-relevant features. We used functional magnetic resonance imaging (fMRI) to examine the neural substrates that are involved in the generation of inverse priming effects with relevant and irrelevant masks. We found a network of brain areas that is accessible to unconscious primes, including supplementary motor area (SMA), anterior insula, middle cingulate cortex, and supramarginal gyrus. Activation of these brain areas were involved in inverse priming when relevant masks were used. With irrelevant masks, however, only SMA activation was involved in inverse priming effects. Activation in SMA correlated with inverse priming effects of individual participants on reaction time, indicating that this brain area reflects the size of inverse priming effects on the behavioral level. Findings are most consistent with the view that a basic inhibitory mechanism contributes to inverse priming with either type of mask and additional processes contribute to the effect with relevant masks. This study provides new evidence showing that cognitive control operations in the human cortex take account of task relevant stimulus information even if this information is not consciously perceived. Read this publication.
Soon, CS, He AH, Bode S, Haynes JD (2013). Predicting free choices for abstract intentions. Proceedings of the National Academy of Sciences of the USA, 110, 6217-6222.
Unconscious neural activity has been repeatedly shown to precede and potentially even influence subsequent free decisions. However, to date, such findings have been mostly restricted to simple motor choices, and despite considerable debate, there is no evidence that the outcome of more complex free decisions can be predicted from prior brain signals. Here, we show that the outcome of a free decision to either add or subtract numbers can already be decoded from neural activity in medial prefrontal and parietal cortex 4 s before the participant reports they are consciously making their choice. These choice-predictive signals co-occurred with the so-called default mode brain activity pattern that was still dominant at the time when the choice-predictive signals occurred. Our results suggest that unconscious preparation of free choices is not restricted to motor preparation. Instead, decisions at multiple scales of abstraction evolve from the dynamics of preceding brain activity. Read this publication.
Bode S, Bogler C, Soon CS, Haynes JD (2012). The neural encoding of guesses in the human brain. Neuroimage, 59(2), 1924-1931.
Human perception depends heavily on the quality of sensory information. When objects are hard to see we often believe ourselves to be purely guessing. Here we investigated whether such guesses use brain networks involved in perceptual decision making or independent networks. We used a combination of fMRI and pattern classification to test how visibility affects the signals, which determine choices. We found that decisions regarding clearly visible objects are predicted by signals in sensory brain regions, whereas different regions in parietal cortex became predictive when subjects were shown invisible objects and believed themselves to be purely guessing. This parietal network was highly overlapping with regions, which have previously been shown to encode free decisions. Thus, the brain might use a dedicated network for determining choices when insufficient sensory information is available. Read this publication.
Bode S, Sewell D, Lilburn S, Forte J, Smith PL, Stahl J (2012). Predicting perceptual decision biases from early brain activity. The Journal of Neuroscience, 32(36): 12488-12498.
Perceptual decision making is believed to be driven by the accumulation of sensory evidence following stimulus encoding. More controversially, some studies report that neural activity preceding the stimulus also affects the decision process. We used a multivariate pattern classification approach for the analysis of the human electroencephalogram (EEG) to decode choice outcomes in a perceptual decision task from spatially and temporally distributed patterns of brain signals. When stimuli provided discriminative information, choice outcomes were predicted by neural activity following stimulus encoding; when stimuli provided no discriminative information, choice outcomes were predicted by neural activity preceding the stimulus. Moreover, in the absence of discriminative information, the recent choice history primed the choices on subsequent trials. A diffusion model fitted to the choice probabilities and response time distributions showed that the starting point of the evidence accumulation process was shifted toward the previous choice, consistent with the hypothesis that choice priming biases the accumulation process toward a decision boundary. This bias is reflected in prestimulus brain activity, which, in turn, becomes predictive of future decisions. Our results provide a model of how non-stimulus-driven decision making in humans could be accomplished on a neural level. Read this publication.
Heinzle J, Anders S, Bode S, Bogler C, Chen Y, Cichy RM, Hackmack K, Kahnt T, Kalberlah C, Reverberi C, Soon SC, Tusche A, Weygandt M, Haynes JD (2012). Multivariate decoding of fMRI data – Towards a content-based cognitive neuroscience. e-Neuroforum, 3(1), 1-16.
Within the last two decades, functional magnetic resonance imaging has become one of the most widely used tools in human cognitive neuroscience. FMRI measures the neural activity in a 3-dimensional grid of roughly 1–3 mm resolution. Most cognitive neuroscience studies measure blood oxygen level dependent (BOLD) signals, which indirectly reflect processes in the underlying neural tissue. One of the major features—but also challenges—of neuroimaging is that it yields very complex, high-dimensional data sets including up to several hundred thousand voxels. Read this publication.
Murawski C, Harris PG, Bode S, Domínguez D. JF, Egan GF (2012). Led into temptation? Rewarding brand logos bias the neural encoding of economic decisions. PLoS ONE, 7(3): e34155.
Human decision-making is driven by subjective values assigned to alternative choice options. These valuations are based on reward cues. It is unknown, however, whether complex reward cues, such as brand logos, may bias the neural encoding of subjective value in unrelated decisions. In this functional magnetic resonance imaging (fMRI) study, we subliminally presented brand logos preceding intertemporal choices. We demonstrated that priming biased participants' preferences towards more immediate rewards in the subsequent temporal discounting task. This was associated with modulations of the neural encoding of subjective values of choice options in a network of brain regions, including but not restricted to medial prefrontal cortex. Our findings demonstrate the general susceptibility of the human decision making system to apparently incidental contextual information. We conclude that the brain incorporates seemingly unrelated value information that modifies decision making outside the decision-maker's awareness. Read this publication.
Bode S, He AH, Soon CS, Trampel R, Turner R, Haynes JD (2011). Tracking the unconscious generation of free decisions using ultra-high field fMRI. PLoS ONE, 6(6):e21612.
Recently, we demonstrated using functional magnetic resonance imaging (fMRI) that the outcome of free decisions can be decoded from brain activity several seconds before reaching conscious awareness. Activity patterns in anterior frontopolar cortex (BA 10) were temporally the first to carry intention-related information and thus a candidate region for the unconscious generation of free decisions. In the present study, the original paradigm was replicated and multivariate pattern classification was applied to functional images of frontopolar cortex, acquired using ultra-high field fMRI at 7 Tesla. Here, we show that predictive activity patterns recorded before a decision was made became increasingly stable with increasing temporal proximity to the time point of the conscious decision. Furthermore, detailed questionnaires exploring subjects' thoughts before and during the decision confirmed that decisions were made spontaneously and subjects were unaware of the evolution of their decision outcomes. These results give further evidence that FPC stands at the top of the prefrontal executive hierarchy in the unconscious generation of free decisions. Read this publication.
Bogler C, Bode S, Haynes JD (2011). Decoding successive computational stages of saliency processing. Current Biology, 21(19), 1667-1671.
An important requirement for vision is to identify interesting and relevant regions of the environment for further processing. Some models assume that salient locations from a visual scene are encoded in a dedicated spatial saliency map [1, 2]. Then, a winner-take-all (WTA) mechanism [1, 2] is often believed to threshold the graded saliency representation and identify the most salient position in the visual field. Here we aimed to assess whether neural representations of graded saliency and the subsequent WTA mechanism can be dissociated. We presented images of natural scenes while subjects were in a scanner performing a demanding fixation task, and thus their attention was directed away. Signals in early visual cortex and posterior intraparietal sulcus (IPS) correlated with graded saliency as defined by a computational saliency model. Multivariate pattern classification [3, 4] revealed that the most salient position in the visual field was encoded in anterior IPS and frontal eye fields (FEF), thus reflecting a potential WTA stage. Our results thus confirm that graded saliency and WTA-thresholded saliency are encoded in distinct neural structures. This could provide the neural representation required for rapid and automatic orientation toward salient events in natural environments. Read this publication.
Müller AD, Bode S, Myer L, Stahl J, von Steinbüchel N (2011). Predictors of adherence to antiretroviral treatment and therapeutic success among children in South Africa. AIDS Care, 23(2), 129-138.
The recent years have shown an up-scaling of treatment programs for HIV-infected children in resource-limited settings, with an increased focus on adherence. Little is known, however, about the influence of socioeconomic as well as caregivers' health beliefs on both adherence and virologic outcome of pediatric antiretroviral treatment in these settings. We conducted a cross-sectional study with 57 caregiver-child dyads at a public hospital in Cape Town, South Africa. Adherence was electronically monitored over three months, viral loads were available pre- and post-study. Caregivers answered questionnaires on their socioeconomic situation, attitudes toward and knowledge about treatment, and quality of life. Young children with a mean age of 51 months (SD 25.6) were investigated, and all were cared for by female caregivers. Mean adherence was 81%, and 67% of children achieved virologic suppression (VS). Household income, educational status, and child characteristics were not significantly correlated with adherence. Disclosure of both the child's and the caregiver's HIV status was linked to achieving VS and was a significant predictor for VS. A model including child's health status, caregiver's language skills, caregiver's disclosure, and perceived stigmatization could explain 95% of the variance in VS. Adherence and VS were not associated with socioeconomic factors in this population. Social factors such as stigmatization, fear of disclosure, and caregivers' attitudes toward the health-care system influenced VS but not adherence. Read this publication.
- Bode S (2010). From stimuli to motor responses: Decoding rules and decision mechanisms in the human brain. Leipzig: Max Planck Institute for Human Cognitive and Brain Sciences, 123 (MPI Series in Human Cognitive and Brain Sciences).
Tusche A, Bode S, Haynes JD (2010). Neural responses to unattended products predict later consumer choices. The Journal of Neuroscience, 30(23), 8024-8031.
Imagine you are standing at a street with heavy traffic watching someone on the other side of the road. Do you think your brain is implicitly registering your willingness to buy any of the cars passing by outside your focus of attention? To address this question, we measured brain responses to consumer products (cars) in two experimental groups using functional magnetic resonance imaging. Participants in the first group (high attention) were instructed to closely attend to the products and to rate their attractiveness. Participants in the second group (low attention) were distracted from products and their attention was directed elsewhere. After scanning, participants were asked to state their willingness to buy each product. During the acquisition of neural data, participants were not aware that consumer choices regarding these cars would subsequently be required. Multivariate decoding was then applied to assess the choice-related predictive information encoded in the brain during product exposure in both conditions. Distributed activation patterns in the insula and the medial prefrontal cortex were found to reliably encode subsequent choices in both the high and the low attention group. Importantly, consumer choices could be predicted equally well in the low attention as in the high attention group. This suggests that neural evaluation of products and associated choice-related processing does not necessarily depend on attentional processing of available items. Overall, the present findings emphasize the potential of implicit, automatic processes in guiding even important and complex decisions. Read this publication.
Bode S, Haynes JD (2009). Decoding sequential stages of task preparation in the human brain. Neuroimage, 45(2), 606-613.
The flow of information from sensory stimuli to motor responses in the human brain can be flexibly re-routed depending on task demands. However, it has remained unclear which sequence of processes is involved in preparing the brain for an upcoming task. Here, we used a combination of fMRI and multivariate pattern classification to decompose the information flow in a task-switching experiment. Specifically, we present a time-resolved decoding approach that allowed us to track the temporal buildup of task-related information. This approach also allowed us to distinguish encoding of the task from encoding of target stimuli and motor responses, thus separating between different components of information processing. We were able to decode from parietal and lateral prefrontal cortex which specific task-set a subject was currently holding. Importantly, this revealed that the intraparietal sulcus encoded task-set information before prefrontal cortex, and it was the only region to encode the specific task-set before the relevant target stimulus was presented. This suggests that task-related information in parietal cortex does not rely on input from prefrontal cortex as previously suggested. In contrast, our findings suggest that parietal cortex might play a role in establishing task-sets in prefrontal cortex. Read this publication.
Müller AD, Bode S, Myer L, Roux P, von Steinbüchel N (2008). Electronic measurement of adherence to paediatric antiretroviral therapy in South Africa. The Paediatric Infectious Disease Journal, 27(3), 257-262.
Little is known about adherence to pediatric antiretroviral regimens in countries of the developing world. Both assessment methods and predictors of adherence need to be examined to deliver appropriate health care to the growing patient population in resource-limited settings. METHODS: We conducted a prospective study of adherence in a pediatric HIV outpatient clinic in Cape Town, South Africa. Adherence was assessed by the Medication Event Monitoring System (MEMS) and caregiver self-report by Visual Analogue Scale (VAS). Virologic response was recorded at study baseline and closest follow-up visit, child and caregiver data were collected by questionnaires. RESULTS: For 73 children followed, median adherence by MEMS was 87.5%; median caregiver reported adherence was 100%. MEMS and caregiver report differed in reporting excellent (>95%) adherence, with MEMS classifying 36% of subjects in this category, whereas caregiver report classified 91%. Overall, 65% of children achieved virologic suppression after the study period. MEMS adherence was significantly associated with virologic suppression. The highest specificity was obtained when adjusting the data for doses taken at the prescribed time (91.3%). No predictors for the differences between MEMS and caregiver reported adherence could be identified. CONCLUSIONS: Adherence to pediatric antiretroviral regimens in South Africa is not lower than in the developed world, yet not high enough to guarantee long-term treatment success. Caregiver report seems unreliable in this setting. MEMS is a feasible and accurate measure of adherence for children on liquid drug formulations. Read this publication.
Bode S, Koeneke S, Jäncke L (2007). Different strategies do not moderate primary motor cortex involvement in mental rotation: a TMS study. Behavioral and Brain Functions, 3:38.
BACKGROUND: Regions of the dorsal visual stream are known to play an essential role during the process of mental rotation. The functional role of the primary motor cortex (M1) in mental rotation is however less clear. It has been suggested that the strategy used to mentally rotate objects determines M1 involvement. Based on the strategy hypothesis that distinguishes between an internal and an external strategy, our study was designed to specifically test the relation between strategy and M1 activity. METHODS: Twenty-two subjects were asked to participate in a standard mental rotation task. We used specific picture stimuli that were supposed to trigger either the internal (e.g. pictures of hands or tools) or the external strategy (e.g. pictures of houses or abstract figures). The strategy hypothesis predicts an involvement of M1 only in case of stimuli triggering the internal strategy (imagine grasping and rotating the object by oneself). Single-pulse Transcranial Magnetic Stimulation (TMS) was employed to quantify M1 activity during task performance by measuring Motor Evoked Potentials (MEPs) at the right hand muscle. RESULTS: Contrary to the strategy hypothesis, we found no interaction between stimulus category and corticospinal excitability. Instead, corticospinal excitability was generally increased compared with a resting baseline although subjects indicated more frequent use of the external strategy for all object categories. CONCLUSION: This finding suggests that M1 involvement is not exclusively linked with the use of the internal strategy but rather directly with the process of mental rotation. Alternatively, our results might support the hypothesis that M1 is active due to a 'spill-over' effect from adjacent brain regions. Read this publication.
Conferences, Symposia & Research talks
Crone DL, Bode S, Murawski C, Laham SM (2016). Probing moral perception with a novel moral image set. Society for Personality and Social Psychology, San Diego, CA, USA.
Bennett D, Brydevall M, Murawski C, Bode S (2015). The feedback-related negativity reflects intrinsic preference for information in decision making under uncertainty. Monash Brain Function Workshop, Melbourne, Australia.
Chan YM, Pianta MJ, Bode S, McKendrick AM (2015). Neural correlates of audiovisual synchrony judgements in older adults. Monash Brain Function Workshop, Melbourne, Australia.
Fung BJ, Bode S, Murawski C (2015). The relationship between time perception, temporal discounting, and cardiac measures. Monash Brain Function Workshop, Melbourne, Australia.
Bode S, Bennett D, Corbett E, Sewell DK, Paton B, Stahl J, Egan GF, Smith PL, Murawski C (2015). Tracking the neural origins of evidence accumulation for perceptual decisions using event-related potentials and functional magnetic resonance imaging. Australasian Cognitive Neuroscience Society, Auckland, New Zealand.
Rens N, Burianova H, Bode S, Cunnington R (2015). Decoding free decisions in a virtual environment. Australasian Cognitive Neuroscience Society, Auckland, New Zealand.
Fung BJ, Bode S, Murawski C (2015). The relation between delay discounting, time perception and cardiac signals. Australasian Cognitive Neuroscience Society, Auckland, New Zealand.
Bennett D, Brydevall M, Murawski C, Bode S (2015). The feedback-related negativity reflects an intrinsic preference for information in decision making under uncertainty. Australasian Cognitive Neuroscience Society, Auckland, New Zealand.
Voigt K, Bode S, Murawski C (2015). Choices of goods systematically change preferences. Australasian Cognitive Neuroscience Society, Auckland, New Zealand.
Bennett D, Brydevall M, Murawski C, Bode S (2015). The feedback-related negativity reflects an intrinsic preference for information in decision making under uncertainty. Students of Brain Research Conference, Melbourne, Australia.
Fung BJ, Bode S, Murawski C (2015). The relation between delay discounting, time perception and cardiac signals. Students of Brain Research Conference, Melbourne, Australia.
Bode S (2015). Decoding cognition from neuroimaging data. IBM and UoM Workshop: Applying advanced technology to advanced precision neuroscience, The University of Melbourne, Australia.
Bode S (2015). Predicting decision-related information from patterns of event-related potentials. NBD Invited Speaker Seminar Series. 10 September, Duke-NUS Graduate Medical School, Singapore.
Bode S (2015). Predicting decision-related information from patterns of event-related potentials. Psychology, Neuroscience & Methods Seminar, 7 September, University of Cologne, Germany.
Bode S (2015). Multivariate pattern classification analysis (MVPA) for EEG data. Workshop: An Introduction to Multivariate Pattern Analysis, 7 September, University of Cologne, Germany.
Bode S (2015). Multivariate pattern classification analysis (MVPA) for fMRI data. Workshop: An Introduction to Multivariate Pattern Analysis, 7 September, University of Cologne, Germany.
Bode S (2015). The application of MVPA to event-related potentials. Part I: Theory and examples. 1 September, Pattern Recognition in Neuroimaging Workshop, Freie University Berlin, Germany.
Bode S (2015). The application of MVPA to event-related potentials. Part II: The Decision Decoding Toolbox. 1 September, Pattern Recognition in Neuroimaging Workshop, Freie University Berlin, Germany.
Bode S (2015). The prediction of decision-related information from brain activity using multivariate analyses of event-related potentials. CCNB Seminar, 31 August, Freie University Berlin, Germany.
Bode S (2015). Free Will and neuroscience – A demystification. 17 August 2015. Forum for Culture and Science, Hannover, Germany.
Bode S (2015). Using multivariate event-related potential analyses to predict decision-related information from brain activity. 30 July, Adelaide University, Adelaide, Australia.
Bode S (2015). Prediction of decision-related information from patterns of event-related potentials. 30 March, Colloquium at the University of South Australia, Adelaide, Australia.
Bode S (2015). Can we use fMRI to read out the content of cognition? 1 April 2015. University of South Australia, Adelaide, Australia.
Morawetz C, Bode S, Baudewig J, Jacobs AM, Heekeren H (2015). Neural representation of emotion regulation goals. Pattern Recognition in Neuroimaging Workshop, Freie University Berlin, Germany.
Bode S, Murawski C (2015). Differences in intertemporal choice are predicted by the neural encoding of numerical magnitude. Organisation for Human Brain Mapping, Honululu, Hawaii, USA.
Bennett D, Bode S, Warren Hi, Murawski C (2015) Decision-makers pay for irrelevant information under uncertainty. Australasian Experimental Psychology Conference, Sydney, Australia.
Voigt K, Murawski C, Bode S (2015). How much you are willing to spend on consumable goods systematically is shaped by past decisions. Australasian Experimental Psychology Conference, Sydney, Australia.
Fung BJ, Murawski C, Bode S (2015). Time estimations are affected by the consumption of primary reward. Australasian Experimental Psychology Conference, Sydney, Australia.
Crone DL, Bode S, Murawski C, Laham, SM (2015). Development and validation of a moral foundations picture set. Society for Australasian Social Psychologists, Newcastle, Australia.
Bennett D, Bode S, Warren H, Murawski C (2015) Computational modelling of information seeking under uncertainty. Australian Mathematical Psychology Conference, Newcastle, Australia.
Morawetz C, Bode S, Baudewig J, Kirilina E, Heekeren H. (2014). Neural representations of emotion regulation strategies. 11th Meeting of the Austrian Society for Psychology (ÖGP), Vienna, Austria.
Murawski C (2014). From Decision Neuroscience to Public Policy. Problem Gambling: An Inter-disciplinary Dialogue between Neuroscientists, Clinicians and Policy Makers. Melbourne, Australia.
Burnett J, Davis K, Murawski C, Wilkins R, Wilkinson N (2014). Measuring adequacy of retirement savings in Australia. 22nd Colloquium of Superannuation Researchers, University of New South Wales, Sydney, Australia.
Murawski, C (2014). Financing our futures. 10th Economic and Social Outlook Conference, Melbourne, Australia.
Bode S, Stahl J (2013). Investigating error processing by decoding patterns of event-related potentials. The 4th Australasian Cognitive Neuroscience Society, Melbourne, Australia.
Bode S, Bennett D, Stahl J, Murawski C (2013). Predicting implicit abstract stimulus attributes from patterns of event-related potentials. The 4th Australasian Cognitive Neuroscience Conference, Melbourne, Australia.
Bennett D, Murawski C, Bode S (2013). Single-Trial P300 amplitudes index feedback information in reinforcement learning. The 4th Australasian Cognitive Neuroscience Conference, Melbourne, Australia.
Thio J, Murawski C, Bode S (2013). The effects of context priming on intertemporal decision-making. The 4th Australasian Cognitive Neuroscience Conference, Melbourne, Australia.
Stahl J, Bierbrauer A, Gommann J, Lenk K, Bode S (2013). Error detection in a force-production task: Testing the force-unit monitoring model. The 4th Australasian Cognitive Neuroscience Conference, Melbourne, Australia.
Bennett D, Murawski C, Bode S (2013). Decoding distinct dimensions of feedback in reinforcement learning: an electroencephalography study. Students of Brain Research Conference, Melbourne, Australia.
Bode S (2013). Informing decision-making models by decoding patterns of brain activity. 2 October, Queensland Brain Institute Seminar, University of Queensland, Brisbane, Australia.
Bode S (2013). Predicting decisions and decision-errors from EEG and fMRI signals using multivariate pattern analysis. 12 September, Seminar Program of the Department of Optometry and Vision Sciences, The University of Melbourne, Australia.
Bode S (2013). Informing models of human decision-making by decoding patterns of brain activity. 14th August, Colloquium, Melbourne School of Psychological Sciences, The University of Melbourne, Australia.
Bode S (2013). Extracting information from patterns of event-related potentials: Using MVPA for EEG data. Innovative Methods in Neuroimaging, Perception in Action Research Workshop. 8-9 August, Macquarie University Sydney, Australia.
Bode S (2013). Decoding decisions and post-decision errors from multivariate patterns of fMRI and EEG activity. 25 February, Colloquium, University of Newcastle, Australia.
Bode S (2013). Predicting decisions and post-decision errors from multivariate patterns of fMRI and EEG activity. 22nd February, Colloquium at Macquarie University Sydney, Australia.
Woolgar A, Bode S, Golland P (2013). Confounds in Multivoxel Pattern Analysis: what should we do about reaction time differences? Innovative Methods in Neuroimaging, Perception in Action Research Workshop. Macquarie University Sydney, Australia.
Bennett D, Murawski C, Bode S (2013). Decoding feedback value and information from EEG signals. Innovative Methods in Neuroimaging, Perception in Action Research Workshop. Macquarie University Sydney, Australia.
Stahl J, Bode S (2013). Perspectives on error processing. A methodological update. Symposium of the University of Cologne and the University of Aachen, Germany.
Bode S (2013). “Mind reading” – A dialog between neuroscience and philosophy about the (im)possibility of one of the oldest dreams of mankind. 29 April 2013, Forum for Culture and Science, Hannover, Germany.
Bode S, Stahl J (2013). ERP-based multivariate pattern classification predicts errors before an overt response is executed. Organisation for Human Brain Mapping, Seattle, WA, USA.
Stahl J, Schmidt-Samoa C, Bode S, Dechent P, Barke A (2013). Neural correlates of perfectionism-specific error processing. An event-related fMRI study. Psychologie & Gehirn, Würzburg, Germany.
Brown S, Murawski C (2013). The riskiness of household portfolios over the life-cycle. Research Seminar, Department of Finance, The University of Melbourne, Australia.
Burnett C, Davis K, Murawski C, Wilkins R, Wilkinson N (2013). Measuring retirement savings adequacy in Australia. Melbourne Money and Finance Conference, Brighton, Australia.
Murawski C (2013). The economics of the long-term. Symposium 'Renaissance of the Humanities', Ormond College, The University of Melbourne, Australia.
Murawski C (2013). Measuring retirement savings adequacy. Research seminar, Department of Finance, The University of Melbourne, Australia.
Bode S, Stahl J (2012). ERP-based multivariate pattern classification predicts errors before an overt response is executed. 3rd Australasian Cognitive Neuroscience Society, Brisbane, Australia.
Bode S, Sewell D, Lilburn S, Forte JD, Smith PL, Stahl J (2012). Early choice-predictive EEG signals reflect starting point bias in evidence accumulation. XXIXI International Congress of Psychology, Cape Town, South Africa.
Bode S (2012). Decoding decisions from brain signals. 18 August. Max Planck Institute for Human Cognitive and Brain Sciences, Dept. Neurology Seminar Series, Germany.
Bode S (2012). Decoding mechanisms for internal decision making from fMRI and EEG signals. 3 May, Monash Biomedical Imaging Science Seminar, Monash University, Australia.
Bode S (2012). Decoding decisions from multivariate fMRI and EEG signals. 1 May, Colloquium Melbourne Neuropsychiatry Centre, The University of Melbourne, Australia.
Morawetz C, Bode S, Baudewig J, Heekeren HR (2012). Skydivers and emotion regulation: training the amygdale. Organisation for Human Brain Mapping, Beijing, China.
Cichy RM, Bode S, Sterzer P, Haynes JD (2012). Object recognition under little and no visibility. Vision Sciences Society, Naples, Italy.
Bode S, Sewell D, Lilburn S, Forte JD, Stahl J, Smith PL (2012). Early choice-predictive EEG signals reflect starting point bias in evidence accumulation. Australian Mathematical Psychology Conference, Adelaide, Australia.
Bode S (2011). Predicting choices from brain activation. Australasian Cognitive Neurosciences Conference, Sydney, Australia.
Bode S, Murawski C, Harris PG, Domínguez D. JF, Egan GF (2011). Led into temptation? Subliminally presented reward cues bias incidental economic decisions. Organisation for Human Brain Mapping, Quebec City, Canada.
Bode S, Murawski C, Harris PG, Domínguez D. JF, Egan GF (2011). Subliminally presented reward cues bias incidental economic decisions and the encoding of subjective values in the brain. Association for the Scientific Studies of Consciousness Kyoto, Japan.
Bode S (2011). Decoding decisions from EEG signals. 24 February, EEG-Group Series, The University of Melbourne, Australia.
Murawski C, Harris PG, Bode S, Domínguez D. JF, Egan GF (2011). Subliminally presented reward cues bias the neural encoding of economic decisions. Cognitive Neuroscience Symposium, The University of Melbourne, Australia.
Harris PG, Murawski C, Bode S, Domínguez D. JF, Egan GF (2010). Brand reactions bias incidental decision-making. Neuroeconomics: Decision Making and the Brain – Society for Neuroeconomics, Evanston, IL, USA.
Murawski C, Harris PG, Bode S, Domínguez D. JF, Egan GF (2010). Brand reactions bias incidental decision-making. Computations, Decisions, and Movement, Schloss Rauischholzhausen, Germany.
Bode S (2010). Decoding decision mechanisms in the human brain. 15 September, Cognitive Neuroscience Symposium, The University of Melbourne, Australia.
Bode S (2010). Decoding rules and decision mechanisms in task preparation. 16 September, Florey Neuroscience Institute Cognitive Neuroscience Symposium, The University of Melbourne, Australia.
Bode S (2010). Decoding decision mechanisms for task preparation from distributed patterns of brain activation. 1 September, Colloquium Psychological Sciences, The University of Melbourne, Australia.
Bode S (2010). From stimuli to motor responses: Decoding rules and decision mechanisms in the human brain. 8 July, Faculty for Biological Sciences, Pharmacy & Psychology, University of Leipzig, Germany.
Bode S, Bogler C, Haynes JD (2009). Two modes of perceptual decision making with and without awareness. Berlin Brain Days, Berlin, Germany.
Bode S, Haynes JD (2009). Decoding the transition from perceptual decision making to free decisions. 24 March, Colloquium Experimental Psychology, University of Göttingen, Germany.
Schafmeister F, Bode S, Gibbons H, Kirchberg W, Menrad N, Blomeyer L, Hartmann M, von Steinbüchel N (2009). Development of a standardized stimulus-set for the investigation of semantic memory. Gehirn & Gesundheit, Deutsche Gesellschaft für Medizinische Psychologie, Göttingen, Germany.
Bode S, Bogler C, Walter Si, Haynes JD (2009). Comparing mechanisms for decision making using multivariate decoding. Organisation for Human Brain Mapping, San Francisco, CA, USA.
Bogler C, Bode S, Haynes JD (2009). Multivariate decoding reveals successive computational stages of saliency processing. Organisation for Human Brain Mapping, San Francisco, CA, USA.
Bode S, Bogler C, Soon SC, Haynes JD (2009). Differential encoding of mechanisms for human decision making. Association for the Scientific Studies of Consciousness, Berlin, Germany.
Bode S, Haynes JD (2008). Neural encoding of object categories with and without awareness: A challenge for signal detection models of human decision making. XXIX International Congress of Psychology, Berlin, Germany.
Müller AD, Bode S, von Steinbüchel N, Myer L (2008). Predictors of antiretroviral treatment adherence and therapeutic success among children in Cape Town, South Africa. XVII International AIDS Conference, Mexico City, Mexico.
Bode S, He AH (2008). Free will in the brain? 10 July, Philosophical Seminar, Leibniz University of Hanover, Germany.
Bode P, Bode S (2008). Is there a God? Proofs of God's existence in history of philosophy and natural sciences. 2 September 2008, Forum for Culture and Science, Hannover, Germany.
Bode S, He AH, Bode P (2008). A neuroscientific perspective on free will. 10 July 2008, Forum for Culture and Science, Hanover, Germany.
Bode S, Haynes JD (2008). Decoding the transformation of sensory information to motor responses. 12 June, Duke-NUS – Graduate Medical School, Singapore.
Bode S, Haynes JD (2008). Neural encoding of perceptual decision making without awareness: Challenges for signal detection models of perception. Organisation for Human Brain Mapping, Melbourne, Australia.
Bode S, Haynes JD (2007). Decoding task-sets from intraparietal sulcus and prefrontal cortex using multivariate pattern classification. 3rd Bernstein Center for Computational Neuroscience Symposium, Göttingen, Germany.
Bode S, Haynes JD (2007). Encoding of sensory stimuli and task sets in prefrontal cortex. Organisation for Human Brain Mapping, Chicago, IL, USA.
Müller AD, Myer L, Jaspan HB, Bode S, Roux P, von Steinbüchel N (2007). Paediatric adherence in South Africa measured by Medication Event Monitoring System and its effect on treatment efficacy. 2nd International Conference on HIV Treatment Adherence, New Jersey City, NY, USA.
Müller AD, Myer L, Jaspan HB, Bode S, Roux P, von Steinbüchel N (2006). Innovative use of electronic measurements for adherence to liquid antiretrovirals in an urban South African paediatric HIV clinic. Priorities in AIDS Treatment and Care, Cape Town, South Africa.
Müller AD, Bode S, Myer L, von Steinbüchel N (2006). Monitoring adherence to liquid antiretroviral medication in South African children – results and challenges. 10th European Symposium on Patient Compliance and Persistence, Bonn, Germany.
Bode S, Jäncke L (2006). Strategy-specific involvement of the primary motor cortex in mental rotation? A TMS-Study. Psychologie & Gehirn, Dresden, Germany.
Tilch HL, Richter S, Bode S, Stiens G, von Steinbüchel N, Hüther G (2006). Therapeutic effects of individual favorite music in elderly people. 48th Tagung experimentell arbeitender Psychologen, Mainz, Germany.
Bode S, Rammsayer T (2004). Correlations between self-estimated stability of behavior and fundamental dimensions of personality. 44th Kongress der Deutschen Gesellschaft für Psychologie, Göttingen, Germany.
Bischof V, Bode S, Grünkorn G, Hübbe L, Jänen I, Schulte K, Ulbricht T, Rammsayer T (2003). Age dependency of Big-Five personality traits and their stability. 7th Arbeitstagung der Fachgruppe für Differentielle Psychologie, Persönlichkeitspsychologie und Psychologische Diagnostik der DGPS, Halle, Germany.
EEG Lab Usage
The Decision Neuroscience Lab assists with coordinating the Electroencephalography (EEG) lab of the Melbourne School of Psychological Sciences (MSPS). This video, produced by our fantastic lab manager Maja Brydevall (and supported by one of our students, Daniel Rosenblatt, and former research intern, Sophia Bock) shows new users how to handle the equipment correctly.
The Decision Decoding Toolbox (or DDTBOX) is our lab's open-source software toolbox for multivariate pattern analysis of EEG data in Matlab and can be accessed through this link: http://ddtbox.github.io/DDTBOX/