Delay discounting and under-valuing of recent information predict poorer adherence to social distancing measures during the COVID-19 pandemic.

The COVID-19 pandemic has brought about unprecedented global changes in individual and collective behaviour. To reduce the spread of the virus, public health bodies have promoted social distancing measures while attempting to mitigate their mental health consequences. The current study aimed to identify cognitive predictors of social distancing adherence and mental health symptoms, using computational models derived from delay discounting (the preference for smaller, immediate rewards over larger, delayed rewards) and patch foraging (the ability to trade-off between exploiting a known resource and exploring an unknown one). In a representative sample of the UK population (N = 442), we find that steeper delay discounting predicted poorer adherence to social distancing measures and greater sensitivity to reward magnitude during delay discounting predicted higher levels of anxiety symptoms. Furthermore, under-valuing recently sampled information during foraging independently predicted greater violation of lockdown guidance. Our results suggest that those who show greater discounting of delayed rewards struggle to maintain social distancing. Further, those who adapt faster to new information are better equipped to change their behaviour in response to public health measures. These findings can inform interventions that seek to increase compliance with social distancing measures whilst minimising negative repercussions for mental health.

Rewards that are near increase impulsive action.

In modern society, the natural drive to behave impulsively in order to obtain rewards must often be curbed. A continued failure to do so is associated with a range of outcomes including drug abuse, pathological gambling, and obesity. Here, we used virtual reality technology to investigate whether spatial proximity to rewards has the power to exacerbate the drive to behave impulsively toward them. We embedded two behavioral tasks measuring distinct forms of impulsive behavior, impulsive action, and impulsive choice, within an environment rendered in virtual reality. Participants responded to three-dimensional cues representing food rewards located in either near or far space. Bayesian analyses revealed that participants were significantly less able to stop motor actions when rewarding cues were near compared with when they were far. Since factors normally associated with proximity were controlled for, these results suggest that proximity plays a distinctive role in driving impulsive actions for rewards.

Waiting in intertemporal choice tasks affects discounting and subjective time perception.

The literature on human delay discounting behavior is dominated by experimental paradigms, which do not impose actual delays. Given that waiting may be aversive even on short timescales, we present a novel delay discounting paradigm to study differences in delay discounting behavior either when real waiting is involved, or not. This paradigm retains the fundamental trade-off between rewards received versus their immediacy. We used hierarchical Bayesian modeling to decompose and test models that separate discounting and subjective time perception mechanisms. We report 2 experiments that also explore the magnitude effect and gain-loss asymmetry. In both experiments, we found greater discounting and nonlinear (convex) time perception in the online waiting task, which required waiting after each choice, compared to a control condition where waiting was deferred until the end of the experiment. Discounting was also measured by a parameter of a hyperbolic-type model and related to reversals of preference between initial desire to wait and then not doing so. These results from our novel paradigm support the emerging view that subjective time perception plays an important role in intertemporal choice in addition to discounting tendencies. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Temporal discounting does not influence body mass index.

The prevalence of obesity has driven searches for cognitive or behavioural economic factors related to Body Mass Index (BMI). One candidate is delay discounting: those who prefer smaller sooner rewards over larger but later rewards are hypothesised to have higher BMI. The findings in the literature are mixed however, with meta analyses suggesting only a very small correlation between discounting and BMI. Here we present novel empirical data (N=381) and Bayesian analyses which suggest no such relationship between discounting of either monetary or weight loss rewards and BMI. We also find evidence against our novel proposal that discounting moderates the rate of BMI gain over time. We also present our data in the context of a random effects Bayesian meta-analytical result which does suggest the presence of a small correlation overall. The strength of the correlation is so weak (2.25% shared variance) that its practical significance may be minor to non existent. However because we found decisive evidence for unaccounted for study-level variance, due to study heterogeneity, we argue that we should treat such meta-analytic correlations with extreme caution. While the relationship between discounting and health outcomes such as BMI remain theoretically appealing, our empirical and meta-analytic results suggest we should be cautious in inferring a correlational, let alone a causal, role for discounting processes in driving BMI or moderating BMI gain with age.

The case of muddled units in temporal discounting.

While parameters are crucial components of cognitive models, relatively little importance has been given to their units. We show that this has lead to some parameters to be contaminated, introducing an artifactual correlation between them. We also show that this has led to the illegal comparison of parameters with different units of measurement - this may invalidate parameter comparisons across participants, conditions, groups, or studies. We demonstrate that this problem affects two related models: Stevens' power law and Rachlin's delay discounting model. We show that it may even affect models which superficially avoid the incompatible units problem, such as hyperbolic discounting. We present simulation results to demonstrate the extent of the issues caused by the muddled units problem. We offer solutions in order to avoid the problem in the future or to aid in re-interpreting existing datasets.

Hunger increases delay discounting of food and non-food rewards.

How do our valuation systems change to homeostatically correct undesirable psychological or physiological states, such as those caused by hunger? There is evidence that hunger increases discounting for food rewards, biasing choices towards smaller but sooner food reward over larger but later reward. However, it is not understood how hunger modulates delay discounting for non-food items. We outline and quantitatively evaluate six possible models of how our valuation systems modulate discounting of various commodities in the face of the undesirable state of being hungry. With a repeated-measures design, an experimental hunger manipulation, and quantitative modeling, we find strong evidence that hunger causes large increases in delay discounting for food, with an approximately 25% spillover effect to non-food commodities. The results provide evidence that in the face of hunger, our valuation systems increase discounting for commodities, which cannot achieve a desired state change as well as for those commodities that can. Given that strong delay discounting can cause negative outcomes in many non-food (consumer, investment, medical, or inter-personal) domains, the present findings suggest caution may be necessary when making decisions involving non-food outcomes while hungry.

Hierarchical Bayesian estimation and hypothesis testing for delay discounting tasks.

A state-of-the-art data analysis procedure is presented to conduct hierarchical Bayesian inference and hypothesis testing on delay discounting data. The delay discounting task is a key experimental paradigm used across a wide range of disciplines from economics, cognitive science, and neuroscience, all of which seek to understand how humans or animals trade off the immediacy verses the magnitude of a reward. Bayesian estimation allows rich inferences to be drawn, along with measures of confidence, based upon limited and noisy behavioural data. Hierarchical modelling allows more precise inferences to be made, thus using sometimes expensive or difficult to obtain data in the most efficient way. The proposed probabilistic generative model describes how participants compare the present subjective value of reward choices on a trial-to-trial basis, estimates participant- and group-level parameters. We infer discount rate as a function of reward size, allowing the magnitude effect to be measured. Demonstrations are provided to show how this analysis approach can aid hypothesis testing. The analysis is demonstrated on data from the popular 27-item monetary choice questionnaire (Kirby, Psychonomic Bulletin & Review, 16(3), 457-462 2009), but will accept data from a range of protocols, including adaptive procedures. The software is made freely available to researchers.

Bayesian accounts of covert selective attention: A tutorial review.

Decision making and optimal observer models offer an important theoretical approach to the study of covert selective attention. While their probabilistic formulation allows quantitative comparison to human performance, the models can be complex and their insights are not always immediately apparent. Part 1 establishes the theoretical appeal of the Bayesian approach, and introduces the way in which probabilistic approaches can be applied to covert search paradigms. Part 2 presents novel formulations of Bayesian models of 4 important covert attention paradigms, illustrating optimal observer predictions over a range of experimental manipulations. Graphical model notation is used to present models in an accessible way and Supplementary Code is provided to help bridge the gap between model theory and practical implementation. Part 3 reviews a large body of empirical and modelling evidence showing that many experimental phenomena in the domain of covert selective attention are a set of by-products. These effects emerge as the result of observers conducting Bayesian inference with noisy sensory observations, prior expectations, and knowledge of the generative structure of the stimulus environment.

How do we use the past to predict the future in oculomotor search?

A variety of findings suggest that when conducting visual search, we can exploit cues that are statistically related to a target's location. But is this the result of heuristic mechanisms or an internal model that tracks the statistics of the environment? Here, connections are made between the two explanations, and four models are assessed to probe the mechanisms underlying prediction in search. Participants conducted a simple gaze-contingent search task with five conditions, each of which consists of different combinations of 1st and 2nd order statistics. People's exploration behaviour adapted to the statistical rules governing target behaviour. Behaviour was most consistent with a model that represents transitions from one location to another, and that makes the underlying assumption that the world is dynamic. This assumption that the world is changeable could not be overridden despite task instruction and nearly 1h of exposure to unchanging statistics. This means that while people may be suboptimal in some experimental contexts, it may be because their internal mental model makes assumptions that are adaptive in a complex, changeable world.

Search asymmetries: parallel processing of uncertain sensory information.

What is the mechanism underlying search phenomena such as search asymmetry? Two-stage models such as Feature Integration Theory and Guided Search propose parallel pre-attentive processing followed by serial post-attentive processing. They claim search asymmetry effects are indicative of finding pairs of features, one processed in parallel, the other in serial. An alternative proposal is that a 1-stage parallel process is responsible, and search asymmetries occur when one stimulus has greater internal uncertainty associated with it than another. While the latter account is simpler, only a few studies have set out to empirically test its quantitative predictions, and many researchers still subscribe to the 2-stage account. This paper examines three separate parallel models (Bayesian optimal observer, max rule, and a heuristic decision rule). All three parallel models can account for search asymmetry effects and I conclude that either people can optimally utilise the uncertain sensory data available to them, or are able to select heuristic decision rules which approximate optimal performance.

Quantitative analysis of three-dimensional human mammary epithelial tissue architecture reveals a role for tenascin-C in regulating c-met function.

Remodeling of the stromal extracellular matrix and elevated expression of specific proto-oncogenes within the adjacent epithelium represent cardinal features of breast cancer, yet how these events become integrated is not fully understood. To address this question, we focused on tenascin-C (TN-C), a stromal extracellular matrix glycoprotein whose expression increases with disease severity. Initially, nonmalignant human mammary epithelial cells (MCF-10A) were cultured within a reconstituted basement membrane (BM) where they formed three-dimensional (3-D) polarized, growth-attenuated, multicellular acini, enveloped by a continuous endogenous BM. In the presence of TN-C, however, acini failed to generate a normal BM, and net epithelial cell proliferation increased. To quantify how TN-C alters 3-D tissue architecture and function, we developed a computational image analysis algorithm, which showed that although TN-C disrupted acinar surface structure, it had no effect on their volume. Thus, TN-C promoted epithelial cell proliferation leading to luminal filling, a process that we hypothesized involved c-met, a proto-oncogene amplified in breast tumors that promotes intraluminal filling. Indeed, TN-C increased epithelial c-met expression and promoted luminal filling, whereas blockade of c-met function reversed this phenotype, resulting in normal BM deposition, proper lumen formation, and decreased cell proliferation. Collectively, these studies, combining a novel quantitative image analysis tool with 3-D organotypic cultures, demonstrate that stromal changes associated with breast cancer can control proto-oncogene function.

Optimal feature integration in visual search.

Despite embodying fundamentally different assumptions about attentional allocation, a wide range of popular models of attention include a max-of-outputs mechanism for selection. Within these models, attention is directed to the items with the most extreme-value along a perceptual dimension via, for example, a winner-take-all mechanism. From the detection theoretic approach, this MAX-observer can be optimal under specific situations, however in distracter heterogeneity manipulations or in natural visual scenes this is not always the case. We derive a Bayesian maximum a posteriori (MAP)-observer, which is optimal in both these situations. While it retains a form of the max-of-outputs mechanism, it is based on the maximum a posterior probability dimension, instead of a perceptual dimension. To test this model we investigated human visual search performance using a yes/no procedure while adding external orientation uncertainty to distracter elements. The results are much better fitted by the predictions of a MAP observer than a MAX observer. We conclude a max-like mechanism may well underlie the allocation of visual attention, but this is based upon a probability dimension, not a perceptual dimension.

Investigating a space-variant weighted salience account of visual selection.

Weighted salience models are a popular framework for image-driven visual attentional processes. These models operate by: sampling the visual environment; calculating feature maps; combining them in a weighted sum and using this to determine where the eye will fixate next. We examine these stages in turn. We find that a biologically plausible non-uniform retinal sampling causes feature coding unreliability. The linear weighted sum operation seems an adequate model if statistical feature dimension dependencies are considered. Using signal detection theory we find good discrimination between targets and non-targets in the weighted sum, but the fixation criterion of 'peak salience' is suboptimal.

The long and the short of it: spatial statistics at fixation vary with saccade amplitude and task.

We recorded over 90,000 saccades while observers viewed a diverse collection of natural images and measured low level visual features at fixation. The features that discriminated between where observers fixated and where they did not varied considerably with task, and the length of the preceding saccade. Short saccades (<8 degrees) are image feature dependent, long are less so. For free viewing, short saccades target high frequency information, long saccades are scale-invariant. When searching for luminance targets, saccades of all lengths are scale-invariant. We argue that models of saccade behaviour must account not only for task but also for saccade length and that long and short saccades are targeted differently.

Is the early visual system optimised to be energy efficient?

This paper demonstrates that a representation which balances natural image encoding with metabolic energy efficiency shows many similarities to the neural organisation observed in the early visual system. A simple linear model was constructed that learned receptive fields by optimally balancing information coding with metabolic expense for an entire visual field in a 2-stage visual system. The input to the model consists of a space variant retinal array of photoreceptors. Natural images were then encoded through a bottleneck such as the retinal ganglion cells that form the optic nerve. The natural images represented by the activity of retinal ganglion cells were then encoded by many more 'cortical' cells in a divergent representation. Qualitatively, the system learnt by optimising information coding and energy expenditure and matched (1) the centre surround organisation of retinal ganglion cells; (2) the Gabor-like organisation of cortical simple cells; (3) higher densities of receptive fields in the fovea decreasing in the periphery; (4) smaller receptive fields in the fovea increasing in size in the periphery; (5) spacing ratios of retinal cells; and (6) aspect ratios of cortical receptive fields. Quantitatively, however, there are small but significant discrepancies between density slopes which may be accounted for by taking optic blur and fixation induced image statistics into account. In addition, the model cortical receptive fields are more broadly tuned than biological cortical neurons; this may be accounted for by the computational limitation of modelling a relatively low number of neurons. This paper shows that retinal receptive field properties can be understood in terms of balancing coding with synaptic energy expenditure and cortical receptive fields with firing rate energy expenditure, and provides a sound biological explanation of why 'sparse' distributions are beneficial.

Synaptic energy efficiency in retinal processing.

Recent work suggests that the visual system may represent early visual information in an energy efficient manner [Nature 381 (1996); Nature, 381 (1996) 607; Neural Comput. 3 (2001) 799; Curr. Opin. Neurobiol. 11 (2001) 475]. This paper applies the idea of energy efficient representations to understand retinal processing, and provides evidence that centre surround processing observed is efficient in terms of minimizing synaptic activity. In particular, it is shown that receptive fields at different retinal eccentricities and at different levels of noise, can be understood in terms of maximizing the transmission of visual information given a constraint on total synaptic strengths and hence energy consumption.