Florian Sandhaeger

Neural activity is selective for the identity and semantic category of natural visual stimuli. In human neuroimaging, this selectivity can be investigated using multivariate pattern analysis. The resulting neural information is thought to reflect neural processes underlying object recognition. However, due to the complexity of natural stimuli, it remains unclear to what extent different visual features drive this information. To address this, we presented natural stimuli from different categories as well as several degraded versions, in which specific visual features were selectively manipulated, to human participants while recording magnetoencephalography (MEG). We applied multivariate pattern analysis and decomposed the resulting high-dimensional neural representational geometry into its distinct contributions from retinotopy, total spatial frequency and orientation energy, global shape, local shape, and textural correlation structure. These features contributed differentially to the neural information, with retinotopy and global shape being the strongest features underlying image and category information, respectively. Our results demonstrate how multiple statistical features of visual stimuli jointly and dynamically account for neural image and category selectivity, providing a basis for a better understanding of human object recognition.

An increasing body of evidence has demonstrated neural representations of choices independent of the motor actions used to report them – so-called abstract choices. However, it remains unclear whether such representations arise due to dynamic changes in choice-response associations or reflect a general property of decision-making. Here, we show that in the human brain, choices are represented abstractly even when choice-response associations remain stable over time. We recorded neural activity using magnetoencephalography while participants performed a motion discrimination task, with choice-response mappings held constant within blocks. We found neural information about participants’ perceptual choices independent of both motor response and visual stimulus. Choice information increased during the stimulus and peaked after the response. Moreover, choice and response information showed distinct cortical distributions, with choice-related signals strongest in frontoparietal regions. Thus, abstract choice representations are not limited to dynamic or action-independent contexts and may be a general feature of decision-making.

Perceptual decisions have long been framed in terms of the actions used to report a choice. Accordingly, studies of perceptual decision-making have historically relied on tasks with fixed choice-response mappings, in which choice and motor response are inextricably linked. Although several studies have since dissociated choice and response, they have typically involved dynamic switching of the choice-response mapping on a trial-by-trial basis. Thus, it remains unclear if abstract choice representations arise specifically when choice-response relationships change dynamically, or if they reflect a more general property of the decision-making process. Here, we show that in the human brain, choices are represented abstractly, even when the association between choice and motor response remains stable over time. We measured neural activity in humans using magnetoencephalography (MEG) while participants performed a motion discrimination task. Importantly, the associations between perceptual choice and motor response were balanced across experimental conditions and remained stable over many trials. We found neural information about the participants’ perceptual choice, independent of both the motor response and the visual stimulus. This abstract choice information increased during the stimulus period and peaked after the response had been made. Furthermore, choice and response information showed distinct cortical distributions, with strongest choice information in frontoparietal regions. Our results suggest that abstract choice representations are not restricted to action-independent contexts or those with dynamic choice-response associations and may therefore reflect a general role in perceptual decision-making.

Humans can make abstract choices independent of motor actions. However, in laboratory tasks, choices are typically reported with an associated action. Consequentially, knowledge about the neural representation of abstract choices is sparse, and choices are often thought to evolve as motor intentions. Here, we show that in the human brain, perceptual choices are represented in an abstract, motor-independent manner, even when they are directly linked to an action. We measured MEG signals while participants made choices with known or unknown motor response mapping. Using multivariate decoding, we quantified stimulus, perceptual choice, and motor response information with distinct cortical distributions. Choice representations were invariant to whether the response mapping was known during stimulus presentation, and they occupied a distinct representational space from motor signals. As expected from an internal decision variable, they were informed by the stimuli, and their strength predicted decision confidence and accuracy. Our results demonstrate abstract neural choice signals that generalize to action-linked decisions, suggesting a general role of an abstract choice stage in human decision-making.

Speech, as the spoken form of language, is fundamental for human communication. The phenomenon of covert inner speech implies functional independence of speech content and motor production. However, it remains unclear how a flexible mapping between speech content and production is achieved on the neural level. To address this, we recorded magnetoencephalography in humans performing a rule-based vocalization task. On each trial, vocalization content (one of two vowels) and production form (overt or covert) were instructed independently. Using multivariate pattern analysis, we found robust neural information about vocalization content and production, mostly originating from speech areas of the left hemisphere. Production signals dynamically transformed upon presentation of the content cue, whereas content signals remained largely stable throughout the trial. In sum, our results show dissociable neural representations of vocalization content and production in the human brain and provide insights into the neural dynamics underlying human vocalization.

Speech, as the spoken form of language, is fundamental for human communication. The phenomenon of covert inner speech implies a functional independence of speech content and motor production. However, it remains unclear how a flexible mapping between speech content and production is achieved on the neural level. To address this, we recorded magnetoencephalography (MEG) in humans performing a rule-based vocalization task. On each trial, vocalization content (one of two vowels) and production form (overt or covert) were instructed independently. Using multivariate pattern analysis, we found robust neural information about vocalization content and production, mostly originating from speech areas of the left hemisphere. Production signals dynamically transformed upon presentation of the content cue, whereas content signals remained largely stable throughout the trial. In sum, our results show dissociable neural representations of vocalization content and production in the human brain and provide new insights into the neural dynamics underlying human vocalization.

Humans can make abstract choices independent of motor actions. However, in laboratory tasks, choices are typically reported with an associated action. Consequentially, knowledge about the neural representation of abstract choices is sparse, and choices are often thought to evolve as motor intentions. Here, we show that in the human brain, perceptual choices are represented in an abstract, motor-independent manner, even when they are directly linked to an action. We measured MEG signals while participants made choices with known or unknown motor response mapping. Using multivariate decoding, we quantified stimulus, perceptual choice and motor response information with distinct cortical distributions. Choice representations were invariant to whether the response mapping was known during stimulus presentation, and they occupied distinct representational spaces from both stimulus and motor signals. Furthermore, their strength predicted decision confidence and accuracy, as expected from an internal decision variable. Our results uncover abstract neural choice signals that generalize to action-linked decisions, suggesting a general role of an abstract choice stage in human decision-making.