Further Evidence for the Binding and Retrieval of Control-States From the Flanker Task

In response-interference tasks, congruency effects are reduced in trials that follow an incongruent trial. This congruence sequence effect (CSE) has been taken to reflect top-down cognitive control processes that monitor for and intervene in case of conflict. In contrast, episodic-memory accounts explain CSEs with bottom-up retrieval of stimulus-response links. Reconciling these opposing views, an emerging perspective holds that memory stores instances of control – abstract control-states – creating a shortcut for effortful control processes. Support comes from a study that assessed CSEs in a prime-target task. Here, repeating an irrelevant context feature boosted CSEs, possibly by retrieving previously stored control-states. We present a conceptual replication using the Eriksen flanker task because previous research found that CSEs in the flanker task reflect different control mechanisms than CSEs in the prime-target task. We measured CSEs while controlling for stimulus–response memory effects and manipulated contextual information (vertical spatial location) independently from the stimulus information, which introduced the conflict (horizontal spatial location). Results replicate previous findings – CSEs increased for context-repetition compared to context-changes. This study shows that retrieval of control-states is not limited to a specific task or context feature and therefore generalizes the notion that abstract control parameters are stored into trial-specific event files.

Adaptive coding of stimulus information in human fronto-parietal cortex during visual classification

Our ability to flexibly adapt to changing demands is supported by flexible coding of task-relevant information in frontal and parietal brain regions. Converging evidence suggest that coding of stimuli and task rules in these regions become stronger as task difficulty increases. Here, we tested whether there is a corresponding change in the representational format as well, an issue that has rarely been addressed directly in past research. Participants performed a visual classification task under varying levels of perceptual difficulty, while we acquired fMRI. Using a model-based representational similarity approach, we tested whether stimulus representations retain exemplar-level information. We expected representations to drop such exemplar-level information as perceptual difficulty increases, which would indicate a focus on representing behaviorally relevant category information. Counter to these expectations, and in contrast to previous research, we found frontal and parietal brain regions contained exemplar-level stimulus information. Interestingly, the anterior intraparietal sulcus (aIPS) retained exemplar-level stimulus information even in perceptually difficult trials, and these representations were directly related to performance. Overall, these findings call for a reassessment of the neural mechanisms underlying human adaptive behavior during visual classification.

Reverse-correlation reveals internal error-corrections during information-seeking

In the area of metacognition research, different methods have been used to study participants’ subjective sense of confidence in their choices. Among the most often used methods are explicit reports of subjective confidence, post-decision wagering and measuring additional info-seeking behavior. While all three methods are thought to measure confidence, they differ greatly in terms of practical execution and theoretical foundation. The method of reverse correlation has previously been used to determine which aspects of the stimulus influence decisions and confidence judgments. Here we compare the three methods of confidence assessment using reverse correlation analysis. Explicit reports and post-decision wagering revealed a positive association of stimulus information with choices and reduced decision weights for low-confidence trials. When confidence was assessed using the info-seeking method, low-confidence trials showed an inverted association with primary stimulus information. Using modelling of the behavioral data, we show how the reverse correlation results of all three methods can be explained by a simple model of confidence when internal error-corrections are allowed during seeking of additional information.

Mechanosensory Hairs and Hair-like Structures in the Animal Kingdom: Specializations and Shared Functions Serve to Inspire Technology Applications

Biological mechanosensation has been a source of inspiration for advancements in artificial sensory systems. Animals rely on sensory feedback to guide and adapt their behaviors and are equipped with a wide variety of sensors that carry stimulus information from the environment. Hair and hair-like sensors have evolved to support survival behaviors in different ecological niches. Here, we review the diversity of biological hair and hair-like sensors across the animal kingdom and their roles in behaviors, such as locomotion, exploration, navigation, and feeding, which point to shared functional properties of hair and hair-like structures among invertebrates and vertebrates. By reviewing research on the role of biological hair and hair-like sensors in diverse species, we aim to highlight biological sensors that could inspire the engineering community and contribute to the advancement of mechanosensing in artificial systems, such as robotics.

Presentation order but not duration affects binary risky choice

Risky choice behaviour often deviates from the predictions of normative models. The information search process has been suggested as a source of some reported "biases". Specifically, gaze-dependent evidence accumulation models, where unfixated alternatives' signals are discounted, propose a mechanistic account of observed associations between eye movements, choices and response times, with longer fixated alternatives being chosen more frequently. It remains debated, however, whether gaze causally influences the choice process, or rather reflects emerging preferences. Furthermore, other aspects the information search process, like the order in which information is inspected, can be confounded with gaze duration, complicating the identification of their causal influences. In our preregistered study 179 participants made repeated incentivized choices between two sequentially presented risky gambles, allowing the experimental control of presentation duration, order, and format (i.e., alternative-wise or attribute-wise). Across presentation formats, we find evidence against an influence of presentation duration on choice. The order in which participants were shown stimulus information, however, causally affected choices, with alternatives shown last being chosen more frequently. Notably, while gaze-dependent accumulation models generally capture effects of gaze duration, causal effects of stimulus order are only predicted by some models, identifying potential for future theory development.

Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission

Our brains process information using a layered hierarchical network architecture, with abundant connections within each layer and sparse long-range connections between layers. As these long-range connections are mostly unchanged after development, each layer has to locally self-organize in response to new inputs to enable information routing between the sparse in- and output connections. Here we demonstrate that this can be achieved by a well-established model of cortical self-organization based on a well-orchestrated interplay between several plasticity processes. After this self-organization, stimuli conveyed by sparse inputs can be rapidly read out from a layer using only very few long-range connections. To achieve this information routing, the neurons that are stimulated form feed-forward projections into the unstimulated parts of the same layer and get more neurons to represent the stimulus. Hereby, the plasticity processes ensure that each neuron only receives projections from and responds to only one stimulus such that the network is partitioned into parts with different preferred stimuli. Along this line, we show that the relation between the network activity and connectivity self-organizes into a biologically plausible regime. Finally, we argue how the emerging connectivity may minimize the metabolic cost for maintaining a network structure that rapidly transmits stimulus information despite sparse input and output connectivity.

Psychophysical profiles in super-recognizers

Facial identity matching ability varies widely, ranging from prosopagnosic individuals (who exhibit profound impairments in face cognition/processing) to so-called super-recognizers (SRs), possessing exceptional capacities. Yet, despite the often consequential nature of face matching decisions—such as identity verification in security critical settings—ability assessments tendentially rely on simple performance metrics on a handful of heterogeneously related subprocesses, or in some cases only a single measured subprocess. Unfortunately, methodologies of this ilk leave contributions of stimulus information to observed variations in ability largely un(der)specified. Moreover, they are inadequate for addressing the qualitative or quantitative nature of differences between SRs’ abilities and those of the general population. Here, therefore, we sought to investigate individual differences—among SRs identified using a novel conservative diagnostic framework, and neurotypical controls—by systematically varying retinal availability, bandwidth, and orientation of faces’ spatial frequency content in two face matching experiments. Psychophysical evaluations of these parameters’ contributions to ability reveal that SRs more consistently exploit the same spatial frequency information, rather than suggesting qualitatively different profiles between control observers and SRs. These findings stress the importance of optimizing procedures for SR identification, for example by including measures quantifying the consistency of individuals’ behavior.

Neuronal selectivity to complex vocalization features emerges in the superficial layers of primary auditory cortex

Early in auditory processing, neural responses faithfully reflect acoustic input. At higher stages of auditory processing, however, neurons become selective for particular call types, eventually leading to specialized regions of cortex that preferentially process calls at the highest auditory processing stages. We previously proposed that an intermediate step in how nonselective responses are transformed into call-selective responses is the detection of informative call features. But how neural selectivity for informative call features emerges from nonselective inputs, whether feature selectivity gradually emerges over the processing hierarchy, and how stimulus information is represented in nonselective and feature-selective populations remain open question. In this study, using unanesthetized guinea pigs (GPs), a highly vocal and social rodent, as an animal model, we characterized the neural representation of calls in 3 auditory processing stages—the thalamus (ventral medial geniculate body (vMGB)), and thalamorecipient (L4) and superficial layers (L2/3) of primary auditory cortex (A1). We found that neurons in vMGB and A1 L4 did not exhibit call-selective responses and responded throughout the call durations. However, A1 L2/3 neurons showed high call selectivity with about a third of neurons responding to only 1 or 2 call types. These A1 L2/3 neurons only responded to restricted portions of calls suggesting that they were highly selective for call features. Receptive fields of these A1 L2/3 neurons showed complex spectrotemporal structures that could underlie their high call feature selectivity. Information theoretic analysis revealed that in A1 L4, stimulus information was distributed over the population and was spread out over the call durations. In contrast, in A1 L2/3, individual neurons showed brief bursts of high stimulus-specific information and conveyed high levels of information per spike. These data demonstrate that a transformation in the neural representation of calls occurs between A1 L4 and A1 L2/3, leading to the emergence of a feature-based representation of calls in A1 L2/3. Our data thus suggest that observed cortical specializations for call processing emerge in A1 and set the stage for further mechanistic studies.

A complex feature-based representation of vocalizations emerges in the superficial layers of primary auditory cortex

Early in auditory processing, neural responses faithfully reflect acoustic input. At higher stages of auditory processing, however, neurons become selective for particular call types, eventually leading to specialized regions of cortex that preferentially process calls at the highest auditory processing stages. We previously proposed that an intermediate step in how non-selective responses are transformed into call-selective responses is the detection of informative call features. But how neural selectivity for informative call features emerges from non-selective inputs, whether feature selectivity gradually emerges over the processing hierarchy, and how stimulus information is represented in non-selective and feature-selective populations remain open questions. In this study, using unanesthetized guinea pigs, a highly vocal and social rodent, as an animal model, we characterized the neural representation of calls in three auditory processing stages: the thalamus (vMGB), and thalamorecipient (L4) and superficial layers (L2/3) of primary auditory cortex (A1). We found that neurons in vMGB and A1 L4 did not exhibit call-selective responses and responded throughout the call durations. However, A1 L2/3 neurons showed high call-selectivity with about a third of neurons responding to only one or two call types. These A1 L2/3 neurons only responded to restricted portions of calls suggesting that they were highly selective for call features. Receptive fields of these A1 L2/3 neurons showed complex spectrotemporal structures that could underlie their high call feature selectivity. Information theoretic analysis revealed that in A1 L4 stimulus information was distributed over the population and was spread out over the call durations. In contrast, in A1 L2/3, individual neurons showed brief bursts of high stimulus-specific information, and conveyed high levels of information per spike. These data demonstrate that a transformation in the neural representation of calls occurs between A1 L4 and A1 L2/3, leading to the emergence of a feature-based representation of calls in A1 L2/3. Our data thus suggest that observed cortical specializations for call processing emerge in A1, and set the stage for further mechanistic studies.