Rewind 2017: My top 5 favourite papers

I read research papers for inspiration and to inform myself about the latest developments in my research field. In this process, I came across some papers that I was very impressed with and started to compile a list of these articles. These are my top 5 favourite papers from 2017. Since I am currently working on serial dependencies in perceptual decision-making, this list might be biased towards that topic.

1. Computational precision of mental inference as critical source of human choice suboptimality, Drugowitsch, Wyart et al. 2016, Neuron (doi: 10.1016/j.neuron.2016.11.005)

Technically this paper is not from 2017 but it came out around Christmas in 2016 so I am including it in the list. In this paper, Drugowitsch, Wyart and colleagues investigate the source of suboptimalities in human choices. Using an elegant task design and a much more elegant quantitative formulation, the authors show that imperfections in inference alone give rise to a dominant fraction of suboptimal choices and that two-thirds of this suboptimality arises from the limited precision of neural computations implementing the inference process itself. This allowed them to suggest an upper bound on the accuracy and predictability of human choices in uncertain environments. Suboptimality in decision making has been well documented in various behavioural experiments across a wide range of species but it did not get the importance it deserves in theoretical models of decision making. Using simple behavioural experimental manipulations and a very thorough mathematical framework, the paper addresses this issue and in my opinion, is a must-read for any researcher working on suboptimality in decision-making.

2. How race affects evidence accumulation during the decision to shoot, Pleskac et al. 2017, Psychon Bull Rev (doi: 10.3758/s13423-017-1369-6)

Accumulation-to-bound class of models, and in particular, the drift diffusion model has been extensively used to model behavioural data from perceptual decision-making studies. This paper uses a similar modelling framework to understand how racial stereotypes bias the observers’ choice to shoot in a first person shooter task. The authors found that racial stereotypes systematically bias the rate at which evidence accumulation takes place in the decision to shoot, with faster rate of accumulation to shoot Black targets. They also found that some participants counteracted this bias by setting a higher decision threshold thereby collecting more evidence for Black targets before reaching a decision. While this study probes how racial stereotypes enter the evidence accumulation process in the context of shooting decisions, the findings have broader implications in understanding how biases affect our decision-making in general. I am especially impressed by the paper because it takes a quantitative framework that was used in studying low-level processes and applied it to a more practical situation and showed analogies between the two.

3. History-based action selection bias in posterior parietal cortex, Hwang et al. 2017 Nature Communications (doi: 10.1038/s41467-017-01356-z)

There has been a recent surge in papers investigating history dependent biases in perceptual decision-making. Among all those papers that provide excellent insights into serial dependencies, this paper stands out as it combines behavioural modelling, two-photon calcium imaging and optogenetic inactivation to show a causal role of posterior parietal cortex (PPC) in mediating the subjective use of history in biasing action selection. The authors found that the activity of PPC neurons during the ITI reflect history-dependent biases and inactivation of the PPC during the ITI but not during task removes the effect of history-dependent biases on behaviour. The paper uses rodent as a model animal and the findings have important implications in decision-making research.

4. Lateral orbitofrontal cortex anticipates choices and integrates prior with current information, Nogueira, Abolafia et al. 2017 Nature Communications (doi: 10.1038/ncomms14823)

Combining prior with current information is a crucial component in decision-making. In this paper, Nogueira, Abolifa et al. used a novel experimental paradigm by introducing outcome-dependent correlations between consecutive stimuli and found that rats adapted their behaviour by learning the task contingency. Interestingly, neurons in the lateral orbitofrontal cortex showed choice-related activity even before stimulus presentation and this activity increased with time following the stimulus onset. This suggests an important role of OFC in transforming immediate prior and stimulus information into choices. I am impressed by the authors’ choice of experimental manipulation to tackle the questions asked. This paper is a very good example of why sufficient thought should be given to the task design and not just to the data analysis pipeline.

5. Dynamic modulation of decision biases by brainstem arousal systems, de Gee et al. 2017 eLife (doi: 10.7554/eLife.23232.001)

Variability in choice behaviour during decision-making has been well observed in decision making community but the underlying neural mechanisms are less understood. de Gee et al. combined behaviour, pupillometry and neuroimaging to show that changes in the brain arousal systems, specifically the activity of locus coeruleus, explains this variability- by boosting the global brain-wide arousal, locus coeruleus reduced the intrinsic biases in subjects’ decisions. This paper is particularly impressive in its methods- the authors used 7T fMRI to study the BOLD activity in locus coeruleus, an extremely challenging task in itself and showed that task-evoked pupil response is predicted by the activity of locus coeruleus, and that the phasic arousal resulting from this activity modulates choice-specific signals but not sensory responses in the brain. In my opinion, this paper acts like a guide to any researcher attempting to do brain-stem fMRI.

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