Biology subjects

Summerfield, C.

Publications and source records attributed to Summerfield, C..

6 recordsLinked to original sources

bioRxiv2018

Neural structure mapping in human probabilistic reward learning

Humans can learn abstract concepts that describe invariances over relational patterns in data. One such concept, known as magnitude, allows stimuli to be compactly represented by a single dimension (i.e. on a mental line), for example according to their cardinality, size or value. Here, we measured representations of magnitude in humans by recording neural signals whilst they viewed symbolic numbers. During a subsequent reward-guided learning task, the neural patterns elicited by novel complex visual images reflected their pay-out probability in a way that suggested they were encoded onto the same mental number line. Our findings suggest that in humans, learning about values is accompanied by structural alignment of value representations with neural codes for the concept of magnitude.

neuroscience↗

bioRxiv2018

A network for computing value homeostasis in the human medial prefrontal cortex

Humans and other animals make decisions in order to satisfy their goals. However, it remains unknown how neural circuits compute which of multiple possible goals should be pursued (e.g. when balancing hunger and thirst) and combine these signals with estimates of available reward alternatives. Here, humans undergoing functional magnetic resonance imaging (fMRI) accumulated two distinct assets over a sequence of trials. Financial outcomes depended on the minimum cumulate of either asset, creating a need to maintain \"value homeostasis\" by redressing any imbalance among the assets. BOLD signals in the dorsal anterior cingulate cortex (dACC) tracked the level of homeostatic imbalance among goals, whereas the ventromedial prefrontal cortex (vmPFC) signalled the level of homeostatic redress incurred by a choice, rather than the overall amount received. These results suggest that a network of medial frontal brain regions compute a value signal that maintains homeostatic balance among internal goals.

neuroscience↗

bioRxiv2018

Human noise blindness drives suboptimal cognitive inference

Humans typically make near-optimal sensorimotor judgments but show systematic biases when making more cognitive judgments. Here we test the hypothesis that, while humans are sensitive to the noise present during early sensory processing, the \"optimality gap\" arises because they are blind to noise introduced by later cognitive integration of variable or discordant pieces of information. In six psychophysical experiments, human observers judged the average orientation of an array of contrast gratings. We varied the stimulus contrast (encoding noise) and orientation variability (integration noise) of the array. Participants adapted near-optimally to changes in encoding noise, but, under increased integration noise, displayed a range of suboptimal behaviours: they ignored stimulus base rates, reported excessive confidence in their choices, and refrained from opting out of objectively difficult trials. These overconfident behaviours were captured by a Bayesian model which is blind to integration noise. Our study provides a computationally grounded explanation of suboptimal cognitive inferences.

neuroscience↗

bioRxiv2018

Focused learning promotes continual task performance in humans

Humans can learn to perform multiple tasks in succession over the lifespan (\"continual\" learning), whereas current machine learning systems fail. Here, we investigated the cognitive mechanisms that permit successful continual learning in humans. Unlike neural networks, humans that were trained on temporally autocorrelated task objectives (focussed training) learned to perform new tasks more effectively, and performed better on a later test involving randomly interleaved tasks. Analysis of error patterns suggested that focussed learning permitted the formation of factorised task representations that were protected from mutual interference. Furthermore, individuals with a strong prior tendency to represent the task space in a factorised manner enjoyed greater benefit of focussed over interleaved training. Building artificial agents that learn to factorise tasks appropriately may be a promising route to solving continual task performance in machine learning.\n\nSignificance StatementHumans learn to perform many different tasks over the lifespan, such as speaking both French and Spanish. The brain has to represent task information without mutual interference. In machine learning, this \"continual learning\" is a major unsolved challenge. Here, we studied the patterns of errors made by humans and state-of-the-art deep networks whilst they learned new tasks from scratch and without instruction. Humans, but not machines, seem to benefit from training regimes that focussed on one task at a time, especially when they had a prior bias to represent stimuli in a way that facilitated task separation. Machines trained to exhibit the same prior bias suffered less interference between tasks, suggesting new avenues for solving continual learning in artificial systems.

neuroscience↗

bioRxiv2017

Adaptive gain control in the human dorsal anterior cingulate cortex

When making decisions, humans are often distracted by irrelevant information. Distraction has different impact on perceptual, cognitive and value-guided choices, giving rise to well-described behavioural phenomena such as the tilt illusion, conflict adaptation, or economic decoy effects. However, a single, unified model that can account for all these phenomena has yet to emerge. Here, we offer one such account, based on adaptive gain control, and additionally show that it successfully predicts a range of counterintuitive new behavioural phenomena on variants of a classic cognitive paradigm, the Eriksen flanker task. We also report that BOLD signals in a dorsal network prominently including the anterior cingulate cortex (dACC), index a gain-modulated decision variable predicted by the model. This work unifies the study of distraction across perceptual, cognitive and economic domains.

neuroscience↗

bioRxiv2017

Robust Averaging Protects Decisions from Noise in Neural Computations

An ideal observer will give equivalent weight to sources of information that are equally reliable. However, when averaging visual information, human observers tend to downweight or discount features that are relatively outlying or deviant ( robust averaging). Why humans adopt an integration policy that discards important decision information remains unknown. Here, observers were asked to judge the average tilt in a circular array of high-contrast gratings, relative to an orientation boundary defined by a central reference grating. Observers showed robust averaging of orientation, but the extent to which they did so was a positive predictor of their overall performance. Using computational simulations, we show that although robust averaging is suboptimal for a perfect integrator, it paradoxically enhances performance in the presence of \"late\" noise, i.e. which corrupts decisions during integration. In other words, robust decision strategies increase the brains resilience to noise arising in neural computations during decision-making.\n\nAuthor SummaryHumans often make decisions by averaging information from multiple sources. When all the sources are equally reliable, they should all have equivalent impact (or weight) on the decisions of an \"ideal\" observer, i.e. one with perfect memory. However, recent experiments have suggested that humans give unequal weight to sources that are deviant or unusual, a phenomenon called \"robust averaging\". Here, we use computer simulations to try to understand why humans do this. Our simulations show that under the assumption that information processing is limited by a source of internal uncertainty that we call \"late\" noise, robust averaging actually leads to improved performance. Using behavioural testing, we replicate the finding of robust averaging in a cohort of healthy humans, and show that those participants that engage in robust averaging perform better on the task. This study thus provides new information about the limitations on human decision-making.

neuroscience↗