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QUANTITATIVE MODELS OF MEMORY

QUANTITATIVE MODELS OF MEMORY. The value of explicit models Precision of thinking Explanatory power Interval- or ratio-scale predictions The macho factor Mathematical models of memory Assumptions about representation What are the stimulus “attributes”? Are traces separate or integrated?

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QUANTITATIVE MODELS OF MEMORY

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  1. QUANTITATIVE MODELS OF MEMORY • The value of explicit models • Precision of thinking • Explanatory power • Interval- or ratio-scale predictions • The macho factor • Mathematical models of memory • Assumptions about representation • What are the stimulus “attributes”? • Are traces separate or integrated? • Nature of item, order, associative info • Local or distributed representation • Assumptions about process • How is context utilized? • Familiarity match or search? • How does the cue “contact” memory?

  2. Implementation of models • Encoding and retrieval mechanisms modeled as equations & flow charts • Model parameters (factors in the equations) can be fixed, or data-based • “solving” equations through simulations can produce predictions • Evaluation of models • Elegance: assumptions should be psychologically plausible and direct • Goodness-of-Fit: the match between predicted and observed data • Efficiency: the model predicting the most phenomena with the fewest parameters wins • Distinctiveness: No other models would make that predictionA functional law, though an equation, is not a model! • Power law of practice • Hick’s law of uncertainty and RT

  3. CAPSULE HISTORY OF MEMORY MODELS • 1955-1965 • Mathematical Learning Theory models specific tasks (e.g., paired-associate learning) • Estes’ (58) Stimulus Sampling Theory • 1965-1975 • Comprehensive models emerge • The Modal Model (Atkinson & Shiffrin, 68); short-term and long-term episodic • HAM (Anderson & Bower, 72); list learning and sentence memory • 1975-1990 • “Global” models appear • Wider range of tasks and processes • All list items and associations are relevant at retrieval • Distributed-network models of association developed • The PDP revolution

  4. Search of Associative Memory(Raaihmakers & Shiffrin, 1981) • Designed for recall and recognition of word lists and associations • Representation and encoding • Episodic study increases memory trace strength (image) of association between the studied item and.. • the “list context” (a)(encoding specificity) • Other items in the rehearsal set (b)(relational processing) • Itself as a potential cue (c)(item distinctiveness) • Parameters (a,b,c) determine mean rate of increase in strength for item Wi with rehearsal time t among n other items S(C,Wi) = ati/n S(WiWh) = btih/n S(WiWi) = cti/n

  5. SAM (Cont’d) • Retrieval: recognition task • Given item Wi as cue, calculate global familiarity • For each item in memory Wk: F(C,Wk) = S(C,Wk) x S(Wi,Wk) • Sum over all list items (k=1 to N)

  6. Recognition in SAM (cont’d) • If summed familiarity exceeds criterion, item is recognized as old • Associative recognition • AB pairs strengthen A,B images as above • At test, pair familiarity obtained by: F(A) x F(B) • Recall • Context serves as cue to sample item(s): Ps(Wi|C) = S(C,Wi)_Σ[S(C,Wk)]then, if strength allows “recovery”, item can serve as part of the cue: Ps(Wi|C,Wh) = _S(C,Wi) x S(Wh,Wi)__ Σ[S(C,Wk) x S(Wh,Wk)]

  7. SAM and Performance • Effects of encoding parameters on performance: • Context (a) • improves recovery, no effect on sampling in recall • no effect on recognition. Why? • Interitem associations (b): • increases familiarity of targets (how?) but not distractors (why not?), so improves recognition • improves recovery, and sampling selectivity, so improves recall • Self-strength (c): • Item familiarity increased, no effect on distractors, so recognition improves • Increases “self-sampling” and so recall (may be?) impaired (cf. part-list cues)

  8. SAM and Performance (cont’d) • Effects of “classic” variables: • Study time: • Associative and item strengths increased, so recall and recognition improve • Retention interval: • Memory images are permanent, so all forgetting is retrieval failure through context changes • Serial position effects: • Smaller rehearsal set for first items, so stronger strengths (see encoding) and primacy effect • Recency? • Word Frequency: • HF words with higher pre-existing associations (d) and easier list associations (b):

  9. Troubles for SAM • The “mirror effect” • In mixed-frequency lists, LF words show both higher hits and lower FA’s • Why is this a problem? • How does REM solve the problem? • The “list strength” effect • In mixed-study-time lists, recall of short-study items should be worse compared to pure-study-time lists. • Why should this happen in SAM? • Well, it doesn’t • How does REM solve the problem? • In Summary: • Models are powerful, but not unfalsifiable • Mix of plausible, less plausible assumptions about representation & process • Sometimes “transparent,” sometimes opaque • Remains relatively unconnected with broader field of memory and cognition

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