Title: Is Perception Probabilistic?
Scientific question
Is perception probabilistic? This is a grand question about the fundamental nature of how our mind operates and how this operation is implemented in brain circuits. The question is also interpreted differently by different people, making progress difficult. In this adversarial collaboration, four scien- tists with different backgrounds and perspectives on this issue1–7 engaged in considerable discussion of how the issue of probabilistic perception can be operationalized as to be made empirically tracta- ble. We have agreed that a critical dividing line between probabilistic and non-probabilistic views of perception is defined by the following question:
Can humans perform flexible and deliberate (i.e., cognitive) computations that require access to more than summary statistics (mean and variance) of a perceptual likelihood function or posterior on a single-trial basis?


Is Perception Probabilistic?

Title: Generalization of information - Integrative encoding or category-based inference?
Scientific question: How do biological organisms generalize previously-learned information for adaptive behaviour in novel experiences? This broad question spans interdisciplinary fields - from decision-making and perception, psychology, memory neuroscience, to theoretical considerations in machine learning and artificial intelligence (Cortese et al. 2019). One prominent theory (integrative encoding) suggests that when new memories are being made, we activate information from overlapping memory representations so that information from both are integrated and reencoded together. Behaviour in novel situations can be guided by simple recall of information related to these extended associative links. Although this mechanism is very simple, computationally this process could become tedious given the inestimable extent of potential (and sometimes unnecessary) associative links that could be formed. A different theory (category-based inference) suggests a computationally simpler mechanism: that humans use abstract thoughts, such as functional categorization, to make their behaviour more efficient. Categories provide a logical structure through which information learned for one stimulus may be generalized to other stimuli (members of the same category). However, this theory requires much higher order, complicated, abstractions than does the former. One possibility is that both of these theories might be valid, with an organism implementing different strategies dependent on their current circumstances. However, to date there exists no study that has clearly dissected the contribution of both theories, nor made formal predictions on how they could or should differ in neural or behavioural terms.

Generalization of information - integrative encoding or category-based inference?

Title: Do grid codes afford generalization and flexible decision-making?
Scientific question: Grid-like codes in the human entorhinal cortex (EC) have been proposed to take part in learning and representing structural knowledge, which enables efficient planning and model-based decision-making. However, it has not yet been established whether these grid-like codes serve to transfer learning and to generalize previous experiences for inferences in novel situations. Do grid codes provide consistent context-invariant representations of structural relationships or do they flexibly switch their reference frame according to the current task demand to plan/discover optimal decision policy? Here we aim to reveal the constraints, conditions, and potential transformations of grid codes in the human brain that would afford flexible generalization.

Do grid codes afford generalization and flexible decision-making?

Title: Is it that simple? The use of linear models in cognitive neuroscience
Scientific question: Do linear models provide an accurate, interpretable, and biologically plausible description of brain activity?

Is it that simple? The use of linear models in cognitive neuroscience

Title: Competing theories of probabilistic computations in the brain. Scientific question
How does neural activity represent probability distributions and how are probabilistic computations implemented in neural circuits?

Competing theories of probabilistic computations in the brain.

Title: Dimensionality and flexibility of learning in biological recurrent neural networks
Scientific Question: Does full-rank gradient descent accurately describe the dynamics of
synaptic plasticity in biological recurrent neural networks?