WEAVER++ (Word Encoding by Activation and VERification) is a computational model designed to explain how we, homo loquens, plan the production of spoken words (also called word finding, word retrieval, or lexical access) and how the planning relates to other cognitive abilities, including perception, action, memory, thinking, and in particular, attention. The model tries to make explicit the many strands of declarative knowledge ("knowing that") that must be woven together using procedural knowledge ("knowing how") and the different mixtures of strands used in different word production tasks. The model falls into the general class of hybrid models of human performance in that it combines a declarative associative network and procedural rule system with retrieval from declarative memory through spreading activation and activation-based rule triggering (cf. ACT-R of John Anderson and colleagues). The distinction between declarative and procedural knowledge in word planning is reminiscent of the distinction between mental contents and acts advanced by Külpe based on the work of his Würzburg group in the early 1900s, and it is supported by accumulating empirical evidence on language production (e.g., Levelt's 1989 Speaking; see also the work of Michael Ullman and colleagues). Milestones in the history of the distinction include Brenda Milner’s (1962) study on mirror-drawing by patient H.M., computational work in Artificial Intelligence in the 1970s, and Neil Cohen and Larry Squire’s (1980) study on mirror-reading by amnesic patients. The WEAVER++ model plans spoken words by activating, selecting, and connecting (weaving together) types of verbal information.

WEAVER++ gives detailed accounts of response time (RT) findings on spoken word production, obtained within a research tradition originating with Donders, Cattell, and Stroop. WEAVER++ has also been applied to eye tracking and neuroimaging findings (i.e., electrophysiological as well as hemodynamic evidence). The neurocognitive extension WEAVER++/ARC synthesizes behavioral psycholinguistic, functional neuroimaging, tractography, and aphasiological evidence. The goal is to provide unified computational explanations for a wide range of relevant empirical findings on spoken word planning (word finding, word retrieval, lexical access) in health and disease. See for example:

Roelofs, A. (2018). A unified computational account of cumulative semantic, semantic blocking, and semantic distractor effects in picture naming. Cognition, 172, 59-72. Article (PDF 574K)

Roelofs, A. (2014). A dorsal-pathway account of aphasic language production: The WEAVER++/ARC model. Cortex, 59, 33-48. Article (PDF 1339K)

The model has been developed for Germanic languages like Dutch and English, but also model versions for Mandarin Chinese and Japanese have been made.




Roelofs, A. (2015). Modeling of phonological encoding in spoken word production: From Germanic languages to Mandarin Chinese and Japanese. Japanese Psychological Research, 57, 22-37. Article (PDF 513K)



Horizontal and vertical threads


Accumulating evidence suggests that linguistic processes underlying the planning of words cannot happen without paying some form of attention. Or as Kussmaul put it in Die Störungen der Sprache (1877), language processes proceed semi-automatically ("halb automatisch").

In their classic article "Attention to action: Willed and automatic control of behavior", Norman and Shallice (1986) made a distinction between "horizontal threads" and "vertical threads" in the control of human performance. Horizontal threads are strands of processing that map perceptions onto actions and vertical threads are attentional influences on these mappings. Characteristics of performance arise from interactions between horizontal and vertical threads. WEAVER++ implements specific claims about how the horizontal and vertical threads are woven together in language performance.

A central claim embodied by WEAVER++ is that the attentional control of language performance is achieved through condition-action rules (cf. EPIC of David Meyer and David Kieras) rather than purely associatively. WEAVER++'s lexical network is accessed by spreading activation while the condition-action rules determine what is done with the activated lexical information depending on the task. When a goal is specified in working memory, the attention of the system is focussed on those rules that include the goal among their conditions.

WEAVER++ plans words by incrementally extending verbal goals, i.e., lemmas are selected for lexical concepts, morphemes for lemmas, segments for morphemes, and syllable motor programs for syllabified segments, whereby the syllabification of segments proceeds incrementally from the beginning of a word to its end. The idea of incrementality in language production originates with Wundt. Furthermore, WEAVER++ is an attempt at incremental or cumulative modeling, i.e., an attempt to extend a model in new directions and to new phenomena by building on earlier modeling results.


Varieties of attention


Attention is an umbrella term covering a number of abilities, such as alerting (brief or sustained), orienting (with or without gaze shifting), and executive control, also called executive attention, attentional control, or cognitive control (see the work of Posner and colleagues, reviewed by Posner in his 2012 book Attention in a Social World). Moreover, executive control consists of a number of abilities, including updating, inhibiting, and shifting (see the work of Akira Miyake and colleagues). These executive abilties are related to the more traditional notions of divided attention (between tasks), selective attention, and attention switching (between tasks). Selective attention includes the ability to resolve conflict among competing types of information, which can be achieved by enhancing the activation of targets, inhibiting competitors, or both.


Moreover, in performing tasks requiring selective attention, such as the color-word Stroop task (e.g., name the ink color of the word GREEN; say "red") or the picture-word interference task (e.g., name a picture of a cat with the word DOG superimposed; say "cat"), WEAVER++ may employ two additional mechanisms of selective attention, referred to as "stimulus set" and "response set" by Donald Broadbent (Decision and Stress book, 1971). Stimulus set ("filtering") refers to selection on the basis of a perceptual attribute, such as spatial location, color, shape, or temporal order. Response set refers to selection on the basis of the vocabulary of allowable responses. Task performance may require one or both of these mechanisms of selective attention:

Roelofs, A. (2003). Goal-referenced selection of verbal action: Modeling attentional control in the Stroop task. Psychological Review, 110, 88-125. Article (PDF 585K)


From Wernicke to WEAVER++

In the early days of experimental psychology, Wundt (1904, Principles of Physiological Psychology book) criticized the now classic, associative Wernicke-Lichtheim model of word production and perception by arguing that the retrieval of words from memory is an active goal-driven process rather than a passive associative process, as held by the model. According to Wundt, an attentional process centered in the frontal lobes of the human brain controls a word perception and production network located in perisylvian brain areas, described by the Wernicke-Lichtheim model. WEAVER++ builds in many respects on the Wernicke-Lichtheim model, but also addresses Wundt's critique by implementing assumptions on how the production-perception network is controlled. Characteristics of language performance, such as latencies and errors, arise from interactions between the lexical network and the attentional control system. For example, patterns of speech errors by aphasic and nonaphasic speakers seem determined, at least in part, by self-monitoring, which is an important attentional control function:

Roelofs, A. (2011). Modeling the attentional control of vocal utterances: From Wernicke to WEAVER++. In J. Guendouzi, F. Loncke, & M. J. Williams (Eds.), The Handbook of Psycholinguistic and Cognitive Processes: Perspectives in Communication Disorders (pp. 189-207). Hove, UK: Psychology Press. Article (PDF 1464K)

Roelofs, A. (2004). Error biases in spoken word planning and monitoring by aphasic and nonaphasic speakers: Comment on Rapp and Goldrick (2000). Psychological Review, 111, 561-572. Article (PDF 150K)



The arcuate fasciculus (AF) is a white-matter fiber tract that arches around the Sylvian fissure, running from temporal to frontal cortex. The AF is much larger in humans than in nonhuman primates, and the cortical terminations of the AF are strongly modified, suggesting a human specialization that is relevant to the evolution of language (see the work of James Rilling and colleagues). It has long been assumed that this dorsal fiber pathway underpins both speech repetition and conceptually driven spoken language production, as underlying picture naming (e.g., Norman Geschwind, book Selected Papers on Language and the Brain, 1974).

Another proposal, however, holds that a ventral pathway underpinned by the uncinate fasciculus (UF) and extreme capsule (EmC) fiber tracts is primarily responsible for spoken language production, whereas the AF primarily underlies speech repetition (see the Lichtheim 2 model of Matthew Lambon Ralph and colleagues).

The extension of WEAVER++ called WEAVER++/ARC (for WEAVER++ Arcuate Repetition and Conversation) provides a computational implementation of the dorsal-pathway view on language production. The model synthesizes behavioral psycholinguistic, functional neuroimaging, tractographic, and aphasiological evidence.

The results of computer simulations with WEAVER++/ARC revealed that the model accounts for the typical patterns of impaired and spared language performance associated with classic acute-onset and progressive aphasias. Moreover, the model accounts for evidence from patients with post-stroke aphasia that damage to the AF but not the EmC/UF pathway predicts impaired production performance. These results demonstrate the viability of a dorsal-pathway account of language production.

Roelofs, A. (2014). A dorsal-pathway account of aphasic language production: The WEAVER++/ARC model. Cortex, 59, 33-48. Article (PDF 1339K)


WEAVER++ on reading and dyslexia

Since the seminal work of Denckla and colleagues in the early 1970s (based on Norman Geschwind's hypothesis that color naming might predict reading), numerous studies have demonstrated that, in addition to phonological deficits, the majority of children and adults with reading disabilities also exhibit pronounced difficulties on naming-speed tasks, such as tests of "rapid automatized naming" (RAN). These tests require simple objects, colors, letters, or numbers to be named as quickly and accurately as possible. Naming speed is highly correlated with performance on word identification tasks, word reading efficiency measures, and measures of reading comprehension. Naming speed predicts later reading ability and helps identify risk at dyslexia in pre-literate children. Dyslexic readers are also known for their poor performance on Stroop color naming. Reading ability is negatively related to Stroop interference. Evidence suggest that attention mechanisms are critically implicated in reading and that disruption of these mechanisms may play a role in reading difficulties and dyslexia.

WEAVER++ provides functional analyses of object, color, and digit naming as well as word reading, and the model makes explicit how attention determines naming and reading. Moreover, the model provides an account of Stroop task performance and explains the negative linear relationship between reading skill and Stroop interference. It has been suggested that RAN is related to reading because reading recruits object-naming circuits in the left cerebral hemisphere. WEAVER++ makes explicit the connection between reading and object naming, both in functional and anatomical terms.

Roelofs, A. (2006). Functional architecture of naming dice, digits, and number words. Language and Cognitive Processes, 21, 78-111. Article (PDF 176K)

Protopapas, A., Archonti, A., & Skaloumbakas, C. (2007). Reading ability is negatively related to Stroop interference. Cognitive Psychology, 54, 251-282.  doi:10.1016/j.cogpsych.2006.07.003




WEAVER++ on specific language impairment

Evidence suggests that (subclinical) attention deficits also contribute to the impaired language performance of individuals with specific language impairment (SLI). This is a disorder of language acquisition and use in children who otherwise appear to be normally developing. The disorder may persist into adulthood. Difficulties concern language production (expressive language disorder) or both production and comprehension (mixed receptive-expressive language disorder). The features of the impaired language performance in SLI are quite variable, but common characteristics are a delay in starting to talk in childhood, deviant production of speech sounds, a restricted vocabulary, slow and inaccurate word retrieval (e.g. in picture naming), and use of simplified grammatical structures, including frequent omission of articles or plural and past tense endings (for a review, see Leonard’s 1998 book Children with Specific Language Impairment). In general, individuals with SLI seem to have a problem in dealing with relatively complex language structures, in both speech production and comprehension. A prominent account of SLI holds that these difficulties with complexity in language reflect a reduced capacity of systems underlying language processes, resulting from a limitation in general processing capacity (see the work of Laurence Leonard and colleagues). Moreover, it is becoming increasingly clear that attention deficits contribute to SLI.

Individuals with SLI appear to have reduced working memory capacity. Moreover, they may have deficits in sustained attention and attentional control. Capacity restrictions concerning language processes, working memory, and attention influence word planning in WEAVER++. For example, a capacity restriction in retrieving morphemes for a lemma may result in omission of inflectional morphemes, such as plural and past tense endings. This type of problem will be reinforced by capacity restrictions in working memory and attention. For word planning to be successful in the model, attention needs to be sustained until the phonological form has been planned and syllable motor programs may be accessed. Difficulties in maintaining attention will impede the planning process, especially when a complex mapping between levels is involved (e.g., such as the mapping between lemmas and morphemes).

Janssen, D. P., Roelofs, A., & Levelt, W.J.M. (2002). Inflectional frames in language production. Language and Cognitive Processes17, 209-236. Article (PDF 246K)

Levelt, W.J.M., Roelofs, A., & Meyer, A.S. (1999). A theory of lexical access in speech production. Behavioral and Brain Sciences, 22, 1-38. Article (PDF 693K)

Roelofs, A. (1996). Serial order in planning the production of successive morphemes of a word. Journal of Memory and Language, 35, 854-876. Article (PDF 249K)

Roelofs, A. (2006). Context effects of pictures and words in naming objects, reading words, and generating simple phrases. Quarterly Journal of Experimental Psychology, 59, 1764-1784. Article (PDF 172K)

Roelofs, A., & Piai, V. (2011). Attention demands of spoken word planning: A review. Frontiers in Psychology, 2, article 307. Article (PDF 976K)


Orienting of attention in dual-task performance

Spatial orienting of attention may occur overtly or covertly, that is, with or without gaze shifting. If the stimuli for one or two tasks have different spatial positions, eye movements (gaze shifts) may need to occur between the stimuli. In an experimental paradigm used in my laboratory, speakers name stimuli (e.g., a picture of a cat with the word DOG superimposed) displayed on the left side of a computer screen and shift their gaze to an arrow (e.g., < or > flanked by two Xs) displayed on the right side of the screen to manually indicate its direction, see figure below. WEAVER++ describes how an attentional control process coordinates the multiple threads of processing in vocal responding, gaze shifting, and manual responding:

Roelofs, A. (2007). Attention and gaze control in picture naming, word reading, and word categorizing. Journal of Memory and Language, 57, 232-251. Article (PDF 311K)

Roelofs, A. (2008). Attention, gaze shifting, and dual-task interference from phonological encoding in spoken word planning. Journal of Experimental Psychology: Human Perception and Performance, 34, 1580-1598. Article (PDF 377K)






Planning stages

The model distinguishes between conceptual preparation, lemma retrieval, and word-form encoding, with the encoding of forms further divided into morphological, phonological, and phonetic encoding. During conceptual preparation, concepts are flagged as goal concepts. In lemma retrieval, a goal concept is used to retrieve a lemma from memory, which is a representation of the syntactic properties of a word, crucial for its use in sentences. For example, the lemma of the word cat says that it is a noun. Lemma retrieval makes these properties available for syntactic encoding processes. In word-form encoding, the lemma is used to retrieve the morpho-phonological properties of the word from memory in order to construct an appropriate articulatory program. For example, for cat the morpheme <cat> and the speech segments /k/, /æ/, and /t/ are retrieved and a phonetic plan for [kæt] is generated. Finally, articulation processes execute the motor program, which yields overt speech.

Assume a speaker wants to name a picture of a cat with the word DOG superimposed. This involves the conceptual identification of the picture based on the perceptual input and its designation as goal concept (i.e., CAT(X)), the retrieval of the lemma of the corresponding word (i.e., cat), and the encoding of the form of the word (i.e., [kæt]). The final result is a motor program for the word "cat", which can be articulated.

In performing the picture-word interference task, aspects of word planning require attention. First, attentional (executive, cognitive) control is needed. The system has to achieve picture naming rather than word reading ("goal control") and the irrelevant input (the word in picture naming) has to be suppressed ("input control"). In general, attentional control in may engage the abilities of updating, inhibiting, and shifting (see the work of Akira Miyake and colleagues). Moreover, attentional control may regulate the sustaining of attention (alertness) and the orienting of attention, with or without shifting of eye gaze (as illustrated above). Attentional control is also needed for self-monitoring (i.e., monitoring internal or external speech), through which language users assess whether planning and performance are consistent with intent.






Neural substrates

According to the model, word planning and attentional control are underpinned by extensive networks of brain areas. The figure below shows a lateral view of the left hemisphere of the human brain. Declarative memory underlying word planning is associated with a network of temporal and frontal areas, including Wernicke's and Broca's areas. Picture naming is achieved through picture perception, conceptual identification, lemma retrieval, word-form encoding, and articulatory processing. Reading aloud minimally involves word-form perception, word-form encoding, and articulatory processing. The attentional control system (indicated by rounded rectangles) is associated with lateral and medial frontal areas (including the anterior cingulate cortex, ACC, not shown) and parietal cortex. Procedural memory, containing the if-then rules, is associated with the basal ganglia, thalamus, frontal cortex (including Broca’s area), and cerebellum. The procedural system mediates between the declarative associative network and the attentional system. It is assumed that the declarative and procedural knowledge is stored cortically, while subcortical structures, like the hippocampus and basal ganglia, mediate the learning of the declarative and procedural knowledge, respectively.    

Roelofs, A. (2014). A dorsal-pathway account of aphasic language production: The WEAVER++/ARC model. Cortex, 59, 33-48. Article (PDF 1339K) 





Much evidence suggests that the dorsolateral prefrontal cortex serves to maintain goals in working memory. ACC involvement in goal-referenced control agrees with the idea that attention is the principal link between cognition and motivation. For action control, it is not enough to have goals in working memory, but one should be motivated to attain them. Extensive projections from the thalamus and brainstem nuclei to the ACC suggest a role for drive and arousal. Extensive reciprocal connections between the ACC and dorsolateral prefrontal cortex suggest a role for working memory. The motor areas of the cingulate sulcus densely project to the brainstem, spinal cord, and motor cortex, which suggests a role of the ACC in motor control (see the work of Tomáš Paus and colleagues).

Roelofs, A., & Hagoort, P. (2002). Control of language use: Cognitive modeling of the hemodynamics of Stroop task performance. Cognitive Brain Research, 15, 85-97. Article (PDF 438K)


Homo Loquens: From monkey calls to human talking

We vocally communicate through speech but also with our cries and laughs. Whereas speech is learned, cries and laughs are innately specified. Such innate "calls" are observed in diverse vertebrates, including fish, amphibians, reptiles, birds, and mammals. Although the sound-producing organs differ (swim bladder in fish, syrinx in birds, larynx in amphibians, reptiles, and mammals), vertebrates seem to share a common brainstem and spinal cord organization for calls. Vocal production learning is common in birds (songbirds, parrots, and hummingbirds), but only humans, bats, cetaceans (dolphins, whales), and pinnipeds (seals) show evidence of vocal learning among mammals. At least in songbirds and humans, some of the forebrain pathways implicated in learned vocalization seem to share homologous components.

Both human and nonhuman primates (monkeys and apes) use their voice for communication. However, whereas an extensive network of perisylvian and medial cortical areas—including the ACC in some circumstances—is involved in the verbal vocal communication of humans (i.e., spoken word production), the only cortical area directly involved in call production by nonhuman primates is the ACC (see the work of Detlev Ploog, Uwe Jürgens, and colleagues). In nonhuman primates, the ACC plays a critical role in the voluntary initiation and suppression of calls (e.g., fear, alarm, aggression, and contact calls), which are all innate. The ACC also controls the innate vocalizations (e.g., crying, laughing, pain shrieking) of humans. The human ACC appears to be the cortical area where the evolutionary older innate-vocalization system and the newer spoken-word production system meet (see the work of Terrence Deacon). For a description of some commonalities of ACC function across call and word production, see

Roelofs, A. (2008). Attention to spoken word planning: Chronometric and neuroimaging evidence. Language and Linguistics Compass, 2, 389-405. Article (PDF 327K)







Declarative aspects

Declarative pieces of information ("facts") about words are stored in a labeled associative network, part of the brain's long-term declarative memory system. The network consists of three major strata: a conceptual stratum, a syntactic stratum, and a word-form stratum, corresponding to the major planning steps. The conceptual stratum represents conceptual facts as nodes and labeled links in a semantic network. For example, the concept CAT is represented by the node CAT(X) connected to ANIMAL(X) by an "is-a" link. The syntactic stratum contains lemma nodes, such as cat, which are connected by a "word class" link to nodes for their syntactic class (e.g., noun). Finally, the form stratum contains nodes representing morphemes (e.g., <cat>), segments (e.g., /k/), and motor programs (e.g., [kæt]). The figure above shows only a small fragment of the lexical network and most of the labels on the links have been omitted.

Another type of memory system that holds declarative information is working memory. In it, production goals, like the goal of naming a picture, are temporarily maintained.





Spreading activation

Information is retrieved from the associative declarative network by spreading activation. For example, a perceived entity (e.g., a cat) activates the corresponding concept node (i.e., CAT(X)) in the network. Activation then spreads through the network following a linear activation rule with a decay factor d. Each node m sends a proportion r of its activation to the nodes n it is connected to. For example, CAT(X) sends activation to other concepts such as ANIMAL(X) and DOG(X) and also to its lemma node cat.


WEAVER++'s lexical network is accessed by spreading activation while condition-action rules (see below) determine what is done with the activated lexical information depending on the goal. When a goal is placed in working memory, processing in the system is focused on those rules that include the goal among their conditions. The rules mediate attentional influences by selectively enhancing the activation of target nodes in the network in order to achieve mappings of targets onto articulatory programs. For example, in naming a picture of a cat, the activation of the concept node CAT(X) is selectively enhanced.


Attentional activation enhancements

The model assumes that the ACC is implicated in the attentional enhancement of the activation of targets (rather than performing conflict monitoring, as is often assumed, see the work of Cohen, Botvinick, and colleagues). The attentional control system determines how strongly and for how long the enhancements occur, depending on the allocation policy (cf. Kahneman, Attention and Effort book, 1973; EPIC of Meyer and Kieras). Attention is assumed to be sustained to word planning just as long as is needed to achieve acceptable levels of speed and accuracy:

Roelofs, A., Van Turennout, M., & Coles, M. G. H. (2006). Anterior cingulate cortex activity can be independent of response conflict in Stroop-like tasks. Proceedings of the National Academy of Sciences USA, 103, 13884-13889. Article (PDF 350K)

Roelofs, A. (2008). Tracing attention and the activation flow in spoken word planning using eye movements. Journal of Experimental Psychology: Learning, Memory, and Cognition, 34, 353-368. Article (PDF 285K)

Roelofs, A. (2008). Attention, gaze shifting, and dual-task interference from phonological encoding in spoken word planning. Journal of Experimental Psychology: Human Perception and Performance, 34, 1580-1598. Article (PDF 377K)





Procedural aspects

Procedural knowledge ("knowing how") is embodied by condition-action (if-then) rules, part of the brain's long-term procedural memory system. The "if-side" of a rule specifies a condition to be satisfied and the "then-side" of a rule specifies an action to be performed when the condition is met.





Procedural knowledge is associated with, among others, the basal ganglia thalamocortical circuitry of the brain. This circuitry consists of a number of parallel loops that run from cortical areas (i.e., temporal, parietal, and frontal cortex, including Broca’s area) via the striatum (i.e., caudate nucleus and putamen) and the subthalamic nucleus to the globus pallidus and via the thalamus back to cortical areas, especially frontal cortex (including Broca’s area). The circuits enable the execution of an action (e.g., selecting a concept or a task set) whose condition (best) matches the current context and momentary goal. There is also a basal ganglia thalamocortical circuitry for the control of eye movements. The basal ganglia circuitry seems especially important for the learning and use of novel rather than expert procedural knowledge (see the work of Ashby and colleagues). Condition-action rules in procedural memory are taken to underlie both attentional control and more automatic processes in speaking. The procedural system is essential for translating declarative knowledge into action.  






Verification means that selections in human performance are accomplished by explicit reference to goals: goal-referenced control. The condition-action rules carry out the selection of nodes. A rule is triggered when its nodes become active. A lemma retrieval rule selects a lemma if the connected concept is flagged as goal concept. For example, cat is selected for CAT(X) if it is the goal concept and cat has reached a critical difference in activation compared to other lemmas. The actual moment in time of firing of the rule is determined by the ratio of activation of the lemma node and the sum of all the others. Thus, how fast a node is selected depends on how active the other nodes are. There is activation-based triggering and firing of condition-action rules.





A morphological rule selects the morpheme nodes that are connected to the selected lemma (<cat> is selected for cat). Phonological rules select the segments that are connected to the selected morphemes (/k/, /æ/, and /t/ for <cat>) and syllabify the segments (e.g., /k/ is made syllable onset: onset(/k/)). Finally, phonetic rules select syllable-based articulatory programs that are appropriately connected to the syllabified segments (i.e., [kæt] is selected for onset(/k/), nucleus(/æ/) and coda(/t)/)). The moment of selection of syllable program nodes is also determined by a ratio of activations, such that how fast selection occurs depends on how active other nodes are.


From Wilhelm Wundt via Watt at Würzburg to WEAVER++


Although issues concerning the attentional control of human performance were explored in the early days of experimental psychology by Donders, Cattell, and Wundt (mental chronometry), no real progress was made in understanding the mechanisms of control. Associationist and behaviorist theories, like those of Hume, the Mills, Watson, and Skinner, accounted for action selection by postulating associations between stimuli and responses. However, if all our actions were determined exclusively by stimulus-response associations, goals could not determine which action to make because the strongest association would automatically determine the response. Around 1900, the Würzburg school with Ach, Külpe, and Watt demonstrated the importance of the task ("Aufgabe") in determining a response. However, how exactly task goals directed processing remained unclear. In the 1910s, Müller proposed an account in associative terms, whereas Selz proposed an account in terms of symbolic structures and rules. Later theoretical developments are descendants of these ideas. On the view that dominates the attention and performance literature, goals associatively bias or "sculpt" the activation of one response pathway (e.g., for picture naming, in responding to the picture of a cat with the word DOG superimposed) rather than another (e.g., for oral reading), following Müller. On another view, following Selz, and computationally implemented in WEAVER++, attentional control arises from explicit reference to goals, accomplished by condition-action rules. Pictures below (from left to right): Watt, Külpe, Selz, and Newell and Simon.




The idea of goal-referenced control that originated with Selz in the 1910s flourished in the work of De Groot, Newell and Simon, and Anderson, among others, on higher-level cognitive processes like problem solving (e.g., playing chess, proving logic theorems, and solving puzzles such as the Tower of Hanoi), where associative models generally failed. However, due to the traditional partitioning of experimental psychology into cognition, perception, and action, with little communication across the boundaries, the idea of goal-referenced control has had little impact in the perception-action literature. Only recently, goal-referenced control made successful strides into the attention and performance literature.

That goal-referenced control underlies both problem-solving and performing Stroop-like tasks agrees with the strong connection between attentional control and general intelligence (Spearman's g, see the work of John Duncan and colleagues, reviewed by Duncan in his 2010 book How Intelligence Happens). Solving intelligence-demanding problems, like the Raven Matrices or the Tower of Hanoi puzzle, involves working serially from one subgoal to another, each with focused attention, until the overall goal is achieved. Individual differences in general intelligence are most pronounced in behavioral measures when attentional control is required (see the work of Randall Engle and colleagues).

In his dissertation work at Würzburg University, Henry Watt found that when verbal responses of the same intrinsic speed were grouped together, a variation of task had a similar effect across latency groups, although he did not quantify this effect. He stated, "The influence of the task is independent of the rapidity of the tendency to reproduction itself" (Watt, 1906, Journal of Anatomy and Physiology, p. 260, original italics). Watt's regularity has recently been confirmed and quantified using modern techniques for analysing response time distributions:

Roelofs, A. (2008). Dynamics of the attentional control of word retrieval: Analyses of response time distributions. Journal of Experimental Psychology: General, 137, 303-323. Article (PDF 392K)





Planning latencies

Given the equations for spreading activation and rule firing, the mathematically expected mean planning latencies can be computed.






Frequently asked questions (FAQs)


Question: Why the name WEAVER?

Answer: Apart from providing a useful acronym (Word Encoding by Activation and VERification) and metaphor, it is accociated with an interesting (hi)story.

Weaving is a psychomotor skill performed already in very early times, known in the prehistoric era well before the ancient Egyptian and Greek civilizations. Weaving plays a prominent role in several Greek myths, including those featuring Arachne, Penelope, and Philomela. Arachne engaged in a weaving contest with the weaver goddess Athena, who transformed her into a spider, destined to weave forever. Penelope wove her design for a shroud by day but unraveled it again at night to keep her suitors from claiming her while waiting for Odysseus to return from the Trojan war. And Philomela, whose tongue was cut out after she was raped, used a loom as her voice to tell about her violation in a woven design. The link between weaving and communication is evident from the English word text, which is derived from the Latin word for weaving, texare. More recently, in the early nineteenth century, Jacquard’s invention of a programmable loom, which used punch cards with stored instructions for weaving patterns, led to the development of the modern computer (for a magnificent account, see James Essinger’s Jacquard’s web: How a Hand-Loom Led to the Birth of the Information Age, 2007).

It is important to note, however, that the "weaving" of words during speaking differs in a fundamental respect from most other human psychomotor skills. As Charles Darwin stated, "Man has an instinctive tendency to speak, as we see in the babble of our young children, whereas no child has an instinctive tendency to bake, brew, or write."


Question: Why do we need labeled links?

Answer: A mere associative link between two nodes tells nothing about the relation between the entities represented. For example, the concept CAT(X) is strongly associated with both DOG(X) and ANIMAL(X) but the relationship between CAT(X) and DOG(X) is very different from the relationship between CAT(X) and ANIMAL(X). The importance of explicitly representing the relation between nodes was recognized by Otto Selz in the early 1900s, and labeled link have become a central part of semantic networks in Artificial Intelligence since the seminal work of Ross Quillian in the late 1960s. Imagine the Internet without labeled (hyper)links!


Question: Why do we need condition-action rules? Wouldn't it be better to have a purely associative model?

Answer: There exists good evidence that language performance involves both declarative aspects (structured symbolic representations) and procedural aspects (if-then production rules), underpinned by the brain's declarative and procedural systems (e.g., the work of Michael Ullman and colleagues). Evidence suggests that condition-action rules underlie both the linguistic processes associated with the word planning stages (i.e., conceptual identification, lemma retrieval, word-form encoding) as well as executive control processes, associated with frontoparietal and basal ganglia thalamocortical networks in the brain (e.g., the work of Earl Miller and colleagues and John Duncan and colleagues).

For a theoretical account of how structured symbolic representations and if-then production rules may be realized by networks of spiking neurons, see the work of Chris Eliasmith and colleagues (see Eliasmith's 2013 book How to Build a Brain).

Question: Where can I find information on WEAVER++?

Answer: Here are a number of references:

Roelofs, A. (2018). A unified computational account of cumulative semantic, semantic blocking, and semantic distractor effects in picture naming. Cognition, 172, 59-72. Article (PDF 574K)

Roelofs, A. (2014). A dorsal-pathway account of aphasic language production: The WEAVER++/ARC model. Cortex, 59, 33-48. Article (PDF 1339K)

Roelofs, A. (2008). Dynamics of the attentional control of word retrieval: Analyses of response time distributions. Journal of Experimental Psychology: General, 137, 303-323. Article (PDF 392K)

Roelofs, A., Van Turennout, M., & Coles, M. G. H. (2006). Anterior cingulate cortex activity can be independent of response conflict in Stroop-like tasks. Proceedings of the National Academy of Sciences USA, 103, 13884-13889. Article (PDF 350K)

Roelofs, A. (2003). Goal-referenced selection of verbal action: Modeling attentional control in the Stroop task. Psychological Review, 110, 88-125. Article (PDF 585K)

Roelofs, A., & Hagoort, P. (2002). Control of language use: Cognitive modeling of the hemodynamics of Stroop task performance. Cognitive Brain Research, 15, 85-97. Article (PDF 438K)

A complete list of articles that report on WEAVER++ simulations can be found here.



Question: What programming language has been used for the model, and is the program available?

Answer: Most of the WEAVER++ and WEAVER++/ARC applications have been programmed in the C programming language using the Microsoft Visual C++ environment. These programs are available from Ardi Roelofs. Applications in Python have been made by Aitor San José.


Track record of WEAVER++ (WEAVER++'s Web)