Integrating Evidence for Decision-Making Over Prolonged Timescales

Post by Stephanie Williams

What's the science?

The human decision-making process includes weighing relevant information and uses these weights to select a choice. Currently, there are several models of decision-making that explain how this happens in terms of an "evidence integration" computation. Although informative, these models have primarily focused on decisions lasting on the order of seconds. It is still unclear whether decisions over longer periods of time can be modelled in the same way, or whether fundamental memory limitations would prevent humans from using integration over longer durations. This week in Current Biology, Waskom and colleagues designed a new task that probed evidence integration over longer periods of time..

How did they do it?

Five participants saw a series of patterns on a computer screen. The patterns were shown at varying levels of contrast against a grey screen, such that some patterns barely contrasted against the grey background, while others stood out. The set of 1-5 patterns, presented sequentially, was randomly sampled from either a) a low contrast distribution or b) a high contrast distribution (see figure). Participants had to decide whether, overall, the series of patterns came from the high contrast distribution or the low contrast distribution. There were either shorter (1-4s) or longer (2-8s) unpredictable gaps between the patterns they saw in each series. When making their decision, participants were instructed to think about the average contrast of the patterns they saw. The authors manipulated several parts of the task— the strength of the contrast of the pattern, the number of patterns (1-5), and the length of the gap between each pattern. To perform well at the task, participants should have used all of the patterns presented to them in the series to make a decision.  

The authors evaluated each participant’s behavior by looking at how the three aspects of the experiment they manipulated influenced the subject's choices. They investigated each of these aspects in both individual subjects and in the aggregated group. They then fit four different computational models and compared the predictions of the models with their data to infer characteristics of the decision-making process over longer timescales.


What did they find?

The behavioral data showed that participants were able to accurately integrate evidence over periods of time on the order of tens of seconds (ranging from 2.2s to 34s). Participants were sensitive to the strength of the contrast in the patterns they saw, and performed better when they saw a greater number of contrast patterns before having to make a decision. Importantly, participants performed with similar accuracy in both task conditions (long vs. short gaps between stimuli), suggesting that evidence integration is a flexible process that can extend across long timescales. Of the four models the authors examined, a linear integration model best fit the data, suggesting subjects summed the evidence from each pattern they encountered to make their decision. Directly modelling two proposed sources of information loss, ‘memory noise’ and ‘memory leak’ (when information presented earlier is forgotten), showed that neither were present in any appreciable magnitude. The subjects’ data were not perfectly explained by the linear integration model, however. Subjects tended to slightly overvalue stimuli that appeared first in each trial, suggesting that they sometimes struggled to change their mind after forming an initial impression.

What's the impact?

The authors’ findings advance our knowledge about how we combine evidence at timescales corresponding to many real-world decisions. The study shows that people are able to integrate data with minimal information loss over relatively long durations. The findings also pose important questions about the biological mechanisms behind evidence integration during natural decision-making, and suggest current network models may need to be amended.


Waskom et al., Decision Making through Integration of Sensory Evidence at Prolonged Timescales. Current Biology (2018). Access the original scientific publication here.

Conscious Awareness is a Key Feature of MTL-Dependent Memory

Post by Shireen Parimoo

What's the science?

Memories guide our eye movements and how we explore visual information. For example, people look at altered regions of familiar scenes more than they look at these same regions in familiar but unaltered scenes – a phenomenon known as the manipulation effect. This effect is observed only when people can also identify the change, suggesting that the eye-movement behavior might reflect awareness of the change, rather than an automatic and unconscious response. Regions of the medial temporal lobe (MTL) – particularly the hippocampus – play a key role in creating and retrieving recent declarative memories, which are memories about facts and events that we can consciously recollect. However, the role of the MTL in the manipulation effect as well as the relationship between the MTL and knowledge about the manipulation is not understood. This week in PNAS, Drs. Smith and Squire investigated the role of the MTL and declarative memory in the manipulation effect by comparing healthy participants and patients with MTL lesions.

How did they do it?

Participants included four patients with hippocampal lesions, one patient with broader MTL damage, and six healthy adults who served as controls. They viewed a series of scene images across three blocks while their eye movements were recorded. The same scenes were presented in the first two blocks and participants simply viewed the scenes. In the third block, half of the scenes were altered, and half were unaltered (repeated). After viewing each scene, participants were asked if the scenes were the same as before or if they had changed. They were then shown the altered scenes and asked to describe how those scenes had changed and where the change had occurred (i.e. the critical region). The authors divided the participants into three groups based on their knowledge of the altered scenes: those who correctly answered all three questions (whether the scene was altered and what and where the changes were) had robust knowledge (awareness) of the change, those who answered some of the questions correctly had partial knowledge about the change, and those who didn’t answer any question correctly were unaware. Eye movement data were analyzed to examine how often participants looked at the critical region of the altered scenes in the third block and how much time they spent viewing this region.

What did they find?

Compared to control participants, patients were unable to discriminate between the repeated and the altered scenes. Moreover, control participants had robust awareness of the changes in 60% of the scenes and were unaware of changes in 18% of the scenes. The reverse pattern was observed in patients, who showed awareness for only 11% of the scenes and were unaware of changes in 56% of the scenes. Thus, patients with MTL lesions had poorer declarative memory than the healthy controls.


Overall, the manipulation effect was observed only in control participants, who viewed the critical region of the scenes more when it was altered than unaltered. However, when participants had robust awareness of the changes in altered scenes, both patients and controls showed the manipulation effect. In other words, when they could identify that a scene had changed, what the change was, and where the change occurred, both controls and patients viewed the critical region more in the altered scenes than in the unaltered scenes. When participants had only partial awareness or were unaware of the changes, they spent a similar amount of time viewing the altered and repeated scenes, indicating that the manipulation effect is linked to conscious awareness for what has been learned.

What's the impact?

This study found that viewing behavior is related to the conscious recollection of memories, and that the manipulation effect could be driven by regions in the MTL, such as the hippocampus. Although previous studies have also linked viewing behavior to different memory-related eye movements, these findings help us better understand the role of awareness in declarative memory retrieval. Further research is needed to determine the conditions under which declarative or non-declarative memory supports different types of viewing behavior.


Smith & Squire. Awareness of what is learned as a characteristic of hippocampus-dependent memory. Proceedings of the National Academy of Sciences of the United States of America (2018). Access the original scientific publication here.

Changes in Cerebral Blood Flow During the Sleep-Wake Cycle

Post by: Amanda McFarlan

What's the science?

The synaptic homeostasis theory of sleep suggests that being awake is associated with strengthening of the brain’s synapses (connections between neurons), which has a high metabolic cost, while sleep is associated with synaptic downscaling. Given that cerebral blood flow in the brain increases with greater metabolic demand, it is hypothesized that cerebral blood flow should increase during the day and decrease during sleep. Previous studies investigating cerebral blood flow have reported mixed results, so it remains unknown how sleep and wake affect cerebral blood flow. This week in NeuroImage, Elvsåshagen and colleagues investigated the effect of a day of wake, sleep and sleep deprivation on resting cerebral blood flow in the brain using neuroimaging.

How did they do it?

The authors examined resting cerebral blood flow in thirty-eight healthy adult males using arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) to determine the effect of wake, sleep and sleep deprivation. On the first day of testing, the participants underwent ASL scanning at two time points: in the morning (after a night of sleep at home) and in the evening (approximately 14 hours after waking). The participants were then separated into two groups: the sleep group and the sleep deprivation group. The participants in the sleep group were sent home for another night of sleep, while the participants in the sleep deprivation group stayed awake all night. Both groups received a third ASL scan the following morning. Additionally, the authors interviewed participants on various aspects of their sleep habits the night, the week and the month prior to the first day of testing. The Horne-Östberg Morningness-Eveningness questionnaire and the Epworth Sleepiness Scale were used to measure participants’ chronotype and daytime sleepiness, respectively. Sleep quality was measured using the Pittsburgh Sleep Quality Index and subjective sleepiness was assessed using the Karolinska Sleepiness Scale. Participants were also asked to perform a task that measured attention by examining each individual’s variability in reaction time immediately after each ASL scan.

What did they find?

The authors compared whole-brain ASL scans taken in the morning to those taken in the evening of the first testing day and determined that resting cerebral blood flow increased bilaterally in the hippocampus, amygdala, thalamus, and sensorimotor cortices over the course of the day. However, they found no differences in resting cerebral blood flow when comparing the first morning versus the second morning in the sleep group, suggesting that resting cerebral blood flow resets after a night of sleep.


Next, the authors examined the effect of sleep deprivation on resting cerebral blood flow. In an interaction analysis, they determined that cerebral blood flow was further increased after a night of sleep deprivation in bilateral lateral and medial occipital cortices, anterior cingulate gyrus and insula, while cerebral blood flow in these areas decreased after a night of sleep. They hypothesized that the changes in resting cerebral blood flow after sleep deprivation may be explained by homeostatic mechanisms. The authors also found  a positive correlation between reaction time and resting cerebral blood flow in the left and right somatosensory and motor cortices and a negative correlation between subjective sleepiness changes between the first and second mornings and cerebral blood flow in the bilateral insula. However, none of the findings related to sleep habits remained significant after being adjusted to account for multiple analyses.

What's the impact?

This is the first study to show that resting cerebral blood flow increases throughout the day in the hippocampus, amygdala, thalamus, and sensorimotor cortices, but resets after a night of sleep. Moreover, this study shows that sleep deprivation is correlated with further increases in resting cerebral blood flow in occipital and temporal cortices as well as the insula. This study provides a foundation for understanding the effect of wake and sleep on cerebral blood flow. In future studies it will be necessary to elucidate the neural mechanisms underlying these changes.

Elvsåshagen et al. Cerebral blood flow changes after a day of wake, sleep, and sleep deprivation. NeuroImage (2018). Access to the original scientific publication here.