Abstract not found

this article are also gratefully acknowledged

The control of routine action is a complex process subject both to minor lapses in normals and to more severe breakdown followingcertain forms of neurological damage. A number of recent empirical studies (e.g. Humphreys & Ford, 1998; Schwartz et al., 1991, 1995, 1998) have examined the details of breakdown in certain classes of patient, and attempted to relate the findings to existing psychological theory. This paper complements those studies by presenting a computational model of the selection of routine actions based on competitive activation within a hierarchically organised network of action schemas (cf. Norman & Shallice, 1980, 1986). Simulations are reported which demonstrate that the model is capable of organised sequential action selection in a complex naturalistic domain. It is further demonstrated that, after lesioning, the model exhibits behaviour qualitatively equivalent to that observed by Schwartz et al., in their action disorganisation syndrome patients.

A distributed neural network model called SPEC for processing sentences with recursive relative clauses is described. The model is based on separating the tasks of segmenting the input word sequence into clauses, forming the case-role representations, and keeping track of the recursive embeddings into di erent modules. The system needs to be trained only with the basic sentence constructs, and it generalizes not only to new instances of familiar relative clause structures, but to novel structures as well. SPEC exhibits plausible memory degradation as the depth of the center embeddings increases, its memory is primed by earlier constituents, and its performance is aided by semantic constraints between the constituents. The ability to process structure is largely due to a central executive network that monitors and controls the execution of the entire system. This way, in contrast to earlier subsymbolic systems, parsing is modeled as a controlled high-level process rather than one based on automatic re ex responses. 1

When subjects switch between a pair of stimulus–response tasks, reaction time is slower on trial N if a different task was performed on trial N � 1. We present a parallel distributed processing (PDP) model that simulates this effect when subjects switch between word reading and color naming in response to Stroop stimuli. Reaction time on ‘‘switch trials’ ’ can be slowed by an extended response selection process which results from (a) persisting, inappropriate states of activation and inhibition of task-controlling representations; and (b) associative learning, which allows stimuli to evoke tasks sets with which they have recently been associated (as proposed by Allport & Wylie, 2000). The model provides a good fit to a large body of empirical data, including findings which have been seen as problematic for this explanation of switch costs, and shows similar behavior when the parameters are set to random values, supporting Allport and Wylie’s proposal. © 2001 Elsevier Science Key Words: task switching; task set; Stroop effect; parallel distributed processing; executive functions. Atkinson and Shiffrin (1968) proposed a distinction between relatively permanent cognitive structures, such as short- and long-term memory, and control processes which harness those fixed structures in order to attain specific goals. This distinction was elaborated in the following years (e.g.,

Although perseveration---the inappropriate repetition of previous responses---is quite common among patients with neurological damage, relatively few detailed computational accounts of its various forms have been put forth. A particularly well-documented variety involves the pattern of errors made by "optic aphasic" patients, who have a selective deficit in naming visually-presented objects. Based on our previous work in modeling impaired reading for meaning in deep dyslexia, we develop a connectionist simulation of visual object naming. The major extension in the present work is the incorporation of short-term correlational weights that bias the network towards reproducing patterns of activity that have occurred on recently preceding trials. Under damage, the network replicates the complex semantic and perseverative effects found in the optic aphasic error pattern. Further analysis reveals that the perseverative effects are strongest when the lesions are near or within semanti...

The current state of A.D. Baddeley and G.J. Hitch’s (1974) multicomponent working memory model is reviewed. The phonological and visuospatial subsystems have been extensively investigated, leading both to challenges over interpretation of individual phenomena and to more detailed attempts to model the processes underlying the subsystems. Analysis of the controlling central executive has proved more challenging, leading to a proposed clarification in which the executive is assumed to be a limited The term working memory appears to have been first proposed by Miller, Galanter, and Pribram (1960) in their classic book Plans and the Structure of Behavior. The term has subsequently been used in computational modeling approaches (Newell & Simon, 1972) and in animal learning studies, in which the participant animals are required to hold information across a number of trials within the same day (Olton, 1979). Finally, within cognitive psychology, the term has been adopted to cover the system or systems involved in the temporary maintenance and manipulation of information. Atkinson and Shiffrin (1968) applied the term to a unitary short-term store, in contrast to the proposal of Baddeley and Hitch (1974), who used it to refer to a system comprising multiple components. They emphasized the functional importance of this system, as opposed to its simple storage capacity. It is this latter concept of a multicomponent working memory that forms the focus of the discussion that follows. I myself have been using the concept for over 25 years; does it still work? Before addressing this issue, it is perhaps appropriate to consider what are the criteria for working. The multicomponent model of working memory was proposed as a theoretical framework whose function was to give

Unexpected interruptions introduced during the execution phase of simple Tower of London problems incurred a time cost when the interrupted goal was retrieved, and this cost was exacerbated the longer the goal was suspended. Furthermore, time taken to retrieve goals was greater following a more complex interruption, indicating that processing limitations may be as important as time-based limitations in determining the ease of goal retrieval. Such findings cannot simply be attributed to task-switching costs and are evaluated in relation to current models of goal memory (E. M. Altmann & G. J. Trafton, 2002; J. R. Anderson & S. Douglass, 2001), which provide a useful basis for the investigation and interpretation of interruption effects.

A series of experiments introduced interruptions to the execution phase of simple Tower of London problems and found that the opportunity for preparation before the break in task reduced the time cost at resumption. Retrieval of the suspended goal was facilitated when participants were given the opportunity to encode retrieval cues during an “interruption lag ” (the brief time before engaging in the interrupting task) but was impeded when these visual cues were subsequently altered following interruption. The results provide useful support for the goal-activation model (E. M. Altmann & G. J. Trafton, 2002), which assumes that context—at the points of both goal suspension and goal retrieval—is critical to efficient interruption recovery.

Humans perform actions to reach particular goals, that is, to intentionally create or modify personally relevant events---we move our eyes to learn more about a novel event, reach for a cup to quench our thirst, and move our lips to share our thoughts with someone else. Accordingly, even primitive actions must involve some kind of planning, some sort of anticipatory control. Indeed, there are at least three defining features that the simplest behavioral acts share with more complex ones. First, all of them are planned in terms of anticipated goal events. In particular, the first step of action planning consists in specifying the features the action is intended to possess; this is achieved by activating the appropriate action-effect codes, i.e., sensory-motor assemblies controlling the production of those features. Action-effect codes emerge through the perception of movement-effect contingencies, and they are acquired from the first months in life on. Besides action planning they are involved in the perception of both one's own actions and actions of others. Second, selected features of an intended action need to be integrated into a coherent, durable action plan, which is achieved by temporarily "binding" distributed feature codes. Third, planning an action turns the cognitive system into a kind of reflex machinery, which facilitates the proper execution of the plan under appropriate circumstances. This involves the implementation of automatic stimulus-response associations and the increase of the salience of action-related situational information, thereby delegating action control to the environment.