Feb 3, 2005 - Although the data on RPS are not as accurate as other data, .... with the recognition process, and this is the basis for my self-interference model.
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MedGenMed. 2005; 7(1): 5.
PMCID: PMC1681430
Published online 2005 Feb 3.
Proactive Interference, Retroactive Interference – What About Self-interference? A New Interpretation of the Recency-Primacy Shift Eugen Tarnow, PhD Disclosure: Eugen Tarnow, PhD, has disclosed no significant financial interests or relationships. Copyright ©2005 Medscape
Abstract and Introduction Abstract
The recency-primacy shift (RPS) indicates that memory for early list items improves and memory for later items becomes worse as the retention interval between study and test increases. In this contribution, this puzzling experimental finding – memory improving with time – is found to be consistent with a model in which recognition is temporarily interfered with by its own storage process (self-interference). I show that this interpretation can qualitatively better account for the RPS experimental data than can the dimensional distinctiveness model, the only other outstanding explanation of the RPS. Two experimental predictions separate the 2 models: The dimensional distinctiveness model predicts no RPS for 2-item lists, in contrast to self-interference, and as the overall timescale is changed, the dimensional distinctiveness model predicts no difference in the RPS whereas self-interference predicts significant changes. Introduction
It is recognized that “the naïve layperson might expect psychological theories of memory to make detailed quantitative claims about the course of forgetting.[1]” Similar frustration is echoed elsewhere: “not one detailed effort to grapple with the theoretical implications of Jost's law [an older memory trace will decay less rapidly in a given period of time than a younger one] can be identified in the 20th-century literature (or, so far, in the 21st-century literature).[2]” Curve fitting may be the key to progress. In the hard sciences, curve fitting to experimental data is of fundamental importance: The data can elicit testable theoretical models and theoretical models can be tested by new experimental data. What emerges is a powerful synergy that allows for an efficient development of theory and experiment. In the memory field, curve fitting to discriminating data is a relatively new endeavor. It was not until 1996 that an effort was made to fit the various retention data with different types of curves.[3] At that time, it was realized that the experimental data were not good enough to show a preference for an algebraic decay, an exponential decay, or even an asymptotic nondecay. Once the curve fitting effort had begun, the lack of quality experimental data bothered apparently nobody but the investigators, who then took it upon themselves to come up with a much better set of experimental data with small enough error bars to be theoretically useful. The data[1] probed recall at time intervals of 6 seconds. At smaller time intervals than 6 seconds, an interesting anomaly in experimental memory research, the recency-primacy shift (RPS), occurs under some circumstances.[4–7] Typically, when we try to remember serially presented items, the most recent item is the one best remembered (referred to as recency). However, in some experimental situations, the first item
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is the one remembered (referred to as primacy). Although the data on RPS are not as accurate as other data, [1] they seem to show conclusively the existence of the RPS in some experimental situations. The existing attempt at an explanation in the literature for the RPS is the dimensional distinctiveness model (DDM).[5] This model suggests that the most distinct stimuli in a series are the first and last, and that recall the first and last stimuli is better than recall of the stimuli in the middle. DDM does not, however, produce the RPS for stimuli presented at constant intervals as used in experiments, but only for stimuli presented at successively longer intervals (a “decreasing schedule[7]”). One does not find this mentioned in the literature.[5] Rather than starting from the DDM, this contribution goes back to the fundamentals of interference theory. Apparently for the first time, a general equation is written down that includes the effects of both proactive and retroactive interference; as a result, an extra term is given to us by mathematics, namely, the selfinterference term. This term seems to explain the RPS data reasonably well and is interpreted as the presence of a memory-storage process that interferes with recognition about 5 seconds after stimulus presentation. Graphing the Experimental Data as a Function of Time In this contribution, I graph the experimental data as a function of time rather than as a function of serial position (contrary to the literature), so that my argument becomes more intuitive. Figure 1[7] shows the proportion of correctly tested list items as a function of time delay in seconds between the test and the study of each item for 4 item series and 4 retention intervals. The RPS is the secondary difference in proportion of correct answers between the first and fourth positions at the 0- and 5-second retention intervals: At 0 seconds this difference is negative but at 5 seconds it is positive. As described in the literature,[1,8] any theory of memory has to account for this shift, in particular, for the rather amazing result that the memory of the fourth position actually improves with increased study-test time. The RPS is relatively small – the secondary difference is only about .1 or 10%. Simulation of the Data With DDM The DDM would simulate the data, as shown in Figure 2 (The data have been normalized for the first test item at 0 seconds to .97; the baseline in the experimental data of Figure 1 is dependent on the relatively small subject sample, and the simulations that follow reflect that particular subject group. I do not use the “weighting factor to avoid symmetries of short lists” used in the DDM predictions,[6] because it seems to have no theoretical basis.) There are several important observations to make. First, the 2 intermediate list items show exactly the same relative distinctiveness, despite the retention interval, and their absolute distinctiveness is just around .55 and varies little. This is in contradiction to the experimental data in Figure 1, in which the 2 intermediate items vary just as much as the first and last list items and seem to exchange values as the retention interval increases. Second, although the model predicts the correct direction of change of the first and last list items, the 2 items never reverse as they do in the experimental data, and there is no RPS. (The interested reader can also check for the absence of RPS in the thought experiment on p. 485.[7]). Indeed, as the retention interval increases, the distinctiveness of the first item becomes equal to the distinctiveness of the last item, .83 and exactly 1.5 as large as the distinctiveness of the intermediate list items. Third, the overall scale of changes in proportion is correct as the function of delay is 5 times larger than found in the experiment. An unusual feature of DDM is not shown in the picture: Although the distinctiveness score is timedependent, it does not scale with time, ie, if the experiment took place over 9 seconds or 18 seconds (or, indeed, 18 years), the model would predict the same result as long as the list items had the same relative time relationship.
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Finally, the author thinks that there is something missing in the model, because one would think that distinctiveness should fade with time passed since the initial series of stimuli presentations, but there is no such time dependence in the model. Mathematical Formulation of Interference Although proactive and retroactive interference are commonly used in the description of memory errors, a mathematical formulation apparently remains to be explicitly written down, eg:
recognition(i, t) = sum(j)I(i, j) ×
(i, j)
where recognition (i, t) is the probability of correctly recognizing at time t that item i was in the list of items presented, and I(i, j) is the proactive or retroactive interference of item j on the recognition of item i. I have assumed that all the items are similar. From now on, let us also assume that I(i, j) can be approximated as I(ti-tj). Notice that the most general way of writing down the equation by necessity includes not only proactive and retroactive interference but also self-interference (when i = j). This self-interference term should not be dropped unless it is shown that the corresponding signal strength is 0. Simulation of the Data With Self-interference The RPS means that memory is improving with time.[9] It is not inconceivable that the storage process at some point could interfere with the recognition process, and this is the basis for my self-interference model of the RPS: The brain is changing to accommodate the new information, and this change could conceivably make recognition of that same information just a little more difficult during the time of the change resulting from the storage process. Let us attempt to simulate the result of Figure 1 with an intraitem interference term (item storage process interfering with same-item recognition) at about 5 seconds after the stimulus presentation. I simulate this self-interference process with an exponential function
I(0) =
a × exp( [ts
tt
N]2 /2s2 )
centered at N seconds with a width s, where ts is the time of study and tt is the time of the test. The proportion correct is then 1 – interference. With the parameters N = 4.8 and a = .074 and s = 2.4, I obtain the graph of Figure 3. Two features are qualitatively similar to the experimental data in Figure 1. First, there is a dip in the data around 5 seconds after the study. This dip automatically leads to the RPS. Second, the intermediate list items are reproduced correctly: They vary just as much as the first and last list items, are not always the same, and change relationships after the 5-second dip. If we add a small amount of interitem interference, the model fits the data even better and makes the test results dependent not only on the delay time between study and test, but also on the position of the item in the list. The result for .63% more erroneous recognition for each previous presentation and N = 4.6 and a = .068 and s = 2.1 in the series is shown in Figure 4. The interitem interference causes the curves to split. Discussion: Comparison Between the 2 Models and Predictions That Further Separate Them
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The self-interference model is superior to DDM in several ways: First, t gives the correct qualitative results for the RPS (the sign change), not just the correct direction of change. Second, it gives the correct qualitative results for the intermediate items rather than making the 2 the same. And third, it gives an intuitive explanation for why memory seems to be getting better with time – one that does not involve organizing memory perceptually. There are several ways in which these 2 models predict different experimental results. Most importantly, the DDM predicts that there is no RPS and, indeed, no change at all for 2-item series, in contradiction to the prediction of the self-interference model in Figure 5. Second, the self-interference model depends strongly on the timescale, whereas the DDM has no dependence on the timescale. Multiplying the experimental timescales by 5 will yield the same prediction for the DDM but the prediction of Figure 6 from the self-interference model. The DDM also does not change with the time passed since the experiment, one would think that the distinctiveness would disappear after enough time has passed. The self-interference model shows an interesting balance of interitem and intraitem interference effects. If one decreases (increases) the ratio of interitem to intraitem interference by decreasing (increasing) the similarity of the stimulus items, the model predicts that the RPS should increase (decrease). In the literature,[1] it was found that the probability of retention under certain conditions can be quantitatively described by a sum of successively smaller exponential terms with successively larger time constants. The smallest time constant was 1.15 × 6 (trial separation in seconds) = 6.9 seconds for a recall condition and 1.15 × 4.5 (trial separation in seconds) = 5.2 seconds for a recognition condition. If this time constant describes the decay of an initial memory store, it is not inconceivable that there may be selfinterference from making the memory move from a first memory store to a second one at our selfinterference timescale of 4.6 seconds. The nature of the physical process behind self-interference remains to be discovered. What makes it larger under the experimental conditions that produce RPS and smaller under the experimental conditions that do not show RPS? Even though the RPS is a small entity, it may prove to give us fundamental information about memory processes, one piece of which is the timescale of 5 seconds. References 1. Rubin DC, Hinton S, Wenzel A. The precise time course of retention. J Exp Psychol: Learning, Memory Cogn. 1999;25:1161–1176. 2. Wixted JT. On common ground: Jost's 1897 law of forgetting and Ribot's (1881) law of retrograde amnesia. Psychol Rev. 2004;111:864–879. [PubMed: 15482065] 3. Rubin DC, Wenzel AE. One hundred years of forgetting: a quantitative description of retention. Psychol Rev. 1996;103:743–760. 4. Wright AA, Santiago HC, Sands SF, Kendrick DF, Cook RG. Memory processing of serial lists by pigeons, monkeys and people. Science. 1985;229:287–289. [PubMed: 9304205] 5. Neath I. Distinctiveness and serial position effects in recognition. Memory Cogn. 1993;19:332–340. 6. Neath I, Knoedler AJ. Distinctiveness and serial position effects in recognition and sentence processing. J Memory Lang. 1994;33:776–795. 7. Knoedler AJ, Hellwig, Neath I. The shift from recency to primacy with increasing delay. J Exp Psych: Learning, Memory Cogn. 1999;25:474–487. 8. Neath I. Human Memory. Pacific Grove, Calif: Brooks/Cole; 1998. pp. 148–151.
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9. Keer JR, Avons SE, Ward G. The effect of retention interval on serial position curves for item recognition of visual patterns and faces. J Exp Psychol: Learning, Memory Cogn. 1999;25:1475–1494. Figures and Tables
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Figure 1
Mean accuracy (proportion correct as measured by hits) for 4-item study/test series as a function of the delay between study and test for 4 retention intervals (0, 1, 2, and 5 seconds) (adapted from: Knoedler et al.[7]).
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Figure 2
Simulation of the mean accuracy for 4-item study-test series as a function of the delay between study and test for 4 retention intervals (0, 1, 2, and 5 seconds) with the dimensional distinctiveness model.
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Figure 3
Simulation of the mean accuracy for 4-item study-test series as a function of the delay between study and test for 4 retention intervals (0, 1, 2, and 5 seconds) with the self-interference model.
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Figure 4
Simulation of the mean accuracy for 4-item study-test series as a function of the delay between study and test for 4 retention intervals (0, 1, 2, and 5 seconds) with the self-interference model, including a .63% interitem interference term.
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Figure 5
Prediction of the mean accuracy for 2-item study-test series as a function of the delay between study and test for 4 retention intervals (0, 1, 2, and 5 seconds) with the self-interference model, including a .63% interitem interference term. Notice the predicted RPS.
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Figure 6
Prediction of the mean accuracy for 4-item study-test series as a function of the delay between study and test for 4 retention intervals with the self-interference model, including a .63% interitem interference term. All timescales are 5 times larger than in the previous figures. Articles from Medscape General Medicine are provided here courtesy of WebMD/Medscape Health Network
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