Can reinforcement learning explain the development of causal inference in multisensory integration?

Author(s):  
Thomas H Weisswange ◽  
Constantin A Rothkopf ◽  
Tobias Rodemann ◽  
Jochen Triesch
2018 ◽  
Vol 8 (1) ◽  
Author(s):  
John F. Magnotti ◽  
Kristen B. Smith ◽  
Marcelo Salinas ◽  
Jacqunae Mays ◽  
Lin L. Zhu ◽  
...  

2020 ◽  
Author(s):  
Hayley M. Dorfman ◽  
Momchil Tomov ◽  
Bernice Cheung ◽  
Dennis Clarke ◽  
Samuel J. Gershman ◽  
...  

AbstractAttributing outcomes to your own actions or to external causes is essential for appropriately learning which actions lead to reward and which actions do not. Our previous work showed that this type of credit assignment is best explained by a Bayesian reinforcement learning model which posits that beliefs about the causal structure of the environment modulate reward prediction errors (RPEs) during action value updating. In this study, we investigated the neural circuits underlying reinforcement learning that are influenced by causal beliefs using functional magnetic resonance imaging (fMRI) while human participants (N = 31; 13 males, 18 females) completed a behavioral task that manipulated beliefs about causal structure. We found evidence that RPEs modulated by causal beliefs are represented in posterior putamen, while standard (unmodulated) RPEs are represented in ventral striatum. Further analyses revealed that beliefs about causal structure are represented in anterior insula and inferior frontal gyrus. Finally, structural equation modeling revealed effective connectivity from anterior insula to posterior putamen. Together, these results are consistent with a neural architecture in which causal beliefs in anterior insula are integrated with prediction error signals in posterior putamen to update action values.Significance StatementLearning which actions lead to reward – a process known as reinforcement learning – is essential for survival. Inferring the causes of observed outcomes – a process known as causal inference – is crucial for appropriately assigning credit to one’s own actions and restricting learning to effective action-outcome contingencies. Previous studies have linked reinforcement learning to the striatum and causal inference to prefrontal regions, yet how these neural processes interact to guide adaptive behavior remains poorly understood. Here, we found evidence that causal beliefs represented in the prefrontal cortex modulate action value updating in posterior striatum, separately from the unmodulated action value update in ventral striatum posited by standard reinforcement learning models.


2019 ◽  
Vol 19 (10) ◽  
pp. 19
Author(s):  
Leslie D Kwakye ◽  
Victoria Fisher ◽  
Margaret Jackson ◽  
Oona Jung-Beeman

2018 ◽  
Author(s):  
Yinan Cao ◽  
Christopher Summerfield ◽  
Hame Park ◽  
Bruno L. Giordano ◽  
Christoph Kayser

When combining information across different senses humans need to flexibly select cues of a common origin whilst avoiding distraction from irrelevant inputs. The brain could solve this challenge using a hierarchical principle, by deriving rapidly a fused sensory estimate for computational expediency and, later and if required, filtering out irrelevant signals based on the inferred sensory cause(s). Analysing time- and source-resolved human magnetoencephalographic data we unveil a systematic spatio-temporal cascade of the relevant computations, starting with early segregated unisensory representations, continuing with sensory fusion in parietal-temporal regions and culminating as causal inference in the frontal lobe. Our results reconcile previous computational accounts of multisensory perception by showing that prefrontal cortex guides flexible integrative behaviour based on candidate representations established in sensory and association cortices, thereby framing multisensory integration in the generalised context of adaptive behaviour.


2019 ◽  
Vol 358 ◽  
pp. 355-368 ◽  
Author(s):  
Ying Fang ◽  
Zhaofei Yu ◽  
Jian K. Liu ◽  
Feng Chen

2018 ◽  
Vol 41 ◽  
Author(s):  
Jean-Paul Noel

AbstractWithin a multisensory context, “optimality” has been used as a benchmark evidencing interdependent sensory channels. However, “optimality” does not truly bifurcate a spectrum from suboptimal to supra-optimal – where optimal and supra-optimal, but not suboptimal, indicate integration – as supra-optimality may result from the suboptimal integration of a present unisensory stimuli and an absent one (audio = audio + absence of vision).


2019 ◽  
Vol 42 ◽  
Author(s):  
Roberto A. Gulli

Abstract The long-enduring coding metaphor is deemed problematic because it imbues correlational evidence with causal power. In neuroscience, most research is correlational or conditionally correlational; this research, in aggregate, informs causal inference. Rather than prescribing semantics used in correlational studies, it would be useful for neuroscientists to focus on a constructive syntax to guide principled causal inference.


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