Meet our new Research Fellows
The School recently appointed two research fellows to boost research strength in its new Decision Science hub. We are delighted to welcome Dr Hinze Hogendoorn from the Netherlands and Dr Nicholas Van Dam from the USA, both of whom commenced work in July. Newsletter interviewed both of these new appointments to find out what makes them tick.
Interview with Dr Hinze Hogendoorn
Much of your previous research has explored visual perception, and especially the role of rhythmic neural processes in it, using EEG, fMRI and eye-tracking among other methods. In simple terms, what have you found in your novel approach to the temporal aspect of visual processing?
My most recent work has investigated how the brain is able to create a continuous, apparently seamless stream of conscious awareness, even though more and more evidence is accumulating that the underlying processes in the brain are themselves not continuous. Instead, the processing seems to take place frame-by-frame, a little analogous to the individual frames of a video file. However, in contrast to the video file, the gaps between the brain's samples are so large that by all rights, we ourselves should be able to perceive them. The fact that we don't notice this at all in daily life, shows that our awareness of the present is not simply the end result of a processing stream, but instead that the brain is constantly and actively piecing together a timeline on which the present unfolds - slotting events into the right places and stitching together the gaps.
Are you planning to extend your work in this area from the study of perception to the study of decision-making, the focus of your fellowship, or do expect to begin a new program of research?
In one of my recent studies, I discovered that the rhythm that determines when new samples of visual information are acquired, surprisingly also determines when we make voluntary eye movements. This means that the underlying rhythm not only dictates what we see, but also when we can act. In the coming years, I will investigate whether decision processes preceding overt action are similarly contingent on ongoing rhythms. I am also very interested in the time-course of neural processing more generally, and look forward to exploring overlapping interests with other members of the School in the near future.
Does your work have practical applications, such as to clinical populations or to the development of decision aids?
My work is primarily fundamental, but I definitely see potential for practical applications in both clinical populations as well as in industry. For example, I am currently exploring whether an eye-tracker mounted in the dashboard of a car might be able to use the driver's microsaccade rate to monitor the driver's ongoing attentional rhythm. This would allow the car's on-board computer to identify the optimal timing to present essential information. I am also talking to the Defense Science and Technology Group (DSTG) to explore possible defense applications.
You have only just arrived, but you have ideas about who you might collaborate with inside or outside the School?
I very much look forward to exploring collaborations here in Melbourne. Due to our shared interest in EEG, particularly combined with multivariate analyses, and of course the focus of my fellowship, I hope to collaborate closely with Stefan Bode and the Decision Science lab. I also look forward to working with (among other) Olivia Carter, Piers Howe, Dan Little, and Philip Smith at the school. Outside the school, I am talking to the DSTG, as well as pursuing a collaboration in decision making in sports.
Although you have worked in the Netherlands, the USA (at Harvard) and in England (University College London), you are no stranger to the Australian academic environment. You have worked with colleagues at the University of Sydney, for example. Is there anything distinctive about the Australian psychology “scene” from what you have observed?
Two things in particular have struck me so far. The first is a shared enthusiasm and curiosity about discovering new things. Rather than individuals protecting their niche for fear of being scooped, people I've spoken to are excited both to hear new things and to share their own work. That is something I love about working in science. The second is the deep engagement with the community, particularly here in Melbourne. My research to date has been largely on fundamental theoretical questions, with relatively indirect societal value, and I look forward to engaging more directly with the community to explore more applied questions.
What are you working on at the moment that excites you the most?
First of all, I think it is very exciting to get to know many of the researchers at the school, and to find out what they are working on and already brainstorm areas of shared interest and potential collaborations. Next to that, I am working on an EEG dataset that explores how the gaps between successive samples of visual input are stitched together in visual awareness. Specifically, it explores the role of prediction and extrapolation, with the idea being that the missing input is essentially filled in with what the brain expects to see.
Can you share with us a recent paper of yours and tell us briefly what its main contribution is?
In a recent paper, I studied the so-called attentional rhythm: the rate at which samples of visual information are acquired and processed. This has always been considered a perceptual phenomenon, but in this paper I show that it also dictates when we make voluntary eye movements. This means that there is an ongoing rhythm (that we are not ourselves aware of) dictating what are optimal and suboptimal moments to act. This opens a whole new set of questions about the processes that determine not just when we acquire new perceptual input, but also when we can use that to inform decisions and when we can act upon those decisions. Needless to say, there are numerous fields where decisions and actions are time-critical (sports, security, air-traffic control, etc.), so I look forward to investigating the applied value of this discovery.
Interview with Dr Nicholas Van Dam
Some of your most influential work to date has examined mindfulness, both in relation to clinical treatment and to the experience of anxiety and depression. Do you think the concept of mindfulness has been over-sold or adulterated in the recent Western enthusiasm for it?
While there is considerable promise to mindfulness in certain specific forms and in certain contexts, it has been touted by some as a panacea. Enthusiasm for using mindfulness in all aspects of daily life has most certainly outstripped its empirical foundations and few have considered the relatively rare, but potentially very serious, adverse experiences that can occur (in some cases, meditation has led to psychosis and suicidality). Some of this has to do with the fact that few scholars and scientists actually agree on what mindfulness is nor how we should measure it. To me, it’s not really a question of whether contemporary definitions of mindfulness jive with historical ones, but more a question of how faithful we are to the particular definition that we are using in a given instantiation. Too many people invoke thousands of years of Buddhist history to support practices that have nothing to do with Buddhism. With co-authors, I wrote a piece about this topic that should be out mid- to late calendar year in Perspectives on Psychological Science.
Much of your work falls within the broad domain of cognitive and clinical neuroscience and you have done important work on an assortment of functional brain networks. But I think you also list “computational psychiatry” as one of your interests. What is computational psychiatry and how has some of your work contributed to it?
Computational psychiatry is the use of advanced behavioural/computational models to try to better classify, predict, and characterize features of relevance to psychiatry (e.g., avoidance behaviour, reward learning, antidepressant response). One aspect of computational psychiatry (the part I think of as top-down) uses novel computational approaches to categorize individuals in clinically meaningful ways (e.g., high risk, good vs. poor treatment response, etc.). Another aspect of computational psychiatry is using specific models of given behaviours (e.g., reinforcement learning per Rescorla and Wagner) to try to characterize the specifics of a known problem within a clinical population (e.g., deficits of reward processing in depression). The goal in this type of computational psychiatry (which I think of as bottom-up) is to better understand which features of a broader deficit are specifically maladaptive. For example, reward processing is multifaceted, with some aspects that are actually intact in depression. My own work has contributed both to top-down and bottom-up aspects of this field. I have looked at the ways in which novel combinations of behavioural features (e.g., childhood adversity, symptom and personality profiles) can be used to examine potentially valuable neurobiological differences (e.g., brain structure, functional brain connectivity). I have also employed economic and other computational models to neuronal and behavioural data to understand things like risk aversion and processing uncertainty.
You received your PhD in clinical psychology (from the University at Albany, State University of New York), and some of your work has addressed anxiety, substance use and autism. How do you think work with a clinical focus, and your work in particular, could contribute to the wider program of research in the School’s emerging decision science research hub?
Decision science is an exciting multidisciplinary area. Broadly speaking, the area focuses on better explaining/predicting how people make choices and implement the resultant behaviours. At the crux of my research program is an effort to understand the neurobehavioral basis of how people make decisions that guide day-to-day actions. I am particularly interested in how the world is represented in a neurocognitive fashion and how that representation is altered/updated/regulated. Moreover, I am interested in how individual differences result in maladaptive behaviours. Many decisions are irrational and non-conscious and one could easily argue that many of the symptoms of neuropsychiatric conditions result from an altered neurocognitive representation of the world, or an inability to update or regulate that representation. For those who don’t work with clinical populations, I think it is worth considering the view that many abnormal behaviours likely represent an extreme variation of normal behaviour (even psychosis, I think). Thus, incorporating consideration of clinical presentations into the study of decision science (where not already present) allows for an important expansion of our models of how people make choices and implement the corresponding behaviours.
Do you have particular research collaborations you would like to build within the School or with its partner organisations?
There are a number of research hubs and people specifically, within MSPS, with whom I am eager to establish relationships. Some specific examples of work that I see as synchronous with my own interests are real-life assessment of emotion/mood via smartphones, computational modelling of various decision processes (from applications of the drift-diffusion model to higher-level choice tasks in neuroeconomics), the use of neurocognitive tasks in the context of potentially maladaptive behaviour, and various examinations of the presentation, course, and treatment of anxiety, depression, and substance use conditions. Outside of the School, I am particularly excited about opportunities for collaboration with Psychiatry and Orygen, especially in the realm of examining trajectories of high-prevalence psychiatric conditions at a broad scale (e.g., HeadSpace) and predictors of real-life treatment outcomes. I am also eager to develop relationships with the Melbourne Neuroscience Institute and Florey Institute towards identifying potential neural biomarkers (especially using MRI) of clinically relevant behaviours.
What are you working on at the moment that excites you the most?
There are two particular projects that I am really excited about. One is a neuroeconomic study of decision making in anxious individuals who underwent fMRI during a probabilistic reward choice task, in some cases with ambiguous probabilities. I find this especially exciting because it allows an opportunity to apply advanced neuroimaging methods and computational modelling to decision-making in a clinical sample while looking for similarities and differences compared to matched healthy controls. As individuals with generalized anxiety often struggle with uncertainty, this model holds promise for helping to understand a critical feature of the condition. As the data is limited in size, it has required me to consider some of the current challenges being faced by the neuroimaging community around sample size and the recruitment of clinical populations. By employing more rigorous statistical methods, I am hopeful that I will being able to generate useful findings that may ultimately inform treatment. The second project that I am especially excited by is an advanced psychometric investigation of mindfulness. After publishing a number of papers criticizing the poor psychometric properties of existing mindfulness scales and learning various techniques for developing new scales (including using Item Response Theory – IRT), I came across a questionnaire format that I thought might help to balance some of the demand characteristics inherent in many mindfulness measures. I am currently analysing the psychometric properties of a beta version of the scale using a new IRT model. Incidentally, I think there is something very relevant about IRT for decision science as the approach models how individuals at varying levels of a latent trait go about choosing a given response option and simultaneously whether that scale can accurately reflect the latent trait.
Do you have a recent paper that you’re especially proud of? What does the paper show, and what questions does it raise for future research?
For more reasons than one, I am especially proud of my recent Biological Psychiatry paper. In the study, we took a large community-ascertained sample of over 300 people (many with psychiatric histories and conditions) and used a complex series of data analytic methods to identify potentially meaningful clinical groups (irrespective of psychiatric nosology). We then looked at how those groups differed in functional brain connectivity. One of the reasons that I was proud of this work is that it allowed me to combine areas in which I am particularly passionate: psychometrics, high prevalence psychiatric conditions, advanced statistical methods, and fMRI. The process that ultimately led to the end product also resulted in me learning how to code/program in a number of languages, which has been immensely helpful in my other work. Apart from personal reasons, the results of the study were compelling. The participants clustered into an adaptive and maladaptive (or healthy vs. unhealthy) group and within the maladaptive group, further subdivided into what were essentially internalizing vs. externalizing problem presentations. From a neurobiological perspective, the adaptive and maladaptive groups differed most prominently with regard to limbic connectivity, but more interestingly, in somatomotor connectivity. This latter point has potentially interesting implications for how bodily representation and action implementation may be different among individuals with psychopathological tendencies, bringing us back to consideration of decision science! The approach we took essentially ignored traditional psychiatric nosology and yielded results that were compatible with we know about psychopathology while also providing potentially new insights. The real implication of the study is in what it could mean for future work in this arena. Rather than focusing on psychiatric groups, our study (along with similar work by others in specific disease groups) shows that there are potentially meaningful, data-driven ways to think about how we classify individuals with psychological issues.