Reading positional codes with fMRI: Problems and solutions.

Kristjan Kalm,Dennis Norris,Floris P De Lange

doi:10.1371/journal.pone.0176585

Kristjan Kalm, Dennis Norris + Show 1 more

Open Access

https://doi.org/10.1371/journal.pone.0176585

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Abstract

Neural mechanisms which bind items into sequences have been investigated in a large body of research in animal neurophysiology and human neuroimaging. However, a major problem in interpreting this data arises from a fact that several unrelated processes, such as memory load, sensory adaptation, and reward expectation, also change in a consistent manner as the sequence unfolds. In this paper we use computational simulations and data from two fMRI experiments to show that a host of unrelated neural processes can masquerade as sequence representations. We show that dissociating such unrelated processes from a dedicated sequence representation is an especially difficult problem for fMRI data, which is almost exclusively the modality used in human experiments. We suggest that such fMRI results must be treated with caution and in many cases the assumed neural representation might actually reflect unrelated processes.

Highlights

One of the most important features of human cognition is the ability to bind individual events into a sequence
We show that with any sequence processing task there are experimental variables which are collinear with the positional signal and which can serve as a positional code
In this paper we have explored two types of processes that could enable an experimenter to read out a positional ‘code’ in the absence of a dedicated positional code

Summary

Introduction

One of the most important features of human cognition is the ability to bind individual events into a sequence. If we assume that any collinear process to sequence position affects all voxels in the brain region uniformly simple de-meaning of the response matrix will eliminate any univariate signal from the data. If we simulate additive interference as described above despite the brain region only encoding item identity information we can linearly separate patterns Y in terms of their position because the total activity increases as a function of position. For the purposes of creating more positions the following plot (Fig 13) displays data generated exactly as above but with 5-item sequences instead of three Such an effect of positional pattern similarity has be observed in a number of animal and human studies [28, 29, 51, 52] and interpreted as a signature of positional code. It follows that the positional lag effect alone is not a sufficient evidence for a neural positional code and additional statistical tests, such as classification analysis, are required

Summary of item interference effects

Discussion

Conclusions