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Prefrontal function and cognitive control: from action to language
... (B) Based on the premises outlined in (A), we conceive of language as an emergent function (iii) derived from functionally specialized modules (i) coming together to form intermediate modules in charge of (at least) semantic cognition and sensorimotor control (ii) (other components might be added in future iterations of the model, cf. Dehaene & Cohen, 2007;Rouault & Koechlin, 2018). Although these modules are capable of functioning autonomously, they can combine at a higher level still to produce linguistically mediated cognition (iii). ...
... Future iterations of this model will hopefully feature other structures as their contribution to linguistic processing becomes clearer. These include the anterior and dorsolateral prefrontal systems whose involvement in action planning and flexibility seems to underlie the various levels of language production (Bourguignon, 2014;Fuster, 2015;Rouault & Koechlin, 2018), the (pre)supplementary motor and This document is copyrighted by the American Psychological Association or one of its allied publishers. ...
... Famous speculations on the evolutionary origins of language have already evoked that some of its core computational features may have been exapted from preexisting cognitive capacities (Hauser et al., 2002). Other candidate features include statistical learning (Frost et al., 2015;Thiessen, 2020), adaptive aspects of sensorimotor control (Houde & Jordan, 1998), or management of uncertainty (Bourguignon, 2014;Rouault & Koechlin, 2018). Integrating these hypotheses within a single computational framework might usefully complement efforts to characterize the neurocognitive architecture of language as emergent property of the mind-brain. ...
The capacity for language has evolved remarkably quickly in recent human history. Its advent likely coincided with a range of cognitive innovations not found elsewhere at this level of complexity in the rest of the animal kingdom. This late yet near-simultaneous florescence of higher language and cognition is difficult to account for in terms of strictly modular neurocognitive systems, each with its own dedicated function and evolutionary trajectory. Nor does it legitimize the neurocognitive study of language in isolation from other systems of human thought and action. In the wake of emergentist approaches to key human cognitive abilities the present paper considers language as the differentiated product of multiple neural networks dedicated to qualitatively distinct cognitive functions – including (at least) the cortical systems of general semantic cognition and control and the sensorimotor systems supporting language production. A model is proposed to account for how these systems congregate to produce language, featuring a dual-stream architecture of the semantic interface into item-based and item-independent semantic knowledge on the one hand, and a notion of the sensorimotor interface as a key component for the temporal tracking and verbal rehearsal of task-relevant information on the other. Avenues are also offered for enriching this architecture in future versions of the model. Finally, it is proposed that language is an ‘optimal’ combination of these neurocognitive systems, enabling fast and cost-effective transfer of information at the systems level. This last point underpins evidence for the privileged status of language as a tool for adaptive thought and behavior, as well as some important features of brain evolution, development and functional organization.
... More than any other area in the brain, the development of the prefrontal cortex (PFC) early in infancy plays a major role for the acquisition of models, patterns and for the manipulation of structured knowledge. For instance, the features of the PFC play an important role for developing logical inference and algebra, for the acquisition of language and music [1], [2], [3], [4], for the learning of task sets and the resolution of rule-based problems [5], [6], [7]. We propose that what does essentially the PFC is to manipulate items and patterns independently; where a pattern is a sequence, a cluster or a group of several items or units. ...
... In line with [1], [79], [12], [80] who supports the view that the brain holds some exclusive mechanisms for manipulating symbolic nested trees, the Broca area appears clearly to hold one of those mechanisms for the detection of the complexity pattern in sequences [4]. We might suspect that the Broca area is functional very rapidly during infancy since babies and even neonates appear to be sensitive to syntax in proto-words [81], [82], [83], [9], [10]; see also the computational models of Dominey in [84], [76], [54]. ...
... In III, Harlow's experiment on learning-to-learn, similar to Piaget's A-not-B problem [86]. Higher-order task sets can be learned to choose and infer the optimal strategy in a hierarchical reward-based decision-making [4], [45], and for which reward conditioning on items does not work. A serial-order rule (a task-set) can solve this problem. ...
In order to keep trace of information and grow up, the infant brain has to resolve the problem about where old information is located and how to index new ones. We propose that the immature prefrontal cortex (PFC) uses its primary functionality of detecting hierarchical patterns in temporal signals as a second feature to organize the spatial ordering of the cortical networks in the developing brain itself. Our hypothesis is that the PFC detects the hierarchical structure in temporal sequences in the shape of ordinal patterns and use them to index information hierarchically in different parts of the brain. Henceforth, we propose that this mechanism for detecting ordinal patterns participates also in
the hierarchical organization of the brain during development; i.e., the bootstrapping of the connectome. By doing so, it gives the tools to the language-ready brain for manipulating abstract knowledge and for planning temporally ordered information; i.e., the emergence of causality and symbolic thinking. In this position paper, we will review several neural models from the literature that support serial ordering and propose an original one. We will confront then our ideas with evidences from developmental, behavioral and brain results.
... More than any other area in the brain, the prefrontal cortex (PFC) plays a major role for the acquisition of models, patterns and for the manipulation of structured knowledge. For instance, the features of the PFC make it an important place for the development of logical inference and algebra, for the acquisition of language and music [34,90,91,138], for the learning of task sets and the resolution of rule-based problems [137,155,176]. We propose that what does essentially the PFC is to separate and to manipulate items and patterns; see tables 1) and 2) in the Annex section for a glossary of the two terms employed in the paper. ...
... In line with [34] who supports the view that the brain holds some exclusive mechanisms for manipulating symbolic nested trees, the Broca area appears clearly to hold one of those mechanisms for the detection of the complexity pattern in sequences [138]. We might suspect that the Broca aera is functional very rapidly during infancy since babies and even neonates appear to be sensitive to syntax in proto-words [139,113,104,64,20]; see also the computational models of the frontal areas done by Dominey to explain these results in [40,38,39]. ...
... Koechlin uses also information theory and Bayesian theory to explain executive and hierarchical control provided by the prefrontal area on more posterior brain areas [58,93,138], which is in support of our ideas. The difference with ours relies on our explanations and on the underlying mechanisms we propose using rank-codes: how information is coded and its potentially strong impact on how the brain is functionally organized to process, keep and retrieve information, as well as its computational cost. ...
In order to keep trace of information, the brain has to resolve the problem where information is and how to index new ones. We propose that the neural mechanism used by the prefrontal cortex (PFC) to detect structure in temporal sequences, based on the temporal order of incoming information, has served as second purpose to the spatial ordering and indexing of brain networks. We call this process, apparent to the manipulation of neural 'addresses' to organize the brain's own network, the 'digitalization' of information. Such tool is important for information processing and preservation, but also for memory formation and retrieval.
... Yet, while a model can include an arbitrary number of hierarchies, there is not an infinity of corresponding specialized brain regions. Computational hierarchies, especially those of higher cognitive functions that can expand to an infinite depth, are therefore likely embodied by information exchanges among a limited number of functionally specialized regions, through reciprocal interactions that can theoretically implement unlimited hierarchical structures using only two abstract chunking levels [57,58]. These information exchanges reflect the probabilistic mappings in the comprehender's internal model, as shown in Figs 2 and 3, and play an important role for linking the model's computational principles to neurophysiological data of speech information processing in the human brain. ...
... The proposed approach is fundamentally different from a purely data-driven one that identifies neural response patterns correlated with pooled activities from hidden layers of a neural network trained on specific tasks of next-input predictions such as in [62][63][64]. The brain interacts with the external stimuli, whether linguistic or not, in a structured fashion that is likely reused across different domains [44,58]. Thus, a clear computational interpretation of brain activity patterns requires an explicit representation of such structures that is lacking in most neural network models. ...
Understanding speech requires mapping fleeting and often ambiguous soundwaves to meaning. While humans are known to exploit their capacity to contextualize to facilitate this process, how internal knowledge is deployed online remains an open question. Here, we present a model that extracts multiple levels of information from continuous speech online. The model applies linguistic and nonlinguistic knowledge to speech processing, by periodically generating top-down predictions and incorporating bottom-up incoming evidence in a nested temporal hierarchy. We show that a nonlinguistic context level provides semantic predictions informed by sensory inputs, which are crucial for disambiguating among multiple meanings of the same word. The explicit knowledge hierarchy of the model enables a more holistic account of the neurophysiological responses to speech compared to using lexical predictions generated by a neural network language model (GPT-2). We also show that hierarchical predictions reduce peripheral processing via minimizing uncertainty and prediction error. With this proof-of-concept model, we demonstrate that the deployment of hierarchical predictions is a possible strategy for the brain to dynamically utilize structured knowledge and make sense of the speech input.
... We found two distinct anterior and posterior subregions of the LPFC associated with representations of high-level musical structure and low-level elementary movements, respectively. This is in accordance with the functional anterior-to-posterior LPFC gradients postulated by models of action control (Miller and Cohen 2001;Koechlin et al. 2003;Wood and Grafman 2003;Badre and Nee 2018;Rouault and Koechlin 2018), based on neuroimaging studies revealing progressively more anterior activity in LPFC during response to progressively more complex stimuli (reviewed by Koechlin & Summerfield, 2007). For example, while movement sequences in response to simple sensory cues evoked activity in motor and premotor regions (BA4/6) (Koechlin et al. 2003;Koechlin and Jubault 2006;Badre and D'Esposito 2007), more complex sequences coordinated across nested temporal frames (Nee and D'Esposito 2017;Shahnazian et al. 2021) or embedded in hierarchical patterns (Koechlin and Jubault 2006) recruited anterior frontal regions (BA44/45/9 up to BA46/10). ...
... Overall, these parallels between spoken language and music production support the idea that the left IFG plays a domain-general role in sequential behaviors (Fitch and Martins 2014;Bornkessel-Schlesewsky et al. 2015;Rouault and Koechlin 2018) by acting as a multimodal association zone or cortical hub (Friederici and Singer 2015) that links cognitive and sensorimotor networks. Our approach using production of musical sequences is promising to further illuminate this link. ...
Complex sequential behaviors, such as speaking or playing music, entail flexible rule-based chaining of single acts. However, it remains unclear how the brain translates abstract structural rules into movements. We combined music production with multimodal neuroimaging to dissociate high-level structural and low-level motor planning. Pianists played novel musical chord sequences on a muted MR-compatible piano by imitating a model hand on screen. Chord sequences were manipulated in terms of musical harmony and context length to assess structural planning, and in terms of fingers used for playing to assess motor planning. A model of probabilistic sequence processing confirmed temporally extended dependencies between chords, as opposed to local dependencies between movements. Violations of structural plans activated the left inferior frontal and middle temporal gyrus, and the fractional anisotropy of the ventral pathway connecting these two regions positively predicted behavioral measures of structural planning. A bilateral frontoparietal network was instead activated by violations of motor plans. Both structural and motor networks converged in lateral prefrontal cortex, with anterior regions contributing to musical structure building, and posterior areas to movement planning. These results establish a promising approach to study sequence production at different levels of action representation.
... The networks associated with high-level structure and low-level motor planning involved anterior and posterior prefrontal regions, respectively. This observation is reminiscent of functional anteriorto-posterior gradients in PFC postulated by models of action control (Badre and Nee 2018;Rouault and Koechlin 2018). Action control is generally referred to as the ability to select and coordinate actions or thoughts in relation to internal higher-level goals, and is a cardinal function of the lateral PFC (Miller and Cohen 2001;Koechlin et al. 2003;Wood and Grafman 2003). ...
... This resonates with the above-raised idea that frequently co-occurring structural elements may consolidate in overlearned chunks that are represented more posteriorly in the frontal hierarchy. Overall, these parallels between speech and music production are in line with ideas that the IFG plays a domaingeneral role in controlling sequential behaviours (Fitch and Martins 2014;Bornkessel-Schlesewsky et al. 2015;Rouault and Koechlin 2018) by acting as a multimodal association zone or "cortical hub" (Friederici and Singer 2015) that links cognitive and sensorimotor networks and thus supports the formation of distributed action circuits. ...
Complex sequential behaviours, such as speaking or playing music, often entail the flexible, rule-based chaining of single acts. However, it remains unclear how the brain translates abstract structural rules into concrete series of movements. Here we demonstrate a multi-level contribution of anatomically distinct cognitive and motor networks to the execution of novel musical sequences. We combined functional and diffusion-weighted neuroimaging to dissociate high-level structural and low-level motor planning of musical chord sequences executed on a piano. Fronto-temporal and fronto-parietal neural networks were involved when sequences violated pianists’ structural or motor plans, respectively. Prefrontal cortex is identified as a hub where both networks converge within an anterior-to-posterior gradient of action control linking abstract structural rules to concrete movement sequences.
... This idea is in line with the view that brain areas supporting language processing are separable from those supporting domain general cognitive control (Fedorenko, 2014). Others argue for a more integrative view, in which language and domain general cognitive control are more intimately intertwined (Rouault and Koechlin, 2018;Bourguignon and Gracco, 2019). Differences between automatic and non-automatic language processing are here explained as differences along the temporal axis of cognitive control, whereby highly automatic language processing involves chunking processes within a single (not hierarchically structured) task-set while non-automatic linguistic processes are supposed to involve the generation of successive independent task-sets (Rouault and Koechlin, 2018). ...
... Others argue for a more integrative view, in which language and domain general cognitive control are more intimately intertwined (Rouault and Koechlin, 2018;Bourguignon and Gracco, 2019). Differences between automatic and non-automatic language processing are here explained as differences along the temporal axis of cognitive control, whereby highly automatic language processing involves chunking processes within a single (not hierarchically structured) task-set while non-automatic linguistic processes are supposed to involve the generation of successive independent task-sets (Rouault and Koechlin, 2018). In a similar vein, an integrative view of language and cognitive control is supported by the observation that brain regions that are specialized in language processing and those that belong to domain-general control networks are closely linked during cognitive control in language production tasks (Bourguignon and Gracco, 2019) 6 . ...
Recent scholarship emphasizes the scaffolding role of language for cognition. Language, it is claimed, is a cognition-enhancing niche (Clark, 2006), a programming tool for cognition (Lupyan and Bergen, 2016), even neuroenhancement (Dove, 2019) and augments cognitive functions such as memory, categorization, cognitive control, and meta-cognitive abilities (“thinking about thinking”). Yet, the notion that language enhances or augments cognition, and in particular, cognitive control does not easily fit in with embodied approaches to language processing, or so we will argue. Accounts aiming to explain how language enhances various cognitive functions often employ a notion of abstract representation. Yet, embodied approaches to language processing have it that language processing crucially, according to some accounts even exclusively, involves embodied, modality-specific, i.e., non-abstract representations. In coming to understand a particular phrase or sentence, a prior experience has to be simulated or reenacted. The representation thus activated is embodied (modality-specific) as sensorimotor regions of the brain are thereby recruited. In this paper, we will first discuss the notion of representation, clarify what it takes for a representation to be embodied or abstract, and distinguish between conceptual and (other) linguistic representations. We will then put forward a characterization of cognitive control and examine its representational infrastructure. The remainder of the paper will be devoted to arguing that language augments cognitive control. To that end, we will draw on two lines of research, which investigate how language augments cognitive control: (i) research on the availability of linguistic labels and (ii) research on the active usage of a linguistic code, specifically, in inner speech. Eventually, we will argue that the cognition-enhancing capacity of language can be explained once we assume that it provides us with (a) abstract, non-embodied representations and with (b) abstract, sparse linguistic representations that may serve as easy-to-manipulate placeholders for fully embodied or otherwise more detailed representations.
... More than any other brain areas, the PFC can extract abstract rules and parametric information within structured data in order to carry out a plan [20,21,22]. This aspect makes it particularly important for problem-solving tasks, language and maths [23,24,25,26]. 80 Experiments carried out on subjects performing hierarchical tasks such as drawing a geometrical figure [27,28] or detecting temporal patterns within action sequences [29,30] have permitted identification of some properties of PFC neurons for binding features and for higher-order sequence planning. ...
... To summarize, we suggest that this system presents some capabilities suited 785 for learning linguistic systems (e.g., a grammar of rules) and timely ordered behaviors. Since Inferno Gate encodes temporal patterns in an abstract manner, like AAB or ABA patterns, we can expect that by adding another layer to the model, presumably the Polar Frontal Cortex as proposed in [26], it may be possible to create sequences of sequences such as ((AAB)B(AAB)) with A=(AAB) 790 and the ABA pattern, mixing two or more temporal patterns in an iterative fashion. In this way, our network may be extended to fractal-coding to have a hierarchical representation of sequences at any depth. ...
We present a framework based on iterative free-energy optimization with spiking neural networks for modeling the fronto-striatal system (PFC-BG) for the generation and recall of audio memory sequences. In line with neuroimaging studies carried out in the PFC, we propose a genuine coding strategy using the gain-modulation mechanism to represent abstract sequences based solely on the rank and location of items within them. Based on this mechanism, we show that we can construct a repertoire of neurons sensitive to the temporal structure in sequences from which we can represent any novel sequences. Free-energy optimization is then used to explore and to retrieve the missing indices of the items in the correct order for executive control and compositionality. We show that the gain-modulation mechanism permits the network to be robust to variabilities and to have long-term dependencies as it implements a gated recurrent neural network. This model, called Inferno Gate, is an extension of the neural architecture Inferno standing for Iterative Free-Energy Optimization of Recurrent Neural Networks with Gating or Gain-modulation. In experiments performed with an audio database of ten thousand MFCC vectors, Inferno Gate is capable of encoding efficiently and retrieving chunks of fifty items length. We then discuss the potential of our network to model the features of working memory in the PFC-BG loop for structural learning, goal-direction and hierarchical reinforcement learning.
... Any framework that uses phase coding for syntactic structure building should find means to encode an adequate level of hierarchical depth while staying within the limits of the oscillatory depth. To this end, we find promise in the idea of implementing recursion through a two-level abstract chunking structure and a backward loop from the lower to the higher level 9,10 . Currently, this is largely an algorithmic level proposal; future research on the underlying neurobiological mechanisms will evaluate its implementational feasibility. ...
... The neural pathways that support these metacognitive computations are distinct from the neural pathways that support first-order action performance and sensorimotor predictions (Beran, 2019;Couchman, Beran, Coutinho, Boomer, & David Smith, 2013;Kepecs & Mainen, 2012;Proust, 2019;Cai et al., 2022). 2. A hierarchy among human brain systems reflects the evolution of control mechanisms towards enhanced exploratory flexibility (Koechlin, Summerfield, & S, 2007;Rouault & Koechlin, 2018). 3. ...
Curious information-seeking is known to be a key driver for learning, but characterizing this important psychological phenomenon remains a challenge. In this article, we argue that solving this challenge requires qualifying the relationships between metacognition and curiosity. The idea that curiosity is a metacognitive competence has been resisted: researchers have assumed both that young children and non-human animals can be genuinely curious, and that metacognition requires conceptual and culturally situated resources that are unavailable to young children and non-human animals. Here, we argue that this resistance is unwarranted given accumulating evidence that metacognition can be deployed procedurally, and we defend the view that curiosity is a metacognitive feeling. Our metacognitive view singles out two monitoring steps as a triggering condition for curiosity: evaluating one's own informational needs, and predicting the likelihood that explorations of the proximate environment afford significant information gains. We review empirical evidence and computational models of curiosity, and show that they fit well with this metacognitive account, while on the contrary, they remain difficult to explain by a competing account according to which curiosity is a basic attitude of questioning. Finally, we propose a new way to construe the relationships between curiosity and the human-specific communicative practice of questioning, discuss the issue of how children may learn to express their curiosity through interactions with others, and conclude by briefly exploring the implications of our proposal for educational practices.
... The neural pathways that support these metacognitive computations are distinct from the neural pathways that support first-order action performance and sensorimotor predictions (Beran, 2019;Couchman, Beran, Coutinho, Boomer, & David Smith, 2013;Kepecs & Mainen, 2012;Proust, 2019;Cai et al., 2022). 2. A hierarchy among human brain systems reflects the evolution of control mechanisms towards enhanced exploratory flexibility (Koechlin, Summerfield, & S, 2007;Rouault & Koechlin, 2018). 3. ...
... The neural pathways that support these metacognitive computations are distinct from the neural pathways that support first-order action performance and sensorimotor predictions (Beran, 2019;Couchman, Beran, Coutinho, Boomer, & David Smith, 2013;Kepecs & Mainen, 2012;Proust, 2019;Cai et al., 2022). 2. A hierarchy among human brain systems reflects the evolution of control mechanisms towards enhanced exploratory flexibility (Koechlin, Summerfield, & S, 2007;Rouault & Koechlin, 2018). 3. ...
Curious information-seeking is known to be a key driver of learning, but characterizing this important psychological phenomenon remains a challenge. In this article, we argue that this requires qualifying the relationships between metacognition and curiosity. The idea that curiosity is a metacognitive competence has been resisted: many researchers have assumed both that young children and non-human animals can be genuinely curious, and that metacognition requires conceptual and culturally situated resources that are unavailable to young children and non-human animals. We suggest that this resistance is unwarranted given accumulating evidence that metacognition can be deployed procedurally, and defend the view that curiosity is a metacognitive feeling. Our metacognitive view singles out two monitoring steps as a triggering condition for curiosity: evaluating one’s own informational needs, and predicting the likelihood that explorations of the proximate environment afford sizeable information gains. We review empirical evidence and computational models of curiosity, and show that they fit well with this metacognitive account, while on the contrary, they remain difficult to explain by a competing account according to which curiosity is a basic attitude of questioning. Finally, we propose a new way to construe the relationships between curiosity and the human-specific communicative practice of questioning, discuss the issue of how children may learn to express their curiosity through interactions with others, and conclude by briefly exploring the implications of our proposal for educational practices.
... This is because linguistic and musical 717 syntax are mainly investigated within one task-set (e.g., sentence processing or short musical 718 sequence processing). As Rouault and Koechlin (2018) pointed out, hierarchical control within 719 a task-set takes place within the IFG and the premotor cortex, while hierarchical control 720 concerning the anterior lateral prefrontal cortex (including BA9 and 46) and the polar lateral 721 prefrontal cortex deals with temporal control over task-sets. They suggested that the former 722 corresponds to sentence generation and linguistic syntax, while the latter conforms to discourse 723 generation. ...
Although comparative research has made substantial progress in clarifying the relationship between language and music as neurocognitive systems from both a theoretical and empirical perspective, there is still no consensus about which mechanisms, if any, are shared and how they bring about different neurocognitive systems. In this paper, we tackle these two questions by focusing on hierarchical control as a neurocognitive mechanism underlying syntax in language and music. We put forward the Coordinated Hierarchical Control (CHC) hypothesis: linguistic and musical syntax rely on hierarchical control, but engage this shared mechanism differently depending on the current control demand. While linguistic syntax preferably engages the abstract rule-based control circuit, musical syntax rather employs the coordination of the abstract rule-based and the more concrete motor-based control circuits. We provide evidence for our hypothesis by reviewing neuroimaging as well as neuropsychological studies on linguistic and musical syntax. The CHC hypothesis makes a set of novel testable predictions to guide future work on the relationship between language and music.
... The authors suggest that elaborated aspects of language processing such as sentence parsing, keeping phrases in verbal memory, or prediction of upcoming words can largely be performed within the core language network, while the multi-demand system gets involved only in case of ''extraneous'' task demands such as plausibility judgments, sentence-picture matching, semantic associations, or complex memory tasks. Further studies have shown that prefrontal regions beyond the ''classical'' Broca's area are relevant for language at the level of discourse rather than single sentences (Kim et al., 2012;Bourguignon, 2014;Moss and Schunn, 2015;Rouault and Koechlin, 2018), based on a meta-analysis of semantic studies . Furthermore, an experimental fMRI study has shown significant functional connectivity of a core semantic region in left posterior middle temporal gyrus to the multi-demand network in case of executive semantic demands such as required in difficult semantic feature selection tasks that cannot be performed by automatic associations (Whitney et al., 2012;Davey et al., 2016). ...
This review article summarizes various functions of the dorsolateral prefrontal cortex (DLPFC) that are related to language processing. To this end, its connectivity with the left-dominant perisylvian language network was considered, as well as its interaction with other functional networks that, directly or indirectly, contribute to language processing. Language-related functions of the DLPFC comprise various aspects of pragmatic processing such as discourse management, integration of prosody, interpretation of nonliteral meanings, inference making, ambiguity resolution, and error repair. Neurophysiologically, the DLPFC seems to be a key region for implementing functional connectivity between the language network and other functional networks, including cortico-cortical as well as subcortical circuits. Considering clinical aspects, damage to the DLPFC causes psychiatric communication deficits rather than typical aphasic language syndromes. Although the number of well-controlled studies on DLPFC language functions is still limited, the DLPFC might be an important target region for the treatment of pragmatic language disorders.
... In the computational neurosciences domain, reactive and proactive control relate to what is called model-free and model-based systems in Reinforcement Learning (RL) [51,[59][60][61], having one system for stimulus-response tasks performing greedy-like optimization -, which means sensorimotor RL tasks (e.g., motor exploration and sound matching),-and the other learning distinct policies for prediction -, which serves for planning goal-directed behaviors (e.g., chunking syllabes into words). Koechlin and colleagues explain how these two systems contribute to adaptive behavior [53] and to language processing [62]. ...
We propose a developmental model inspired by the cortico-basal system (CX-BG) for vocal learning in babies and for solving the correspondence mismatch problem they face when they hear unfamiliar voices, with different tones and pitches. This model is based on the neural architecture INFERNO standing for Iterative Free-Energy Optimization of Recurrent Neural Networks. Free-energy minimization is used for rapidly exploring, selecting and learning the optimal choices of actions to perform (eg sound production) in order to reproduce and control as accurately as possible the spike trains representing desired perceptions (eg sound categories). We detail in this paper the CX-BG system responsible for linking causally the sound and motor primitives at the order of a few milliseconds. Two experiments performed with a small and a large audio database show the capabilities of exploration, generalization and robustness to noise of our neural architecture in retrieving audio primitives during vocal learning and during acoustic matching with unheared voices (different genders and tones).
... Cognitive control is a sort of cognitive ability that is involved in the adjustment of perceptual selection and action; namely, cognitive control can be regarded as a flexible, goal-directed behavior that is essential for efficient information processing and behavioral response under conditions of uncertainty and underlies a broad range of executive functions (1,2). AD is associated with cognitive control dysfunctions, and cognitive control is mediated through the interaction between inherent large-scale brain networks involved in externally oriented executive functioning and internally focused thought processing (3). ...
Alcohol dependence (AD) presents cognitive control deficits. Event-related potential (ERP) P300 reflects cognitive control-related processing. The aim of this study was to investigate whether cognitive control deficits are a trait biomarker or a state biomarker in AD. Participants included 30 AD patients and 30 healthy controls (HCs). All participants were measured with P300 evoked by a three-stimulus auditory oddball paradigm at a normal state (time 1, i.e., just after the last alcohol intake) and abstinence (time 2, i.e., just after a 4-week abstinence). The results showed that for P3a and P3b amplitude, the interaction effect for group × time point was significant, the simple effect for group at time 1 level and time 2 level was significant, and the simple effect for time point at AD group level was significant; however, the simple effect for time point at HC group level was not significant. Above results indicated that compared to HCs, AD patients present reductions of P3a/3b amplitude, and after 4-week alcohol abstinence, although P3a/3b amplitudes were improved, they were still lower than those of HCs. For P3a and P3b latencies, no significant differences were observed. These findings conclude that AD patients present cognitive control deficits that are reflected by P3a/3b and that cognitive control deficits in AD are trait- and state-dependent. The implication of these findings is helpful to understand the psychological and neural processes for AD, and these findings suggest that improving the cognitive control function may impact the treatment effect for AD.
... Regarding functional gradients, most studies on the core language regions are more or less restricted to phonology, syntax, and semantics up to the sentence level. However, cognitive control of language and speech has also to consider the superordinate level of discourse generation and management which seems to rely on dorsal and medial prefrontal regions beyond the "classical" Broca area, (Kim et al., 2012;Bourguignon, 2014;Moss and Schunn, 2015;Rouault and Koechlin, 2018;Panikratova et al., 2020). Further examples for the recruitment of prefrontal cortex are the task of verb generation including the management of competition among words, with additional activation of anterior cingulate cortex (Bourguignon et al., 2018), and the management of discourse coherence at the perceptual level, with additional activity in angular gyrus and posterior cingulate cortex (Moss and Schunn, 2015). ...
This review paper summarizes the various brain modules that are involved in speech and language communication in addition to a left-dominant “core” language network that, for the present purpose, has been restricted to elementary formal-linguistic and more or less disembodied functions such as abstract phonology, syntax, and very basic lexical functions. This left-dominant perisylvian language network comprises parts of inferior frontal gyrus, premotor cortex, and upper temporal lobe, and a temporoparietal interface. After introducing this network, first, the various roles of neighboring and functionally connected brain regions are discussed. As a second approach, entire additional networks were considered rather than single regions, mainly motivated by resting-state studies indicating more or less stable connectivity patterns within these networks. Thirdly, some examples are provided for language tasks with functional demands exceeding the operating domain of the core language network. The rationale behind this approach is to present some outline of how the brain produces and perceives language, accounting, first, for a bulk of clinical studies showing typical forms of aphasia in case of left-hemispheric lesions in the core language network and second, for wide-spread activation patterns beyond this network in various experimental studies with language tasks. Roughly, the brain resources that complement the core language system in a task-specific way can be described as a number of brain structures and networks that are related to (1) motor representations, (2) sensory-related representations, (3) non-verbal memory structures, (4) affective/emotional processing, (5) social cognition and theory of mind, (6) meaning in context, and (7) cognitive control. After taking into account all these aspects, first, it seems clear that natural language communication cannot really work without additional systems. Second, it also becomes evident that during language acquisition the core language network has to be built up from outside, that is, from various neuronal activations that are related to sensory input, motor imitation, nursing, pre-linguistic sound communication, and pre-linguistic pragmatics. Furthermore, it might be worth considering that also in cases of aphasia the language network might be restored by being trained from outside.
... However, everyday listening conditions are rarely as good as in the laboratory, and speech understanding is often compromised by noisy environments, low-fidelity digital communication, and by hearing impairment across a large portion of the population including older adults. When such degradation occurs, people must recruit effortful cognitive control to successfully perceive speech (Broadbent, 1958;Eckert et al., 2016;Fedorenko, 2014;Heald & Nusbaum, 2014;Johnsrude & Rodd, 2016;Pichora-Fuller et al., 2016;Rouault & Koechlin, 2018;Vaden et al., 2013). ...
Speech is often degraded by environmental noise or hearing impairment. People can compensate for degradation, but this requires cognitive effort. Previous research has identified frontotemporal networks involved in effortful perception, but materials in these works were also less intelligible, and so it is not clear whether activity reflected effort or intelligibility differences. We used functional magnetic resonance imaging to assess the degree to which spoken sentences were processed under distraction, and whether this depended on speech quality even when intelligibility of degraded speech was matched to that of clear speech (i.e., 100%). On each trial, participants either attended to a sentence, or to a concurrent multiple object tracking (MOT) task that imposed parametric cognitive load. Activity in bilateral anterior insula reflected task demands: during the MOT task, activity increased as cognitive load increased, and during speech listening, activity increased as speech became more degraded. In marked contrast, activity in bilateral anterior temporal cortex was speech-selective, and gated by attention when speech was degraded. In this region, performance of the MOT task with a trivial load blocked processing of degraded speech whereas processing of clear speech was unaffected. As load increased, responses to clear speech in these areas declined, consistent with reduced capacity to process it. This result dissociates cognitive control from speech processing: substantially less cognitive control is required to process clear speech than is required to understand even very mildly degraded, 100% intelligible, speech. Perceptual and control systems clearly interact dynamically during real-world speech comprehension.
Significance Statement
Speech is often perfectly intelligible even when degraded, e.g., by background sound, phone transmission, or hearing loss. How does degradation alter cognitive demands? Here, we use fMRI to demonstrate a novel and critical role for cognitive control in the processing of mildly degraded but perfectly intelligible speech. We compare speech that is matched for intelligibility but differs in putative control demands, dissociating cognitive control from speech processing. We also impose a parametric cognitive load during perception, dissociating processes that depend on tasks from those that depend on available capacity. Our findings distinguish between frontal and temporal contributions to speech perception and reveal a hidden cost to processing mildly degraded speech, underscoring the importance of cognitive control for everyday speech comprehension.
... Human language-written, spoken or signed-is a salient example of the complexity of the binding problem, because it features syntactically organized dependencies between semantic units [10]. Yet the problem of building complex representations is also relevant to complex action sequences [8,11,12], music [8,13,14], mathematics [9,15] and cognition in general [11,13]. Moreover, some of these systems are not unique to humans, since songbirds can construct complex vocalization sequences [16], an ability supported by a forebrain neural system [17], and correspondences have been established between humans and a number of species in processing adjacent and non-adjacent sequencing dependencies [18][19][20]. ...
Understanding how the brain forms representations of structured information distributed in time is a challenging endeavour for the neuroscientific community, requiring computationally and neurobiologically informed approaches. The neural mechanisms for segmenting continuous streams of sensory input and establishing representations of dependencies remain largely unknown, as do the transformations and computations occurring between the brain regions involved in these aspects of sequence processing. We propose a blueprint for a neurobiologically informed and informing computational model of sequence processing (entitled: Vector-symbolic Sequencing of Binding INstantiating Dependencies, or VS-BIND). This model is designed to support the transformation of serially ordered elements in sensory sequences into structured representations of bound dependencies, readily operates on multiple timescales, and encodes or decodes sequences with respect to chunked items wherever dependencies occur in time. The model integrates established vector symbolic additive and conjunctive binding operators with neurobiologically plausible oscillatory dynamics, and is compatible with modern spiking neural network simulation methods. We show that the model is capable of simulating previous findings from structured sequence processing tasks that engage fronto-temporal regions, specifying mechanistic roles for regions such as prefrontal areas 44/45 and the frontal operculum during interactions with sensory representations in temporal cortex. Finally, we are able to make predictions based on the configuration of the model alone that underscore the importance of serial position information, which requires input from time-sensitive cells, known to reside in the hippocampus and dorsolateral prefrontal cortex.
This article is part of the theme issue ‘Towards mechanistic models of meaning composition’.
... However, the function such as maintenance and manipulation could be same for all of them. Similarly, there is a rostro-caudal gradient of memory, control, and goal representation in the frontal cortex with motor part in the most caudal part and cognitive or abstract part in the most rostral part (Badre & D'Esposito, 2009;Badre & Nee, 2018;Fuster, 2008b;Koechlin & Jubault, 2006;Rouault & Koechlin, 2018;Uddén & Bahlmann, 2012). Language and music can be also differently represented on this axis. ...
The current thesis tackles the question “Why is music the way it is?” within a comparative biomusicology framework by focusing on musical syntax and its relation to syntax in language. Comparative biomusicology integrates different comparative approaches, biological frameworks as well as levels of analysis in cognitive science, and puts forward principled explanations, regarding cognitive systems as different instances of the same principles, as its central research strategy. The main goal is to provide a preliminary answer to this question in form of hypotheses about neurocognitive mechanisms, i.e., cognitive and neural processes, underlying a core function of syntactic computation in language and music, i.e., mapping hierarchical structure and temporal sequence. In particular, the relationship between language and music is discussed on the basis of a top-down approach taking syntax as combinatorial principles and a bottom-up approach taking neural structures and operations as implementational principles. On the basis of the top-down approach, the thesis identifies computational problems of musical syntax, cognitive processes and neural correlates of music syntactic processing, and the relationship to language syntax and syntactic processing. The neural correlates of music syntactic processing are investigated by ALE meta-analyses. The bottom-up approach then studies the relationship between language and music on the basis of neural processes implemented in the cortico-basal ganglia-thalamocortical circuits. The main result of the current thesis suggests that the relationship between language and music syntactic processing can be explained in terms of the same neurocognitive mechanisms with different expressions on the motor-to-cognitive gradient. The current thesis, especially its bottom-up approach, opens up a possible way going toward comparative cognitive biology, i.e., a comparative approach to cognitive systems with a greater emphasis on the biology.
This chapter takes an evolutionary perspective on the innovativeness of Homo sapiens. It starts with a set of observations pertaining to the emergence of new skills in these species. Then it concatenates diverse lines of evidence from three connected but different types of innovation generating new skills—spoken language, written language, and the language of digital communication, d-language, also known as electronics or e-language. These three types of language skills are compared and contrasted, focusing on three dimensions: (1) the origin and timeline of their emergence; (2) their acquisition, functional properties, and utility for the wise man; and (3) their biological bases.Keywords
Homo sapiens
InnovativenessSpoken, written, and digital language
Cognitive effort is thought to be familiar in everyday life, ubiquitous across multiple variations of task and circumstance, and integral to cost/benefit computations that are themselves central to the proper functioning of cognitive control. In particular, cognitive effort is thought to be closely related to the assessment of cognitive control's costs. I argue here that the construct of cognitive effort, as it is deployed in cognitive psychology and neuroscience, is problematically unclear. The result is that talk of cognitive effort may paper over significant disagreement regarding the nature of cognitive effort, and its key functions for cognitive control. I highlight key points of disagreement, and several open questions regarding what causes cognitive effort, what cognitive effort represents, cognitive effort's relationship to action, and cognitive effort's relationship to consciousness. I also suggest that pluralism about cognitive effort—that cognitive effort may manifest as a range of intentional or non‐intentional actions the function of which is to promote greater success at paradigmatic cognitive control tasks—may be a fruitful and irenic way to conceive of cognitive effort. Finally, I suggest that recent trends in work on cognitive control suggests that we might fruitfully conceive of cognitive effort as one key node in a complex network of mental value, and that studying this complex network may illuminate the nature of cognitive control, and the role of consciousness in cognitive control's proper functioning.
This article is categorized under: Philosophy > Consciousness
Philosophy > Psychological Capacities
Neuroscience > Cognition
I seek an explanation for the etiology and the function of mind wandering episodes. My proposal—which I call the cognitive control proposal—is that mind wandering is a form of non-conscious guidance due to cognitive control. When the agent’s current goal is deemed insufficiently rewarding, the cognitive control system initiates a search for a new, more rewarding goal. This search is the process of unintentional mind wandering. After developing the proposal, and relating it to the literature on mind wandering and on cognitive control, I discuss explanations the proposal affords, testable predictions the proposal makes, and philosophical implications the proposal has.
The discovery in FLK West lowermost level 6 (Olduvai Gorge, Tanzania) of a massive and symmetrical handaxe, at odds both with the other LCT specimens so far unearthed from this site and with the technological traits commonly reported for the earliest Acheulean ca. 1.7 Ma, challenges the hypothesis of a gradual development of technological and cognitive qualities during the Acheulean. This thought-provoking specimen points to a less sharp difference between the first and later phases of the Early Acheulean (i.e. between ca. 1.7 and 1.2 Ma) and supports a somewhat punctuated flourishing of the handaxe technical and cognitive conceptualization. In this regard, this paper provides a detailed descriptive study of the large olivine basalt handaxe, in the framework of both the FLK West L6 lithic collection and the LCT shaping strategies. This analysis includes a morphometric description of the specimen and a comprehensive analysis of the knapping and shaping sequencing, through a diacritical study. It also includes a techno-functional assessment of the final design and symmetry. Finally, this paper examines some of the most significant implications of this large tool for our current views on temporal divisions within the African Acheulean and the cognitive abilities of the individual that made it.
Humans are set apart from other animals by many elements of advanced cognition and behaviour, including language, judgement and reasoning. What is special about the human brain that gives rise to these abilities? Could the foremost part of the prefrontal cortex (the frontopolar cortex), which has become considerably enlarged in humans during evolution compared with other animals, be important in this regard, especially as, in primates, it contains a unique cytoarchitectural field, area 10? The first studies of the function of the frontopolar cortex in monkeys have now provided critical new insights about its precise role in monitoring the significance of current and alternative goals. In human evolution, the frontopolar cortex may have acquired a further role in enabling the monitoring of the significance of multiple goals in parallel, as well as switching between them. Here, we argue that many other forms of uniquely human behaviour may benefit from this cognitive ability mediated by the frontopolar cortex.
The lateral prefrontal cortex (LPFC) is essential for higher-level cognition, but how its interactions support cognitive control remains elusive. Previously (Nee and D'Esposito, 2016), dynamic causal modeling (DCM) indicated that mid LPFC integrates abstract, rostral and concrete, caudal influences to inform context-appropriate action. Here, we use continuous theta-burst transcranial magnetic stimulation (cTBS) to causally test this model. cTBS was applied to three LPFC sites and a control site in counterbalanced sessions. Behavioral modulations resulting from cTBS were largely predicted by information flow within the previously estimated DCM. However, cTBS to caudal LPFC unexpectedly impaired processes presumed to involve rostral LPFC. Adding a pathway from caudal to mid-rostral LPFC significantly improved the model fit and accounted for the observed behavioral findings. These data provide causal evidence for LPFC dynamics supporting cognitive control and demonstrate the utility of combining DCM with causal manipulations to test and refine models of cognition.
The human prefrontal cortex (PFC) subserves cognitive control, that is, the ability to form behavioral strategies that coordinate actions and thoughts in relation to internal goals. Cognitive control involves the medial and lateral PFC but we still poorly understand how these regions guide strategy selection according to expected rewards. We addressed this issue using neuroimaging, computational modeling and model-based analyses of information flows between medial and lateral PFC. We show here that the (dorsal) medial PFC encodes and conveys to lateral PFC reward expectations driving strategy selection, while strategy selection originates in lateral PFC and propagates backward to medial PFC. This functional loop through lateral PFC enables strategy selection to further comply with learned rules encoded in lateral PFC rather than with reward expectations conveyed from medial PFC. Thus, the medial and lateral PFC are functionally coupled in cognitive control for integrating expected rewards and learned rules in strategy selection.
Humans and animals are capable of evaluating actions by considering their long-run future
rewards through a process described using model-based reinforcement learning (RL) algorithms.
The mechanisms by which neural circuits perform the computations prescribed by model-based
RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits
supporting model-based behavior are structurally homologous to and overlapping with those
thought to carry out model-free temporal difference (TD) learning. Here, we lay out a family of
approaches by which model-based computation may be built upon a core of TD learning. The
foundation of this framework is the successor representation, a predictive state representation
that, when combined with TD learning of value predictions, can produce a subset of the
behaviors associated with model-based learning at a fraction of the computational cost. Using
simulations, we delineate the precise behavioral capabilities enabled by evaluating actions using
this approach, and compare them to those demonstrated by biological organisms. We then
introduce two new algorithms that build upon the successor representation while progressively
mitigating its limitations. Because this framework can account for the full range of observed
putatively model-based behaviors while still utilizing a core TD framework, we suggest that it
represents a neurally plausible family of mechanisms for model-based evaluation.
Planning allows actions to be structured in pursuit of a future goal. However, in natural environments, planning over multiple possible future states incurs prohibitive computational costs. To represent plans efficiently, states can be clustered hierarchically into “contexts”. For example, representing a journey through a subway network as a succession of individual states (stations) is more costly than encoding a sequence of contexts (lines) and context switches (line changes). Here, using functional brain imaging, we asked humans to perform a planning task in a virtual subway network. Behavioral analyses revealed that humans executed a hierarchically organized plan. Brain activity in the dorsomedial prefrontal cortex and premotor cortex scaled with the cost of hierarchical plan representation and unique neural signals in these regions signaled contexts and context switches. These results suggest that humans represent hierarchical plans using a network of caudal prefrontal structures.
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The prefrontal cortex (PFC) subserves higher cognitive abilities such as planning, reasoning and creativity. Here we review recent findings from both empirical and theoretical studies providing new insights about these cognitive abilities and their neural underpinnings in the PFC as overcoming key adaptive limitations in reinforcement learning. We outline a unified theoretical framework describing the PFC function as implementing an algorithmic solution approximating statistically optimal, but computationally intractable, adaptive processes. The resulting PFC functional architecture combines learning, planning, reasoning and creativity processes for balancing exploitation and exploration behaviors and optimizing behavioral adaptations in uncertain, variable and open-ended environments.
Frontal neocortex is thought to support our highest intellectual abilities, including our ability to plan and enact a sequence of tasks toward a desired goal. In everyday life, such task sequences are abstract in that they do not require consistent movement sequences and are often assembled "on the fly." Yet, remarkably little is known about the necessity of frontal sub-regions for such control. Participants repeatedly completed sequences of simple tasks during fMRI scanning. Rostrolateral prefrontal cortex (RLPFC) activation ramped over sequence position and reset at the initiation of each new sequence. To establish the necessity and function of RLPFC in this task, participants performed the sequential task while undergoing transcranial magnetic stimulation (TMS) of the RLPFC versus two prefrontal control regions. Across two independent experiments, only RLPFC stimulation increasingly disrupted task performance as each sequence progressed. These data establish RLPFC as necessary for uncertainty resolution during sequence-level control.
A key process in decision-making is estimating the value of possible outcomes. Growing evidence suggests that different types of values are automatically encoded in the ventromedial prefrontal cortex (VMPFC). Here we extend this idea by suggesting that any overt judgment is accompanied by a second-order valuation (a confidence estimate), which is also automatically incorporated in VMPFC activity. In accordance with the predictions of our normative model of rating tasks, two behavioral experiments showed that confidence levels were quadratically related to first-order judgments (age, value or probability ratings). The analysis of three functional magnetic resonance imaging data sets using similar rating tasks confirmed that the quadratic extension of first-order ratings (our proxy for confidence) was encoded in VMPFC activity, even if no confidence judgment was required of the participants. Such an automatic aggregation of value and confidence in a same brain region might provide insight into many distortions of judgment and choice.
Working memory is often described as a system for simultaneous storage and processing. Much research - and most measures of working-memory capacity - focus on the storage component only, that is, peoplés ability to recall or recognize items after short retention intervals. The mechanisms of processing information are studied in a separate research tradition, concerned with the selection and control of actions in simple choice situations, dual-task constellations, or task-switching setups. Both research traditions investigate performance based on representations that are temporarily maintained in an active, highly accessible state, and constrained by capacity limits. In this article an integrated theoretical framework of declarative and procedural working memory is presented that relates the two domains of research to each other. Declarative working memory is proposed to hold representations available for processing (including recall and recognition), whereas procedural working memory holds representations that control processing (i.e., task sets, stimulus-response mappings, and executive control settings). The framework motivates two hypotheses: Declarative and procedural working memory have separate capacity limits, and they operate by analogous principles. The framework also suggests a new characterization of executive functions as the subset of processes governed by procedural working memory that has as its output a change in the conditions of operation of the working-memory system.
The number of papers about the orbitofrontal cortex (OFC) has grown from 1 per month in 1987 to a current rate of over 50 per month. This publication stream has implicated the OFC in nearly every function known to cognitive neuroscience and in most neuropsychiatric diseases. However, new ideas about OFC function are typically based on limited data sets and often ignore or minimize competing ideas or contradictory findings. Yet true progress in our understanding of an area's function comes as much from invalidating existing ideas as proposing new ones. Here we consider the proposed roles for OFC, critically examining the level of support for these claims and highlighting the data that call them into question.
The dorsolateral prefrontal cortex (PFC), with more anterior areas [Brodmann area (BA) 45, 47, and 10], has been known to be activated as cognitive hierarchy increases. However, this does not hold for highly automatic processes such as first language (L1), where the posterior region (BA 44) is known as the key area for the processing of complex linguistic hierarchy. Discussing this disparity, we propose that the degree of automaticity (DoA) is a crucial factor for the framework of functional mapping in the PFC: the posterior-to-anterior gradient system for more controlled processes and the posterior-confined system for automatic processes. We support this view with previous findings and provide a new perspective on the functional organization of the PFC.
Copyright © 2015 Elsevier Ltd. All rights reserved.
The frontal cortex mediates cognitive control and motivation to shape human behavior. It is generally observed that medial frontal areas are involved in motivational aspects of behavior, whereas lateral frontal regions are involved in cognitive control. Recent models of cognitive control suggest a rostro-caudal gradient in lateral frontal regions, such that progressively more rostral (anterior) regions process more complex aspects of cognitive control. How motivation influences such a control hierarchy is still under debate. Although some researchers argue that both systems work in parallel, others argue in favor of an interaction between motivation and cognitive control. In the latter case it is yet unclear how motivation would affect the different levels of the control hierarchy. This was investigated in the present functional MRI study applying different levels of cognitive control under different motivational states (low vs high reward anticipation). Three levels of cognitive control were tested by varying rule complexity: stimulus-response mapping (low-level), flexible task updating (mid-level), and sustained cue-task associations (high-level). We found an interaction between levels of cognitive control and motivation in medial and lateral frontal subregions. Specifically, flexible updating (mid-level of control) showed the strongest beneficial effect of reward and only this level exhibited functional coupling between dopamine-rich midbrain regions and the lateral frontal cortex. These findings suggest that motivation differentially affects the levels of a control hierarchy, influencing recruitment of frontal cortical control regions depending on specific task demands.
Copyright © 2015 the authors 0270-6474/15/353207-11$15.00/0.
Everyday activities, such as e.g., driving a car or preparing a meal, require the hierarchical organisation and processing of several individual actions. Currently, the neural mechanisms underlying the control of action sequences are not well understood. Here, the authors demonstrate that the right inferior frontal gyrus (rIFG) plays a key role in implementing the strategy used to cascade different actions. Continuous theta burst stimulation (TBS) applied to the rIFG results in a less efficient action cascading strategy, whereas intermittent TBS results in a more efficient strategy, compared to a sham TBS control condition. These effects are confirmed in electrophysiological data showing that activity differences in the rIFG are related to alterations in response selection processes. Overall, these results suggest that the neural dynamics of the rIFG determine the strategy used during some forms of everyday multi-component behaviour.
The prefrontal cortex (PFC) subserves decision-making and executive control. Here we review recent empirical and modeling works with a focus on neuroimaging studies, which start unifying these two conceptual approaches of PFC function. We propose that the PFC comprises two arbitration systems: (1) a peripheral system comprising premotor/caudal PFC regions and orbitofrontal regions involved in the selection of actions based on perceptual cues and reward values, respectively, and embedded in behavioral sets associated with external contingencies inferred as being stable; (2) a core system comprising ventromedial, dorsomedial, lateral and polar PFC regions involved in superordinate probabilistic reasoning for arbitrating online between exploiting/adjusting previously learned behavioral sets and exploring/creating new ones for efficient adaptive behavior in variable and open-ended environments. Current Opinion in Behavioral Sciences 2015, 1:xx–yy This review comes from a themed issue on Cognitive neuroscience Edited by Cindy Lustig and Howard Eichenbaum The prefrontal cortex is often described as subserving decision-making and executive control. Decision-making research focuses on the PFC function in action selection according to perceptual cues and reward values [1,2]. Executive control research focuses on the PFC function in learning and switching between behavioral rules or sets that guide action [1,3–10]. These two lines of research have often been carried out independently. Here we review recent findings and outline a theoretical frame-work unifying these two conceptual approaches of PFC function. From simple decisions to task sets
The prefrontal cortex subserves executive control and decision-making, that is, the coordination and selection of thoughts and actions in the service of adaptive behaviour. We present here a computational theory describing the evolution of the prefrontal cortex from rodents to humans as gradually adding new inferential Bayesian capabilities for dealing with a computationally intractable decision problem: exploring and learning new behavioural strategies versus exploiting and adjusting previously learned ones through reinforcement learning (RL). We provide a principled account identifying three inferential steps optimizing this arbitration through the emergence of (i) factual reactive inferences in paralimbic prefrontal regions in rodents; (ii) factual proactive inferences in lateral prefrontal regions in primates and (iii) counterfactual reactive and proactive inferences in human frontopolar regions. The theory clarifies the integration of model-free and model-based RL through the notion of strategy creation. The theory also shows that counterfactual inferences in humans yield to the notion of hypothesis testing, a critical reasoning ability for approximating optimal adaptive processes and presumably endowing humans with a qualitative evolutionary advantage in adaptive behaviour.
The prefrontal cortex (PFC) subserves reasoning in the service of adaptive behavior. Little is known, however, about the architecture
of reasoning processes in the PFC. Using computational modeling and neuroimaging, we show here that the human PFC has two
concurrent inferential tracks: (i) one from ventromedial to dorsomedial PFC regions that makes probabilistic inferences about
the reliability of the ongoing behavioral strategy and arbitrates between adjusting this strategy versus exploring new ones
from long-term memory, and (ii) another from polar to lateral PFC regions that makes probabilistic inferences about the reliability
of two or three alternative strategies and arbitrates between exploring new strategies versus exploiting these alternative
ones. The two tracks interact and, along with the striatum, realize hypothesis testing for accepting versus rejecting newly
created strategies.
Human cognition is flexible and adaptive, affording the ability to detect and leverage complex structure inherent in the environment and generalize this structure to novel situations. Behavioral studies show that humans impute structure into simple learning problems, even when this tendency affords no behavioral advantage. Here we used electroencephalography to investigate the neural dynamics indicative of such incidental latent structure. Event-related potentials over lateral prefrontal cortex, typically observed for instructed task rules, were stratified according to individual participants' constructed rule sets. Moreover, this individualized latent rule structure could be independently decoded from multielectrode pattern classification. Both neural markers were predictive of participants' ability to subsequently generalize rule structure to new contexts. These EEG dynamics reveal that the human brain spontaneously constructs hierarchically structured representations during learning of simple task rules.
The cascade model of cognitive control, mostly relying on functional neuroimaging studies, stipulates that the lateral frontal cortex (LFC) is organized as a cascade of executive processes involving three levels of cognitive control, implemented in distinct LFC areas from the premotor to the anterior prefrontal regions. The present experiment tested this model in patients with LFC lesions and studied the hierarchy of executive functions along the caudo-rostral axis, i.e. the respective roles of the different LFC areas in the control of behavior. Voxel-based lesion-symptom mapping and region of interest group analyses were conducted in 32 patients with focal LFC lesions who performed cognitive tasks assessing the cascade model. We first showed that three different LFC areas along the caudo-rostral axis subserved three distinct control levels, whose integrity is necessary for adaptive behavior. Second, we found that prefrontal cognitive control has an asymmetric organization: higher control processes involving more anterior prefrontal regions rely on the integrity of lower control processes in more posterior regions, while lower control processes can operate irrespective of the integrity of higher control processes. Altogether, these findings support a caudo-rostral cascade of executive processes from premotor to anterior prefrontal regions.
Learning and executive functions such as task-switching share common neural substrates, notably prefrontal cortex and basal ganglia. Understanding how they interact requires studying how cognitive control facilitates learning but also how learning provides the (potentially hidden) structure, such as abstract rules or task-sets, needed for cognitive control. We investigate this question from 3 complementary angles. First, we develop a new context-task-set (C-TS) model, inspired by nonparametric Bayesian methods, specifying how the learner might infer hidden structure (hierarchical rules) and decide to reuse or create new structure in novel situations. Second, we develop a neurobiologically explicit network model to assess mechanisms of such structured learning in hierarchical frontal cortex and basal ganglia circuits. We systematically explore the link between these modeling levels across task demands. We find that the network provides an approximate implementation of high-level C-TS computations, with specific neural mechanisms modulating distinct C-TS parameters. Third, this synergism yields predictions about the nature of human optimal and suboptimal choices and response times during learning and task-switching. In particular, the models suggest that participants spontaneously build task-set structure into a learning problem when not cued to do so, which predicts positive and negative transfer in subsequent generalization tests. We provide experimental evidence for these predictions and show that C-TS provides a good quantitative fit to human sequences of choices. These findings implicate a strong tendency to interactively engage cognitive control and learning, resulting in structured abstract representations that afford generalization opportunities and, thus, potentially long-term rather than short-term optimality. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
The frontal lobes subserve decision-making and executive control--that is, the selection and coordination of goal-directed behaviors. Current models of frontal executive function, however, do not explain human decision-making in everyday environments featuring uncertain, changing, and especially open-ended situations. Here, we propose a computational model of human executive function that clarifies this issue. Using behavioral experiments, we show that unlike others, the proposed model predicts human decisions and their variations across individuals in naturalistic situations. The model reveals that for driving action, the human frontal function monitors up to three/four concurrent behavioral strategies and infers online their ability to predict action outcomes: whenever one appears more reliable than unreliable, this strategy is chosen to guide the selection and learning of actions that maximize rewards. Otherwise, a new behavioral strategy is tentatively formed, partly from those stored in long-term memory, then probed, and if competitive confirmed to subsequently drive action. Thus, the human executive function has a monitoring capacity limited to three or four behavioral strategies. This limitation is compensated by the binary structure of executive control that in ambiguous and unknown situations promotes the exploration and creation of new behavioral strategies. The results support a model of human frontal function that integrates reasoning, learning, and creative abilities in the service of decision-making and adaptive behavior.
Decision making and learning in a real-world context require organisms to track not only the choices they make and the outcomes that follow but also other untaken, or counterfactual, choices and their outcomes. Although the neural system responsible for tracking the value of choices actually taken is increasingly well understood, whether a neural system tracks counterfactual information is currently unclear. Using a three-alternative decision-making task, a Bayesian reinforcement-learning algorithm, and fMRI, we investigated the coding of counterfactual choices and prediction errors in the human brain. Rather than representing evidence favoring multiple counterfactual choices, lateral frontal polar cortex (lFPC), dorsomedial frontal cortex (DMFC), and posteromedial cortex (PMC) encode the reward-based evidence favoring the best counterfactual option at future decisions. In addition to encoding counterfactual reward expectations, the network carries a signal for learning about counterfactual options when feedback is available-a counterfactual prediction error. Unlike other brain regions that have been associated with the processing of counterfactual outcomes, counterfactual prediction errors within the identified network cannot be related to regret theory. Furthermore, individual variation in counterfactual choice-related activity and prediction error-related activity, respectively, predicts variation in the propensity to switch to profitable choices in the future and the ability to learn from hypothetical feedback. Taken together, these data provide both neural and behavioral evidence to support the existence of a previously unidentified neural system responsible for tracking both counterfactual choice options and their outcomes.
Simultaneously Serving Two Goals
What are the neuronal mechanisms underlying dual-task performance? Charron and Koechlin (p. 360 ) used functional magnetic resonance imaging to study this question under conditions of varying incentive for two tasks. Subjects performed letter-matching tasks either sequentially or concurrently. Reward value for performance of each of the two tasks was manipulated orthogonally. Under conditions of single-task (sequential) performance, they observed bilateral brain activity in the dorsal anterior cingulate and the premotor cortex that correlated with the reward value of the current task. However, under dual-task conditions, activity in these regions on the left corresponded to primary-task reward performance, while on the right, activity corresponded to secondary-task performance. Under dual-task conditions, both reward and stimulus-response representations are thus segregated according to hemisphere.
When people hold several objects (such as digits or words) in working memory and select one for processing, switching to a new object takes longer than selecting the same object as that on the preceding processing step. Similarly, selecting a new task incurs task- switching costs. This work investigates the selection of objects and of tasks in working memory using a combination of object-switching and task-switching paradigms. Participants used spatial cues to select one digit held in working memory and colour cues to select one task (addition or subtraction) to apply to it. Across four experiments the mapping between objects and their cues and the mapping between tasks and their cues were varied orthogonally. When mappings varied from trial to trial for both objects and tasks, switch costs for objects and tasks were additive, as predicted by sequential selection or resource sharing. When at least one mapping was constant across trials, allowing learning of long-term associations, switch costs were underadditive, as predicted by partially parallel selection. The number of objects in working memory affected object-switch costs but not task-switch costs, counter to the notion of a general resource of executive attention.
The prefrontal cortex (PFC) subserves cognitive control, that is, the ability to select thoughts or actions in relation to internal goals. Little is known, however, about how the PFC combines motivation and the selection processes underlying cognitive control. We used functional magnetic resonance imaging in humans and found that the medial and lateral PFC have a parallel hierarchical organization from posterior to anterior regions for motivating and selecting behaviors, respectively. Moreover, using functional connectivity analyses, we found that functional interactions in this parallel system from medial to lateral PFC regions convey motivational incentives on the basis of rewards/penalties regulating the differential engagement of lateral PFC regions in top-down selection. Our results indicate that motivation is a dissociable function, reveal how the PFC integrates motivation and cognitive control in the service of decision-making, and have major implications for current theories of prefrontal executive function.
Complex problem-solving and planning involve the most anterior part of the frontal lobes including the fronto-polar prefrontal cortex (FPPC), which is especially well developed in humans compared with other primates. The specific role of this region in human cognition, however, is poorly understood. Here we show, using functional magnetic resonance imaging, that bilateral regions in the FPPC alone are selectively activated when subjects have to keep in mind a main goal while performing concurrent (sub)goals. Neither keeping in mind a goal over time (working memory) nor successively allocating attentional resources between alternative goals (dual-task performance) could by themselves activate these regions. Our results indicate that the FPPC selectively mediates the human ability to hold in mind goals while exploring and processing secondary goals, a process generally required in planning and reasoning.
The anterior medial prefrontal cortex (AMPC) in humans is involved in affect and in regulating goal-directed behaviors. The precise function of the AMPC, however, is poorly understood. Using magnetic resonance imaging, we found that bilateral regions in the AMPC were selectively recruited to compute the reliability of subjects' expectations that developed when subjects were learning sequences of cognitive tasks. In contrast, regions similarly recruited in learning sequences of motor acts were found in the ventral striatum. Our results show that beyond the execution of motor acts, the AMPC is selectively engaged in computing the relevance of cognitive goals that subjects intend to achieve. This indicates that the fronto-striatal circuit, including the ventral striatum and AMPC, subserves hierarchically distinct evaluative processes mediating the human ability to build behavioral plans, ranging from motor to cognitive action plans.
The prefrontal cortex (PFC) subserves cognitive control: the ability to coordinate thoughts or actions in relation with internal goals. Its functional architecture, however, remains poorly understood. Using brain imaging in humans, we showed that the lateral PFC is organized as a cascade of executive processes from premotor to anterior PFC regions that control behavior according to stimuli, the present perceptual context, and the temporal episode in which stimuli occur, respectively. The results support an unified modular model of cognitive control that describes the overall functional organization of the human lateral PFC and has basic methodological and theoretical implications.
The prefrontal cortex subserves executive control, i.e., the organization of action or thought in relation to internal goals. This brain region hosts a system of executive processes extending from premotor to the most anterior prefrontal regions that governs the temporal organization of behavior. Little is known, however, about the prefrontal executive system involved in the hierarchical organization of behavior. Here, we show using magnetic resonance imaging in humans that the posterior portion of the prefrontal cortex, including Broca's area and its homolog in the right hemisphere, contains a system of executive processes that control start and end states and the nesting of functional segments that combine in hierarchically organized action plans. Our results indicate that Broca's area and its right homolog process hierarchically structured behaviors regardless of their temporal organization, suggesting a fundamental segregation between prefrontal executive systems involved in the hierarchical and temporal organization of goal-directed behaviors.
This is the fourth edition of the undisputed classic on the prefrontal cortex, the principal "executive" structure of the brain. Because of its role in such cognitive functions as working memory, planning, and decision-making, the prefrontal cortex is critically involved in the organization of behavior, language, and reasoning. Prefrontal dysfunction lies at the foundation of several psychotic and neurodegenerative disorders, including schizophrenia and dementia. © 2015, 2008, 1997, 1989, 1980 Elsevier Ltd. All rights reserved.
Non-adjacent dependencies are challenging for the language learning machinery and are acquired later than adjacent dependencies. In this transcranial magnetic stimulation (TMS) study, we show that participants successfully discriminated between grammatical and non-grammatical sequences after having implicitly acquired an artificial language with crossed non-adjacent dependencies. Subsequent to transcranial magnetic stimulation of Broca’s region, discrimination was impaired compared to when a language-irrelevant control region (vertex) was stimulated. These results support the view that Broca’s region is engaged in structured sequence processing and extend previous functional neuroimaging results on artificial grammar learning (AGL) in two directions: first, the results establish that Broca’s region is a causal component in the processing of non-adjacent dependencies, and second, they show that implicit processing of non-adjacent dependencies engages Broca’s region. Since patients with lesions in Broca’s region do not always show grammatical processing difficulties, the result that Broca’s region is causally linked to processing of non-adjacent dependencies is a step towards clarification of the exact nature of syntactic deficits caused by lesions or perturbation to Broca’s region. Our findings are consistent with previous results and support a role for Broca’s region in general structured sequence processing, rather than a specific role for the processing of hierarchically organized sentence structure.
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Although the orbitofrontal cortex (OFC) has been studied intensely for decades, its precise functions have remained elusive. We recently hypothesized that the OFC contains a “cognitive map” of task space in which the current state of the task is represented, and this representation is especially critical for behavior when states are unobservable from sensory input. To test this idea, we apply pattern-classification techniques to neuroimaging data from humans performing a decision-making task with 16 states. We show that unobservable task states can be decoded from activity in OFC, and decoding accuracy is related to task performance and the occurrence of individual behavioral errors. Moreover, similarity between the neural representations of consecutive states correlates with behavioral accuracy in corresponding state transitions. These results support the idea that OFC represents a cognitive map of task space and establish the feasibility of decoding state representations in humans using non-invasive neuroimaging.
A sequence of images, sounds, or words can be stored at several levels of detail, from specific items and their timing to abstract structure. We propose a taxonomy of five distinct cerebral mechanisms for sequence coding: transitions and timing knowledge, chunking, ordinal knowledge, algebraic patterns, and nested tree structures. In each case, we review the available experimental paradigms and list the behavioral and neural signatures of the systems involved. Tree structures require a specific recursive neural code, as yet unidentified by electrophysiology, possibly unique to humans, and which may explain the singularity of human language and cognition.
Many daily behaviors require us to actively focus on the current task and ignore all other distractions. Yet, ignoring everything else might hinder the ability to discover new ways to achieve the same goal. Here, we studied the neural mechanisms that support the spontaneous change to better strategies while an established strategy is executed. Multivariate neuroimaging analyses showed that before the spontaneous change to an alternative strategy, medial prefrontal cortex (MPFC) encoded information that was irrelevant for the current strategy but necessary for the later strategy. Importantly, this neural effect was related to future behavioral changes: information encoding in MPFC was changed only in participants who eventually switched their strategy and started before the actual strategy change. This allowed us to predict spontaneous strategy shifts ahead of time. These findings suggest that MPFC might internally simulate alternative strategies and shed new light on the organization of PFC.
Copyright © 2015 Elsevier Inc. All rights reserved.
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Behavioral choices that ignore prior experience promote exploration and unpredictability but are seemingly at odds with the brain’s tendency to use experience to optimize behavioral choice. Indeed, when faced with virtual competitors, primates resort to strategic counterprediction rather than to stochastic choice. Here, we show that rats also use history- and model-based strategies when faced with similar competitors but can switch to a “stochastic” mode when challenged with a competitor that they cannot defeat by counterprediction. In this mode, outcomes associated with an animal’s actions are ignored, and normal engagement of anterior cingulate cortex (ACC) is suppressed. Using circuit perturbations in transgenic rats, we demonstrate that switching between strategic and stochastic behavioral modes is controlled by locus coeruleus input into ACC. Our findings suggest that, under conditions of uncertainty about environmental rules, changes in noradrenergic input alter ACC output and prevent erroneous beliefs from guiding decisions, thus enabling behavioral variation.
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Human ventrolateral frontal cortex (vlFC) is identified with cognitive processes such as language and cognitive flexibility. The relationship between it and the vlFC of other primates has therefore been the subject of particular speculation. We used a combination of structural and functional neuroimaging methods to identify key components of human vlFC. We compared how vlFC areas interacted with other brain areas in 25 humans and 25 macaques using the same methods. We identified a core set of 11 vlFC components that interacted in similar ways with similar distributed circuits in both species and, in addition, one distinctively human component in ventrolateral frontal pole. Fundamental differences in interactions with posterior auditory association areas in the two species were also present—these were ubiquitous throughout posterior human vlFC but channeled to different frontal regions in monkeys. Finally, there were some differences in interregional interactions within vlFC in the two species.
Orbitofrontal cortex (OFC) has long been known to play an important role in decision making. However, the exact nature of that role has remained elusive. Here, we propose a unifying theory of OFC function. We hypothesize that OFC provides an abstraction of currently available information in the form of a labeling of the current task state, which is used for reinforcement learning (RL) elsewhere in the brain. This function is especially critical when task states include unobservable information, for instance, from working memory. We use this framework to explain classic findings in reversal learning, delayed alternation, extinction, and devaluation as well as more recent findings showing the effect of OFC lesions on the firing of dopaminergic neurons in ventral tegmental area (VTA) in rodents performing an RL task. In addition, we generate a number of testable experimental predictions that can distinguish our theory from other accounts of OFC function.
Recent computational theories of decision making in humans and animals have portrayed 2 systems locked in a battle for control of behavior. One system-variously termed model-free or habitual-favors actions that have previously led to reward, whereas a second-called the model-based or goal-directed system-favors actions that causally lead to reward according to the agent's internal model of the environment. Some evidence suggests that control can be shifted between these systems using neural or behavioral manipulations, but other evidence suggests that the systems are more intertwined than a competitive account would imply. In 4 behavioral experiments, using a retrospective revaluation design and a cognitive load manipulation, we show that human decisions are more consistent with a cooperative architecture in which the model-free system controls behavior, whereas the model-based system trains the model-free system by replaying and simulating experience. (PsycINFO Database Record (c) 2012 APA, all rights reserved).
Behavioral economic studies involving limited numbers of choices have provided key insights into neural decision-making mechanisms.
By contrast, animals’ foraging choices arise in the context of sequences of encounters with prey or food. On each encounter,
the animal chooses whether to engage or, if the environment is sufficiently rich, to search elsewhere. The cost of foraging
is also critical. We demonstrate that humans can alternate between two modes of choice, comparative decision-making and foraging,
depending on distinct neural mechanisms in ventromedial prefrontal cortex (vmPFC) and anterior cingulate cortex (ACC) using
distinct reference frames; in ACC, choice variables are represented in invariant reference to foraging or searching for alternatives.
Whereas vmPFC encodes values of specific well-defined options, ACC encodes the average value of the foraging environment and
cost of foraging.
The prefrontal cortex is critical to many cognitive abilities that are considered particularly human, and forms a large part of a neural system crucial for normal socio-emotional and executive functioning in humans and other primates. In this chapter, we survey the literature regarding prefrontal development and pathology in humans as well as comparative studies of the region in humans and closely related primate species. The prefrontal cortex matures later in development than more caudal regions, and some of its neuronal subpopulations exhibit more complex dendritic arborizations. Comparative work suggests that the human prefrontal cortex differs from that of closely related primate species less in relative size than it does in organization. Specific reorganizational events in neural circuitry may have taken place either as a consequence of adjusting to increases in size or as adaptive responses to specific selection pressures. Living in complex environments has been recognized as a considerable factor in the evolution of primate cognition. Normal frontal lobe development and function are also compromised in several neurological and psychiatric disorders. A phylogenetically recent reorganization of frontal cortical circuitry may have been critical to the emergence of human-specific executive and social-emotional functions, and developmental pathology in these same systems underlies many psychiatric and neurological disorders, including autism and schizophrenia.
Although we often encounter circumstances with which we have no prior experience, we rapidly learn how to behave in these novel situations. Such adaptive behavior relies on abstract behavioral rules that are generalizable, rather than concrete rules mapping specific cues to specific responses. Although the frontal cortex is known to support concrete rule learning, less well understood are the neural mechanisms supporting the acquisition of abstract rules. Here, we use a reinforcement learning paradigm to demonstrate that more anterior regions along the rostro-caudal axis of frontal cortex support rule learning at higher levels of abstraction. Moreover, these results indicate that when humans confront new rule learning problems, this rostro-caudal division of labor supports the search for relationships between context and action at multiple levels of abstraction simultaneously.
Behavioral flexibility is the hallmark of goal-directed behavior. Whereas a great deal is known about the neural substrates of behavioral adjustment when it is explicitly cued by features of the external environment, little is known about how we adapt our behavior when such changes are made on the basis of uncertain evidence. Using a Bayesian reinforcement-learning model and fMRI, we show that frontopolar cortex (FPC) tracks the relative advantage in favor of switching to a foregone alternative when choices are made voluntarily. Changes in FPC functional connectivity occur when subjects finally decide to switch to the alternative behavior. Moreover, interindividual variation in the FPC signal predicts interindividual differences in effectively adapting behavior. By contrast, ventromedial prefrontal cortex (vmPFC) encodes the relative value of the current decision. Collectively, these findings reveal complementary prefrontal computations essential for promoting short- and long-term behavioral flexibility.
The lack of a single anatomical or functional definition of 'prefrontal cortex' has led to different and, in some respects, controversial views on the existence of a prefrontal cortex in non-primate mammals, in particular in rats. Until the classic paper by Rose and Woolsey [Res. Publ. Assoc. Nerv. Ment. Dis. 27 (1948) 210], the general idea was that a prefrontal cortex is unique to primate species. Rose and Woolsey's 'prefrontal cortex' definition was based upon a single anatomical criterion, i.e. the cortical projection area of the mediodorsal thalamic nucleus. Single criteria, however, do not appear to be sufficient for defining the prefrontal cortex. Therefore, other anatomical and functional characteristics are currently used to identify the prefrontal cortex in different species. Yet, recently the debate about the nature of the prefrontal cortex in non-primate species has been resumed. In the present paper we will compare the structural and functional characteristics of the prefrontal cortex of nonhuman primates and rats. We will argue that rats have a functionally divided prefrontal cortex that includes not only features of the medial and orbital areas in primates, but also some features of the primate dorsolateral prefrontal cortex.
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Badre D, Hoffman J, Cooney JW, D'Esposito M: Hierarchical
cognitive control deficits following damage to the human
frontal lobe. Nat Neurosci 2009, 12:515-522.
The hierarchical organization of the lateral prefrontal cortex
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Nee DE, D'Esposito M: The hierarchical organization of the
lateral prefrontal cortex. eLife 2016, 5:e12112.
Zé non A: Disruption of Broca's area alters higherorder chunking processing during perceptual sequence learning
- A Alamia
- O Solopchuk
- D 'ausilio
- Van Bever
- V Fadiga
- L Olivier
Alamia A, Solopchuk O, D'Ausilio A, Van Bever V, Fadiga L,
Olivier E, Zé non A: Disruption of Broca's area alters higherorder chunking processing during perceptual sequence
learning. J Cogn Neurosci 2016, 28:402-417.
Comprehensive review detailing the functional similarities and differences between monkey and human frontopolar cortex. The review proposes a model describing the specificity of human compared to monkey frontopolar function based on the notion of directed versus undirected exploration
- F A Mansouri
- E Koechlin
- M G Rosa
- M J Buckley
Mansouri FA, Koechlin E, Rosa MG, Buckley MJ: Managing
competing goals -a key role for the frontopolar cortex. Nat
Rev Neurosci 2017, 18:645.
Comprehensive review detailing the functional similarities and differences
between monkey and human frontopolar cortex. The review proposes a
model describing the specificity of human compared to monkey frontopolar function based on the notion of directed versus undirected
exploration.