Causal inference

How reliably can non-invasive brain stimulation establish causal relationships in cognition, and which factors change its effects? This workshop examines the empirical, methodological and theoretical limits of causal inference with transcranial stimulation. It connects stimulation protocols, individual variability and biomarkers with multi-laboratory replication and open-science practice. The programme covers concurrent TMS-fMRI, white-matter pathways, paired associative stimulation, electric-field modelling and memory-related electrical stimulation. A satellite session includes practical TMS-

…Professor of Epidemiology and Causal Inference, Erasmus MC, Rotterdam) on 'dark causal inference' — implied but unstated causal reasoning that pervades epidemiology — the misuse of 'association' language, and gaps in teaching causal reasoning.

Center-surround processing (CSP) is a ubiquitous feature of the brain, starting from early sensory processing (e.g., context-dependent retinal encoding) to representing abstract concepts (e.g., context-dependent object representation), manifesting itself in both behavior and neural responses. While CSP in neural responses is typically modeled using divisive normalization or in purely descriptive terms, none of them can account for the bewildering range of effects documented for CSP of motion alone. Furthermore, a normative model that links these neural effects to function and behavior is still

…Our novel inference algorithm performs approximate Bayesian filtering and smoothing within a structured varia

In everyday life, the brain integrates information from different sensory modalities to form a coherent perception of the surrounding environment. This process, known as multisensory integration, enhances the brain’s response to redundant congruent sensory cues and emerges gradually during the developmental period, based on our experience with cross-modal stimuli. Consequently, integrative abilities develop in an altered form in individuals subjected to atypical multisensory experiences during the developmental period, such as those on the autistic spectrum. However, to interact with and corre

Center-surround processing (CSP) is a ubiquitous feature of the brain, starting from early sensory processing (e.g., context-dependent retinal encoding) to representing abstract concepts (e.g., context-dependent object representation), manifesting itself in both behavior and neural responses. CPS in neural responses is typically modeled using normalization models or in purely descriptive terms. However, a normative model that could enhance our understanding of the function of CSP and link these neural effects to behavior is still lacking. Here, we build on a recently developed normative model

…In this study our aim is to conduct inference and prediction on the local and global dynamics using virtual brain models, affected by disorders. Inference algorithms are necessary to efficiently estimate the unknown parameters (such as regional excitability parameters and global scaling factor on connectome), ideally incorporating measures of uncertainty. In this work, we provide f

Causal inference based on temporal relationships is fundamental for adaptive, intelligent behavior. However, standard value-based reinforcement learning (RL) models learn estimates of temporally discounted average future reward, leading to ambiguity about future reward timing and magnitude. Extending these algorithms to learn a set of values that differ in their sensitivity to reward prediction errors (RPEs) and temporal discounting can support inference. Midbrain dopamine (DA) neurons, known to encode RPEs, display signatures of learning about distributions of reward magnitude. However, it is

The idea of estimating synaptic connectivity and strength from parallel spike trains has been entertained since the late 1960s, but the last few years have seen a resurgence in proposed methods. A majority of model-free techniques estimate synaptic coupling via a “separation of timescales” (SOT) principle applied to the cross-correlogram (CCG). That is, a joint distribution is implicitly specified on paired spike trains where it is common to include a fast timescale component (modeling synaptic coupling) and a coarse timescale component (modeling network effects). Such models provide insights

Animals learn from experience the value of environmental stimuli and adjust their behavior accordingly. At the core of animal conditioning lies the capacity to associate a neural activity pattern induced by an unconditioned stimulus (US) with the pattern arising in response to a conditioned stimulus (CS). Reward-modulated associative synaptic plasticity has been successful in explaining conditioning when the neural representations of behavioral stimuli are unmixed. However, this assumption is inconsistent with the fact that neurons --- particularly in high-level, cognitive areas --- display mi

Causal inference (CI) has been proposed as a universal computational motif in the brain [Shams & Beierholm 2020]. However, its neural implementation is unclear. Likewise, Divisive Normalization (DN) has been proposed as a canonical circuit motif [Heeger 2011], but there are competing theories about the computations that DN implements (gain control, attention). In this work, we unified both by showing how an extended DN model can account for neural predictions made by a CI model [Shivkumar et al. 2022] in the context of hierarchical motion perception. Specifically, we generated CI predicti

Learning to predict rewards based on environmental cues is essential for survival. It is widely believed that animals learn to predict rewards by updating predictions whenever the outcome deviates from expectations. Such violations of predictions are called reward prediction errors (RPEs). RPEs are the critical teaching signal in the most widely accepted model for associative learning—temporal difference reinforcement learning (TDRL). As TDRL RPE has been successful at explaining the activity dynamics of dopamine, it has become the dominant theory of dopamine’s role as the critical regulator o

…regions, its usefulness in determining causal link between specific brain regions and given behav

A major challenge for causal inference from time-series data is the trade-off between computational feasibility and accuracy. Motivated by process motifs for lagged covariance in an autoregressive model with slow mean-reversion, we propose to infer networks of causal relations via pairwise edge measure (PEMs) that one can easily compute from lagged correlation matrices. Motivated by contributions of process motifs to covariance and lagged variance, we formulate two PEMs that correct for confounding factors and for reverse causation. To demonstrate the performance of our PEMs, we consider netwo

Causal inference (CI) has recently been proposed as a universal computational motif in the brain [Shams \& Beierholm 2020]. However, how CI is implemented by neural circuits, and its signatures in terms of single neuron responses, are still unclear. We have investigated this question in the context of complex motion processing. Motion perception deviates from retinal motion [Johansson 1973] a computation that can be understood in terms of hierarchical CI over which moving elements to integrate into coherent 'groups' vs segment into different ones [Gershman et al. 2016, Shivkumar et al. 202

Natural perception relies inherently on inferring causal structure in the environment. However, the neural mechanisms and functional circuits that are essential for representing and updating the hidden causal structure during multisensory processing are unknown. To address this, monkeys were trained to infer the probability of a potential common source from visual and proprioceptive signals on the basis of their spatial disparity in a virtual reality system. The proprioceptive drift reported by monkeys demonstrated that they combined historical information and current multisensory signals to e

…First, we need to solve the causal inference problem - deciding whether signals come from a common cause and thus should be integrated, or come from different sources and be treated independently. Second, when there is a common cause, we should integrate signals across the senses weighted in proportion to their sensory reliabilities. I discuss recent research at the behavioural, computational and neu