Reading Hidden Intentions in the Human Brain

Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany.
Current Biology (Impact Factor: 9.57). 03/2007; 17(4):323-8. DOI: 10.1016/j.cub.2006.11.072
Source: PubMed


When humans are engaged in goal-related processing, activity in prefrontal cortex is increased. However, it has remained unclear whether this prefrontal activity encodes a subject's current intention. Instead, increased levels of activity could reflect preparation of motor responses, holding in mind a set of potential choices, tracking the memory of previous responses, or general processes related to establishing a new task set. Here we study subjects who freely decided which of two tasks to perform and covertly held onto an intention during a variable delay. Only after this delay did they perform the chosen task and indicate which task they had prepared. We demonstrate that during the delay, it is possible to decode from activity in medial and lateral regions of prefrontal cortex which of two tasks the subjects were covertly intending to perform. This suggests that covert goals can be represented by distributed patterns of activity in the prefrontal cortex, thereby providing a potential neural substrate for prospective memory. During task execution, most information could be decoded from a more posterior region of prefrontal cortex, suggesting that different brain regions encode goals during task preparation and task execution. Decoding of intentions was most robust from the medial prefrontal cortex, which is consistent with a specific role of this region when subjects reflect on their own mental states.

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Available from: Chris D Frith
    • "With the adoption of information-based imaging, it has become accepted practice to apply the same second-level inferential procedures to the results of first-level multivariate analyses, in particular classification accuracy (see e.g. Haxby et al., 2001; Spiridon and Kanwisher, 2002; Haynes et al., 2007): A classifier is trained on part of the data and is tested on another part, using each part for testing once (cross-validation). The classification performance is then quantified in the form of an accuracy, the fraction of correctly classified test data points. "
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    ABSTRACT: In multivariate pattern analysis of neuroimaging data, 'second-level' inference is often performed by entering classification accuracies into a t-test vs chance level across subjects. We argue that while the random effects analysis implemented by the t-test does provide population inference if applied to activation differences, it fails to do so in the case of classification accuracy or other 'information-like' measures, because the true value of such measures can never be below chance level. This constraint changes the meaning of the population-level null hypothesis being tested, which becomes equivalent to the global null hypothesis that there is no effect in any subject in the population. Consequently, rejecting it only allows to infer that there are some subjects in which there is an information effect, but not that it generalizes. This statement is supported by theoretical arguments as well as simulations. We review possible alternative approaches to population inference for information-based imaging, converging on the idea that it should not target the mean, but the prevalence of the effect in the population. One method to do so, 'permutation-based information prevalence inference using the minimum statistic', is described in detail and applied to empirical data.
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    • "There has been considerable neuroscience research reported on the primary brain system(s) involved in immediate intentions, in large part because of the greater simplicity of the problem and the greater ease with which it can be represented and manipulated experimentally. Much of this research involves studies of a phenomenon known as prospective memory (remembering to perform an intended action in the future), with results indicating that several areas within the prefrontal cortex play a central role in the formation and maintenance of intentions delayed into the future (den Ouden, Frith, Frith et al., 2005; Haynes, Sakai, Rees et al., 2007; Burgess, Gonen-Yaacovi, & Volle, 2011). "
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    • "However, to gain deeper insight into cognitive processing, it is important to assess information processing in these regions throughout the course of the task. Multivariate methods provide a possible way to achieve this, by discerning more finely grained aspects of information processing from patterns of voxel activation that form the multivariate fMRI blood oxygenation level dependent (BOLD) time series (Haynes and Rees, 2006; Kriegeskorte et al., 2006; Norman et al., 2006; Haynes et al., 2007). These methods can illuminate different stages of cognitive processing and thereby enhance our understanding of the computations occurring in specific regions. "
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    ABSTRACT: Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze (RAM) task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity.
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