Meaningful patterns of information in the brain revealed through analysis of errors

Abstract

Great excitement has surrounded our ability to decode task information from human brain activity patterns, reinforcing the dominant view of the brain as an information processor. We tested a fundamental but overlooked assumption: that such decodable information is actually used by the brain to generate cognition and behaviour.Participants performed a challenging stimulus-response task during fMRI. Our novel analyses trained a pattern classifier on data from correct trials, and used it to examine stimulus and rule coding on error trials. There was a striking interaction in which frontoparietal cortex systematically represented incorrect rule but correct stimulus information when participants used the wrong rule, and incorrect stimulus but correct rule information on other types of errors. Visual cortex, by contrast, did not code correct or incorrect information on error. Thus behaviour was tightly linked to coding in frontoparietal cortex and only weakly linked to coding in visual cortex. Human behaviour may indeed result from information-like patterns of activity in the brain, but this relationship is stronger in some brain regions than in others. Testing for information coding on error can help establish which patterns constitute behaviourally-meaningful information.

Temporal variabilities provide additional category-related information in object category decoding: a systematic comparison of informative EEG features

“…Another important question in cognitive neuroscience has been whether (if at all) neuroimaging data can explain behavior (Williams et al, 2007;Ritchie et al, 2015;Woolgar et al, 2019). Although many recent studies have found correlations between the neural decoding and behavioral performance in object and face recognition (Karimi-Rouzbahani et al, 2019;Karimi-Rouzbahani et al, 2020a;Dobs et al, 2019), as we also did in the current study, one question that has remained unanswered was whether a more optimal neural code for objects, which is found using feature extraction, could explain the behavioral performance more accurately.…”

“…A major open question in neuroimaging is whether the information that is extracted from neural activity is relevant or is just epiphenomenal to the target conditions. To answer this question, recent efforts have tried to explain behavioral performance using the neural decoding results on the same experiment (Williams et al, 2007;Grootswagers et al, 2018;Woolgar et al, 2019). These studies found that the decoding accuracy obtained by analyzing mean signal activations can predict the behavioral performance in object recognition (Ritchie, et al, 2015).…”

Neural signatures of vigilance decrements predict behavioural errors before they occur

“…Fourth, building upon our recently-developed method of error analysis (Woolgar et al, 2019), we were able to predict forthcoming behavioural misses before the response was given. This method only used correct trials for training, which makes its implementation plausible for real-world situations since we usually have plenty of correct trials and only few miss trials (i.e., cases when the railway controller diverts the trains correctly vs. misses and a collision happens).…”

“…We next move on to our second question, which is whether these neural representations change when overt behaviour is affected, and therefore, whether we can use the neural activity as measured by MEG to predict behavioural errors before they occur. We used our method of error data analysis (Woolgar et al, 2019) to examine whether the patterns of information coding on miss trials differed from correct trials (see Methods). For these analyses we used only attended dots, as unattended dots do not have behavioural responses, and we matched the total number of trials in our implementation of correct and miss classification.…”

Concurrent neuroimaging and neurostimulation reveals a causal role for dlPFC in coding of task-relevant information

“…One possibility is that TMS reduced the person's ability to benefit from congruency because it interfered with processes involved in recognising or responding to congruency signals, but this is not something we could decode with this design. Nonetheless, part of the promise of using concurrent TMS-fMRI with cognitive tasks is the possibility of relating neural activity to behaviour, and future work may capitalise on this by using simpler designs where both neural data and behaviour can be analysed for the same information, or, for example, by interrogating the information content on behavioural errors (Woolgar, Dermody, Afshar, Williams, & Rich, 2019).…”