The Use of Our Language in Higher Cognition and Other Tasks

Kashima has provided a demonstration of how our language can be used to propose hypotheses about some tasks in social cognition.   We think that this demonstration is especially useful because social cognition has traditionally viewed memory as simply involving trace access and has relied heavily on search (e.g., Srull & Wyer, 1989).   To be useful our functions only have to be more similar to the algorithmic-level mechanisms than are the functions currently employed in social cognition.   Similarly, Murnane has shown how our language is flexible enough to extend to more complex tasks involving context.   Sloman has suggested, however, that it would be as easy to use an algorithmic-level language (also see Lewis).

Sloman's point about the use of an algorithmic-level language is predicated on the observation that most of the difficulty involved in extending our specifications to new tasks lies in the identification of the knowledge and actions needed to perform the task.   These are indeed the difficult parts of the task and it is also true that once you have identified the knowledge and actions required you could use any algorithmic-level language that computes our functions.   This use of an algorithmic-level language to describe tasks is actually very close to what Humphreys, et al. (1989b) attempted and there are pitfalls to this approach.   It is very difficult for some modellers to accept that algorithmic-level ideas can be used to describe a task.   That is, they have a tendency to reject the approach unless "you show that your model actually fits the data." There is merit in fitting data but there are problems.   If your purpose is to point out similarities and differences across a large number of tasks, formal modelling can be premature.   That is, in many cases systematic experiments comparing the tasks should follow the identification of similarities and differences and precede a model fitting effort.   Formal modeling can also get in the way of effective communication.   That is, the number of arbitrary assumptions that are needed to actually fit data can detract from the argument over the identification of the knowledge and actions required.   An illustration of this problem occurs in Lindsay's (1991) comment on Metcalfe (1990).   Metcalfe had provided an identification of the knowledge and actions involved in the misleading postevent paradigm.   She had also shown that her model (CHARM) could provide a reasonable fit to the data.   In his commentary Lindsay invoked Pike's (1984) criticism of the mathematics of CHARM.   This completely ignored the fact that, for the purpose of describing the knowledge and actions required, Pike's and Metcalfe's mathematics are interchangeable.   The knowledge and actions required could also be described in the language of the target article and this might better serve to focus the debate.

Kinoshita has compared the use of our language with the language of an algorithmic-level theory.   Not surprisingly she found our language was inadequate.   We have never claimed that our language could substitute for an algorithmic-level language and our position - like Marr's - is that theories at all three levels are required (computational, algorithmic, and implementational).   What our language can do is to: a) help determine if Kinoshita and the other researchers in the area are asking all of the right questions, b) suggest alternative explanations for experimental results, and c) indicate the potential relevance of results.

One cannot conclude that an ceptual fluency (i. e., an ahistoric source of information) is involved in single item recognition without varying the episodes in which different items have occurred and using instructions to direct subjects to particular episodes.   With rare exceptions (Parkin, Leng, & Hunkin, 1990; Jacoby, 1991) researchers who have addressed Kinoshita's concerns have not used list- specific tasks.   Thus, they are not asking all of the right questions.   Kinoshita also assumes that a masked identity prime serves to increase perceptual fluency.   Presenting a masked identity prime just prior to the presentation of a to-be-recognized word is, however, the same sequence of events as occurs in JPTS.   Our specifications for LSIR and JPTS suggest an alternative to the perceptual fluency interpretation.   That is, they suggest that subjects are computing the intersection between the to-be-recognized word and the words in the study list in order to recognize a word.   If the to-be-recognized word did not occur in the study list or if the association with context was not learned then the intersection will be empty.   Under these conditions the presence of a masked identity prime may result in a nonempty intersection just as it is assumed to do in JPTS (Humphreys & Bain, 1992).   Some support for this prediction comes from Bernstein and Welch's (1991) observation of a very high correlation between similarity and recognition judgments.

As an example of how our theory can identify potentially relevant results, Jacoby (1983) had subjects study a long list of words and then gave them a perceptual identification test.   When the study list was made salient via instructions or the use of a short retention interval, the probability of the correct identification of an old word increased and the probability of the correct identification of a new word decreased.   This pattern of facilitation and inhibition is the same pattern that is found when direct (explicit) and indirect (implicit) retrieval instructions are used with partword cues (Roediger, et al., 1992).   This connection may not have been noticed because Roediger et al.   thought of their results as arising from a generation/recognition process instead of a more generic intersection process.   Jacoby's (1983) results are unlikely to arise from a generate-recognize process but they strongly suggest that subjects are combining information from two sources (perceptual information from the target and list-membership information) in a manner approximating an intersection.   This observation raises several important questions including: a) Is the use of list-membership information in this task in any sense deliberate? and b) In what way is the use of list-membership information when the subject has stayed in the same setting performing the same task, similar to the use of list-membership information invoked by instructions?

Oscar-Berman questioned whether our theory could help in understanding anterograde amnesia.   What it provides here is a language for describing hypotheses about the knowledge and actions needed to perform tasks.   For example, in Humphreys and Dennis' (1994) commentary on Eichenbaum et al.   (1994) our language was used to propose hypotheses about how some animal memory tasks were being performed.   Previously Humphreys, et al. (1989a) had used the algorithmic-level language of Humphreys, et al. (1989b) to propose hypotheses about a wider variety of tasks.   Because our language was designed to subsume the Humphreys, et al. (1989b) language, amongst many other algorithmic-level languages, it will be relatively straightforward to translate the Humphreys, et al. (1989a) hypotheses into our language.   It should be apparent that our language is far more complete, powerful, and precise than is the language of associative learning, familiarity, configural memory, declarative memory, etc.

Our analysis also suggests that Oscar-Berman and other researchers are not asking all of the right questions.   For example, in section 2.5 we showed how a task (AB ABr learning) that requires a three-way binding can be performed using two-way bindings and a series of retrievals.   This procedure is an unlikely way to perform AB ABr learning, given the ease with which untrained subjects perform this task.   However, the possibility that hippocampectimized rats are performing some tasks, which require three-way bindings, by using two-way bindings and a series of retrievals needs to be explored (Humphreys & Dennis, 1994).

Miscellaneous Issues and Misconceptions
Clarke draws a parallel between the bottom up versus top down approach and the split in cognitive science between Searle (1992) and Dennett (1991).   Ours and Marr's advocacy of the importance of a computational-level theory does not presuppose an acceptance of Dennett's position.   Even in a strong specification the functions are simply abstract characterizations of the mechanisms/processes that need to be implemented.   Such important issues as the type and number of errors and possibly even subjective experiences will emerge from the details of the implementation or even of the neural substrate.

Lewis has questioned whether our distinction between tasks that functionally do and do not require an episodic input maps onto the implicit/explicit distinction of Graf and Schacter (1985).   Our distinction directly maps onto the Dunn and Kirsner (1989) distinction between implicit and explicit tasks.   To map onto the Graf and Schacter distinction we have to assume, as they do, that subject supplied instructions can turn an implicit task into an explicit task.   Neither our approach nor the Graf and Schacter approach can unambiguously classify all tasks without some additional assumptions.

Murnane suggests that we are caught in a "bind" because a three-way binding does not suffice for nested context problems.   A three-way binding sufficed for the task we analyzed and we designed our language so that it could be extended to arbitrarily complex problems.   There is no "bind" here as Murnane's extension of our analysis shows.

Murray drew a parrallel between our classification scheme and the classification of psychophysical methods in accordance with the number of stimuli involved.   Yet, there is only a superficial resemblance, as one of our crucial distinctions between AB ABr learning and CREA involves the same number of stimuli.   In addition, modern psychophysical theories may assume that the same processes underlie the different methods.   Modern memory theories, however, assume that different processes underlie memory access with partword and word cues.

In arguing for the inclusion of a filtering process in a computational-level theory Murray implies that we are assuming perfect storage.   In order to correctly perform a task such as CREA a subject must recall a word from the list that is related to the cue.   We have described the complexity of the representation that is required to correctly perform this task.   In addition, in describing the input/output mapping, we have used a language that suggests similarities and differences between this task and other tasks and may even provide a rough description of some of the mechanisms and processes involved.   That is, we are trying to describe the components which are necessary in order to correctly perform the task.   If a representation of sufficient complexity is not available (i.e., if storage has failed) the task will not be performed correctly.

Tiberghien questions the autonomy of the computational level because the alternative specifications for LSIR could have arisen from implicit algorithmic-level hypotheses.   This question reflects the tension that exists in our article between thinking of our specifications as weak or strong.   As a weak specification the alternatives are simply an allowable rearrangement of a formal mathematical expression.   As a strong specification they are alternative algorithmic-level hypotheses.   Their strength in the latter role comes from the fact that our language, unlike Tiberghien's language, does not make a commitment to particular mechanisms (context as a tag) and processes (search).

There are, however, some algorithmic-level hypotheses we cannot express at the computational level.   For example, we could not express the hypothesis that the degree of similarity between the study and test contexts was playing a crucial role (Tiberghien expresses this idea as an association between the list context and the test context).   Some hypotheses must be expressed at the algorithmic level.   This requirement means that they must be expressed in terms of a specific form of representation and in terms of specific processes.   As a consequence the hypothesis will not generalize to other processes and other forms of representation.   The strength of an hypothesis stated in our language is that it generalizes to different forms of representation and different processes.   The weakness is that we lose the fine detail.

Van der Velde et al.  argue that our memory access functions are based on the notion that bounded sets exist.   In fact, our preferred method of implementation is to represent a set as a composite vector where the elements of the set vary in terms of the strength by which they are included.   Again this is an issue for the algorithmic level and is not an assumption of our approach.

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