Semantic and Lexical Processes: PDP Models that Capture Patterns in Neuropsychological Data

1. Semantic and Lexical Processes: PDP Models that Capture Patterns in Neuropsychological Data PDP Class February 18, 2011

2. The Traditional �Neuropsychological� Method One begins by wishing to �carve the bird at its joints�. The key assumption is that distinct mechanisms are used for different tasks (or sometimes the same task but different types of items). One relies on double dissociations: Patient A fails on Task 1 but not Task 2. Patient B fails on Task 2 but not Task 1. One then posits separate mechanisms, one for Task 1,another for Task 2. One also relies on different error types: semantic errors suggest a semantic deficit, visual errors a visual deficit, etc. Often this is done with a minimal commitment to a specific framework or theory of normal function.

3. An Example: Dual Route Model of Reading The basic idea: One can read a word either: By using grapheme-phoneme correspondence rules By accessing its meaning and then pronouncing the word that has that meaning If this is true then: A lesion that disrupts meaning should allow one still to �sound the word out�. Expect regularization errors with exceptions. A lesion that disrupts �sounding out� should allow one still to read via meaning. Expect non-word reading to be impossible; not entirely clear what else to expect.

4. The �Central Dyslexias� Fluent surface dyslexia (�preserved phonological reading�): Patient has a semantic deficit (e.g., has a deficit in a word-picture matching task). Patient reads regular words (HINT) and nonwords (VINT) at near normal reaction time and accuracy, but has trouble reading exception words (PINT) and often �regularizes� them (to rhyme with MINT). Phonological dyslexia Severe deficit reading non-words. Some patients read none correctly Patient reads many words correctly, with few visual or semantic errors (usually some omission errors). Deep dyslexia Severe deficit reading non-words. Patient reads some words correctly, but makes both visual and semantic errors.

5. Problematic Aspects of Deep Dyslexia Deep dyslexics all make visual as well as semantic and derivational errors, and mixed errors: APRICOT->�peach� SYMPATHY->�symphony� SYMPATHY->�concerto� FLEEING -> �flee� Deep dyslexics generally show an imageability effect and a part of speech effect. Deep dyslexia typically co-occurs with phonological and syntactic deficits as well.

6. The Morton-Patterson Account Multiple pathways. Multiple types of semantics. Multiple lesion loci. No account of why visual errors generally co-occur with semantic errors. The authors themselves were not very well satisfied!

7. Problematic aspects of fluent surface dyslexia Although patients may be profoundly semantically impaired, they can often still read many exception words correctly. There is a very strong frequency effect in reading exceptions. Frequently, errors are regularizations, but sometimes they are partial regularizations (YACHT -> �yat�). Regularity appears to be a matter of degree, and the patients show the greatest deficits with �Very irregular� words.

8. Coltheart�s �DRC� Model Reading occurs primarily by GPC�s or via lexical representations. Semantics is relied on only if both other pathways are cut. GPC and Lexical routes are implemented as localist connectionist networks (although Coltheart does not let them to work together as much as this approach would allow).

9. The PDP Approach There are two routes, but neither is strictly a �GPC� route. The direct route is sensitive to frequency and regularity. The indirect route is not sensitive to regularity, but is sensitive to frequency, imagability, and semantic relatedness.

10. Simulating Deep Dyslexia Plaut and Shallice (1993) employed a network like the triangle model but without a direct pathway. They constructed �semantic� patterns and used these as targets for their semantic layer. DD was simulated as damage (removal) of either connection weights or units in this network. The model had bidirectional connections and/or recurrent connections. Interestingly, damage either to connections or to units tended to produce both visual and semantic errors. Lesions change shape of basins of attraction and/or where input puts the starting point of the settling process. Both visual and semantic errors are obtained. Imagery effects were captured by assuming concrete words have denser semantic representations.

11. Simulating Surface Dyslexia Plaut et al (1996) first capture key aspects of normal word reading using a model containing only the �direct route�. Frequency, Regularity, and FxR �Interaction� Human-like level and pattern of errors in non-word reading. Then they consider the role of semantics.

12. Human and Model Performance Reading Non-Words Derived from Exceptions

13. Role of Semantics in PMSP The model did not actually include semantic patterns; instead, the model simply provided direct support for the correct interpretation (net input of +/-2 to each output unit, present during training, so that direct pathway could be �lazy�). Semantic damage was simulated by simply reducing the �semantic� contribution to the net input. The model simulates data from two fluent surface dyslexia patients fairly accurately.

14. Lexical Decision without Lexical Entries Plaut used the overall strength of activation (deviation from .5) at the semantic layer as the basis for lexical decision: Words generate higher �stress� than non-words; it is easy to choose a threshold that separates words from non-words. Many cognitive neuroscience theories of familiarity rely on an activation-based measure of familiarity like this.

15. A problem patient: Impaired semantics�

16. With spared LD and Single Word Reading

17. Categorical Dissociations vs. Continuous Variation Classic �Dissociation Logic�: Cases like EM tell us that lexical tasks are handled by a system independent of semantics, which is intact in their cases. (e.g. Coltheart, 2004; Blazely et al., 2005) Continuous Variation Perhaps patients come from a single distribution, with apparent dissociation cases simply showing a relative sparing of one task relative to another. It could still be the case that for all patients, there is a relationship between reading and semantics. (e.g. Patterson et al., 2006; Plaut et al., 1996; Woollams et al., 2007)

18. Patterson et al: Six Deficits in Search of an Explanation Tested 14 SD patients Assigned �Semantic Score� based on: Picture Naming, WP Matching, Pyramids and Palm Trees w/ Pictures Reading HF&LF Reg. and Exc. Words Spelling HF&LF Reg. and Exc. Words Past Tense Inflection, HF&LF R&E Words Lexical Decision: fruit/frute, flute/fluit Object Decision: See Next slide Delayed (Picture) Copy Test

21. What about EM?Data from A large group of SD patients, some tested at multiple time points, suggests (a) that best readers may be part of a distribution and (b) reading always deteriorates eventually as the disease progresses.

22. What accounts for the Individual Differences? three hypotheses about individual differences: Differences in experience with reading Differences in the neural pathways used for lexical tasks such as reading Differences in the distribution of the lesion

23. Dilkina, McClelland and Plaut (2008) Includes an integrative layer that integrates all different kinds of information about objects, including the spelling and sound of the word for it. Trained by presenting any of the four inputs, allowing the network to settle to produce any one or all four outputs.

24. Training Set generated probabilistically based on 12 category prototypes generated probabilistically based on 12 category prototypes

25. Similarity structure from human ratings

26. Phonological & orthographic patterns based on lemma frequency counts of ~1, 300, 000 spoken word lemmas from the CELEX English lemma database (Burnage, 1990) HF > 70 LF < 30 (Kucera & Francis written frequency) in the model: HF = 8*LFbased on lemma frequency counts of ~1, 300, 000 spoken word lemmas from the CELEX English lemma database (Burnage, 1990) HF > 70 LF < 30 (Kucera & Francis written frequency) in the model: HF = 8*LF

27. Individual differences variables Relative amount of reading (O:V: 1:2; 1:1; 2:1) Capacity of direct pathway (10, 20, or 30 units) Distribution of damage Unbiased Biased �toward Orthography� Biased toward �Vision�

28. Game Plan Examine reading and naming of each version of the model at several levels of lesion severity. Examine the �typical pattern� with default parameters at an intermediate level of damage, as well as: Overall pattern of fall off in naming and reading with damage. Range of performance for each ID manipulation and for all three taken together. Then consider whether individual patient data can be explained as arising from effects of 1 or more of the individual differences variables. Finally, consider whether we can provide a simplified �model� of the model!

29. Pattern of performance with default parameters,

30. Overall trend and range of individual differences

31. Fitting the patient data: JL

32. Fitting the patient data: EM

33. logistic regression results

34. logistic regression results

35. logistic regression results

36. frequency and regularity in reading (except for orthographically-biased lesion, O:V=1:1, direct 20: p = .033)(except for orthographically-biased lesion, O:V=1:1, direct 20: p = .033)

37. Take Home Messages for Cognitive Neuropsychology PDP models capture the graceful degredation of function and the kinds of errors patients make. Sometimes they offer �simpler� explanations (fewer separate routes or disorders required) Although simplicity can be in the eye of the beholder� Instead of making us reply on simple categorical disociation (�spared� vs. �intact�), They make it easy and natural to explore continuous variation� In neuroanatomical parameters In experience In stimulus variables (frequency, �regularity��) And in the details of the distribution of the lesion. They can be complex, but sometimes their behavior can be captured by a simpler descriptive characterization.