Conservation of information-processing capacity in paired-associate memorizing

Data that impose constraints on hypotheses regarding the role of temporal variables in memorizing are reviewed, including results that apparently disconfirm Greeno's (1967) time-sharing hypothesis. An alternative hypothesis is proposed, in which it is assumed that S occasionally attenuates his rate of processing information for memory, with the probability of attentuation being relatively high when the item being presented is still in short-term memory as a result of a recent presentation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/32693/1/0000060.pd

Matrix analysis of identifiability of some finite markov models

Methods developed by Bernbach [1966] and Millward [1969] permit increased generality in analyses of identifiability. Matrix equations are presented that solve part of the identifiability problem for a class of Markov models. Results of several earlier analyses are shown to involve special cases of the equations developed here. And it is shown that a general four-state chain has the same parameter space as an all-or-none model if and only if its representation with an observable absorbing state is lumpable into a Markov chain with three states.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45730/1/11336_2005_Article_BF02291365.pd

Attentional biases for food stimuli in external eaters: Possible mechanism for stress-induced eating?

External eaters reportedly increase snack intake when stressed, which could be due to an attentional shift towards food stimuli. Attentional biases for food stimuli were tested in high and low external eaters in stress and control conditions, using a computerised Stroop. A significant interaction was observed between external eating group and condition for snack word bias. This suggested that low external eaters have a greater bias for snack words when unstressed and that stressed, high external eaters have a greater bias for snack words than stressed, low external eaters, which could contribute to stress-induced snack intake in high external eaters

Interpretation of the two-stage analysis of paired-associate memorizing

Four groups were run with response difficulty and stimulus difficulty varied factorially. A two-stage Markov model fit the data adequately. The parameter associated with the first stage depended on stimulus difficulty as well as response difficulty, refuting an interpretation of the first stage as response learning. The learning parameters associated with the second stage seemed to depend only on stimulus difficulty. The results suggest that the first stage of learning involves storage of the stimulus-response pair in memory, and the second stage involves learning to retrieve the item reliably.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/32746/1/0000115.pd

Combining Cytotoxic and Immune-Mediated Gene Therapy to Treat Brain Tumors

Glioblastoma (GBM) is a type of intracranial brain tumor, for which there is no cure. In spite of advances in surgery, chemotherapy and radiotherapy, patients die within a year of diagnosis. Therefore, there is a critical need to develop novel therapeutic approaches for this disease. Gene therapy, which is the use of genes or other nucleic acids as drugs, is a powerful new treatment strategy which can be developed to treat GBM. Several treatment modalities are amenable for gene therapy implementation, e.g. conditional cytotoxic approaches, targeted delivery of toxins into the tumor mass, immune stimulatory strategies, and these will all be the focus of this review. Both conditional cytotoxicity and targeted toxin mediated tumor death, are aimed at eliminating an established tumor mass and preventing further growth. Tumors employ several defensive strategies that suppress and inhibit anti-tumor immune responses. A better understanding of the mechanisms involved in eliciting anti-tumor immune responses has identified promising targets for immunotherapy. Immunotherapy is designed to aid the immune system to recognize and destroy tumor cells in order to eliminate the tumor burden. Also, immune-therapeutic strategies have the added advantage that an activated immune system has the capability of recognizing tumor cells at distant sites from the primary tumor, therefore targeting metastasis distant from the primary tumor locale. Pre-clinical models and clinical trials have demonstrated that in spite of their location within the central nervous system (CNS), a tissue described as \u27immune privileged\u27, brain tumors can be effectively targeted by the activated immune system following various immunotherapeutic strategies. This review will highlight recent advances in brain tumor immunotherapy, with particular emphasis on advances made using gene therapy strategies, as well as reviewing other novel therapies that can be used in combination with immunotherapy. Another important aspect of implementing gene therapy in the clinical arena is to be able to image the targeting of the therapeutics to the tumors, treatment effectiveness and progression of disease. We have therefore reviewed the most exciting non-invasive, in vivo imaging techniques which can be used in combination with gene therapy to monitor therapeutic efficacy over time

An analysis of some conditions for representing N state Markov processes as general all or none models

Recently Markov learning models with two unidentifiable presolution success states, an error state, and an absorbing learned state, have been suggested to handle certain aspects of data better than the three state Markov models of the General All or None model type. In attempting to interpret psychologically, and evaluate statistically the adequacy of various classes of Markov models, a knowledge of the relationship between the classes of models would be helpful. This paper considers some aspects of the relationship between the class of General All or None models and the class of Stationary Absorbing Markov models with N error states, and M presolution success states.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45728/1/11336_2005_Article_BF02290602.pd

The company a word keeps: the role of neighbourhood density in verbal short-term memory

Psycholinguistic information plays an important role in verbal memory over the short-term (vSTM). One such linguistic feature is neighbourhood density (ND)—the number of words that can be derived from a given word by changing a single phoneme or single letter—so that vSTM performance is better when word sequences are from dense rather than sparse neighbourhoods, an effect attributed to higher levels of supportive activation among neighbouring words. Generally, it has been assumed that lexical variables influence item memory but not order memory, and we show that the typical vSTM advantage for dense neighbourhood words in serial recall is eliminated when using serial recognition. However, we also show that the usual effect of ND is reversed—for both serial recall and serial recognition—when using a subset of those same words. The findings call into question the way in which ND has been incorporated into accounts of vSTM that invoke mutual support from long-term representations on either encoding or retrieval

Equivalence classes of functions of finite Markov chains

A matrical representation of a Markov chain consists of the initial vector and transition matrix of the chain, along with matrices that specify which observable response occurs for each state. The likelihood function based on a Markov model can be stated in a general way using the components of the model's matrical representation. It follows directly from that statement that two models are equivalent in likelihood if they are related through matrix operations that constitute a change of basis of the matrical representation. Two necessary properties of a change matrix associating two Markov models that are members of the same equivalence class with respect to likelihood are derived. Examples are provided, involving use of the results in analyzing identifiability of Markov models, including a useful application of diagonalization that provides a connection between the problem of identifiability and the eigenvalue problem.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/22239/1/0000675.pd